Enhancing Digital Reading Comprehension in Secondary Education: Effects of Question Placement and Feedback Type

Enhancing Digital Reading Comprehension in Secondary Education: Effects of Question Placement and Feedback Type | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Enhancing Digital Reading Comprehension in Secondary Education: Effects of Question Placement and Feedback Type Laura Gil, Amelia Mañá, Mario Romero-Palau, Marian Serrano-Mendizabal, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8995659/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The rapid expansion of digital reading in schools has outpaced the development of empirical evidence about how digital learning environments can effectively support reading comprehension for diverse learners. This study examined the effectiveness of a classroom-based digital reading intervention and investigated whether key instructional design features—question placement and feedback type—interact with students’ initial digital reading competence. A total of 1227 secondary-school students (Grades 7–10) from 12 schools participated in a cluster-randomized trial including four intervention conditions and a waiting-list control group. Within the intervention group, classrooms were assigned to a 2 × 2 factorial design manipulating the placement of comprehension questions (embedded vs. post-reading) and feedback type (corrective vs. elaborated). Over a 16-week period, students completed weekly digital reading units consisting of one or more texts accompanied by comprehension questions designed to engage strategic reading processes. Digital reading comprehension was assessed using a standardized measure administered before and after the intervention. Linear mixed-effects models showed that baseline digital reading competence was the strongest predictor of post-test performance. No overall intervention effects emerged across conditions. However, for older students (Grades 9–10), a significant interaction indicated that the combination of embedded questions and corrective feedback was associated with comparatively stronger post-test outcomes, particularly for students with higher baseline competence. These findings highlight the importance of considering learner characteristics and instructional design when developing digital reading interventions and provide insights for refining literacy practices within Multi-Tiered Systems of Support frameworks. digital reading comprehension secondary school comprehension questions feedback Multi-Tiered System of Supports Figures Figure 1 Figure 2 Introduction As with many educational innovations, the integration of digital reading tools in classrooms has progressed faster than the development of scientific evidence on how digital environments can effectively support reading comprehension for all students. Recent meta-analyses and large-scale studies indicate that the transition from paper to digital reading may pose challenges for some learners. For instance, digital texts have been found to be slightly less comprehensible than their printed counterparts across different age groups (Clinton, 2019 ; Delgado et al., 2018 ; Furenes et al., 2021 ; Kong et al., 2018 ). Similarly, more frequent classroom use of digital reading tools has been negatively associated with students’ comprehension performance in large representative samples of American students in Grades 4 and 8 (Salmerón et al., 2023 ). Digital reading exposure has also been proposed as a factor affecting sustained attention during reading (Wolf, 2018). These findings suggest that the introduction of digital reading activities in schools cannot be assumed to have neutral or automatically positive effects on comprehension. At the same time, digital reading comprehension has become a core competence for participation in contemporary learning environments and for achieving broader educational goals such as those outlined in the Sustainable Development Goals (OECD, 2023 ). As digital reading underpins academic, social, and professional activities, identifying effective instructional practices to support comprehension in digital contexts has become a key educational priority. Such practices should also account for individual differences among learners, including gender, socioeconomic background, and variability in reading ability. Despite its growing importance, recent large-scale evidence indicates that many secondary school students struggle to process complex digital texts, particularly those requiring the coordination of multiple cognitive processes such as identifying main ideas, integrating information across documents, and evaluating sources (OECD, 2023 ). Data from PISA 2022 illustrate this challenge: although most 15-year-old students achieve basic proficiency levels, only about one quarter reach advanced levels requiring integration across multiple sources and critical evaluation of claims. These findings highlight the need for instructional approaches capable of strengthening higher-order digital reading skills across diverse student populations. In response to this need, the present study examines how specific instructional design features embedded in digital reading activities may support comprehension development in secondary school students. Specifically, we investigate the effects of two common components of reading interventions—comprehension question placement (embedded vs. post-reading) and feedback type (corrective vs. elaborated)—while considering their interaction with students’ initial levels of digital reading competence. This study addresses principles underlying the multi-tiered system of supports (MTSS) framework by examining instructional practices that can be flexibly adapted to students’ literacy needs (Brown-Chidsey, 2016 ). Individual differences in digital reading strategies To function effectively in contemporary information environments, students must comprehend digital texts both strategically and critically. Beyond traditional comprehension strategies used in print reading, digital reading requires additional skills such as navigating information spaces, integrating multiple sources and formats, and evaluating the credibility of online information (Richter & Maier, 2017 ; Salmerón et al., 2018 ). Understanding how students differ in these skills is particularly relevant from an MTSS perspective, where instruction is expected to respond to learner variability through progressively differentiated supports. Research suggests that findings from print literacy cannot always be directly generalized to digital reading contexts (Leu et al., 2015 ). Accordingly, growing attention has examined how learner characteristics shape performance in digital reading tasks (Afflerbach, 2015; Cho et al., 2021 ; Coiro, 2021 ; Salmerón et al., 2018 ). Although a comprehensive review is beyond the scope of this article, several learner characteristics consistently emerge as relevant, including gender, cognitive resources, reading competence, and metacognitive strategy use. Regarding gender, the well-documented advantage of girls in print reading comprehension appears to persist in digital contexts. In PISA 2022, girls outperformed boys in digital reading across participating countries, with an average difference of nearly 30 score points across OECD nations (OECD, 2023 ). Similar patterns have been reported in studies of digital reading comprehension among secondary school students (Forzani, 2018 ). Cognitive and linguistic resources also play an important role. Factors such as prior knowledge, vocabulary, language proficiency, working memory capacity, and strategic reading behaviors support effective digital text processing (Wylie et al., 2018). Among these variables, print reading competence consistently emerges as one of the strongest predictors of digital reading performance (Afflerbach & Cho, 2009 ; Cho & Afflerbach, 2017 ; Coiro, 2011 ; Leu et al., 2015 ). Students with stronger foundational reading skills tend to achieve better outcomes when reading digital texts (Kanniainen et al., 2019 ). Additionally, metacognitive reading strategies significantly predict comprehension in digital environments (Salmerón et al., 2018 ). Together, these findings highlight the importance of considering both foundational literacy skills and strategic regulation processes when designing digital reading interventions. The role of adjunct questions in students’ reading comprehension Digital reading comprehension depends not only on textual content and reader characteristics but also on the design of the tasks accompanying the reading activity. One common classroom practice involves answering questions related to a text (i.e., text-based questions), a format that also underlies many standardized comprehension assessments (Ness, 2011 ). According to the RESOLV model (Rouet et al., 2017 ), such questions function as task instructions that guide readers in establishing reading goals (Cerdán et al., 2009 ; McCrudden & Schraw, 2007 ). By directing attention to specific parts of the text and prompting readers to generate inferences or evaluate information, questions can shape the strategic processes underlying comprehension (Rouet et al., 2017 , 2018). However, their effectiveness depends on several design features, including question complexity, text availability during answering, and question placement relative to the reading process (Cerdán et al., 2009 ; Jensen et al., 2014 ; Ozuru et al., 2007; Tawfik et al., 2020 ). Among these variables, question placement has received considerable attention. Embedded questions appear during reading and require immediate engagement with the text (Dornisch, 2012), whereas post-reading questions are presented only after the text has been read in full. Evidence comparing these approaches remains mixed. Some studies report advantages for embedded questions, suggesting that they direct attention to relevant information and support ongoing comprehension processes (Kapp et al., 2015 ; Peverly & Wood, 2001 ; Philips et al., 2020; Rubio et al., 2022 ; van den Broek et al., 2001 ). Others have found no significant differences (Uner & Roediger, 2018 ; Weinstein et al., 2016 ), and some suggest that embedded questions may hinder comprehension for highly skilled readers by fragmenting the mental representation of the text (Cerdán et al., 2009 ; Salmerón et al., 2015 ). Most prior studies have examined short-term effects of adjunct questions on comprehension of specific texts. Less is known about whether extended practice with such questions leads to broader improvements in digital reading comprehension. The present study addresses this issue by examining whether sustained exposure to comprehension questions during a 16-week intervention transfers to performance on a standardized measure of digital reading comprehension. Feedback in the context of answering comprehension questions In digital reading instruction, comprehension questions are frequently complemented by feedback provided after students respond, potentially enhancing their instructional impact. Feedback is widely recognized as a central instructional tool for supporting learning (Hattie & Gan, 2011 ; Shute, 2008 ). Broadly defined, it refers to information—oral, written, or visual—provided during or after task performance with the aim of reducing the gap between current performance and a desired goal (Hattie & Timperley, 2007 ). In this sense, feedback functions as a regulatory scaffold that informs learners about their level of achievement, clarifies expectations, and guides subsequent improvement (Boud & Molloy, 2013 ). This view aligns with approaches that use feedback as instructional assistance when readers encounter comprehension difficulties. For example, Golke et al. ( 2015 ) implemented elaborated feedback designed to help students recognize comprehension problems signaled by incorrect responses to text-based questions and to provide cognitive and metacognitive hints supporting error correction and transfer to subsequent tasks. Grounded in the notion of the zone of proximal development, this approach is consistent with principles of dynamic assessment, in which learners’ potential within a performance domain is inferred from the extent to which their performance improves following feedback (Sternberg & Grigorenko, 2002 ). From this perspective, feedback functions not merely as an evaluative device but also as a scaffold that reveals and fosters learners’ developing comprehension abilities. The present study adopts a similar view, conceptualizing feedback as an instructional support aimed at facilitating students’ digital reading comprehension during sustained classroom practice. In reading comprehension contexts, feedback can be conceptualized as information provided in response to students’ answers to text-based questions. A meta-analysis by Swart et al. ( 2019 ) reported a positive overall effect of feedback on comprehension outcomes (d = 0.35), although its effectiveness depended on design characteristics such as timing and message richness. Regarding timing, feedback delivered after reading can support comprehension outcomes (Swart et al., 2019 ), whereas feedback provided during reading may facilitate strategic processes such as information search and self-regulation (Swart et al., 2022 ). Immediate feedback guiding the processes required to reach correct answers may also improve later reading performance (Llorens et al., 2016 ). In digital reading contexts, feedback timing is often closely linked to task design. When comprehension questions are embedded within the text, feedback occurs during reading, whereas post-reading questions entail feedback delivered after text processing. The present study reflects this distinction: feedback was consistently provided after students answered comprehension questions, but the questions were positioned either during reading or after reading the text. In both conditions, students were allowed to revisit the text when answering the questions, ensuring comparable access to textual information. Message richness also plays a decisive role. Feedback may range from simple corrective responses (e.g., “correct” or “incorrect”) to more elaborated forms that provide explanatory information or strategic guidance. Meta-analytic evidence suggests that elaborated feedback, which focuses on the processes required to solve the task, is generally more effective than purely corrective feedback (Hattie & Timperley, 2007 ; van der Kleij et al., 2015 ). Moreover, students with lower proficiency levels tend to benefit more from elaborated feedback than more advanced readers (Shute, 2008 ), likely because it provides additional information that helps identify and correct misunderstandings (van der Kleij et al., 2015 ). Digital learning environments further expand the instructional potential of feedback through their capacity for personalization. However, research indicates that written feedback in digital contexts is often processed superficially, potentially limiting its impact (Golke et al., 2015 ; Máñez et al., 2019 ). For example, Golke et al. ( 2015 ) found that computer-delivered elaborated feedback did not improve sixth graders’ deep-level text comprehension when students showed limited engagement with feedback messages, whereas improvements emerged when feedback was mediated by a person. Related evidence suggests that students sometimes devote more attention to simple corrective feedback than to elaborated feedback, particularly after incorrect responses (Máñez et al., 2019 ). Beyond immediate performance effects, feedback has also been shown to foster the development and transfer of reading strategies. A meta-analysis by Swart et al. ( 2022 ) found that comprehension-focused feedback significantly increased strategy use (d = 0.61) and improved text comprehension (d = 0.34), with effects extending to new reading tasks. However, emerging evidence suggests that the effectiveness of feedback may vary across educational stages and learners’ self-regulatory profiles. For example, recent intervention research has reported differential effects of elaborated and corrective feedback depending on students’ grade level and patterns of strategic engagement (Maña et al., 2026). Taken together, this evidence indicates that well-designed feedback—particularly when enriched with explanatory or strategic information—can enhance comprehension outcomes and support the strategic processes underlying literacy development. From an MTSS perspective, such feedback mechanisms contribute to differentiated literacy support within classroom instruction. The current study Against this backdrop, the present study examines the effectiveness of a classroom-based digital reading intervention designed to support secondary students’ digital reading comprehension while accounting for variability in learners’ literacy profiles. The study focuses on how instructional design features—specifically questions placement and feedback type—may interact with students’ initial digital reading competence to influence comprehension outcomes in digital environments. In the Spanish secondary education system (Grades 7–10), schooling is typically organized into two educational cycles that differ in students’ reading development (Llorens et al., 2011 ). To ensure appropriate task difficulty, the intervention materials were adapted to each cycle. Although no major differences between cycles were anticipated, analyses were conducted separately to account for potential developmental variation. A large and diverse sample of secondary school students participated in a classroom-based intervention implemented under ecologically valid conditions. Activities were integrated into regular classroom instruction and delivered by students’ own teachers. Within the experimental condition, classes were assigned to a 2 × 2 factorial design, manipulating (a) questions placement (embedded vs. post-reading) and (b) feedback type (corrective vs. elaborated). Over a 16-week period, students engaged with digital reading activities involving comprehension questions designed to foster strategic processing. Elaborated feedback messages focused on the comprehension strategies required to answer each question. Standardized measures of digital reading comprehension were administered before and after the intervention to assess potential transfer effects. Based on prior research, four hypotheses were formulated. First, sustained practice with digital reading activities incorporating comprehension questions and feedback was expected to improve students’ digital reading comprehension relative to regular instruction. Second, embedded questions were expected to yield greater benefits than post-reading questions, particularly for students with lower initial digital reading competence. Third, elaborated feedback providing strategic guidance was expected to produce stronger gains than purely corrective feedback. Finally, instructional design features were expected to interact with students’ baseline digital reading comprehension. Accordingly, the present study addressed the following research questions: To what extent does sustained practice with digital reading activities incorporating comprehension questions and feedback improve students’ digital reading comprehension compared with regular classroom instruction? Does the placement of comprehension questions (embedded vs. post-reading) differentially affect digital reading comprehension outcomes? Does feedback type (corrective vs. elaborated) influence digital reading comprehension development over time? Do these instructional design features interact with students’ initial digital reading comprehension—and potentially with educational cycle—suggesting differential benefits across learner profiles? Method Participants Our initial sample consisted of 1682 secondary-school students (Grades 7–10, ages 11–15). After applying the inclusion criteria for the analyses—namely, having parental consent and completing at least 80% of the intervention sessions—the final sample comprised 1227 students (53.8% female) from 12 schools (4 public, 8 private) and 74 classrooms supervised by 39 teachers (M age = 46.6 years, SD = 11.0; 79.1% female; M teaching experience = 18.4 years, SD = 10.2). Participating schools represented diverse socio-educational contexts. Within this sample, 140 students were reported by their teachers as having diagnosed special educational needs (SEN), reflecting the diversity of regular classroom settings. Schools were invited through their management teams and teachers volunteered to participate. Students were randomly assigned to experimental conditions through a cluster-randomized design at the classroom level. The study was approved by the Research Ethics Commission of the University of Valencia (reference PSILOG-3176086) and conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from parents or legal guardians. Design and Experimental Conditions This study employed a quasi-experimental, pre-post, waiting-list control-group design, with the intervention group divided into four subgroups based on question placement (embedded vs. post-reading questions) and feedback type (corrective vs. elaborated feedback) (Table 1 ). In accordance with ethical guidelines for school-based research, the control group received the intervention after completion of the post-test assessment. All experimental conditions were implemented through an online platform (see Fig. 1 for an example of an embedded question, which illustrates the platform interface and task presentation). Table 1 Research design overview Experimental conditions Question placement Feedback type Embedded Post-reading Corrective Elaborated IEC + + IEE + + IPC + + IPE + + Note . IEC: Intervention with Embedded questions and Corrective feedback; IEE: Intervention with Embedded questions and Elaborated feedback; IPC: Intervention with Post-reading questions and Corrective feedback; IPE: Intervention with Post-reading and Elaborated feedback. Question placement conditions. The intervention included two conditions differing in the placement of comprehension questions relative to the reading process within each text. In the embedded-question condition, comprehension questions were presented at predetermined points within the text, requiring students to respond before continuing to subsequent sections. In the post-reading condition, students read the entire text without interruption and answered all comprehension questions only after completing the reading. In both conditions, students were allowed to revisit the text while answering the questions if they considered it necessary. Feedback conditions. During the intervention, participants were given two attempts to answer the comprehension questions. In all experimental groups, when a correct response was provided on the first attempt, simple corrective feedback (e.g., “Correct answer”) was displayed. However, feedback messages following an initial incorrect response differed across experimental conditions. Groups assigned to the corrective feedback condition received the message “Incorrect answer, please try again” before proceeding to the second attempt. In contrast, groups assigned to the elaborated feedback condition received a more detailed message combining notification of incorrectness with a reading strategy recommendation aimed at supporting completion of the task required by the comprehension question (see Table 2 for examples of elaborated feedback messages). After the second attempt, all experimental groups received corrective feedback indicating response accuracy. Elaborated feedback was explicitly designed to scaffold students’ strategic reading processes rather than merely indicate answer correctness. Table 2 Elaborated feedback messages per question type Type of question Elaborated feedback message Structure Pay attention to how the parts of the text are articulated to understand its structure. Linguistic Knowledge To answer this question, it is important to keep grammatical knowledge in mind. Literary Knowledge Try to consider the genre of the text, the characteristics of its author, the time it was written, and the literary movement it belongs to. Vocabulary Considering the context, thinking of synonyms, or analyzing the words can help you with vocabulary questions. Remember you can use a dictionary. Location The information you need appears clearly in the text or can be easily inferred. Inferences It is important to combine the information in the text with your own knowledge. Predictions Try to make a reasonable prediction based on what the text indicates and what you know about it. Paraphrases Even if they are expressed differently, the idea which is present in the text and the question should remain the same. Summary When summarizing, it is important to focus on the most relevant information; try to locate it well. Application Even though the situation may be different, there are ideas in the text that can be transferred here. Reflection It is important to make an assessment and ensure it is well-reasoned. Author's Intention Regardless of what the text conveys, try to think about what the author intended to communicate by writing it. Intertextual Integration To answer this question, you need to look at how the ideas in different texts are related, especially their similarities, contributions, and disagreements. Text-Image Integration To answer correctly, you must combine the written information with the visual information from images or graphs. Source Evaluation Remember that the most reliable sources are those with qualified and impartial authors, published in specialized media, well-documented, and more up-to-date. Intervention program Purpose and organization. The intervention aimed to improve students’ digital reading comprehension through 16 reading units. Each unit included one or more texts—depending on whether it belonged to the single-source or multiple-source category—together with a set of comprehension questions. Students completed one unit per week through an online platform during regular classroom sessions under their teachers’ supervision. Teachers received written instructions, an intervention schedule, and an 8-minute instructional video explaining the platform features, according to the experimental condition assigned to each classroom. Each week, they were given access to the corresponding reading unit and could schedule it within a regular 50-minute class session. The intervention included a curated set of reading units, aligned with secondary-school curriculum requirements and drawn from textbooks, institutional websites (e.g., museum sites), or materials specifically developed by literacy professionals. As noted above, due to age-related differences among secondary-school students, two versions of the program were created to ensure grade-appropriate materials: one for Grades 7–8 and another for Grades 9–10. Both versions included the same number of reading units, genres, and question types, although they differed in the specific texts and, in some cases, the questions associated with those texts. These two versions were piloted with a sample of 340 secondary-school students. Reading comprehension items showed moderate difficulty levels (M = .50, SD = .08 for Grades 7–8; M = .56, SD = .11 for Grades 9–10) and acceptable internal reliability (McDonald’s ω: M = .64, SD = .09 and M = .68, SD = .10, respectively). The final version of the program was developed after revising the most problematic items. Additionally, the sequencing of reading units was informed by overall comprehension scores for each text, ensuring a gradual increase in difficulty throughout the intervention. Texts. The texts covered a variety of topics and were categorized along three dimensions: genre (narrative vs. informational), source (single-source vs. multiple-source texts), and format (continuous vs. mixed texts). Following the PISA 2018 reading framework (OECD, 2019 ), single-source texts are defined by a common authorship, publication context, or reference framework, whereas multiple-source texts include materials from different authors, publication dates, or reference sources. Continuous texts consist of sentences organized into paragraphs (e.g., narratives, science texts), whereas mixed texts combine continuous text with non-continuous elements such as tables, graphs, maps, or diagrams. In addition to overall text difficulty, the sequencing of reading units ensured systematic variation across these three dimensions. The average length of the texts was 1083.69 words (SD = 492.58) for Grades 7–8 and 1150.06 words (SD = 585.74) for Grades 9–10. Comprehension questions. Each reading unit included 14 comprehension questions: primarily multiple-choice items (11–12 per unit) together with a small number of associating or ordering tasks (2–3 per unit). These questions targeted a range of reading processes, including understanding text structure, linguistic and literary knowledge, vocabulary, information retrieval, inference-making, prediction, paraphrasing, summarization, application, reflection, author intention, intertextual integration, text–image integration, and source evaluation. Table 2 presents the elaborated feedback messages associated with each question type and illustrates the reading processes and strategies that students were expected to activate when answering the questions. The average reliability of comprehension scores (McDonald’s ω) was .70 (SD = .03, range [.67, .77]) for Grades 7–8 and .71 (SD = .04, range [.65, .77]) for Grades 9–10. Fidelity of Implementation . Both the platform company and part of the research team were in direct contact with the teachers to resolve potential technical issues and schedule the reading sessions on new dates (e.g., due to local holidays), in which case two readings were completed the following week on separate days. Additionally, the fidelity measures of the implementation were measured at student and teacher level. At the end of each week, teachers completed a fidelity report per classroom on the implementation of that week's assigned reading unit (i.e. dosage). Furthermore, the student data registered in the system indicated the actual number of reading units completed by the participants (i.e., student exposure). The mean percentage of reading units’ dosage was 98.5% (SD = 3.72%), while the average student exposure was 84.0% (SD = 18.1%), indicating that, overall, teachers and students adhered to the intervention as planned. Measures Digital reading comprehension. Students’ digital reading comprehension was measured using a standardized test for Spanish secondary-school students (CompLEC; Llorens et al., 2011 ). The test includes five digital expository texts of different types: a single text, multiple texts, a text with graphics, and a diagram. The structure of the test and the types of questions are aligned with the PISA framework for digital reading literacy (OECD, 2023 ), and some of the texts included in the instrument are drawn from released PISA reading materials. The test consisted of 20 multiple-choice questions requiring students to locate (e.g., For Arturo, nuclear energy is the best alternative. Why ?), integrate (e.g., Although there are many points of disagreement, Arturo and Sonia agree that …), and reflect on textual information (e.g., Luis is an engineer who has been working at a nuclear power plant for years and believes that his work makes a significant contribution to society. Who do you think he would agree with (Arturo or Sonia) ?). The original CompLEC test was used as the pre-test to assess students’ digital reading comprehension, whereas a parallel version with similar characteristics was piloted and subsequently used as the post-test. Regarding reliability, omega coefficients for the digital reading comprehension scores were .816 and .782 for the pre-test and post-test, respectively. Procedure The intervention was conducted from October 2023 to February 2024 under the online supervision of both the research team and the platform company. Prior to the start of the intervention, teachers received calendars, written instructions, and instructional videos explaining how to use the platform. These materials varied slightly depending on the experimental condition assigned to each classroom, particularly regarding the moment when comprehension questions appeared and the type of feedback students would receive. Teachers were also informed that students should complete the activities autonomously, limiting their intervention to assistance related to the technical use of the platform. This preparation period allowed teachers to familiarize themselves with the platform and to address any questions with the research team before the intervention began. Students’ digital reading comprehension was assessed via the platform during the first week of the study (pre-test). Over the subsequent 16 weeks, students completed the reading units. One week after completing the intervention, digital reading comprehension was reassessed using the post-test, coinciding with the start of the control group’s intervention. After the intervention concluded, participating schools were thanked and provided with a summary report of the study results. Data Analysis Following What Works Clearinghouse guidelines (2022), baseline equivalence across the four intervention conditions and the control group was first examined. To this end, linear mixed-effects models (LMMs) were estimated with initial digital reading comprehension as the outcome variable and intervention condition as the fixed predictor, controlling for gender and school type. Classrooms were specified as random intercepts to account for the clustered assignment at the classroom level. Intervention effects were subsequently analyzed separately for the Grades 7–8 and Grades 9–10 cohorts using LMMs. Post-test reading comprehension scores served as the dependent variable. Fixed effects included baseline digital reading comprehension (grand-mean centered), gender, school type, intervention condition, and the interaction between baseline digital reading comprehension and intervention condition. Random intercepts for classrooms were included to account for the nested data structure. All models were estimated using restricted maximum likelihood (REML), which provides unbiased estimates of variance components and accommodates incomplete data under missing-at-random assumptions (Enders, 2010). To ensure adequate implementation fidelity, analyses were restricted to classrooms delivering at least 80% of the scheduled sessions, consistent with commonly applied thresholds for structural dosage in intervention research (Carroll et al., 2007 ; O’Donnell, 2008 ). All classrooms met this criterion; therefore, no exclusions were necessary. The control group was retained in all analyses. All statistical analyses were conducted in R (version 4.5.1; R Core Team, 2023) using the following packages: dplyr (Wickham et al., 2021 ), lme4 (Bates et al., 2015 ), lmerTest (Kuznetsova et al., 2017 ), emmeans (Lenth, 2023), and ggplot2 (Wickham, 2016 ). Results To address the study objectives, analyses first examined baseline equivalence across intervention conditions before evaluating intervention effects on digital reading comprehension. Table 3 presents pre- and post-test descriptive statistics of digital reading comprehension scores for both grade cohorts, disaggregated by experimental condition and control group. Table 3 Pre-test and post-test means (SE) of digital reading comprehension scores by grade cohort and condition. Grades 7–8 Grades 9–10 IEC IEE IPC IPE CG IEC IEE IPC IPE CG Pre-test 8.88 (4.21) 9.36 (3.83) 10.55 (4.03) 9.24 (3.75) 9.91 (4.39) 12.38 (4.19) 12.62 (4.08) 12.16 (3.71) 11.90 (4.00) 12.47 (4.18) Post-test 9.79 (4.25) 10.52 (4.24) 11.35 (4.05) 9.63 (3.85) 11.60 (4.24) 12.73 (4.74) 13.13 (3.77) 13.15 (3.88) 12.69 (3.77) 12.40 (3.76) n 177 197 214 154 248 153 90 117 158 174 Note . IEC: Intervention with Embedded questions and Corrective feedback; IEE: Intervention with Embedded questions and Elaborated feedback; IPC: Intervention with Post-reading questions and Corrective feedback; IPE: Intervention with Post-reading and Elaborated feedback; CG: Control Group. Baseline equivalence between the four intervention conditions and the control group was assessed using linear mixed-effects models predicting initial digital reading comprehension scores, controlling for gender and school type and including random intercepts for classrooms. No statistically significant differences were found in baseline digital reading comprehension scores between any intervention condition and the control group (all ps > .190). Furthermore, post hoc pairwise comparisons (Tukey-adjusted) revealed no significant differences among the intervention conditions (all ps > .270), supporting satisfactory baseline equivalence for both the Grades 7–8 and Grades 9–10 samples. For the model estimated for Grades 7–8 students (Table 4 ), no significant interactions were found between intervention condition and baseline digital reading comprehension, suggesting that the association between baseline and post-test digital reading comprehension scores did not differ across instructional conditions. Neither the main effects of intervention condition nor the effect of school type (included as a control variable) were statistically significant predictors of post-test digital reading comprehension scores. In contrast, baseline digital reading comprehension scores was a significant predictor (p < .001), indicating that students with higher baseline scores tended to achieve higher post-test digital reading comprehension scores. Gender (included as a control variable) was also significant (p < .001), with girls outperforming boys overall. Table 4 Linear mixed-effects model predicting post-test digital reading comprehension for 7th−8th grade students (≥ 80% dosage fidelity) Fixed effects β (Estimate) SE 95% CI t(df) p Intercept 11.87 0.71 10.48–13.26 16.80 (49.75) < 0.001 Baseline digital reading comprehension 0.60 0.06 0.49–0.72 10.30 (649.98) < 0.001 Gender (1 = Male) -0.99 0.24 -1.47 –-0.52 -4.13 (640.36) < 0.001 School (1 = Non-public) -0.43 0.56 -1.53–0.66 -0.78 (44.48) 0.438 IEE vs CG -0.65 0.74 -2.10–0.79 -0.89 (38.30) 0.376 IEC vs CG -0.90 0.79 -2.45–0.64 -1.15 (41.12) 0.252 IPE vs CG -0.93 0.79 -2.49–0.63 -1.17 (41.26) 0.241 IPC vs CG 0.09 0.77 -1.43–1.60 0.11 (39.54) 0.910 Baseline digital reading comprehension x IEE -0.05 0.09 -0.22–0.13 -0.52 (667.88) 0.606 Baseline digital reading comprehension x IEC -0.05 0.10 -0.24–0.14 -0.53 (660.37) 0.595 Baseline digital reading comprehension x IPE -0.00 0.11 -0.22–0.22 -0.02 (667.89) 0.982 Baseline digital reading comprehension x IPC -0.11 0.09 -0.29–0.07 -1.15 (666.61) 0.249 Random Effects Variance SD Class 2.14 1.46 Residual 9.44 3.07 Model Fit ICC 0.19 Marginal R2 0.340 Conditional R2 0.462 N Class 51 Observations 681 Note . SE = standard error; CI = confidence interval; ICC = Intraclass Correlation Coefficient ; SD = standard deviation. Baseline reading comprehension was mean-centered prior to analysis, and all interaction terms used the centered scores. For the model estimated for Grades 9–10 students (Table 5 ), a significant interaction was found between baseline digital reading comprehension and one intervention condition (IEC; p = .043) (Fig. 2 ). This finding indicates that the relationship between baseline and post-test digital reading comprehension scores differed across instructional conditions. Specifically, compared with the control group, the association between baseline and post-test scores was stronger in this condition, suggesting that students with higher initial digital reading comprehension tended to achieve relatively higher post-test scores in this experimental condition. As observed in the younger cohort, baseline digital reading comprehension was a strong positive predictor of post-test scores (p < .001). Gender also remained significant (p = .001), with girls outperforming boys overall. Neither the main effects of the remaining intervention conditions nor the effect of school type reached statistical significance Table 5 Linear mixed-effects model predicting post-test digital reading comprehension for 9th−10th grade students (≥ 80% dosage fidelity) Fixed effects Predictors β (Estimate) SE 95% CI t(df) p Intercept 12.06 1.23 9.65–14.48 9.81 (22.08) < 0.001 Baseline digital reading comprehension 0.36 0.07 0.22–0.49 5.14 (518.47) < 0.001 Gender (1 = Male) -0.92 0.27 -1.45 – -0.40 -3.46 (506.86) 0.001 School (1 = Non-public) 0.47 0.88 -1.27–2.21 0.53 (23.20) 0.595 IEE vs CG 1.39 1.36 -1.27–4.06 1.03 (21.98) 0.306 IEC vs CG 0.16 1.21 -2.22–2.53 0.13 (20.67) 0.897 IPE vs CG 0.28 1.24 -2.16–2.72 0.23 (21.56) 0.821 IPC vs CG 0.51 1.23 -1.89–2.92 0.42 (21.58) 0.675 Baseline digital reading comprehension x IEE 0.22 0.12 -0.01–0.45 1.85 (526.85) 0.065 Baseline digital reading comprehension x IEC 0.21 0.10 0.01–0.41 2.03 (532.27) 0.043 Baseline digital reading comprehension x IPE 0.11 0.10 -0.09–0.31 1.05 (526.07) 0.294 Baseline digital reading comprehension x IPC -0.07 0.11 -0.29–0.16 -0.59 (531.86) 0.554 Random Effects Variance SD Class 4.47 2.11 Residual 8.82 2.97 Model Fit ICC 0.34 Marginal R2 0.223 Conditional R2 0.484 N Class 37 Observations 546 Note. SE = standard error; CI = confidence interval; ICC = Intraclass Correlation Coefficient ; SD = standard deviation; Baseline reading comprehension was mean-centered prior to analysis, and all interaction terms used the centered scores. Taken together, the findings provide limited evidence for overall intervention effects on digital reading comprehension. Baseline digital reading comprehension emerged as the strongest predictor of post-test performance, underscoring the stability of individual differences in digital literacy outcomes. While no consistent main effects of question placement or feedback type were observed, the significant interaction identified in the older cohort suggests that the impact of specific instructional design features may vary depending on students’ initial reading proficiency. This pattern highlights the importance of examining how instructional components interact with learner characteristics when evaluating digital literacy interventions in authentic classroom contexts. Discussion The present study examined the effectiveness of a classroom-based digital reading intervention and investigated whether specific instructional design features—namely question placement and feedback type—interacted with students’ initial digital reading comprehension. Drawing on prior research, we hypothesised that sustained engagement with structured digital reading activities would improve digital reading comprehension outcomes, that embedded questions and elaborated feedback would yield stronger benefits than their respective alternatives, and that these effects might vary depending on students’ baseline digital reading levels. Overall effects of the digital reading intervention Contrary to expectations, the intervention did not produce robust overall improvements in standardized post-test digital reading comprehension beyond differences explained by baseline comprehension. In both cohorts, initial digital reading comprehension emerged as the strongest predictor of post-test performance, indicating substantial continuity in students’ literacy profiles over time. These findings suggest that, within the duration and structure of the intervention, incorporating adjunct questions and feedback into digital reading practice was insufficient to generate broad transfer effects to a standardized measure of digital reading comprehension. Importantly, these findings should be interpreted in light of methodological differences from much of the prior literature. Many previous studies on adjunct questions and feedback have examined short-term interventions and evaluated outcomes using task-specific questions closely aligned with the instructional activities. In contrast, the present study implemented a longer classroom intervention with a large and diverse sample and assessed outcomes through a standardized digital reading comprehension measure designed to capture transfer beyond the practiced texts. While this design enhances ecological validity and educational relevance, it may also reduce the likelihood of detecting large effects, as transfer to generalized reading comprehension typically requires more sustained and explicit instructional support than improvements observed on task-aligned assessments. Effects of question placement With respect to question placement, the study did not detect consistent advantages of embedded over post-reading questions in terms of transfer to new texts. This contrasts with prior research suggesting that embedded questions foster active and continuous processing (e.g., Peverly & Wood, 2001 ; Rubio et al., 2022 ). One possible explanation is that embedded questions may enhance local processing within specific texts but do not necessarily promote the metacognitive awareness or strategic abstraction required for transfer. Without explicit guidance helping students reflect on how and why embedded questioning supports comprehension, the benefits may remain task-bound. At the same time, the absence of clear differences between embedded and post-reading questions is consistent with studies reporting comparable learning outcomes across question placements. For instance, Uner and Roediger ( 2018 ) found that retrieval practice improved learning from textbook materials regardless of whether questions were presented during reading or after completing the text. These findings suggest that the benefits of question-based activities may depend less on their exact position in the reading sequence than on the opportunity they provide for retrieval and engagement with the material. Interestingly, in the older cohort, the condition combining embedded questions with corrective feedback showed comparatively more favourable post-test results. Although limited in scope and requiring cautious interpretation, this pattern suggests that the effects of embedded questioning may depend on learners’ developmental characteristics and reading proficiency. Developmental research supports this interpretation. Van den Broek et al. ( 2001 ) found that questioning during reading facilitated comprehension mainly among more advanced readers, whereas it could interfere with younger or less skilled students due to the additional processing demands imposed by answering questions while reading. They argued that as reading skills become more automatic, inferential questions help direct attention to relevant information; however, for less proficient readers, such questions may increase cognitive load and disrupt comprehension processes. This developmental pattern may help explain why potential benefits of embedded questions were more evident among older students in the present study. Effects of feedback type Contrary to expectations derived from meta-analytic evidence (e.g., van der Kleij et al., 2015 ; Shute, 2008 ), elaborated feedback did not demonstrate overall superiority over corrective feedback. Several factors—largely related to the design of the feedback messages implemented in the present intervention—may account for this finding. First, the effectiveness of elaborated feedback depends not only on informational richness but also on students’ engagement with the feedback message (Golke et al., 2015 ; Máñez et al., 2019 ). In digital environments, written feedback may be processed superficially, particularly when embedded in low-stakes activities or when messages are perceived as repetitive. For example, Golke et al. ( 2015 ) reported that computer-delivered elaborated feedback did not improve deep-level comprehension when students showed limited engagement with feedback messages, whereas improvements emerged when feedback was mediated by a person. In the present study, teachers were instructed not to provide additional support in order to preserve the experimental manipulation. While this decision strengthened internal validity, it may also have reduced opportunities for instructional scaffolding. Second, the elaborated feedback used in the intervention primarily indicated the reading strategy required to answer each question but did not explicitly guide students through the procedural steps needed to implement that strategy. Thus, although it highlighted the strategic focus of the task, it may not have provided sufficiently detailed scaffolding to support effective strategy enactment. Third, the diversity of texts across the 16 reading units—varying in topic, genre, source structure, and format—may have increased the cognitive demands associated with transferring strategic reading skills across heterogeneous materials, thereby attenuating observable effects on a standardized outcome measure. Notably, in the older cohort, the combination of embedded questions and corrective feedback yielded comparatively stronger post-test outcomes. One possible interpretation is that concise corrective messages were more readily processed than elaborated strategy-oriented feedback, particularly if the latter were perceived as repetitive or insufficiently instructive. This interpretation aligns with prior findings suggesting that feedback effectiveness may depend on learners’ self-regulatory profiles and developmental characteristics (Golke et al., 2015 ; Máñez et al., 2026). Individual differences in digital reading comprehension A significant interaction between baseline digital reading comprehension and one instructional condition was observed in the older cohort in relation to post-test performance. Specifically, the combination of embedded questions and corrective feedback showed comparatively more favourable outcomes. Although limited in scope and requiring cautious interpretation, this finding suggests that certain instructional configurations may differentially influence longer-term comprehension outcomes depending on students’ initial literacy levels. Rather than assuming that greater informational richness uniformly enhances learning, these results indicate that the clarity, usability, and perceived relevance of feedback may be equally important determinants of its impact. In addition, gender emerged as a significant covariate in both cohorts, with girls outperforming boys in post-test digital reading comprehension. This pattern is consistent with large-scale literacy assessments documenting gender differences in reading proficiency across both print and digital contexts (OECD, 2023 ). Although gender was not a focal variable in the present study, this finding underscores the importance of considering variability in learner characteristics when designing digital literacy interventions. It is also noteworthy that the sample included 140 students identified by their teachers as having diagnosed special educational needs (SEN). Although subgroup analyses were not conducted due to limited statistical power, their inclusion strengthens the ecological validity of the sample and reflects the heterogeneity typical of regular secondary classrooms. From a Multi-Tiered System of Supports (MTSS) perspective, these results reinforce the notion that universal instructional practices may not affect all students equally. Responsiveness to intervention appears to vary as a function of prior competence and developmental stage, and may also reflect broader learner characteristics such as gender differences in reading proficiency. The absence of strong universal effects suggests that embedding questions and feedback into digital reading tasks may not be sufficient as a Tier 1 instructional strategy. Instead, differentiated or intensified supports—such as explicit strategy instruction, adaptive feedback systems, or increased teacher mediation—may be necessary to address the needs of diverse learner profiles. Limitations and educational implications Several limitations should be acknowledged. Although the intervention spanned 16 weeks and was implemented under ecologically valid classroom conditions, the dosage may still have been insufficient to produce measurable gains in standardized digital reading outcomes. Feedback was delivered exclusively in written form, and qualitative aspects of teacher mediation and students’ depth of processing were not directly assessed. Furthermore, the interaction observed in the older cohort was limited to one condition and therefore warrants replication. Despite these limitations, the study contributes meaningfully to both research and practice. The intervention involved the development of a substantial corpus of diverse digital texts and comprehension questions designed for authentic classroom implementation and delivered by regular teachers. While the findings indicate that further refinement is needed—particularly regarding feedback depth, explicit strategy scaffolding, and support for transfer—the program provides a feasible framework for integrating structured digital reading practice into secondary education. Rather than offering definitive solutions, the present work should be understood as part of an iterative process aimed at identifying pedagogically meaningful and evidence-informed approaches to fostering digital reading comprehension. Advancing such efforts will require continued collaboration between researchers and educators to ensure that digital literacy interventions are not only theoretically grounded but also responsive to the complexities of classroom practice. Declarations Author Contribution - Conceptualization: Laura Gil, Amelia Mañá, Ladislao Salmerón, Cristina Vargas-- Methodology: Laura Gil, Amelia Mañá, Mario Romero-Palau, Marian Serrano-Mendizábal, Cristina Vargas- Formal analysis: Cristina Vargas- Data curation: Laura Gil, Amelia Mañá, Marian Serrano-Mendizábal, Mario Romero-Palau.- Writing – original draft: Laura Gil, Amelia Mañá, Mario Romero-Palau, Marian Serrano-Mendizábal, Ladislao Salmeron, Cristina Vargas- Supervision: Laura Gil, Ladislao Salmeon.- Funding acquisition: Ladislao Salmerón Data Availability The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. References Afflerbach, P., Cho, B. Y., & Kim, J. Y. (2015). Conceptualizing and assessing higher-order thinking in reading. Theory into Practice , 54 (3), 203–212. https://doi.org/10.1080/00405841.2015.1044367 Afflerbach, P., & Cho, B. Y. (2009). 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Barzillai, J. Thomson, S. Schroeder, & P. Broek (Eds.), Learning to read in a digital world (pp. 57–90). John Benjamins Publishing Company. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8995659","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600659883,"identity":"cf5e6e0f-76b2-491a-8374-e2d1f7db9e7e","order_by":0,"name":"Laura Gil","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYBACPijNw8feQKQWNpgWNp4DJGphYJNIIFYL+xmzDz8YamXYJN+YSTBU1BGhhSfHeGYPw3EeNukcoJYzh4lxWI4xAw/DMZAWYwPGtgNEaOF/Y8z4B6RF8gxQyz9iHCaRY8zMw1DDwybBY/iAsYGZGC3PipllDA4AAzmt8EHCMSL8ws+fvJnxTUWdPT/74Q0HPtQQ4TAIMIAankCsBiAg2vBRMApGwSgYiQAAU+UpQw8Vn2EAAAAASUVORK5CYII=","orcid":"","institution":"University of Valencia","correspondingAuthor":true,"prefix":"","firstName":"Laura","middleName":"","lastName":"Gil","suffix":""},{"id":600659884,"identity":"c046d8c4-940e-4d01-89ec-06d1bf39db87","order_by":1,"name":"Amelia Mañá","email":"","orcid":"","institution":"University of Valencia","correspondingAuthor":false,"prefix":"","firstName":"Amelia","middleName":"","lastName":"Mañá","suffix":""},{"id":600659888,"identity":"dba72f1e-c3d9-40d3-bdb5-a705d6ec8855","order_by":2,"name":"Mario Romero-Palau","email":"","orcid":"","institution":"University of Valencia","correspondingAuthor":false,"prefix":"","firstName":"Mario","middleName":"","lastName":"Romero-Palau","suffix":""},{"id":600659889,"identity":"f8eea420-3366-4404-b7cc-1d6aabc823b0","order_by":3,"name":"Marian Serrano-Mendizabal","email":"","orcid":"","institution":"University of Valencia","correspondingAuthor":false,"prefix":"","firstName":"Marian","middleName":"","lastName":"Serrano-Mendizabal","suffix":""},{"id":600659890,"identity":"a979c539-ab0f-4bf8-ac6b-90d74d14f298","order_by":4,"name":"Ladislao Salmeron","email":"","orcid":"","institution":"University of Valencia","correspondingAuthor":false,"prefix":"","firstName":"Ladislao","middleName":"","lastName":"Salmeron","suffix":""},{"id":600659891,"identity":"559d9b3b-bcb8-465f-8e3f-5cefc0d42cbb","order_by":5,"name":"Cristina Vargas","email":"","orcid":"","institution":"University of Valencia","correspondingAuthor":false,"prefix":"","firstName":"Cristina","middleName":"","lastName":"Vargas","suffix":""}],"badges":[],"createdAt":"2026-02-28 13:24:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8995659/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8995659/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103994233,"identity":"0954ed74-5133-44aa-90eb-f4c05836d5c5","added_by":"auto","created_at":"2026-03-05 12:18:36","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":172363,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eExample of text and embedded comprehension question on the online platform\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8995659/v1/9b71969c165ab1723a9afc40.jpg"},{"id":103994232,"identity":"664089a1-4b77-4431-9ae8-0749dd45f25d","added_by":"auto","created_at":"2026-03-05 12:18:36","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":49118,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eInteraction between baseline digital reading comprehension and IEC compared to CG.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8995659/v1/b40027e804458fc7ba6b466c.jpg"},{"id":104402267,"identity":"e9d951cd-845f-47c5-ba23-9edf7122bba8","added_by":"auto","created_at":"2026-03-11 12:14:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1350044,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8995659/v1/a70b5614-44b8-47c1-971a-3dd501b63362.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing Digital Reading Comprehension in Secondary Education: Effects of Question Placement and Feedback Type","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs with many educational innovations, the integration of digital reading tools in classrooms has progressed faster than the development of scientific evidence on how digital environments can effectively support reading comprehension for all students. Recent meta-analyses and large-scale studies indicate that the transition from paper to digital reading may pose challenges for some learners. For instance, digital texts have been found to be slightly less comprehensible than their printed counterparts across different age groups (Clinton, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Delgado et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Furenes et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kong et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similarly, more frequent classroom use of digital reading tools has been negatively associated with students\u0026rsquo; comprehension performance in large representative samples of American students in Grades 4 and 8 (Salmer\u0026oacute;n et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Digital reading exposure has also been proposed as a factor affecting sustained attention during reading (Wolf, 2018). These findings suggest that the introduction of digital reading activities in schools cannot be assumed to have neutral or automatically positive effects on comprehension.\u003c/p\u003e \u003cp\u003eAt the same time, digital reading comprehension has become a core competence for participation in contemporary learning environments and for achieving broader educational goals such as those outlined in the Sustainable Development Goals (OECD, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As digital reading underpins academic, social, and professional activities, identifying effective instructional practices to support comprehension in digital contexts has become a key educational priority. Such practices should also account for individual differences among learners, including gender, socioeconomic background, and variability in reading ability. Despite its growing importance, recent large-scale evidence indicates that many secondary school students struggle to process complex digital texts, particularly those requiring the coordination of multiple cognitive processes such as identifying main ideas, integrating information across documents, and evaluating sources (OECD, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Data from PISA 2022 illustrate this challenge: although most 15-year-old students achieve basic proficiency levels, only about one quarter reach advanced levels requiring integration across multiple sources and critical evaluation of claims. These findings highlight the need for instructional approaches capable of strengthening higher-order digital reading skills across diverse student populations.\u003c/p\u003e \u003cp\u003eIn response to this need, the present study examines how specific instructional design features embedded in digital reading activities may support comprehension development in secondary school students. Specifically, we investigate the effects of two common components of reading interventions\u0026mdash;comprehension question placement (embedded vs. post-reading) and feedback type (corrective vs. elaborated)\u0026mdash;while considering their interaction with students\u0026rsquo; initial levels of digital reading competence. This study addresses principles underlying the multi-tiered system of supports (MTSS) framework by examining instructional practices that can be flexibly adapted to students\u0026rsquo; literacy needs (Brown-Chidsey, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eIndividual differences in digital reading strategies\u003c/h3\u003e\n\u003cp\u003eTo function effectively in contemporary information environments, students must comprehend digital texts both strategically and critically. Beyond traditional comprehension strategies used in print reading, digital reading requires additional skills such as navigating information spaces, integrating multiple sources and formats, and evaluating the credibility of online information (Richter \u0026amp; Maier, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Salmer\u0026oacute;n et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Understanding how students differ in these skills is particularly relevant from an MTSS perspective, where instruction is expected to respond to learner variability through progressively differentiated supports. Research suggests that findings from print literacy cannot always be directly generalized to digital reading contexts (Leu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Accordingly, growing attention has examined how learner characteristics shape performance in digital reading tasks (Afflerbach, 2015; Cho et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Coiro, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Salmer\u0026oacute;n et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Although a comprehensive review is beyond the scope of this article, several learner characteristics consistently emerge as relevant, including gender, cognitive resources, reading competence, and metacognitive strategy use.\u003c/p\u003e \u003cp\u003eRegarding gender, the well-documented advantage of girls in print reading comprehension appears to persist in digital contexts. In PISA 2022, girls outperformed boys in digital reading across participating countries, with an average difference of nearly 30 score points across OECD nations (OECD, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similar patterns have been reported in studies of digital reading comprehension among secondary school students (Forzani, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCognitive and linguistic resources also play an important role. Factors such as prior knowledge, vocabulary, language proficiency, working memory capacity, and strategic reading behaviors support effective digital text processing (Wylie et al., 2018). Among these variables, print reading competence consistently emerges as one of the strongest predictors of digital reading performance (Afflerbach \u0026amp; Cho, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Cho \u0026amp; Afflerbach, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Coiro, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Leu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Students with stronger foundational reading skills tend to achieve better outcomes when reading digital texts (Kanniainen et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, metacognitive reading strategies significantly predict comprehension in digital environments (Salmer\u0026oacute;n et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Together, these findings highlight the importance of considering both foundational literacy skills and strategic regulation processes when designing digital reading interventions.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eThe role of adjunct questions in students\u0026rsquo; reading comprehension\u003c/h2\u003e \u003cp\u003eDigital reading comprehension depends not only on textual content and reader characteristics but also on the design of the tasks accompanying the reading activity. One common classroom practice involves answering questions related to a text (i.e., text-based questions), a format that also underlies many standardized comprehension assessments (Ness, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). According to the RESOLV model (Rouet et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), such questions function as task instructions that guide readers in establishing reading goals (Cerd\u0026aacute;n et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; McCrudden \u0026amp; Schraw, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). By directing attention to specific parts of the text and prompting readers to generate inferences or evaluate information, questions can shape the strategic processes underlying comprehension (Rouet et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, 2018). However, their effectiveness depends on several design features, including question complexity, text availability during answering, and question placement relative to the reading process (Cerd\u0026aacute;n et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Jensen et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ozuru et al., 2007; Tawfik et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong these variables, question placement has received considerable attention. Embedded questions appear during reading and require immediate engagement with the text (Dornisch, 2012), whereas post-reading questions are presented only after the text has been read in full. Evidence comparing these approaches remains mixed. Some studies report advantages for embedded questions, suggesting that they direct attention to relevant information and support ongoing comprehension processes (Kapp et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Peverly \u0026amp; Wood, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Philips et al., 2020; Rubio et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; van den Broek et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Others have found no significant differences (Uner \u0026amp; Roediger, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Weinstein et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and some suggest that embedded questions may hinder comprehension for highly skilled readers by fragmenting the mental representation of the text (Cerd\u0026aacute;n et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Salmer\u0026oacute;n et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMost prior studies have examined short-term effects of adjunct questions on comprehension of specific texts. Less is known about whether extended practice with such questions leads to broader improvements in digital reading comprehension. The present study addresses this issue by examining whether sustained exposure to comprehension questions during a 16-week intervention transfers to performance on a standardized measure of digital reading comprehension.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFeedback in the context of answering comprehension questions\u003c/h3\u003e\n\u003cp\u003eIn digital reading instruction, comprehension questions are frequently complemented by feedback provided after students respond, potentially enhancing their instructional impact. Feedback is widely recognized as a central instructional tool for supporting learning (Hattie \u0026amp; Gan, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Shute, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Broadly defined, it refers to information\u0026mdash;oral, written, or visual\u0026mdash;provided during or after task performance with the aim of reducing the gap between current performance and a desired goal (Hattie \u0026amp; Timperley, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In this sense, feedback functions as a regulatory scaffold that informs learners about their level of achievement, clarifies expectations, and guides subsequent improvement (Boud \u0026amp; Molloy, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This view aligns with approaches that use feedback as instructional assistance when readers encounter comprehension difficulties. For example, Golke et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) implemented elaborated feedback designed to help students recognize comprehension problems signaled by incorrect responses to text-based questions and to provide cognitive and metacognitive hints supporting error correction and transfer to subsequent tasks. Grounded in the notion of the zone of proximal development, this approach is consistent with principles of dynamic assessment, in which learners\u0026rsquo; potential within a performance domain is inferred from the extent to which their performance improves following feedback (Sternberg \u0026amp; Grigorenko, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). From this perspective, feedback functions not merely as an evaluative device but also as a scaffold that reveals and fosters learners\u0026rsquo; developing comprehension abilities. The present study adopts a similar view, conceptualizing feedback as an instructional support aimed at facilitating students\u0026rsquo; digital reading comprehension during sustained classroom practice.\u003c/p\u003e \u003cp\u003eIn reading comprehension contexts, feedback can be conceptualized as information provided in response to students\u0026rsquo; answers to text-based questions. A meta-analysis by Swart et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported a positive overall effect of feedback on comprehension outcomes (d\u0026thinsp;=\u0026thinsp;0.35), although its effectiveness depended on design characteristics such as timing and message richness. Regarding timing, feedback delivered after reading can support comprehension outcomes (Swart et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), whereas feedback provided during reading may facilitate strategic processes such as information search and self-regulation (Swart et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Immediate feedback guiding the processes required to reach correct answers may also improve later reading performance (Llorens et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In digital reading contexts, feedback timing is often closely linked to task design. When comprehension questions are embedded within the text, feedback occurs during reading, whereas post-reading questions entail feedback delivered after text processing. The present study reflects this distinction: feedback was consistently provided after students answered comprehension questions, but the questions were positioned either during reading or after reading the text. In both conditions, students were allowed to revisit the text when answering the questions, ensuring comparable access to textual information.\u003c/p\u003e \u003cp\u003eMessage richness also plays a decisive role. Feedback may range from simple corrective responses (e.g., \u0026ldquo;correct\u0026rdquo; or \u0026ldquo;incorrect\u0026rdquo;) to more elaborated forms that provide explanatory information or strategic guidance. Meta-analytic evidence suggests that elaborated feedback, which focuses on the processes required to solve the task, is generally more effective than purely corrective feedback (Hattie \u0026amp; Timperley, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; van der Kleij et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Moreover, students with lower proficiency levels tend to benefit more from elaborated feedback than more advanced readers (Shute, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), likely because it provides additional information that helps identify and correct misunderstandings (van der Kleij et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Digital learning environments further expand the instructional potential of feedback through their capacity for personalization. However, research indicates that written feedback in digital contexts is often processed superficially, potentially limiting its impact (Golke et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; M\u0026aacute;\u0026ntilde;ez et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For example, Golke et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) found that computer-delivered elaborated feedback did not improve sixth graders\u0026rsquo; deep-level text comprehension when students showed limited engagement with feedback messages, whereas improvements emerged when feedback was mediated by a person. Related evidence suggests that students sometimes devote more attention to simple corrective feedback than to elaborated feedback, particularly after incorrect responses (M\u0026aacute;\u0026ntilde;ez et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond immediate performance effects, feedback has also been shown to foster the development and transfer of reading strategies. A meta-analysis by Swart et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that comprehension-focused feedback significantly increased strategy use (d\u0026thinsp;=\u0026thinsp;0.61) and improved text comprehension (d\u0026thinsp;=\u0026thinsp;0.34), with effects extending to new reading tasks. However, emerging evidence suggests that the effectiveness of feedback may vary across educational stages and learners\u0026rsquo; self-regulatory profiles. For example, recent intervention research has reported differential effects of elaborated and corrective feedback depending on students\u0026rsquo; grade level and patterns of strategic engagement (Ma\u0026ntilde;a et al., 2026).\u003c/p\u003e \u003cp\u003eTaken together, this evidence indicates that well-designed feedback\u0026mdash;particularly when enriched with explanatory or strategic information\u0026mdash;can enhance comprehension outcomes and support the strategic processes underlying literacy development. From an MTSS perspective, such feedback mechanisms contribute to differentiated literacy support within classroom instruction.\u003c/p\u003e\n\u003ch3\u003eThe current study\u003c/h3\u003e\n\u003cp\u003eAgainst this backdrop, the present study examines the effectiveness of a classroom-based digital reading intervention designed to support secondary students\u0026rsquo; digital reading comprehension while accounting for variability in learners\u0026rsquo; literacy profiles. The study focuses on how instructional design features\u0026mdash;specifically questions placement and feedback type\u0026mdash;may interact with students\u0026rsquo; initial digital reading competence to influence comprehension outcomes in digital environments.\u003c/p\u003e \u003cp\u003eIn the Spanish secondary education system (Grades 7\u0026ndash;10), schooling is typically organized into two educational cycles that differ in students\u0026rsquo; reading development (Llorens et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). To ensure appropriate task difficulty, the intervention materials were adapted to each cycle. Although no major differences between cycles were anticipated, analyses were conducted separately to account for potential developmental variation.\u003c/p\u003e \u003cp\u003eA large and diverse sample of secondary school students participated in a classroom-based intervention implemented under ecologically valid conditions. Activities were integrated into regular classroom instruction and delivered by students\u0026rsquo; own teachers. Within the experimental condition, classes were assigned to a 2 \u0026times; 2 factorial design, manipulating (a) questions placement (embedded vs. post-reading) and (b) feedback type (corrective vs. elaborated). Over a 16-week period, students engaged with digital reading activities involving comprehension questions designed to foster strategic processing. Elaborated feedback messages focused on the comprehension strategies required to answer each question. Standardized measures of digital reading comprehension were administered before and after the intervention to assess potential transfer effects.\u003c/p\u003e \u003cp\u003eBased on prior research, four hypotheses were formulated. First, sustained practice with digital reading activities incorporating comprehension questions and feedback was expected to improve students\u0026rsquo; digital reading comprehension relative to regular instruction. Second, embedded questions were expected to yield greater benefits than post-reading questions, particularly for students with lower initial digital reading competence. Third, elaborated feedback providing strategic guidance was expected to produce stronger gains than purely corrective feedback. Finally, instructional design features were expected to interact with students\u0026rsquo; baseline digital reading comprehension.\u003c/p\u003e \u003cp\u003eAccordingly, the present study addressed the following research questions:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo what extent does sustained practice with digital reading activities incorporating comprehension questions and feedback improve students\u0026rsquo; digital reading comprehension compared with regular classroom instruction?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDoes the placement of comprehension questions (embedded vs. post-reading) differentially affect digital reading comprehension outcomes?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDoes feedback type (corrective vs. elaborated) influence digital reading comprehension development over time?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDo these instructional design features interact with students\u0026rsquo; initial digital reading comprehension\u0026mdash;and potentially with educational cycle\u0026mdash;suggesting differential benefits across learner profiles?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eOur initial sample consisted of 1682 secondary-school students (Grades 7\u0026ndash;10, ages 11\u0026ndash;15). After applying the inclusion criteria for the analyses\u0026mdash;namely, having parental consent and completing at least 80% of the intervention sessions\u0026mdash;the final sample comprised 1227 students (53.8% female) from 12 schools (4 public, 8 private) and 74 classrooms supervised by 39 teachers (M age\u0026thinsp;=\u0026thinsp;46.6 years, SD\u0026thinsp;=\u0026thinsp;11.0; 79.1% female; M teaching experience\u0026thinsp;=\u0026thinsp;18.4 years, SD\u0026thinsp;=\u0026thinsp;10.2). Participating schools represented diverse socio-educational contexts. Within this sample, 140 students were reported by their teachers as having diagnosed special educational needs (SEN), reflecting the diversity of regular classroom settings. Schools were invited through their management teams and teachers volunteered to participate. Students were randomly assigned to experimental conditions through a cluster-randomized design at the classroom level.\u003c/p\u003e \u003cp\u003e The study was approved by the Research Ethics Commission of the University of Valencia (reference PSILOG-3176086) and conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from parents or legal guardians.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDesign and Experimental Conditions\u003c/h2\u003e \u003cp\u003eThis study employed a quasi-experimental, pre-post, waiting-list control-group design, with the intervention group divided into four subgroups based on question placement (embedded vs. post-reading questions) and feedback type (corrective vs. elaborated feedback) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In accordance with ethical guidelines for school-based research, the control group received the intervention after completion of the post-test assessment. All experimental conditions were implemented through an online platform (see Fig.\u0026nbsp;1 for an example of an embedded question, which illustrates the platform interface and task presentation).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eResearch design overview\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExperimental conditions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eQuestion placement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eFeedback type\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmbedded\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePost-reading\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCorrective\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eElaborated\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote\u003c/em\u003e. IEC: Intervention with Embedded questions and Corrective feedback; IEE: Intervention with Embedded questions and Elaborated feedback; IPC: Intervention with Post-reading questions and Corrective feedback; IPE: Intervention with Post-reading and Elaborated feedback.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003cp\u003e \u003cb\u003eQuestion placement conditions.\u003c/b\u003e The intervention included two conditions differing in the placement of comprehension questions relative to the reading process within each text. In the embedded-question condition, comprehension questions were presented at predetermined points within the text, requiring students to respond before continuing to subsequent sections. In the post-reading condition, students read the entire text without interruption and answered all comprehension questions only after completing the reading. In both conditions, students were allowed to revisit the text while answering the questions if they considered it necessary.\u003c/p\u003e \u003cp\u003e\u003cb\u003eFeedback conditions.\u003c/b\u003e During the intervention, participants were given two attempts to answer the comprehension questions. In all experimental groups, when a correct response was provided on the first attempt, simple corrective feedback (e.g., \u0026ldquo;Correct answer\u0026rdquo;) was displayed. However, feedback messages following an initial incorrect response differed across experimental conditions. Groups assigned to the corrective feedback condition received the message \u0026ldquo;Incorrect answer, please try again\u0026rdquo; before proceeding to the second attempt. In contrast, groups assigned to the elaborated feedback condition received a more detailed message combining notification of incorrectness with a reading strategy recommendation aimed at supporting completion of the task required by the comprehension question (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for examples of elaborated feedback messages). After the second attempt, all experimental groups received corrective feedback indicating response accuracy. Elaborated feedback was explicitly designed to scaffold students\u0026rsquo; strategic reading processes rather than merely indicate answer correctness.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eElaborated feedback messages per question type\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of question\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElaborated feedback message\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePay attention to how the parts of the text are articulated to understand its structure.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLinguistic Knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTo answer this question, it is important to keep grammatical knowledge in mind.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiterary Knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTry to consider the genre of the text, the characteristics of its author, the time it was written, and the literary movement it belongs to.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVocabulary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConsidering the context, thinking of synonyms, or analyzing the words can help you with vocabulary questions. Remember you can use a dictionary.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe information you need appears clearly in the text or can be easily inferred.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInferences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIt is important to combine the information in the text with your own knowledge.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTry to make a reasonable prediction based on what the text indicates and what you know about it.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParaphrases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEven if they are expressed differently, the idea which is present in the text and the question should remain the same.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSummary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhen summarizing, it is important to focus on the most relevant information; try to locate it well.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApplication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEven though the situation may be different, there are ideas in the text that can be transferred here.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReflection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIt is important to make an assessment and ensure it is well-reasoned.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor's Intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegardless of what the text conveys, try to think about what the author intended to communicate by writing it.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntertextual Integration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTo answer this question, you need to look at how the ideas in different texts are related, especially their similarities, contributions, and disagreements.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eText-Image Integration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTo answer correctly, you must combine the written information with the visual information from images or graphs.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource Evaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRemember that the most reliable sources are those with qualified and impartial authors, published in specialized media, well-documented, and more up-to-date.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eIntervention program\u003c/h2\u003e \u003cp\u003e \u003cb\u003ePurpose and organization.\u003c/b\u003e The intervention aimed to improve students\u0026rsquo; digital reading comprehension through 16 reading units. Each unit included one or more texts\u0026mdash;depending on whether it belonged to the single-source or multiple-source category\u0026mdash;together with a set of comprehension questions. Students completed one unit per week through an online platform during regular classroom sessions under their teachers\u0026rsquo; supervision. Teachers received written instructions, an intervention schedule, and an 8-minute instructional video explaining the platform features, according to the experimental condition assigned to each classroom. Each week, they were given access to the corresponding reading unit and could schedule it within a regular 50-minute class session. The intervention included a curated set of reading units, aligned with secondary-school curriculum requirements and drawn from textbooks, institutional websites (e.g., museum sites), or materials specifically developed by literacy professionals. As noted above, due to age-related differences among secondary-school students, two versions of the program were created to ensure grade-appropriate materials: one for Grades 7\u0026ndash;8 and another for Grades 9\u0026ndash;10. Both versions included the same number of reading units, genres, and question types, although they differed in the specific texts and, in some cases, the questions associated with those texts. These two versions were piloted with a sample of 340 secondary-school students. Reading comprehension items showed moderate difficulty levels (M = .50, SD = .08 for Grades 7\u0026ndash;8; M = .56, SD = .11 for Grades 9\u0026ndash;10) and acceptable internal reliability (McDonald\u0026rsquo;s ω: M = .64, SD = .09 and M = .68, SD = .10, respectively). The final version of the program was developed after revising the most problematic items. Additionally, the sequencing of reading units was informed by overall comprehension scores for each text, ensuring a gradual increase in difficulty throughout the intervention.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTexts.\u003c/b\u003e The texts covered a variety of topics and were categorized along three dimensions: genre (narrative vs. informational), source (single-source vs. multiple-source texts), and format (continuous vs. mixed texts). Following the PISA 2018 reading framework (OECD, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), single-source texts are defined by a common authorship, publication context, or reference framework, whereas multiple-source texts include materials from different authors, publication dates, or reference sources. Continuous texts consist of sentences organized into paragraphs (e.g., narratives, science texts), whereas mixed texts combine continuous text with non-continuous elements such as tables, graphs, maps, or diagrams. In addition to overall text difficulty, the sequencing of reading units ensured systematic variation across these three dimensions. The average length of the texts was 1083.69 words (SD\u0026thinsp;=\u0026thinsp;492.58) for Grades 7\u0026ndash;8 and 1150.06 words (SD\u0026thinsp;=\u0026thinsp;585.74) for Grades 9\u0026ndash;10.\u003c/p\u003e \u003cp\u003e \u003cb\u003eComprehension questions.\u003c/b\u003e Each reading unit included 14 comprehension questions: primarily multiple-choice items (11\u0026ndash;12 per unit) together with a small number of associating or ordering tasks (2\u0026ndash;3 per unit). These questions targeted a range of reading processes, including understanding text structure, linguistic and literary knowledge, vocabulary, information retrieval, inference-making, prediction, paraphrasing, summarization, application, reflection, author intention, intertextual integration, text\u0026ndash;image integration, and source evaluation. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the elaborated feedback messages associated with each question type and illustrates the reading processes and strategies that students were expected to activate when answering the questions. The average reliability of comprehension scores (McDonald\u0026rsquo;s ω) was .70 (SD = .03, range [.67, .77]) for Grades 7\u0026ndash;8 and .71 (SD = .04, range [.65, .77]) for Grades 9\u0026ndash;10.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFidelity of Implementation\u003c/b\u003e. Both the platform company and part of the research team were in direct contact with the teachers to resolve potential technical issues and schedule the reading sessions on new dates (e.g., due to local holidays), in which case two readings were completed the following week on separate days. Additionally, the fidelity measures of the implementation were measured at student and teacher level. At the end of each week, teachers completed a fidelity report per classroom on the implementation of that week's assigned reading unit (i.e. dosage). Furthermore, the student data registered in the system indicated the actual number of reading units completed by the participants (i.e., student exposure). The mean percentage of reading units\u0026rsquo; dosage was 98.5% (SD\u0026thinsp;=\u0026thinsp;3.72%), while the average student exposure was 84.0% (SD\u0026thinsp;=\u0026thinsp;18.1%), indicating that, overall, teachers and students adhered to the intervention as planned.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cp\u003e\u003cb\u003eDigital reading comprehension.\u003c/b\u003e Students\u0026rsquo; digital reading comprehension was measured using a standardized test for Spanish secondary-school students (CompLEC; Llorens et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The test includes five digital expository texts of different types: a single text, multiple texts, a text with graphics, and a diagram. The structure of the test and the types of questions are aligned with the PISA framework for digital reading literacy (OECD, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and some of the texts included in the instrument are drawn from released PISA reading materials. The test consisted of 20 multiple-choice questions requiring students to locate (e.g., \u003cem\u003eFor Arturo, nuclear energy is the best alternative. Why\u003c/em\u003e?), integrate (e.g., \u003cem\u003eAlthough there are many points of disagreement, Arturo and Sonia agree that\u003c/em\u003e\u0026hellip;), and reflect on textual information (e.g., \u003cem\u003eLuis is an engineer who has been working at a nuclear power plant for years and believes that his work makes a significant contribution to society. Who do you think he would agree with (Arturo or Sonia)\u003c/em\u003e?). The original CompLEC test was used as the pre-test to assess students\u0026rsquo; digital reading comprehension, whereas a parallel version with similar characteristics was piloted and subsequently used as the post-test. Regarding reliability, omega coefficients for the digital reading comprehension scores were .816 and .782 for the pre-test and post-test, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eProcedure\u003c/h2\u003e \u003cp\u003eThe intervention was conducted from October 2023 to February 2024 under the online supervision of both the research team and the platform company. Prior to the start of the intervention, teachers received calendars, written instructions, and instructional videos explaining how to use the platform. These materials varied slightly depending on the experimental condition assigned to each classroom, particularly regarding the moment when comprehension questions appeared and the type of feedback students would receive. Teachers were also informed that students should complete the activities autonomously, limiting their intervention to assistance related to the technical use of the platform. This preparation period allowed teachers to familiarize themselves with the platform and to address any questions with the research team before the intervention began. Students\u0026rsquo; digital reading comprehension was assessed via the platform during the first week of the study (pre-test). Over the subsequent 16 weeks, students completed the reading units. One week after completing the intervention, digital reading comprehension was reassessed using the post-test, coinciding with the start of the control group\u0026rsquo;s intervention. After the intervention concluded, participating schools were thanked and provided with a summary report of the study results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003e Following What Works Clearinghouse guidelines (2022), baseline equivalence across the four intervention conditions and the control group was first examined. To this end, linear mixed-effects models (LMMs) were estimated with initial digital reading comprehension as the outcome variable and intervention condition as the fixed predictor, controlling for gender and school type. Classrooms were specified as random intercepts to account for the clustered assignment at the classroom level. Intervention effects were subsequently analyzed separately for the Grades 7\u0026ndash;8 and Grades 9\u0026ndash;10 cohorts using LMMs. Post-test reading comprehension scores served as the dependent variable. Fixed effects included baseline digital reading comprehension (grand-mean centered), gender, school type, intervention condition, and the interaction between baseline digital reading comprehension and intervention condition. Random intercepts for classrooms were included to account for the nested data structure.\u003c/p\u003e \u003cp\u003eAll models were estimated using restricted maximum likelihood (REML), which provides unbiased estimates of variance components and accommodates incomplete data under missing-at-random assumptions (Enders, 2010). To ensure adequate implementation fidelity, analyses were restricted to classrooms delivering at least 80% of the scheduled sessions, consistent with commonly applied thresholds for structural dosage in intervention research (Carroll et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; O\u0026rsquo;Donnell, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). All classrooms met this criterion; therefore, no exclusions were necessary. The control group was retained in all analyses. All statistical analyses were conducted in R (version 4.5.1; R Core Team, 2023) using the following packages: dplyr (Wickham et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), lme4 (Bates et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), lmerTest (Kuznetsova et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), emmeans (Lenth, 2023), and ggplot2 (Wickham, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTo address the study objectives, analyses first examined baseline equivalence across intervention conditions before evaluating intervention effects on digital reading comprehension. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents pre- and post-test descriptive statistics of digital reading comprehension scores for both grade cohorts, disaggregated by experimental condition and control group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003ePre-test and post-test means (SE) of digital reading comprehension scores by grade cohort and condition.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eGrades 7\u0026ndash;8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e \u003cp\u003eGrades 9\u0026ndash;10\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIEC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIEE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIPC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIPE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIEC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIEE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIPC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eIPE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCG\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.88 (4.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.36 (3.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.55 (4.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.24 (3.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.91 (4.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.38 (4.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.62 (4.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.16 (3.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.90 (4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e12.47 (4.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.79 (4.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.52 (4.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.35 (4.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.63 (3.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.60 (4.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.73 (4.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.13 (3.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.15\u003c/p\u003e \u003cp\u003e(3.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12.69 (3.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e12.40 (3.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cem\u003eNote\u003c/em\u003e. IEC: Intervention with Embedded questions and Corrective feedback; IEE: Intervention with Embedded questions and Elaborated feedback; IPC: Intervention with Post-reading questions and Corrective feedback; IPE: Intervention with Post-reading and Elaborated feedback; CG: Control Group.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBaseline equivalence between the four intervention conditions and the control group was assessed using linear mixed-effects models predicting initial digital reading comprehension scores, controlling for gender and school type and including random intercepts for classrooms. No statistically significant differences were found in baseline digital reading comprehension scores between any intervention condition and the control group (all ps \u0026gt; .190). Furthermore, post hoc pairwise comparisons (Tukey-adjusted) revealed no significant differences among the intervention conditions (all ps \u0026gt; .270), supporting satisfactory baseline equivalence for both the Grades 7\u0026ndash;8 and Grades 9\u0026ndash;10 samples.\u003c/p\u003e \u003cp\u003eFor the model estimated for Grades 7\u0026ndash;8 students (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), no significant interactions were found between intervention condition and baseline digital reading comprehension, suggesting that the association between baseline and post-test digital reading comprehension scores did not differ across instructional conditions. Neither the main effects of intervention condition nor the effect of school type (included as a control variable) were statistically significant predictors of post-test digital reading comprehension scores. In contrast, baseline digital reading comprehension scores was a significant predictor (p \u0026lt; .001), indicating that students with higher baseline scores tended to achieve higher post-test digital reading comprehension scores. Gender (included as a control variable) was also significant (p \u0026lt; .001), with girls outperforming boys overall.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eLinear mixed-effects model predicting post-test digital reading comprehension for 7th\u0026minus;8th grade students (\u0026ge;\u0026thinsp;80% dosage fidelity)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (Estimate)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et(df)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.48\u0026ndash;13.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.80 (49.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline digital reading comprehension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49\u0026ndash;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.30 (649.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (1\u0026thinsp;=\u0026thinsp;Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.47 \u0026ndash;-0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.13 (640.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool (1\u0026thinsp;=\u0026thinsp;Non-public)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.53\u0026ndash;0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.78 (44.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIEE vs CG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.10\u0026ndash;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.89 (38.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIEC vs CG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.45\u0026ndash;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.15 (41.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPE vs CG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.49\u0026ndash;0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.17 (41.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPC vs CG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.43\u0026ndash;1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11 (39.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline digital reading comprehension x IEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.22\u0026ndash;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.52 (667.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline digital reading comprehension x IEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.24\u0026ndash;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.53 (660.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline digital reading comprehension x IPE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.22\u0026ndash;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.02 (667.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline digital reading comprehension x IPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.29\u0026ndash;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.15 (666.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eRandom Effects Variance SD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eClass 2.14 1.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eResidual 9.44 3.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eModel Fit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarginal R2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConditional R2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN Class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e681\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote\u003c/em\u003e. SE\u0026thinsp;=\u0026thinsp;standard error; CI\u0026thinsp;=\u0026thinsp;confidence interval; ICC\u0026thinsp;=\u0026thinsp;Intraclass Correlation Coefficient ; SD\u0026thinsp;=\u0026thinsp;standard deviation. Baseline reading comprehension was mean-centered prior to analysis, and all interaction terms used the centered scores.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003eFor the model estimated for Grades 9\u0026ndash;10 students (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), a significant interaction was found between baseline digital reading comprehension and one intervention condition (IEC; p = .043) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This finding indicates that the relationship between baseline and post-test digital reading comprehension scores differed across instructional conditions. Specifically, compared with the control group, the association between baseline and post-test scores was stronger in this condition, suggesting that students with higher initial digital reading comprehension tended to achieve relatively higher post-test scores in this experimental condition. As observed in the younger cohort, baseline digital reading comprehension was a strong positive predictor of post-test scores (p \u0026lt; .001). Gender also remained significant (p = .001), with girls outperforming boys overall. Neither the main effects of the remaining intervention conditions nor the effect of school type reached statistical significance\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eLinear mixed-effects model predicting post-test digital reading comprehension for 9th\u0026minus;10th grade students (\u0026ge;\u0026thinsp;80% dosage fidelity)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (Estimate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et(df)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.65\u0026ndash;14.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.81 (22.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline digital reading comprehension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u0026ndash;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.14 (518.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (1\u0026thinsp;=\u0026thinsp;Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.45 \u0026ndash; -0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.46 (506.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool (1\u0026thinsp;=\u0026thinsp;Non-public)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.27\u0026ndash;2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.53 (23.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIEE vs CG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.27\u0026ndash;4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03 (21.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIEC vs CG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.22\u0026ndash;2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13 (20.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPE vs CG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.16\u0026ndash;2.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23 (21.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPC vs CG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.89\u0026ndash;2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.42 (21.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline digital reading comprehension x IEE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u0026ndash;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.85 (526.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline digital reading comprehension x IEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u0026ndash;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.03 (532.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline digital reading comprehension x IPE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.09\u0026ndash;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05 (526.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline digital reading comprehension x IPC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.29\u0026ndash;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.59 (531.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eRandom Effects Variance SD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eClass 4.47 2.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eResidual 8.82 2.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eModel Fit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarginal R2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConditional R2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN Class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003e546\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote.\u003c/em\u003e SE\u0026thinsp;=\u0026thinsp;standard error; CI\u0026thinsp;=\u0026thinsp;confidence interval; ICC\u0026thinsp;=\u0026thinsp;Intraclass Correlation Coefficient ; SD\u0026thinsp;=\u0026thinsp;standard deviation; Baseline reading comprehension was mean-centered prior to analysis, and all interaction terms used the centered scores.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTaken together, the findings provide limited evidence for overall intervention effects on digital reading comprehension. Baseline digital reading comprehension emerged as the strongest predictor of post-test performance, underscoring the stability of individual differences in digital literacy outcomes. While no consistent main effects of question placement or feedback type were observed, the significant interaction identified in the older cohort suggests that the impact of specific instructional design features may vary depending on students\u0026rsquo; initial reading proficiency. This pattern highlights the importance of examining how instructional components interact with learner characteristics when evaluating digital literacy interventions in authentic classroom contexts.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study examined the effectiveness of a classroom-based digital reading intervention and investigated whether specific instructional design features\u0026mdash;namely question placement and feedback type\u0026mdash;interacted with students\u0026rsquo; initial digital reading comprehension. Drawing on prior research, we hypothesised that sustained engagement with structured digital reading activities would improve digital reading comprehension outcomes, that embedded questions and elaborated feedback would yield stronger benefits than their respective alternatives, and that these effects might vary depending on students\u0026rsquo; baseline digital reading levels.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eOverall effects of the digital reading intervention\u003c/h2\u003e \u003cp\u003eContrary to expectations, the intervention did not produce robust overall improvements in standardized post-test digital reading comprehension beyond differences explained by baseline comprehension. In both cohorts, initial digital reading comprehension emerged as the strongest predictor of post-test performance, indicating substantial continuity in students\u0026rsquo; literacy profiles over time. These findings suggest that, within the duration and structure of the intervention, incorporating adjunct questions and feedback into digital reading practice was insufficient to generate broad transfer effects to a standardized measure of digital reading comprehension.\u003c/p\u003e \u003cp\u003eImportantly, these findings should be interpreted in light of methodological differences from much of the prior literature. Many previous studies on adjunct questions and feedback have examined short-term interventions and evaluated outcomes using task-specific questions closely aligned with the instructional activities. In contrast, the present study implemented a longer classroom intervention with a large and diverse sample and assessed outcomes through a standardized digital reading comprehension measure designed to capture transfer beyond the practiced texts. While this design enhances ecological validity and educational relevance, it may also reduce the likelihood of detecting large effects, as transfer to generalized reading comprehension typically requires more sustained and explicit instructional support than improvements observed on task-aligned assessments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eEffects of question placement\u003c/h2\u003e \u003cp\u003eWith respect to question placement, the study did not detect consistent advantages of embedded over post-reading questions in terms of transfer to new texts. This contrasts with prior research suggesting that embedded questions foster active and continuous processing (e.g., Peverly \u0026amp; Wood, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Rubio et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). One possible explanation is that embedded questions may enhance local processing within specific texts but do not necessarily promote the metacognitive awareness or strategic abstraction required for transfer. Without explicit guidance helping students reflect on how and why embedded questioning supports comprehension, the benefits may remain task-bound.\u003c/p\u003e \u003cp\u003eAt the same time, the absence of clear differences between embedded and post-reading questions is consistent with studies reporting comparable learning outcomes across question placements. For instance, Uner and Roediger (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) found that retrieval practice improved learning from textbook materials regardless of whether questions were presented during reading or after completing the text. These findings suggest that the benefits of question-based activities may depend less on their exact position in the reading sequence than on the opportunity they provide for retrieval and engagement with the material.\u003c/p\u003e \u003cp\u003eInterestingly, in the older cohort, the condition combining embedded questions with corrective feedback showed comparatively more favourable post-test results. Although limited in scope and requiring cautious interpretation, this pattern suggests that the effects of embedded questioning may depend on learners\u0026rsquo; developmental characteristics and reading proficiency. Developmental research supports this interpretation. Van den Broek et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) found that questioning during reading facilitated comprehension mainly among more advanced readers, whereas it could interfere with younger or less skilled students due to the additional processing demands imposed by answering questions while reading. They argued that as reading skills become more automatic, inferential questions help direct attention to relevant information; however, for less proficient readers, such questions may increase cognitive load and disrupt comprehension processes. This developmental pattern may help explain why potential benefits of embedded questions were more evident among older students in the present study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eEffects of feedback type\u003c/h2\u003e \u003cp\u003eContrary to expectations derived from meta-analytic evidence (e.g., van der Kleij et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Shute, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), elaborated feedback did not demonstrate overall superiority over corrective feedback. Several factors\u0026mdash;largely related to the design of the feedback messages implemented in the present intervention\u0026mdash;may account for this finding.\u003c/p\u003e \u003cp\u003eFirst, the effectiveness of elaborated feedback depends not only on informational richness but also on students\u0026rsquo; engagement with the feedback message (Golke et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; M\u0026aacute;\u0026ntilde;ez et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In digital environments, written feedback may be processed superficially, particularly when embedded in low-stakes activities or when messages are perceived as repetitive. For example, Golke et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) reported that computer-delivered elaborated feedback did not improve deep-level comprehension when students showed limited engagement with feedback messages, whereas improvements emerged when feedback was mediated by a person. In the present study, teachers were instructed not to provide additional support in order to preserve the experimental manipulation. While this decision strengthened internal validity, it may also have reduced opportunities for instructional scaffolding. Second, the elaborated feedback used in the intervention primarily indicated the reading strategy required to answer each question but did not explicitly guide students through the procedural steps needed to implement that strategy. Thus, although it highlighted the strategic focus of the task, it may not have provided sufficiently detailed scaffolding to support effective strategy enactment. Third, the diversity of texts across the 16 reading units\u0026mdash;varying in topic, genre, source structure, and format\u0026mdash;may have increased the cognitive demands associated with transferring strategic reading skills across heterogeneous materials, thereby attenuating observable effects on a standardized outcome measure.\u003c/p\u003e \u003cp\u003eNotably, in the older cohort, the combination of embedded questions and corrective feedback yielded comparatively stronger post-test outcomes. One possible interpretation is that concise corrective messages were more readily processed than elaborated strategy-oriented feedback, particularly if the latter were perceived as repetitive or insufficiently instructive. This interpretation aligns with prior findings suggesting that feedback effectiveness may depend on learners\u0026rsquo; self-regulatory profiles and developmental characteristics (Golke et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; M\u0026aacute;\u0026ntilde;ez et al., 2026).\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003eIndividual differences in digital reading comprehension\u003c/h2\u003e \u003cp\u003eA significant interaction between baseline digital reading comprehension and one instructional condition was observed in the older cohort in relation to post-test performance. Specifically, the combination of embedded questions and corrective feedback showed comparatively more favourable outcomes. Although limited in scope and requiring cautious interpretation, this finding suggests that certain instructional configurations may differentially influence longer-term comprehension outcomes depending on students\u0026rsquo; initial literacy levels. Rather than assuming that greater informational richness uniformly enhances learning, these results indicate that the clarity, usability, and perceived relevance of feedback may be equally important determinants of its impact.\u003c/p\u003e \u003cp\u003eIn addition, gender emerged as a significant covariate in both cohorts, with girls outperforming boys in post-test digital reading comprehension. This pattern is consistent with large-scale literacy assessments documenting gender differences in reading proficiency across both print and digital contexts (OECD, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although gender was not a focal variable in the present study, this finding underscores the importance of considering variability in learner characteristics when designing digital literacy interventions.\u003c/p\u003e \u003cp\u003eIt is also noteworthy that the sample included 140 students identified by their teachers as having diagnosed special educational needs (SEN). Although subgroup analyses were not conducted due to limited statistical power, their inclusion strengthens the ecological validity of the sample and reflects the heterogeneity typical of regular secondary classrooms.\u003c/p\u003e \u003cp\u003eFrom a Multi-Tiered System of Supports (MTSS) perspective, these results reinforce the notion that universal instructional practices may not affect all students equally. Responsiveness to intervention appears to vary as a function of prior competence and developmental stage, and may also reflect broader learner characteristics such as gender differences in reading proficiency. The absence of strong universal effects suggests that embedding questions and feedback into digital reading tasks may not be sufficient as a Tier 1 instructional strategy. Instead, differentiated or intensified supports\u0026mdash;such as explicit strategy instruction, adaptive feedback systems, or increased teacher mediation\u0026mdash;may be necessary to address the needs of diverse learner profiles.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and educational implications\u003c/h2\u003e \u003cp\u003eSeveral limitations should be acknowledged. Although the intervention spanned 16 weeks and was implemented under ecologically valid classroom conditions, the dosage may still have been insufficient to produce measurable gains in standardized digital reading outcomes. Feedback was delivered exclusively in written form, and qualitative aspects of teacher mediation and students\u0026rsquo; depth of processing were not directly assessed. Furthermore, the interaction observed in the older cohort was limited to one condition and therefore warrants replication.\u003c/p\u003e \u003cp\u003eDespite these limitations, the study contributes meaningfully to both research and practice. The intervention involved the development of a substantial corpus of diverse digital texts and comprehension questions designed for authentic classroom implementation and delivered by regular teachers. While the findings indicate that further refinement is needed\u0026mdash;particularly regarding feedback depth, explicit strategy scaffolding, and support for transfer\u0026mdash;the program provides a feasible framework for integrating structured digital reading practice into secondary education. Rather than offering definitive solutions, the present work should be understood as part of an iterative process aimed at identifying pedagogically meaningful and evidence-informed approaches to fostering digital reading comprehension.\u003c/p\u003e \u003cp\u003eAdvancing such efforts will require continued collaboration between researchers and educators to ensure that digital literacy interventions are not only theoretically grounded but also responsive to the complexities of classroom practice.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e- Conceptualization: Laura Gil, Amelia Ma\u0026ntilde;\u0026aacute;, Ladislao Salmer\u0026oacute;n, Cristina Vargas-- Methodology: Laura Gil, Amelia Ma\u0026ntilde;\u0026aacute;, Mario Romero-Palau, Marian Serrano-Mendiz\u0026aacute;bal, Cristina Vargas- Formal analysis: Cristina Vargas- Data curation: Laura Gil, Amelia Ma\u0026ntilde;\u0026aacute;, Marian Serrano-Mendiz\u0026aacute;bal, Mario Romero-Palau.- Writing \u0026ndash; original draft: Laura Gil, Amelia Ma\u0026ntilde;\u0026aacute;, Mario Romero-Palau, Marian Serrano-Mendiz\u0026aacute;bal, Ladislao Salmeron, Cristina Vargas- Supervision: Laura Gil, Ladislao Salmeon.- Funding acquisition: Ladislao Salmer\u0026oacute;n\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAfflerbach, P., Cho, B. 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John Benjamins Publishing Company.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"digital reading comprehension, secondary school, comprehension questions, feedback, Multi-Tiered System of Supports","lastPublishedDoi":"10.21203/rs.3.rs-8995659/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8995659/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid expansion of digital reading in schools has outpaced the development of empirical evidence about how digital learning environments can effectively support reading comprehension for diverse learners. This study examined the effectiveness of a classroom-based digital reading intervention and investigated whether key instructional design features\u0026mdash;question placement and feedback type\u0026mdash;interact with students\u0026rsquo; initial digital reading competence. A total of 1227 secondary-school students (Grades 7\u0026ndash;10) from 12 schools participated in a cluster-randomized trial including four intervention conditions and a waiting-list control group. Within the intervention group, classrooms were assigned to a 2 \u0026times; 2 factorial design manipulating the placement of comprehension questions (embedded vs. post-reading) and feedback type (corrective vs. elaborated). Over a 16-week period, students completed weekly digital reading units consisting of one or more texts accompanied by comprehension questions designed to engage strategic reading processes. Digital reading comprehension was assessed using a standardized measure administered before and after the intervention. Linear mixed-effects models showed that baseline digital reading competence was the strongest predictor of post-test performance. No overall intervention effects emerged across conditions. However, for older students (Grades 9\u0026ndash;10), a significant interaction indicated that the combination of embedded questions and corrective feedback was associated with comparatively stronger post-test outcomes, particularly for students with higher baseline competence. These findings highlight the importance of considering learner characteristics and instructional design when developing digital reading interventions and provide insights for refining literacy practices within Multi-Tiered Systems of Support frameworks.\u003c/p\u003e","manuscriptTitle":"Enhancing Digital Reading Comprehension in Secondary Education: Effects of Question Placement and Feedback Type","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-05 12:18:31","doi":"10.21203/rs.3.rs-8995659/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"328a4644-0af5-4ab9-a56f-61d54adad570","owner":[],"postedDate":"March 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-05T12:18:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-05 12:18:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8995659","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8995659","identity":"rs-8995659","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}