Effects of emotional visual and auditory cues on Chinese poetry learning: An eye-tracking study

Effects of emotional visual and auditory cues on Chinese poetry learning: An eye-tracking study | 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 Effects of emotional visual and auditory cues on Chinese poetry learning: An eye-tracking study Yi Wang, Man Zhang, Zhen Xia, Zhijin Zhou, Fred Paas This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7731687/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 Background Chinese classical poetry, known for its concise and evocative language, often challenges learners due to its subtle emotional expressions. Affective scaffolds, such as emotional visual and auditory cues, may enhance emotional engagement and comprehension. Aim This study investigated whether emotional visual and auditory cues facilitate learners’ emotional engagement and understanding of Chinese classical poetry. Method Two experiments were conducted. In Experiment 1, a between-subjects design with 139 high school students examined the effects of emotional visual cues (no cues, emotion label words, emotion laden words, and combined cues). In Experiment 2, a single-factor between-subjects design with 67 undergraduates explored emotional narration versus neutral narration, using eye-tracking to measure emotional engagement via pupil dilation and blink rate. Results In Experiment 1, emotion laden word cues increased negative emotional valence ( d = 0.49) but did not affect comprehension. In Experiment 2, emotional narration improved poetic comprehension ( d = 0.56) and motivation ( d = 2.32), with greater pupil dilation ( d = 2.10) and reduced blink rate ( d = 0.70) indicating enhanced emotional engagement. Conclusion Emotional visual cues alone foster emotional engagement without improving comprehension, while combining them with emotional narration significantly enhances both motivation and understanding. These findings extend the cueing principle by demonstrating that emotional visual and auditory cues enhance engagement with affective content and support the emotional design hypothesis by showing that cues aligned with inherent emotional content improve comprehension and motivation in literary learning. Emotional design cues Emotion label words Emotion laden words Emotional narration Chinese classical poetry Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Chinese classical poetry, known for its concise and emotionally evocative language, often uses metaphorical imagery (e.g., ‘moon’ symbolizing longing) to convey complex emotions (Xia, 2021 ; Lei & He, 2025 ). However, novice learners, especially those with limited cultural or linguistic knowledge, struggle to interpret these subtle cues due to insufficient prior exposure and limited instructional support. Current instructional approaches often focus primarily on linguistic and historical aspects, overlooking emotional engagement (Levine et al., 2013; Eva-Wood, 2004 ). Cues (or signals) are instructional design elements in multimedia learning that capture learners’ attention and assist in the selection, organization, and integration of essential information, thereby enhancing learning outcomes (Alpizar et al., 2020 ; De Koning et al., 2009 ). Recent studies have explored the supportive role of cues such as visual color and embedded textual annotations in language learning (Teng, 2023 ; Pi et al., 2023 ). Furthermore, Poetry, as an emotional linguistic text, is not merely about understanding lexical semantics; its primary purpose lies in expressing emotions (Johnson Laird & Oatley, 2022 ). The emotional design hypothesis in multimedia learning suggests that inducing positive and negative emotions can facilitate learners’ emotional experience and learning motivation, thus enhancing learning outcomes (Beege et al., 2018 , 2023; Stark et al., 2018 ), this study explored the effects of emotional text-based visual cues, e.g., visual underlines highlighting emotional words, and emotional narration cues on poetry instruction. Specifically, the integration of these emotional cues was used to enhance learners’ affective response to poetry, potentially reducing extraneous cognitive load, increasing learning motivation, and deepening understanding. Additionally, eye-tracking metrics, such as pupil dilation and blink rate, were employed to provide objective measures of emotional engagement, complementing self-reports and enhancing our understanding of affective processing in poetry learning (Bradley et al., 2008 ; Maffei & Angrilli, 2019 ). Literature Review Cues and Emotional Design in Multimedia Learning Cues in multimedia learning, such as visual highlights or textual annotations, guide learners’ attention, reduce extraneous cognitive load, and promote information integration (Alpizar et al., 2020 ; De Koning et al., 2009 ). According to De Koning et al. ( 2009 ), cues serve three functions: selecting information by directing attention to key elements, organizing information by emphasizing structure, and integrating information by clarifying relationships, all of which enhance germane cognitive load and learning outcomes. Visual cues, like coloring or underlines, attract attention to critical content (Pi et al., 2023 ; X. Wang et al., 2020 ), while text cues, such as annotations, provide semantic priming to improve processing speed and accuracy (Pi et al., 2023 ). For example, X. Wang et al. ( 2020 ) found that visual lines in instructional videos increased attention to geographical content, improving learning performance. Emotional design cues, including colors, facial expressions, narrative voices, and emotional words, enhance motivation and learning by eliciting affective responses (Mayer & Estrella, 2014 ; Beege et al., 2023; Pi et al., 2025 ). The emotional design hypothesis posits that emotions elicited during learning influence outcomes by either facilitating or hindering cognitive processing (Beege et al., 2020 ; Plass & Kalyuga, 2019 ). When aligned with learning goals, emotional cues can reduce extraneous cognitive load and enhance engagement, as seen in studies where emotional design improved positive emotions and achievement (Chang & Chen, 2024 ). However, misaligned cues, such as adding emotional words to neutral scientific texts, may increase extraneous load (Stark et al., 2018 ). In poetry, aligning cues with inherent emotional content (e.g., highlighting “moonlight” to evoke longing) may reduce cognitive load and enhance comprehension by facilitating mood-affect congruency, where the emotional tone of instructional materials matches learners’ affective states (Beege et al., 2018 ). While Stark et al. ( 2018 ) found that emotional words increased extraneous load in scientific texts, poetry’s inherent emotional content may allow cues to enhance germane load by emphasizing affective meaning, a hypothesis tested in this study. Emotional Scaffolds in Poetry Instruction Poetry primarily aims to express emotions through carefully selected language, encompassing literal and metaphorical meanings (Eva-Wood, 2004 ; Xia, 2021 ). Emotion laden words, such as “moon” or “coffin”, evoke feelings like longing or sadness without directly naming emotions, unlike emotion label words (e.g., “happiness”, “anger”) (Zhang et al., 2023 ; Zhang et al., 2017 ). This subtlety engages readers’ imaginations but challenges novices, who require reflective thinking to interpret emotional nuances (Johnson-Laird & Oatley, 2022). Educational strategies, such as affective appraisal and think-aloud methods, enhance emotional engagement and interpretive skills by guiding students to analyze emotion laden words (Levine et al., 2013, 2014, 2021 ; Eva-Wood, 2004 ). Multimedia advancements offer new opportunities, with sensory cues like olfactory stimuli improving memory and emotional connections in poetry learning (Li et al., 2024 ). However, practical limitations, such as the availability of poems with consistent sensory references, restrict widespread application. This study explores visual and auditory emotional cues to enhance engagement and comprehension in Chinese classical poetry, leveraging its inherent affective content. The Current Study This study investigated the effects of emotional visual and auditory cues on learners’ emotional engagement and comprehension of Chinese classical poetry, which is characterized by subtle, culturally embedded affective expressions (Xia, 2021 ). Building on the cueing principle and emotional design hypothesis (Alpizar et al., 2020 ; Beege et al., 2018 ), we designed multimedia learning materials incorporating visual cues (underlined emotional words) in Experiment 1 and auditory cues (emotional narration) in Experiment 2 to enhance learners’ affective responses and understanding. Eye-tracking metrics, such as pupil dilation and blink rate, were used in Experiment 2 to provide objective measures of emotional engagement, complementing self-reports by capturing real-time physiological responses to affective stimuli (Bradley et al., 2008 ; Maffei & Angrilli, 2019 ). This study extended emotional design research into the domain of poetry, which differs from typical scientific or expository texts in that its primary purpose is affective expression. By examining emotional cues in a literary context, we contribute both theoretically—showing how the cueing principle and emotional design hypothesis operate with inherently affective material—and practically, by informing poetry instruction where engaging with emotions is essential for comprehension. Experiment 1 employed a one-factor between-subjects design with four levels to examine the impact of emotional text-based visual cues on poetic learning. Visual cues consisted of red underlines on emotion label words (e.g., “sorrow”), emotion laden words (e.g., “moonlight”), or both, compared to a no-cue condition. Based on the mood-affect congruency effect (Beege et al., 2018 ), we hypothesized that emotional cues aligned with the poems’ negative emotional tone would enhance emotional engagement (H1a) by priming affective processing, reduce extraneous cognitive load (H1b) by directing attention to relevant emotional content, increase motivation (H1c), and improve comprehension (H1d) by facilitating deeper emotional processing (Stark et al., 2018 ). Experiment 2 utilized a one-factor between-subjects design with two levels to explore the effects of emotional narration versus neutral narration. Emotional narration used a melancholic tone with varied pitch. We hypothesized that emotional narration would enhance emotional engagement (H2a), reduce extraneous cognitive load (H2b), increase motivation (H2c), and improve comprehension (H2d) by aligning with the poem’s affective tone (Beege et al., 2020 ). Additionally, eye-tracking metrics were expected to reveal greater pupil dilation and reduced blink rate in the emotional narration condition (H2e), reflecting heightened emotional arousal and engagement (Bradley et al., 2008 ; Maffei & Angrilli, 2019 ). Experiment 1 Method Design and Participants Sample size was estimated using G-Power 3.1.7 ( η ² >0.10), yielding a required sample of 112 participants to achieve 0.95 statistical power. A total of 139 high school students (76 female, M age = 16.91, SD age = 1.51) participated in the study. A single-factor, four-level between-subjects design was employed, with emotional cue type as the independent variable (no cues vs. emotional label cues vs. emotional laden cues vs. combined label + laden cues). Measures Demographics Basic demographic information, including gender and age, was collected. Emotional Response Two self-rating items assessed valence and arousal on a 7-point scale immediately after they finished learning (1 = very unhappy/very calm, 7 = very happy/very excited). The Cronbach’s α was 0.69 in this experiment. Cognitive Load Three items scored on a 7-point scale (1 = extremely low, 7 = extremely high) measured intrinsic, germane, and extraneous cognitive load (Paas et al., 1994 ). These items were adapted from a prior Chinese study (Wang et al., 2019 ). Learning Motivation An 8-item self-rating scale scored on a 7-point scale (Isen & Reeve, 2005 ) assessed learning motivation, with Cronbach’s α = 0.87 in this experiment. Comprehension Test Three open-ended questions assessed semantic understanding, emotional experience, and aesthetic appreciation of the poems (Pan et al., 2022). Each question was rated by two independent raters trained on a standardized rubric aligned with the Chinese poetry curriculum. They evaluated the accuracy of imagery identification (Q1), depth of emotional interpretation (Q2), and quality of aesthetic analysis (Q3) using a 10-point scale (inter-rater reliability, ICC = 0.81). Total scores ranged from 0 to 30, and the Cronbach’s α was 0.65, indicating moderate reliability, likely due to the subjective nature of aesthetic judgments. Control Variable Prior knowledge of classical poetry was measured using a 30-point scale, including four 5-point Likert items and one open-ended question (Y. Wang et al., 2023 ). Cronbach’s α = 0.63 in this experiment. Learning Materials Poetic Texts Two Tang Dynasty poems, Gu Yi (《古意》) , and Chun Si (《春思》), by lesser known poets were selected to ensure quality while controlling for familiarity. Thirty-one randomly selected high school students (18 female, M age = 16.27, SD age = 1.29) rated the poems on difficulty, familiarity, and emotional tone. Both poems were moderately difficult ( M 1 = 4.67, SD 1 = 0.97; M 2 = 4.74, SD 2 = 0.81), low in familiarity ( M 1 = 2.38, SD 1 = 0.96; M 2 = 2.26, SD 2 = 1.06), and emotionally negative ( M 1 = 2.64, SD 1 = 0.87; M 2 = 2.71, SD 2 = 0.82). No significant differences were found between the poems on these dimensions, t (30) = 0.29, p = 0.77; t (30) = 0.53, p = 0.59; t (30) = 0.25, p = 0.80, respectively. Emotion laden words and emotion label words were identified for each poem through collaboration with two Chinese teachers. For Gu Yi , emotion labels were “cold” (寒) and “sorrow” (愁), and emotion laden words were “epistles and tidings” (音书) and “moonlight” (明月). For Chun Si , emotion labels were “regret” (恨) and “smile” (笑), and emotion laden words were “moonlight” (明月) and “solitary slumber” (独眠). Poetic Videos Learning materials consisted of 6.5-minute instructional videos for each poem, presented in a seven-slide PowerPoint format. The poem’s full text was displayed in black 22-point Kaiti font, centered on a white background, across all slides. Each slide presents the poem's title, author, and full text. In addition, beneath the full text, the second slide also provided a brief overview of the author's biography and the historical context (e.g., Tang Dynasty cultural references). The third to sixth slides briefly presented the key imagery and deeper meanings of the first to fourth stanzas, respectively. The final slide summarized the poem’s emotions and themes. The presentation time for each slide was fixed: the first and last slides were 45 seconds, while the others were 60 seconds. In the cue conditions, visual cues were red underlines (2-point thickness) applied to emotion label words (e.g., ‘sorrow’[愁]), emotion laden words (e.g., ‘moonlight’[明月]), or both, depending on the condition. Slide transitions were automated to ensure consistent timing across participants. Procedure The experiment was conducted in a classroom setting using individual 20-inch monitors at 1280x1024 resolution. Participants (groups of 5–10) were seated at separate workstations to minimize distractions. The procedure lasted approximately 50 minutes. Participants first completed a 15-minute pretest assessing demographics and prior poetry knowledge via an online questionnaire. After a 2-minute rest, they were randomly assigned to one of four conditions (no cues, emotion label cues, emotion laden cues, combined cues) and viewed the corresponding 6.5-minute video. Slides auto-advanced every 45–60 seconds based on content length. Immediately after, participants completed a 25-minute posttest, typing responses to open-ended comprehension questions and self-rating emotional responses and motivation on an online interface. Figure 1 illustrated the first slide in four experimental conditions, showing the poem’s text with no cues, red underlines on emotion label words (e.g., ‘sorrow’), emotion laden words (e.g., ‘moonlight’), or both. And Fig. 2 illustrates the experimental flow. (Fig. 1 and Fig. 2 ) Results The descriptive statistics for the variable indicators under four conditions are shown in Table 1 below. Table 1 Descriptive statistics of Experiment 1. Experimental Conditions No cues (n = 34) Emotion label words (n = 36) Emotion laden words (n = 32) Emotion label + Emotion laden words (n = 37) Variables Prior knowledge 12.56 (1.78) 12.38 (1.74) 12.28 (1.63) 12.41 (1.91) Valence 4.15 (1.71) 4.43 (1.51) 3.81 (1.84) 3.59 (1.88) Arousal 4.57 (1.46) 4.79 (1.47) 4.66 (1.66) 5.10 (1.26) Intrinsic cognitive load 3.53 (1.42) 3.58 (1.13) 3.28 (1.98) 3.35 (1.18) Germane cognitive load 4.64 (1.57) 4.77(1.62) 4.59(1.79) 4.70(1.58) Extraneous cognitive load 4.64 (1.29) 4.72 (1.20) 4.46 (1.29) 4.54 (1.46) Learning motivation 4.50 (0.56) 4.49 (0.55) 4.67 (0.52) 4.58 (0.44) Comprehension test 13.70 (3.00) 13.50 (3.32) 13.00 (2.82) 13.36 (2.58) (Table 1 ) A one-way ANOVA revealed no significant differences in prior knowledge across four conditions, F < 1. Consequently, prior knowledge was excluded as a covariate in subsequent analyses. A one-way ANOVA revealed no significant differences in emotional valence across the four conditions, F (3, 135) = 1.64, p = 0.18, η² = 0.04. The small to medium effect size ( η² = 0.04) suggests a limited impact of visual cues on emotional experience (Cohen, 1988 ). Post hoc comparisons indicated that the combined emotional label + laden cue condition elicited a more negative valence than the emotion label cue condition ( p = 0.04, d = 0.49), but this effect’s practical significance is modest given the non-significant omnibus test and small effect size. No significant differences were found in emotional arousal, F (3, 135) = 0.87, p = 0.46, η² = 0.02. Additionally, no significant differences were found across the four groups in terms of intrinsic, germane, or extraneous cognitive load, learning motivation, or comprehension performance, all F s 0.05, with η² = 0.007, η² = 0.001, η² = 0.005 for intrinsic, germane, or extraneous cognitive load respectively, and η² = 0.018, η² = 0.007 for learning motivation and comprehension. Discussion This experiment was built on prior research on visual cues (Pi et al., 2023 ; X. Wang et al., 2020 ) and emotional text design (Stark et al., 2018 ) by using red underlines to highlight emotion label and emotion laden words in mute instructional videos. The results showed a modest effect of emotion laden word cues on negative emotional valence ( p = 0.04, d = 0.49 in post hoc comparison), partially supporting H1a, but the non-significant omnibus test ( p = 0.18, η² = 0.04) suggests limited emotional impact. Notably, this result emerged from a post-hoc comparison in the absence of a significant omnibus effect, so it should be interpreted as exploratory rather than confirmatory evidence. No effects were observed on cognitive load, motivation, or comprehension, failing to support H1b, H1c, and H1d. First, emotion laden word cues elicited a slightly more negative emotional valence, consistent with the poems’ emotional tone, but the effect was small and not statistically significant at the omnibus level (Beege et al., 2018 ). This may reflect partial mood-affect congruency, where cues aligned with the poem’s negative emotions primed affective processing, though the effect’s practical significance is limited (Cohen, 1988 ). Second, emotional visual cues did not reduce cognitive load compared to the no-cue condition, failing to support H1b. This may be due to the high baseline cognitive demands of interpreting classical poetry’s condensed imagery and symbolism, which could have overshadowed the benefits of visual cues (Sweller et al., 2019 ). Unlike science texts where cues reduce extraneous load (Xie et al., 2017 ), poetry’s emotional complexity may require more dynamic cues to enhance germane load. Third, emotional cues did not enhance motivation, failing to support H1c. The passive learning mode, where participants viewed videos without interactive elements, may have limited engagement, consistent with the ICAP framework’s emphasis on active learning for motivation (Chi & Wylie, 2014 ). The static nature of visual cues, compared to dynamic auditory cues, may also have reduced their motivational impact (Höffler & Leutner, 2007 ). In sum, while emotion laden word cues showed a modest effect on emotional valence, their static nature limited their impact on motivation and comprehension, suggesting that visual cues alone are insufficient for engaging learners with poetry’s complex emotions (Höffler & Leutner, 2007 ). Multimodal cues, combining visual and auditory elements, may enhance engagement by providing dynamic social cues, such as expressive narration, that align with the poem’s affective content (Beege et al., 2020 ). Experiment 2 addressed this by incorporating emotional narration to examine its effects on motivation, comprehension, and emotional engagement, using eye-tracking to capture physiological responses. Experiment 2 Method Participants and Design Sample size estimation was conducted using GPower 3.1.7, referencing effect sizes from similar studies and our experimental design (d > 0.50), which indicated a required sample of 61 participants to achieve 0.95 statistical power. A total of 67 undergraduate students (51 female; M age = 20.15, SD age = 1.39) participated. We employed a single-factor, two-level between-subjects design, manipulating the narration of the voice (neutral vs. emotional) as the independent variable.​ Manipulation Check of Narration To verify the effectiveness of our emotional narration manipulation, 26 students (21 female; M age = 20.91, SD age = 1.29) were randomly selected to rate the emotional narration and text matching of each poem on a 7-point scale (1 = emotionally insufficient, 7 = emotionally sufficient; 1 = very mismatched, 7 = very matched). For “Gu Yi”, emotional narration received significantly higher ratings for both emotional sufficiency and matching compared to neutral narration, t (25) = 7.06, p < .001, t (25) = 7.01, p < .001, respectively. Similarly, for “Chun Si”, emotional narration received higher ratings for emotional sufficiency and matching than neutral narration, t (25) = 9.92, p < .001, t (25) = 10.28, p < .001, respectively. In addition, we quantified the differences in phonetic characteristics between the two narratives using pitch as an example. Thirty random time points from each narrative were analyzed with Praat (Chang et al., 2023 ). The results showed that the pitch of the expressive narrative was significantly higher than the neutral one, t (58) = 6.34, p < .001, indicating a more dynamic and fluctuating pattern, as shown in Fig. 3 . Measures and equipment Measures Dependent variables included learners’ emotional responses, learning motivation, poetry comprehension tests, and control variables as in experiment 1. Furthermore, two eye tracking metrics, e.g., pupil dilation and blink rate were introduced to assess the impact of emotional narration on the emotional process of poetry learning.​ Pupil dilation has been related to cognitive load (Van der Wel & Van Steenbergen, 2018 ), emotional stimuli elicit pupil changes as well, serving as indicators of emotional responses such as the emotional arousal and valence of visual stimuli (Bradley et al., 2008 ). Negative emotions are more likely to cause pupil dilation than positive emotions (Bradley et al., 2008 ). In poetry appreciation, violations of rhyme have been reported to lead to a delayed increase in subjects’ pupil dilation when listening to English limerick poetry (Scheepers et al., 2013 ). Moreover, an increase in pupil size can significantly predict higher beauty ratings of the poems, suggesting pupil dilation reflects enhanced emotional engagement or aesthetic pleasure during poetic processing (Hitsuwari & Nomura, 2025 ). Meanwhile, the blink rate can serve as a sensitive physiological indicator of emotion and motivational orientation. Specifically, the modulation of blink rate varies depending on emotional context, with stronger emotional content often leading to greater inhibition of blinking, for example, empathy related film clips, e.g., crying characters elicited the lowest blink rate, indicating strong visual engagement and emotional absorption (Maffei & Angrilli, 2019 ). Eye tracking data were collected using an Eyelink1000 Desktop eye tracker (SR Research Ltd., Canada) with a sampling rate of 1000 Hz, screen refresh rate of 60 Hz, and resolution of 1280×1024 pixels. Participants’ heads were stabilized using a chin forehead rest, maintaining a distance of approximately 60 cm from the screen. Data were recorded in monocular pupil corneal reflection mode, with a 9-point calibration focusing on the right eye. The learning materials were identical to those in experiment 1 except for auditory narrations. Procedure This eye tracking experiment was conducted in a laboratory with constant light to minimize external interference. Participants had normal or corrected to normal vision and no history of neurological or psychological disorders.​ Before the experiment, participants randomly selected one poem as their learning material. The experiment was conducted via computer. Participants first completed a pretest, including demographic and prior knowledge questionnaires. After a 2-minute rest, they were briefed on the experimental task, completed eye tracking calibration, and entered the learning phase. Participants were randomly assigned to one of two conditions and viewed the corresponding learning video while their eye movements were recorded.​ In the posttest, participants reported their emotions, cognitive load, and motivation. They then completed a poetry comprehension test. This experiment lasted approximately 50 minutes. Results The descriptive statistics for dependent variables under the two experimental conditions are shown in Table 2 below. Table 2 Descriptive Statistics of behavioral data for Experiment 2. Experimental Conditions Neutral narration (n = 32) Emotional narration (n = 35) Variables Prior knowledge 11.94 (1.90) 12.06 (1.73) Valence 4.07 (1.92) 4.19 (1.48) Arousal 4.75 (1.08) 4.54 (1.15) Intrinsic cognitive load 4.40 (1.56) 4.34 (1.41) Germane cognitive load 4.25 (1.52) 4.40 (1.52) Extraneous cognitive load 4.56 (1.54) 4.68 (1.62) Learning motivation 4.24 (0.38) 5.17 (0.42) Comprehension test 13.68 (3.42) 15.60 (3.38) (Table 2 ) Independent samples t-tests revealed no significant differences between the two groups in prior knowledge, t (65) = 0.27, p = 0.78, d = 0.06, or emotional valence, t (65) = 0.29, p = 0.77, d = 0.07, or emotional arousal, t (65) = 0.80, p = 0.43, d = -0.19. Emotional narration did not affect intrinsic, germane, or extraneous cognitive load, with t (65) = 0.17, p = 0.86, d = -0.04, t (65) = 0.40, p = 0.70, d = 0.09, t (65) = 0.32, p = 0.75, d = 0.08, respectively. However, emotional narration significantly increased learning motivation ( t (65) = 9.66, p < 0.001, d = 2.32) and comprehension performance ( t (65) = 2.30, p < 0.05, d = 0.56) compared to neutral narration. The large motivation effect size (d = 2.32) aligns with studies on expressive narration (Beege et al., 2020 ) but may be inflated due to the small sample size ( N = 67), warranting replication. To control for individual differences, pupil size was baseline-corrected using the average within 500 ms post-stimulus onset (Mathôt et al., 2018 ). An independent samples t-test showed significantly greater pupil dilation in the emotional narration condition ( t (65) = 8.57, p < 0.001, d = 2.10), consistent with heightened emotional arousal (Bradley et al., 2008 ). Blink rate, calculated as blinks per 6.5-minute video, was lower in the emotional narration condition ( t (65) = 2.88, p = 0.005, d = 0.70). These large effect sizes ( d = 2.10, 0.70) align with prior findings in aesthetic contexts (Laeng et al., 2016 ) but may reflect sample size limitations. Descriptive Statistics of eye tracking metrics for Experiment 2 were displayed in Table 3 and Fig. 4 . Table 3 Descriptive Statistics of eye tracking metrics for Experiment 2. Eye movement indicators Neutral narration (n = 32) Emotional narration (n = 35) Pupil dilation -72.67 (74.40) 58.89 (49.87) Blink rate 30.13 (14.98) 20.24 (13.15) (Table 3 , Fig. 4 ) Discussion Experiment 2 extended experiment 1 by incorporating emotional narration into poetry learning material to examine the effects of combined emotional cues on Chinese poetry learning. The results showed that the emotional narration has no effect on learners’ emotions but promotes learning motivation and comprehension performance. Regarding eye movement indicators, under the emotional narration condition, learners had a stronger emotional response to the poetry. On behavioral indicators, the lack of significant effects on emotional valence and arousal (H2a) may reflect the poems’ complex emotional tone, blending negative (e.g., longing) and positive (e.g., aesthetic liking) elements, which could dilute self-reported emotional changes (Johnson-Laird & Oatley, 2022). This contrasts with Experiment 1, where emotion laden cues elicited a modest valence effect, suggesting visual cues may be more effective for static emotional priming. Second, Emotional narration positively influenced learning motivation compared to neutral narration, fully supporting H2c. This aligns with the emotion as facilitator hypothesis (Beege et al., 2020 ). Emotional narration with mixed emotional tones elicited social responses, making learners perceive the narrator as engaging and experienced, thereby increasing their motivation (Zhao & Mayer, 2023 ). Third, emotional narration improved comprehension (H2d, d = 0.56), likely because expressive vocal cues highlighted the poems’ affective content, facilitating deeper processing of emotional and semantic elements (Levine et al., 2014). This alignment of narration with the poem’s emotional tone may have enhanced learners’ ability to interpret imagery and themes, consistent with the emotional design hypothesis (Beege et al., 2020 ). Eye-tracking metrics revealed greater pupil dilation ( d = 2.10) and reduced blink rate ( d = 0.70) in the emotional narration condition, supporting H2e and indicating heightened emotional arousal and engagement (Bradley et al., 2008 ; Maffei & Angrilli, 2019 ). These findings align with studies linking pupil dilation to emotional processing in aesthetic contexts (Laeng et al., 2016 ) and reduced blink rate to immersive emotional engagement during narrative videos (Maffei & Angrilli, 2019 ). The discrepancy with null self-reported emotional effects (H2a) may reflect peak arousal occurring mid-video, captured by eye-tracking but dissipated by posttest self-reports, as physiological measures are more sensitive to transient emotional changes (Hitsuwari & Nomura, 2025 ). The large effect sizes may be inflated due to the small sample size ( N = 67), necessitating replication. Given the relatively small sample size, these unusually large effects may partly reflect sample-specific variance or Type I error. The findings should therefore be considered preliminary until replicated with larger and more diverse cohorts. General Discussion This study investigated the effects of emotional visual and auditory cues on Chinese classical poetry learning, leveraging the cueing principle and emotional design hypothesis to enhance emotional engagement and comprehension (Alpizar et al., 2020 ; Beege et al., 2018 ). Across two experiments, we examined how visual cues (emotion label words, emotion laden words, or both) and auditory cues (emotional narration) influence learners’ emotional responses, cognitive load, motivation, and comprehension. The findings provide nuanced insights into the role of emotional cues in literary learning, particularly for culturally complex texts like Chinese classical poetry. In Experiment 1, emotion laden word cues modestly increased negative emotional valence ( d = 0.49), partially supporting H1a, but the non-significant omnibus test ( F (3, 135) = 1.64, p = 0.18, η² = 0.04) suggests limited practical impact. No effects were observed on cognitive load, motivation, or comprehension (H1b–H1d, η² ≤ 0.02), indicating that static visual cues alone may not sufficiently engage learners with poetry’ s intricate emotional and semantic content. This aligns with prior research suggesting that visual cues are less effective when cognitive demands are high, as in interpreting poetry’ s condensed imagery (Sweller et al., 2019 ). In contrast, Experiment 2 demonstrated that emotional narration significantly enhanced motivation ( d = 2.32), comprehension ( d = 0.56), and eye-tracking indicators of emotional arousal (pupil dilation, d = 2.10; blink rate, d = 0.70), supporting H2c, H2d, and H2e, but not H2a or H2b (emotional valence, cognitive load). These findings suggest that dynamic auditory cues, which align with the poem’ s affective tone through expressive vocal delivery, are more effective than static visual cues in facilitating emotional and cognitive processing (Beege et al., 2020 ). The discrepancy between the experiments highlights the importance of cue modality. Emotional narration likely outperformed visual cues because its dynamic, temporal nature mirrors the rhythmic and emotive qualities of poetry, fostering deeper engagement through mood-affect congruency (Beege et al., 2018 ). The null effects on self-reported emotional valence and arousal in Experiment 2, despite significant eye-tracking results, may reflect the transient nature of emotional arousal, captured in real-time by pupil dilation but dissipated by posttest self-reports (Hitsuwari & Nomura, 2025 ). This suggests that physiological measures are more sensitive to immediate affective responses in literary contexts, consistent with prior aesthetic research (Laeng et al., 2016 ). Theoretically, these findings extend the cueing principle by demonstrating that emotional cues enhance engagement when aligned with a text's inherent affective content, particularly in literary learning where emotions are central (Mayer & Estrella, 2014 ). They also support the emotional design hypothesis by showing that emotionally salient cues (e.g., narration) improve motivation and comprehension by facilitating germane cognitive load, though visual cues alone may be insufficient in complex tasks (Stark et al., 2018 ). Practically, emotional narration offers a promising tool for poetry instruction, but its implementation requires teacher training and multimedia resources, which may be challenging in resource-limited settings. Limitations include the passive learning mode, which likely constrained engagement, as active learning enhances outcomes per the ICAP framework (Chi & Wylie, 2014 ). Future studies should test interactive methods, such as AI-supported annotations or generative drawing (Chen et al., 2024 ; Xie et al., 2024 ). The small sample size in Experiment 2 ( N = 67) may have inflated effect sizes, necessitating replication with larger, diverse samples. The moderate comprehension test reliability (Cronbach’s α = 0.65) suggests scoring challenges, which could be addressed with refined rubrics, expanded item pools, or more intensive rater training. An alternative would be to incorporate objective items (e.g., multiple-choice imagery identification) alongside open-ended analysis to increase reliability. Longitudinal designs are also needed to assess the durability of emotional cue effects on retention and appreciation of Chinese poetry’s cultural nuances (Xia, 2021 ). Furthermore, both samples were Chinese high school and university students. While appropriate for studying classical Chinese poetry, this restricts generalizability. Future studies should investigate whether emotional cueing effects extend to learners from other cultural or linguistic backgrounds who approach Chinese poetry as second-language or world literature. In conclusion, this study underscores the value of emotional auditory cues in enhancing poetry learning, offering a foundation for future research into multimodal instructional strategies that use poetry’s affective power to foster deeper learning experiences. Implications and limitations This study provides novel insights into the role of emotional visual and auditory cues in enhancing engagement and comprehension in Chinese classical poetry learning. The findings from Experiment 1 suggest that emotion laden word cues can modestly enhance emotional valence, while Experiment 2 demonstrates that emotional narration significantly improves motivation and comprehension, with eye-tracking metrics (pupil dilation, blink rate) indicating heightened emotional arousal (Bradley et al., 2008 ; Maffei & Angrilli, 2019 ). These results extend the cueing principle by showing that emotional cues, when aligned with poetry’s affective content, enhance engagement and learning outcomes, supporting the emotional design hypothesis in a literary context (Beege et al., 2018 ; Mayer & Estrella, 2014 ). Practically, integrating emotional cues into poetry instruction shows promise for fostering deeper emotional and cognitive processing, particularly through dynamic auditory narration. However, implementation requires teacher training to design and deliver emotionally aligned cues and access to multimedia tools, which may limit scalability in resource-constrained settings, such as rural schools or underfunded districts. Despite these contributions, the study has limitations. First, the passive learning mode, where participants viewed videos without interactive tasks, likely restricted engagement, as predicted by the ICAP framework, which emphasizes active and constructive learning for deeper processing (Chi & Wylie, 2014 ). Future studies should incorporate interactive elements, such as generative drawing of poetic imagery (Xie et al., 2024 ) or AI-supported annotations to highlight emotional content (Chen et al., 2024 ), to enhance active learning and motivation. Second, the small sample size in Experiment 2 ( N = 67) may have inflated effect sizes (e.g., d = 2.32 for motivation), necessitating replication with larger, more diverse samples to confirm generalizability. Third, the study focused on short-term effects, with posttests administered immediately after learning. Longitudinal designs are needed to assess the durability of emotional cue effects on comprehension and retention, particularly for novice learners navigating the complex emotional and cultural nuances of Chinese poetry (Xia, 2021 ). Finally, the moderate reliability of the comprehension test (Cronbach’s α = 0.65) suggests challenges in scoring subjective responses, which could be addressed by refining the rubric or incorporating objective measures, such as multiple-choice questions on imagery identification. References Ahmad S, Asghar MZ, Alotaibi FM, Khan S (2020) Classification of poetry text into the emotional states using deep learning technique. IEEE Access 8:73865–73878 Alemdag E, Cagiltay K (2018) A systematic review of eye tracking research on multimedia learning. Comput Educ 125:413–428 Alpizar D, Adesope OO, Wong RM (2020) A meta-analysis of signaling principle in multimedia learning environments. Education Tech Research Dev 68(5):2095–2119 Beege M, Schneider S (2023) Emotional design of pedagogical agents: The influence of enthusiasm and model observer similarity. Education Tech Research Dev 71(3):859–880 Beege M, Schneider S, Nebel S, Häßler A, Rey GD (2018) Mood-affect congruency. Exploring the relation between learners’ mood and the affective charge of educational videos. Comput Educ 123:8596 Beege M, Schneider S, Nebel S, Rey GD (2020) Does the effect of enthusiasm in a pedagogical Agent's voice depend on mental load in the Learner's working memory? Comput Hum Behav 112:106483 Bradley MM, Miccoli L, Escrig MA, Lang PJ (2008) The pupil as a measure of emotional arousal and autonomic activation. Psychophysiology 45(4):602–607 Chan KY, Lyons C, Kon LL, Stine K, Manley M, Crossley A (2020) Effect of on-screen text on multimedia learning with native and foreign-accented narration. Learn Instruction 67:101305 Chang CC, Chen TC (2024) Emotion, cognitive load and learning achievement of students using e textbooks with/without emotional design and paper textbooks. Interact Learn Environ 32(2):674–692 Chang HS, Lee CY, Wang X, Young ST, Li CH, Chu WC (2023) Emotional tones of voice affect the acoustics and perception of Mandarin tones. PLoS ONE, 18(4), e0283635 Chen Y, Zhang X, Hu L (2024) A progressive prompt-based image generative AI approach to promoting students’ achievement and perceptions in learning ancient Chinese poetry. Educational Technol Soc 27(2):284–305 Chi MT, Wylie R (2014) The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychol 49(4):219–243 Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd edn. Lawrence Erlbaum Associates Creely E (2018) What's poetry got to do with it? The importance of poetry for enhancing literacy and fostering student engagement. Lit Learning: Middle Years 26(3):64–70 De Koning BB, Tabbers HK, Rikers RM, Paas F (2009) Towards a framework for attention cueing in instructional animations: Guidelines for research and design. Educational Psychol Rev 21:113–140 Eva-Wood AL (2004) Thinking and feeling poetry: Exploring meanings aloud. J Educ Psychol 96(1):182–191 Hitsuwari J, Nomura M (2025) Effects of emotional and cognitive changes on aesthetic evaluation of poetry based on subjective and physiological continuous responses with pupil diameter measurements. Perceptual and Motor Skills Advance online publication Horovitz T, Mayer RE (2021) Learning with human and virtual instructors who display happy or bored emotions in video lectures. Comput Hum Behav 119:106724 Höffler TN, Leutner D (2007) Instructional animation versus static pictures: A meta–analysis. Learn Instruction 17(6):722–738 Isen AM, Reeve J (2005) The influence of positive affect on intrinsic and extrinsic motivation: Facilitating enjoyment of play, responsible work behavior, and self-control. Motivation Emot 29(4):295–323 Johnson Laird PN, Oatley K (2022) How poetry evokes emotions. Acta Psychol 224:103506 Lei Y, He X (2025) The effect of imagery on the comprehension and aesthetic appreciation of Chinese ancient poetry. Psychology of Aesthetics, Creativity, and the Arts. Advance online publication Laeng B, Eidet LM, Sulutvedt U, Panksepp J (2016) Music chills: The eye pupil as a mirror to music’s soul. Conscious Cogn 44:161–178 Li W, Qian L, Feng Q, Luo H (2024) Using olfactory cues in text materials benefits delayed retention and schemata construction. Sci Rep 14(1):17819 Levine S (2014) Making interpretation visible with an affect-based strategy. Reading Res Q 49(3):283–303 Levine S, Horton WS (2013) Using affective appraisal to help readers construct literary interpretations. Sci Study Literature 3(1):105–136 Levine S, Trepper K, Chung RH, Coelho R (2021) How feeling supports students’ interpretive discussions about literature. J Lit Res 53(4):491–515 Maffei A, Angrilli A (2019) Spontaneous blink rate as an index of attention and emotion during film clips viewing. Physiol Behav 204:256–263 Mathôt S, Fabius J, van Heusden E, van Der Stigchel S (2018) Safe and sensible preprocessing and baseline correction of pupil size data. Behav Res Methods 50(1):94–106 Osowiecka M, Kolańczyk A (2018) Let’s read a poem! What type of poetry boosts creativity? Front Psychol 9:1781 Paas F, Van Merrienboer JJG, Adam JJ (1994) Measurement of cognitive load in instructional research. Percept Motor Skills 79:419–430 Pan Y, Cheng X, Hu Y (2023) Three heads are better than one: cooperative learning brains wire together when a consensus is reached. Cereb Cortex 33(4):1155–1169 Park B, Knörzer L, Plass JL, Brünken R (2015) Emotional design and positive emotions in multimedia learning: An eyetracking study on the use of anthropomorphisms. Comput Educ 86:30–42 Pi Z, Huang X, Wen Y, Wang Q, Zhao X, Li X (2025) Happy facial expressions and mouse pointing enhance EFL vocabulary learning from instructional videos. Br J Edu Technol 56(1):388–409 Pi Z, Liu C, Wang L, Yang J, Li X (2023) Cues facilitate foreign language vocabulary learning from instructional videos: Behavioral and neural evidence. Lang Teach Res, 13621688231164724 Plass JL, Heidig S, Hayward EO, Homer BD, Um E (2014) Emotional design in multimedia learning: Effects of shape and color on affect and learning. Learn Instruction 29:128–140 Plass JL, Kalyuga S (2019) Four ways of considering emotion in cognitive load theory. Educational Psychol Rev 31:339–359 Stark L, Brünken R, Park B (2018) Emotional text design in multimedia learning: A mixed-methods study using eye tracking. Comput Educ 120:185–196 Sweller J, van Merriënboer JJ, Paas F (2019) Cognitive architecture and instructional design: 20 years later. Educational Psychol Rev 31(2):261–292 Teng MF (2023) Language learning strategies. Cogn Individual Differences Second Lang Acquisition: Theor Assess Pedagogy 19:147 Van der Wel P, Van Steenbergen H (2018) Pupil dilation as an index of effort in cognitive control tasks: A review. Psychon Bull Rev 25:2005–2015 Reynaert V, Possik J, Demarey C, Kieken D, Abert B, De Witte B (2024, November) Immersive poetry learning: a field study with middle school students. Front Educ 9:1463635 Scheepers C, Mohr S, Fischer MH, Roberts AM (2013) Listening to limericks: a pupillometry investigation of perceivers’ expectancy. PLoS ONE, 8(9), e74986 van Gog T (2022) The signaling (or cueing) principle in multimedia learning. In Mayer, R.E., & L. Fiorella (Eds.), The Cambridge Handbook of Multimedia Learning (pp. 221–230). 3rd edition. Cambridge University Press Wang X, Mayer RE, Han M, Zhang L (2023) Two emotional design features are more effective than one in multimedia learning. J Educational Comput Res 60(8):1991–2014 Wang X, Lin L, Han M, Spector JM (2020) Impacts of cues on learning: Using eye tracking technologies to examine the functions and designs of added cues in short instructional videos. Comput Hum Behav 107:106279 Wang Y, Zhang M, Yu Q, Zhou Z, Paas F (2025) Effects of an onscreen instructor’s emotions and picture types on poetry appreciation. J Experimental Educ, 1–23 Wang Y, Zhou Z, Paas F (2023) Effects of instruction colour and learner empathy on aesthetic appreciation of Chinese poetry. Instr Sci 51(4):617–637 Wang Z, Gong SY, Xu S, Hu XE (2019) Elaborated feedback and learning: Examining cognitive and motivational influences. Comput Educ 136:130–140 Mayer RE, Estrella G (2014) Benefits of emotional design in multimedia instruction. Learn Instruction 33:12–18 Xia C (2021) Poetry and emotion in classical Chinese literature. The Routledge Handbook of Chinese Studies. Routledge, pp 289–303 Xie H, Lin D, He W, Chen Q (2024) The aesthetics at a pencil tip: The effects of drawing on learning poems. Learn Instruction 91:101881 Xie H, Wang F, Hao Y, Chen J, An J, Wang Y, Liu H (2017) The more total cognitive load is reduced by cues, the better retention and transfer of multimedia learning: A meta-analysis and two meta regression analyses. PLoS ONE 12(8):e0183884 Zhang Q, Chen C, Lu J, Zhang P (2016) The mechanism of emotional contagion. Acta Physiol Sinica 48(11):1423–1436 Zhang J, Wu C, Meng Y, Yuan Z (2017) Different neural correlates of emotion label words and emotion laden words: An ERP study. Front Hum Neurosci 11:455 Zhang J, Bürkner PC, Kiesel A, Dignath D (2023) How emotional stimuli modulate cognitive control: A meta-analytic review of studies with conflict tasks. Psychol Bull 149(12):25 Zhao F, Mayer RE (2023) Benefits of turning the illustrations in a narrated slideshow into cartoons: An extension of the positivity principle. Learn Instruction 86:101779 Declarations Ethics Statement The studies involving human participants were reviewed and approved by the local ethics committee at the School of Psychology, Central China Normal University. The legal guardians of the participants in experiment 1 consented to participate in the study. Additional Declarations The authors declare no competing interests. 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-7731687","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":521656687,"identity":"01990ca7-8705-4903-afbf-7dfc53d46351","order_by":0,"name":"Yi Wang","email":"","orcid":"https://orcid.org/0000-0002-9042-0417","institution":"Jianghan University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Wang","suffix":""},{"id":521657868,"identity":"22b7f083-b266-4416-be27-362ce0c430c7","order_by":1,"name":"Man Zhang","email":"","orcid":"https://orcid.org/0000-0003-1436-8669","institution":"Central China Normal University","correspondingAuthor":false,"prefix":"","firstName":"Man","middleName":"","lastName":"Zhang","suffix":""},{"id":521657869,"identity":"2aa30856-d8ab-431c-964a-c49ebfab1309","order_by":2,"name":"Zhen Xia","email":"","orcid":"","institution":"Wuhan Optics Valley Experimental Middle School","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Xia","suffix":""},{"id":521657870,"identity":"c3ecb204-5ea6-4ed4-8f1a-0d7212e46ed6","order_by":3,"name":"Zhijin Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYDACCRDBYwPlsRGvJY1kLQyHSdAiP7v5mOQPmfPy/LN7DBg+lB1m4J/dgF8L45xjaRISPLcNZ9w5Y8A449xhBok7B/BrYZbIMZMw4LmdYCCRY8DM23aYwUAiAb8WNpCWBJ5zEC1/idHCA9JygOcARAsjMVokJNKSLRt4kg1n3EgrONhzLp1H4gYBLfIzkg/e/NljJ88/I3njgx9l1nL8MwhoAQPGHgh9AORSItSDwA8i1Y2CUTAKRsHIBABnOTnvpbhd+QAAAABJRU5ErkJggg==","orcid":"","institution":"Central China Normal University","correspondingAuthor":true,"prefix":"","firstName":"Zhijin","middleName":"","lastName":"Zhou","suffix":""},{"id":521657871,"identity":"71ee6c48-8f82-4e3f-85c7-2cb46324523f","order_by":4,"name":"Fred Paas","email":"","orcid":"https://orcid.org/0000-0002-1647-5305","institution":"Erasmus University Rotterdam","correspondingAuthor":false,"prefix":"","firstName":"Fred","middleName":"","lastName":"Paas","suffix":""}],"badges":[],"createdAt":"2025-09-28 04:34:17","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7731687/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7731687/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92657962,"identity":"8eec5323-d8f9-4052-8c37-9c84474b65b3","added_by":"auto","created_at":"2025-10-02 14:06:06","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":726911,"visible":true,"origin":"","legend":"","description":"","filename":"Emotionalvisualandauditorycuesonpoetrylearning0913.docx","url":"https://assets-eu.researchsquare.com/files/rs-7731687/v1/b5e1770817eca042cb831d04.docx"},{"id":92657958,"identity":"2e400eb5-8260-4ca1-b656-f5b8949d9617","added_by":"auto","created_at":"2025-10-02 14:06:06","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":342,"visible":true,"origin":"","legend":"","description":"","filename":"rs7731687.json","url":"https://assets-eu.researchsquare.com/files/rs-7731687/v1/d9fa8b5fa6b0abea0c7163f1.json"},{"id":92657964,"identity":"7d8fa866-eaa8-4db9-85ff-ea1a4a8ec8f4","added_by":"auto","created_at":"2025-10-02 14:06:06","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":136520,"visible":true,"origin":"","legend":"","description":"","filename":"rs77316870enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7731687/v1/380592c6a9082c347b1b839a.xml"},{"id":92658621,"identity":"88772a47-8dfa-4d95-866c-fe58f285da8f","added_by":"auto","created_at":"2025-10-02 14:14:06","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":60306,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7731687/v1/eb2e074b501c8426b89faf47.png"},{"id":92658911,"identity":"d8df21fa-1354-495b-8c68-6e5c3e623ba6","added_by":"auto","created_at":"2025-10-02 14:22:06","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32493,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7731687/v1/ce8f9feb92fee5f46b184c02.png"},{"id":92657965,"identity":"b9d62421-7a22-413e-8b3a-80a23dcfb8bc","added_by":"auto","created_at":"2025-10-02 14:06:06","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":130766,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7731687/v1/61087950b58b72824d963265.png"},{"id":92658623,"identity":"ff27d413-ab66-487d-b496-9b25a7905e67","added_by":"auto","created_at":"2025-10-02 14:14:06","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":37301,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7731687/v1/9c0a67e14ff04d6fa3f5475a.png"},{"id":92657968,"identity":"ff8d2cc1-81e8-43d4-9181-9368f01cee27","added_by":"auto","created_at":"2025-10-02 14:06:06","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":133251,"visible":true,"origin":"","legend":"","description":"","filename":"rs77316870structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7731687/v1/595a6b29bd5df97a4fd8a812.xml"},{"id":92658624,"identity":"9f98f8e2-51ce-41bc-8a1c-fff98c71c996","added_by":"auto","created_at":"2025-10-02 14:14:06","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":144343,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7731687/v1/0c165187bf35e920632be2b6.html"},{"id":92658620,"identity":"6905265b-0149-49c3-ba40-6133ac001e2d","added_by":"auto","created_at":"2025-10-02 14:14:06","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":168427,"visible":true,"origin":"","legend":"\u003cp\u003eAn example of learning materials for an ancient poem (Clockwise from top left are: no cues, emotional label word cues, emotional label word + laden word cues, emotional laden word cues).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7731687/v1/b4a3634f2f9fdc82dea9833b.jpeg"},{"id":92657956,"identity":"676bb622-1cb8-484c-b14f-c3fcfd42b4d3","added_by":"auto","created_at":"2025-10-02 14:06:06","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":96162,"visible":true,"origin":"","legend":"\u003cp\u003eProcedure of Experiment 1.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7731687/v1/3462f2fe78f7c4767db9288f.jpeg"},{"id":92657961,"identity":"19af6956-a1c3-4290-b962-05a209db9e62","added_by":"auto","created_at":"2025-10-02 14:06:06","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":375540,"visible":true,"origin":"","legend":"\u003cp\u003ePitch (Hz) at 30 random points across narrations.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7731687/v1/2c2c28a772c86c033537fcf9.jpeg"},{"id":92657963,"identity":"beadb657-122b-4525-9e4a-693cdae91fbe","added_by":"auto","created_at":"2025-10-02 14:06:06","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":237688,"visible":true,"origin":"","legend":"\u003cp\u003eBaseline-corrected pupil dilation (in pixels) and blink rate (per 6.5-min video) as a function of narration type.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7731687/v1/3c253b22db296c611d01f2ee.jpeg"},{"id":92659594,"identity":"3db586cd-9435-40b8-b9a3-ec838e9b5287","added_by":"auto","created_at":"2025-10-02 14:30:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1772767,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7731687/v1/26a9adf2-2e35-402c-aa23-37fadaa78d6f.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eEffects of emotional visual and auditory cues on Chinese poetry learning: An eye-tracking study\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChinese classical poetry, known for its concise and emotionally evocative language, often uses metaphorical imagery (e.g., \u0026lsquo;moon\u0026rsquo; symbolizing longing) to convey complex emotions (Xia, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lei \u0026amp; He, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, novice learners, especially those with limited cultural or linguistic knowledge, struggle to interpret these subtle cues due to insufficient prior exposure and limited instructional support. Current instructional approaches often focus primarily on linguistic and historical aspects, overlooking emotional engagement (Levine et al., 2013; Eva-Wood, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCues (or signals) are instructional design elements in multimedia learning that capture learners\u0026rsquo; attention and assist in the selection, organization, and integration of essential information, thereby enhancing learning outcomes (Alpizar \u003csup\u003eet al., 2020\u003c/sup\u003e; De Koning et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Recent studies have explored the supportive role of cues such as visual color and embedded textual annotations in language learning (Teng, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pi et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, Poetry, as an emotional linguistic text, is not merely about understanding lexical semantics; its primary purpose lies in expressing emotions (Johnson Laird \u0026amp; Oatley, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The emotional design hypothesis in multimedia learning suggests that inducing positive and negative emotions can facilitate learners\u0026rsquo; emotional experience and learning motivation, thus enhancing learning outcomes (Beege et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, 2023; Stark et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), this study explored the effects of emotional text-based visual cues, e.g., visual underlines highlighting emotional words, and emotional narration cues on poetry instruction. Specifically, the integration of these emotional cues was used to enhance learners\u0026rsquo; affective response to poetry, potentially reducing extraneous cognitive load, increasing learning motivation, and deepening understanding. Additionally, eye-tracking metrics, such as pupil dilation and blink rate, were employed to provide objective measures of emotional engagement, complementing self-reports and enhancing our understanding of affective processing in poetry learning (Bradley et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Maffei \u0026amp; Angrilli, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eCues and Emotional Design in Multimedia Learning\u003c/p\u003e\u003cp\u003eCues in multimedia learning, such as visual highlights or textual annotations, guide learners\u0026rsquo; attention, reduce extraneous cognitive load, and promote information integration (Alpizar et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; De Koning et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). According to De Koning et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), cues serve three functions: selecting information by directing attention to key elements, organizing information by emphasizing structure, and integrating information by clarifying relationships, all of which enhance germane cognitive load and learning outcomes. Visual cues, like coloring or underlines, attract attention to critical content (Pi et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; X. Wang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), while text cues, such as annotations, provide semantic priming to improve processing speed and accuracy (Pi et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For example, X. Wang et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that visual lines in instructional videos increased attention to geographical content, improving learning performance.\u003c/p\u003e\u003cp\u003eEmotional design cues, including colors, facial expressions, narrative voices, and emotional words, enhance motivation and learning by eliciting affective responses (Mayer \u0026amp; Estrella, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Beege et al., 2023; Pi et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The emotional design hypothesis posits that emotions elicited during learning influence outcomes by either facilitating or hindering cognitive processing (Beege et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Plass \u0026amp; Kalyuga, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). When aligned with learning goals, emotional cues can reduce extraneous cognitive load and enhance engagement, as seen in studies where emotional design improved positive emotions and achievement (Chang \u0026amp; Chen, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, misaligned cues, such as adding emotional words to neutral scientific texts, may increase extraneous load (Stark et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In poetry, aligning cues with inherent emotional content (e.g., highlighting \u0026ldquo;moonlight\u0026rdquo; to evoke longing) may reduce cognitive load and enhance comprehension by facilitating mood-affect congruency, where the emotional tone of instructional materials matches learners\u0026rsquo; affective states (Beege et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). While Stark et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) found that emotional words increased extraneous load in scientific texts, poetry\u0026rsquo;s inherent emotional content may allow cues to enhance germane load by emphasizing affective meaning, a hypothesis tested in this study.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eEmotional Scaffolds in Poetry Instruction\u003c/h2\u003e\u003cp\u003ePoetry primarily aims to express emotions through carefully selected language, encompassing literal and metaphorical meanings (Eva-Wood, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Xia, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Emotion laden words, such as \u0026ldquo;moon\u0026rdquo; or \u0026ldquo;coffin\u0026rdquo;, evoke feelings like longing or sadness without directly naming emotions, unlike emotion label words (e.g., \u0026ldquo;happiness\u0026rdquo;, \u0026ldquo;anger\u0026rdquo;) (Zhang et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This subtlety engages readers\u0026rsquo; imaginations but challenges novices, who require reflective thinking to interpret emotional nuances (Johnson-Laird \u0026amp; Oatley, 2022). Educational strategies, such as affective appraisal and think-aloud methods, enhance emotional engagement and interpretive skills by guiding students to analyze emotion laden words (Levine et al., 2013, 2014, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Eva-Wood, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Multimedia advancements offer new opportunities, with sensory cues like olfactory stimuli improving memory and emotional connections in poetry learning (Li et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, practical limitations, such as the availability of poems with consistent sensory references, restrict widespread application. This study explores visual and auditory emotional cues to enhance engagement and comprehension in Chinese classical poetry, leveraging its inherent affective content.\u003c/p\u003e\u003c/div\u003e"},{"header":"The Current Study","content":"\u003cp\u003eThis study investigated the effects of emotional visual and auditory cues on learners\u0026rsquo; emotional engagement and comprehension of Chinese classical poetry, which is characterized by subtle, culturally embedded affective expressions (Xia, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Building on the cueing principle and emotional design hypothesis (Alpizar et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Beege et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), we designed multimedia learning materials incorporating visual cues (underlined emotional words) in Experiment 1 and auditory cues (emotional narration) in Experiment 2 to enhance learners\u0026rsquo; affective responses and understanding. Eye-tracking metrics, such as pupil dilation and blink rate, were used in Experiment 2 to provide objective measures of emotional engagement, complementing self-reports by capturing real-time physiological responses to affective stimuli (Bradley et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Maffei \u0026amp; Angrilli, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study extended emotional design research into the domain of poetry, which differs from typical scientific or expository texts in that its primary purpose is affective expression. By examining emotional cues in a literary context, we contribute both theoretically\u0026mdash;showing how the cueing principle and emotional design hypothesis operate with inherently affective material\u0026mdash;and practically, by informing poetry instruction where engaging with emotions is essential for comprehension.\u003c/p\u003e\u003cp\u003eExperiment 1 employed a one-factor between-subjects design with four levels to examine the impact of emotional text-based visual cues on poetic learning. Visual cues consisted of red underlines on emotion label words (e.g., \u0026ldquo;sorrow\u0026rdquo;), emotion laden words (e.g., \u0026ldquo;moonlight\u0026rdquo;), or both, compared to a no-cue condition. Based on the mood-affect congruency effect (Beege et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), we hypothesized that emotional cues aligned with the poems\u0026rsquo; negative emotional tone would enhance emotional engagement (H1a) by priming affective processing, reduce extraneous cognitive load (H1b) by directing attention to relevant emotional content, increase motivation (H1c), and improve comprehension (H1d) by facilitating deeper emotional processing (Stark et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Experiment 2 utilized a one-factor between-subjects design with two levels to explore the effects of emotional narration versus neutral narration. Emotional narration used a melancholic tone with varied pitch. We hypothesized that emotional narration would enhance emotional engagement (H2a), reduce extraneous cognitive load (H2b), increase motivation (H2c), and improve comprehension (H2d) by aligning with the poem\u0026rsquo;s affective tone (Beege et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, eye-tracking metrics were expected to reveal greater pupil dilation and reduced blink rate in the emotional narration condition (H2e), reflecting heightened emotional arousal and engagement (Bradley et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Maffei \u0026amp; Angrilli, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e"},{"header":"Experiment 1","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eMethod\u003c/h2\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003eDesign and Participants\u003c/h2\u003e\u003cp\u003eSample size was estimated using G-Power 3.1.7 (\u003cem\u003eη\u003c/em\u003e² \u0026gt;0.10), yielding a required sample of 112 participants to achieve 0.95 statistical power. A total of 139 high school students (76 female, \u003cem\u003eM\u003c/em\u003e\u003csub\u003eage\u003c/sub\u003e = 16.91, \u003cem\u003eSD\u003c/em\u003e\u003csub\u003eage\u003c/sub\u003e = 1.51) participated in the study. A single-factor, four-level between-subjects design was employed, with emotional cue type as the independent variable (no cues vs. emotional label cues vs. emotional laden cues vs. combined label + laden cues).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eMeasures\u003c/h2\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003eDemographics\u003c/h2\u003e\u003cp\u003eBasic demographic information, including gender and age, was collected.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eEmotional Response\u003c/h3\u003e\n\u003cp\u003eTwo self-rating items assessed valence and arousal on a 7-point scale immediately after they finished learning (1 = very unhappy/very calm, 7 = very happy/very excited). The Cronbach’s \u003cem\u003eα\u003c/em\u003e was 0.69 in this experiment.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCognitive Load\u003c/h2\u003e\u003cp\u003eThree items scored on a 7-point scale (1 = extremely low, 7 = extremely high) measured intrinsic, germane, and extraneous cognitive load (Paas et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). These items were adapted from a prior Chinese study (Wang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eLearning Motivation\u003c/h2\u003e\u003cp\u003eAn 8-item self-rating scale scored on a 7-point scale (Isen \u0026amp; Reeve, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) assessed learning motivation, with Cronbach’s \u003cem\u003eα\u003c/em\u003e = 0.87 in this experiment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eComprehension Test\u003c/h2\u003e\u003cp\u003eThree open-ended questions assessed semantic understanding, emotional experience, and aesthetic appreciation of the poems (Pan et al., 2022). Each question was rated by two independent raters trained on a standardized rubric aligned with the Chinese poetry curriculum. They evaluated the accuracy of imagery identification (Q1), depth of emotional interpretation (Q2), and quality of aesthetic analysis (Q3) using a 10-point scale (inter-rater reliability, ICC = 0.81). Total scores ranged from 0 to 30, and the Cronbach’s \u003cem\u003eα\u003c/em\u003e was 0.65, indicating moderate reliability, likely due to the subjective nature of aesthetic judgments.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eControl Variable\u003c/h2\u003e\u003cp\u003ePrior knowledge of classical poetry was measured using a 30-point scale, including four 5-point Likert items and one open-ended question (Y. Wang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Cronbach’s \u003cem\u003eα\u003c/em\u003e = 0.63 in this experiment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eLearning Materials\u003c/h2\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003ePoetic Texts\u003c/h2\u003e\u003cp\u003eTwo Tang Dynasty poems, \u003cem\u003eGu Yi (《古意》)\u003c/em\u003e, and \u003cem\u003eChun Si\u003c/em\u003e (《春思》), by lesser known poets were selected to ensure quality while controlling for familiarity. Thirty-one randomly selected high school students (18 female, \u003cem\u003eM\u003c/em\u003e\u003csub\u003eage\u003c/sub\u003e = 16.27, \u003cem\u003eSD\u003c/em\u003e\u003csub\u003eage\u003c/sub\u003e = 1.29) rated the poems on difficulty, familiarity, and emotional tone. Both poems were moderately difficult (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e = 4.67, \u003cem\u003eSD\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e = 0.97; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e = 4.74, \u003cem\u003eSD\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e = 0.81), low in familiarity (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e = 2.38, \u003cem\u003eSD\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e = 0.96; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e = 2.26, \u003cem\u003eSD\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e = 1.06), and emotionally negative (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e = 2.64, \u003cem\u003eSD\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e = 0.87; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e = 2.71, \u003cem\u003eSD\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e = 0.82). No significant differences were found between the poems on these dimensions, \u003cem\u003et\u003c/em\u003e(30) = 0.29, \u003cem\u003ep\u003c/em\u003e = 0.77; \u003cem\u003et\u003c/em\u003e(30) = 0.53, \u003cem\u003ep\u003c/em\u003e = 0.59; \u003cem\u003et\u003c/em\u003e(30) = 0.25, \u003cem\u003ep\u003c/em\u003e = 0.80, respectively. Emotion laden words and emotion label words were identified for each poem through collaboration with two Chinese teachers. For \u003cem\u003eGu Yi\u003c/em\u003e, emotion labels were “cold” (寒) and “sorrow” (愁), and emotion laden words were “epistles and tidings” (音书) and “moonlight” (明月). For \u003cem\u003eChun Si\u003c/em\u003e, emotion labels were “regret” (恨) and “smile” (笑), and emotion laden words were “moonlight” (明月) and “solitary slumber” (独眠).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003ePoetic Videos\u003c/h2\u003e\u003cp\u003eLearning materials consisted of 6.5-minute instructional videos for each poem, presented in a seven-slide PowerPoint format. The poem’s full text was displayed in black 22-point Kaiti font, centered on a white background, across all slides. Each slide presents the poem's title, author, and full text. In addition, beneath the full text, the second slide also provided a brief overview of the author's biography and the historical context (e.g., Tang Dynasty cultural references). The third to sixth slides briefly presented the key imagery and deeper meanings of the first to fourth stanzas, respectively. The final slide summarized the poem’s emotions and themes. The presentation time for each slide was fixed: the first and last slides were 45 seconds, while the others were 60 seconds. In the cue conditions, visual cues were red underlines (2-point thickness) applied to emotion label words (e.g., ‘sorrow’[愁]), emotion laden words (e.g., ‘moonlight’[明月]), or both, depending on the condition. Slide transitions were automated to ensure consistent timing across participants.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eProcedure\u003c/h2\u003e\u003cp\u003eThe experiment was conducted in a classroom setting using individual 20-inch monitors at 1280x1024 resolution. Participants (groups of 5–10) were seated at separate workstations to minimize distractions. The procedure lasted approximately 50 minutes. Participants first completed a 15-minute pretest assessing demographics and prior poetry knowledge via an online questionnaire. After a 2-minute rest, they were randomly assigned to one of four conditions (no cues, emotion label cues, emotion laden cues, combined cues) and viewed the corresponding 6.5-minute video. Slides auto-advanced every 45–60 seconds based on content length. Immediately after, participants completed a 25-minute posttest, typing responses to open-ended comprehension questions and self-rating emotional responses and motivation on an online interface. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrated the first slide in four experimental conditions, showing the poem’s text with no cues, red underlines on emotion label words (e.g., ‘sorrow’), emotion laden words (e.g., ‘moonlight’), or both. And Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the experimental flow.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eResults\u003c/h3\u003e\n\u003cp\u003eThe descriptive statistics for the variable indicators under four conditions are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\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\u003eDescriptive statistics of Experiment 1.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperimental Conditions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNo cues\u003c/p\u003e\u003cp\u003e(n = 34)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eEmotion label words\u003c/p\u003e\u003cp\u003e(n = 36)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eEmotion laden words\u003c/p\u003e\u003cp\u003e(n = 32)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eEmotion label + \u003c/p\u003e\u003cp\u003eEmotion laden words (n = 37)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrior knowledge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.56 (1.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.38 (1.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.28 (1.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.41 (1.91)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eValence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.15 (1.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.43 (1.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.81 (1.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.59 (1.88)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArousal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.57 (1.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.79 (1.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.66 (1.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.10 (1.26)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntrinsic cognitive load\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.53 (1.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.58 (1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.28 (1.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.35 (1.18)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGermane cognitive load\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.64 (1.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.77(1.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.59(1.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.70(1.58)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtraneous cognitive load\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.64 (1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.72 (1.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.46 (1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.54 (1.46)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLearning motivation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.50 (0.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.49 (0.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.67 (0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.58 (0.44)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComprehension test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.70 (3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.50 (3.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.00 (2.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.36 (2.58)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eA one-way ANOVA revealed no significant differences in prior knowledge across four conditions, \u003cem\u003eF\u003c/em\u003e \u0026lt; 1. Consequently, prior knowledge was excluded as a covariate in subsequent analyses.\u003c/p\u003e\u003cp\u003eA one-way ANOVA revealed no significant differences in emotional valence across the four conditions, \u003cem\u003eF\u003c/em\u003e(3, 135) = 1.64, \u003cem\u003ep\u003c/em\u003e = 0.18, \u003cem\u003eη²\u003c/em\u003e = 0.04. The small to medium effect size (\u003cem\u003eη²\u003c/em\u003e = 0.04) suggests a limited impact of visual cues on emotional experience (Cohen, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Post hoc comparisons indicated that the combined emotional label + laden cue condition elicited a more negative valence than the emotion label cue condition (\u003cem\u003ep\u003c/em\u003e = 0.04, \u003cem\u003ed\u003c/em\u003e = 0.49), but this effect’s practical significance is modest given the non-significant omnibus test and small effect size. No significant differences were found in emotional arousal, \u003cem\u003eF\u003c/em\u003e(3, 135) = 0.87, \u003cem\u003ep\u003c/em\u003e = 0.46, \u003cem\u003eη²\u003c/em\u003e = 0.02.\u003c/p\u003e\u003cp\u003eAdditionally, no significant differences were found across the four groups in terms of intrinsic, germane, or extraneous cognitive load, learning motivation, or comprehension performance, all \u003cem\u003eF\u003c/em\u003es \u0026lt; 1, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05, with \u003cem\u003eη²\u003c/em\u003e = 0.007, \u003cem\u003eη²\u003c/em\u003e = 0.001, \u003cem\u003eη²\u003c/em\u003e = 0.005 for intrinsic, germane, or extraneous cognitive load respectively, and \u003cem\u003eη²\u003c/em\u003e = 0.018, \u003cem\u003eη²\u003c/em\u003e = 0.007 for learning motivation and comprehension.\u003c/p\u003e\n\u003ch3\u003eDiscussion\u003c/h3\u003e\n\u003cp\u003eThis experiment was built on prior research on visual cues (Pi et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; X. Wang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and emotional text design (Stark et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) by using red underlines to highlight emotion label and emotion laden words in mute instructional videos. The results showed a modest effect of emotion laden word cues on negative emotional valence (\u003cem\u003ep\u003c/em\u003e = 0.04, \u003cem\u003ed\u003c/em\u003e = 0.49 in post hoc comparison), partially supporting H1a, but the non-significant omnibus test (\u003cem\u003ep\u003c/em\u003e = 0.18, \u003cem\u003eη²\u003c/em\u003e = 0.04) suggests limited emotional impact. Notably, this result emerged from a post-hoc comparison in the absence of a significant omnibus effect, so it should be interpreted as exploratory rather than confirmatory evidence. No effects were observed on cognitive load, motivation, or comprehension, failing to support H1b, H1c, and H1d.\u003c/p\u003e\u003cp\u003eFirst, emotion laden word cues elicited a slightly more negative emotional valence, consistent with the poems’ emotional tone, but the effect was small and not statistically significant at the omnibus level (Beege et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This may reflect partial mood-affect congruency, where cues aligned with the poem’s negative emotions primed affective processing, though the effect’s practical significance is limited (Cohen, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1988\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSecond, emotional visual cues did not reduce cognitive load compared to the no-cue condition, failing to support H1b. This may be due to the high baseline cognitive demands of interpreting classical poetry’s condensed imagery and symbolism, which could have overshadowed the benefits of visual cues (Sweller et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Unlike science texts where cues reduce extraneous load (Xie et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), poetry’s emotional complexity may require more dynamic cues to enhance germane load. Third, emotional cues did not enhance motivation, failing to support H1c. The passive learning mode, where participants viewed videos without interactive elements, may have limited engagement, consistent with the ICAP framework’s emphasis on active learning for motivation (Chi \u0026amp; Wylie, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The static nature of visual cues, compared to dynamic auditory cues, may also have reduced their motivational impact (Höffler \u0026amp; Leutner, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn sum, while emotion laden word cues showed a modest effect on emotional valence, their static nature limited their impact on motivation and comprehension, suggesting that visual cues alone are insufficient for engaging learners with poetry’s complex emotions (Höffler \u0026amp; Leutner, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Multimodal cues, combining visual and auditory elements, may enhance engagement by providing dynamic social cues, such as expressive narration, that align with the poem’s affective content (Beege et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Experiment 2 addressed this by incorporating emotional narration to examine its effects on motivation, comprehension, and emotional engagement, using eye-tracking to capture physiological responses.\u003c/p\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003cdiv id=\"Sec23\" class=\"Section4\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003cdiv id=\"Sec26\" class=\"Section4\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Experiment 2","content":"\u003ch2\u003eMethod\u003c/h2\u003e\u003ch2\u003eParticipants and Design\u003c/h2\u003e\u003cp\u003eSample size estimation was conducted using GPower 3.1.7, referencing effect sizes from similar studies and our experimental design (d \u0026gt; 0.50), which indicated a required sample of 61 participants to achieve 0.95 statistical power. A total of 67 undergraduate students (51 female; \u003cem\u003eM\u003c/em\u003e\u003csub\u003eage\u003c/sub\u003e = 20.15, \u003cem\u003eSD\u003c/em\u003e\u003csub\u003eage\u003c/sub\u003e = 1.39) participated. We employed a single-factor, two-level between-subjects design, manipulating the narration of the voice (neutral vs. emotional) as the independent variable.​\u003c/p\u003e\u003ch2\u003eManipulation Check of Narration\u003c/h2\u003e\u003cp\u003eTo verify the effectiveness of our emotional narration manipulation, 26 students (21 female; \u003cem\u003eM\u003c/em\u003e\u003csub\u003eage\u003c/sub\u003e = 20.91, \u003cem\u003eSD\u003c/em\u003e\u003csub\u003eage\u003c/sub\u003e = 1.29) were randomly selected to rate the emotional narration and text matching of each poem on a 7-point scale (1 = emotionally insufficient, 7 = emotionally sufficient; 1 = very mismatched, 7 = very matched). For “Gu Yi”, emotional narration received significantly higher ratings for both emotional sufficiency and matching compared to neutral narration, \u003cem\u003et\u003c/em\u003e(25) = 7.06, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, \u003cem\u003et\u003c/em\u003e(25) = 7.01, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, respectively. Similarly, for “Chun Si”, emotional narration received higher ratings for emotional sufficiency and matching than neutral narration, \u003cem\u003et\u003c/em\u003e(25) = 9.92, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, \u003cem\u003et\u003c/em\u003e(25) = 10.28, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, respectively. In addition, we quantified the differences in phonetic characteristics between the two narratives using pitch as an example. Thirty random time points from each narrative were analyzed with Praat (Chang et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The results showed that the pitch of the expressive narrative was significantly higher than the neutral one, \u003cem\u003et\u003c/em\u003e(58) = 6.34, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, indicating a more dynamic and fluctuating pattern, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003ch2\u003eMeasures and equipment\u003c/h2\u003e\u003ch2\u003eMeasures\u003c/h2\u003e\u003cp\u003eDependent variables included learners’ emotional responses, learning motivation, poetry comprehension tests, and control variables as in experiment 1. Furthermore, two eye tracking metrics, e.g., pupil dilation and blink rate were introduced to assess the impact of emotional narration on the emotional process of poetry learning.​ Pupil dilation has been related to cognitive load (Van der Wel \u0026amp; Van Steenbergen, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), emotional stimuli elicit pupil changes as well, serving as indicators of emotional responses such as the emotional arousal and valence of visual stimuli (Bradley et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Negative emotions are more likely to cause pupil dilation than positive emotions (Bradley et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In poetry appreciation, violations of rhyme have been reported to lead to a delayed increase in subjects’ pupil dilation when listening to English limerick poetry (Scheepers et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Moreover, an increase in pupil size can significantly predict higher beauty ratings of the poems, suggesting pupil dilation reflects enhanced emotional engagement or aesthetic pleasure during poetic processing (Hitsuwari \u0026amp; Nomura, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Meanwhile, the blink rate can serve as a sensitive physiological indicator of emotion and motivational orientation. Specifically, the modulation of blink rate varies depending on emotional context, with stronger emotional content often leading to greater inhibition of blinking, for example, empathy related film clips, e.g., crying characters elicited the lowest blink rate, indicating strong visual engagement and emotional absorption (Maffei \u0026amp; Angrilli, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEye tracking data were collected using an Eyelink1000 Desktop eye tracker (SR Research Ltd., Canada) with a sampling rate of 1000 Hz, screen refresh rate of 60 Hz, and resolution of 1280×1024 pixels. Participants’ heads were stabilized using a chin forehead rest, maintaining a distance of approximately 60 cm from the screen. Data were recorded in monocular pupil corneal reflection mode, with a 9-point calibration focusing on the right eye. The learning materials were identical to those in experiment 1 except for auditory narrations.\u003c/p\u003e\u003ch2\u003eProcedure\u003c/h2\u003e\u003cp\u003eThis eye tracking experiment was conducted in a laboratory with constant light to minimize external interference. Participants had normal or corrected to normal vision and no history of neurological or psychological disorders.​ Before the experiment, participants randomly selected one poem as their learning material. The experiment was conducted via computer. Participants first completed a pretest, including demographic and prior knowledge questionnaires. After a 2-minute rest, they were briefed on the experimental task, completed eye tracking calibration, and entered the learning phase. Participants were randomly assigned to one of two conditions and viewed the corresponding learning video while their eye movements were recorded.​ In the posttest, participants reported their emotions, cognitive load, and motivation. They then completed a poetry comprehension test. This experiment lasted approximately 50 minutes.\u003c/p\u003e\n\u003ch3\u003eResults\u003c/h3\u003e\n\u003cp\u003eThe descriptive statistics for dependent variables under the two experimental conditions are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e below.\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\u003eDescriptive Statistics of behavioral data for Experiment 2.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperimental Conditions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNeutral narration\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eEmotional narration (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrior knowledge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11.94 (1.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.06 (1.73)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eValence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.07 (1.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.19 (1.48)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArousal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.75 (1.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.54 (1.15)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntrinsic cognitive load\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.40 (1.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.34 (1.41)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGermane cognitive load\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.25 (1.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.40 (1.52)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtraneous cognitive load\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.56 (1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.68 (1.62)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLearning motivation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.24 (0.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.17 (0.42)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComprehension test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.68 (3.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.60 (3.38)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eIndependent samples t-tests revealed no significant differences between the two groups in prior knowledge, \u003cem\u003et\u003c/em\u003e(65)\u0026thinsp;=\u0026thinsp;0.27, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.78, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06, or emotional valence, \u003cem\u003et\u003c/em\u003e(65)\u0026thinsp;=\u0026thinsp;0.29, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.77, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.07, or emotional arousal, \u003cem\u003et\u003c/em\u003e(65)\u0026thinsp;=\u0026thinsp;0.80, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.43, \u003cem\u003ed\u003c/em\u003e = -0.19. Emotional narration did not affect intrinsic, germane, or extraneous cognitive load, with \u003cem\u003et\u003c/em\u003e(65)\u0026thinsp;=\u0026thinsp;0.17, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.86, \u003cem\u003ed\u003c/em\u003e = -0.04, \u003cem\u003et\u003c/em\u003e(65)\u0026thinsp;=\u0026thinsp;0.40, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.70, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.09, \u003cem\u003et\u003c/em\u003e(65)\u0026thinsp;=\u0026thinsp;0.32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.75, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08, respectively. However, emotional narration significantly increased learning motivation (\u003cem\u003et\u003c/em\u003e(65)\u0026thinsp;=\u0026thinsp;9.66, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.32) and comprehension performance (\u003cem\u003et\u003c/em\u003e(65)\u0026thinsp;=\u0026thinsp;2.30, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.56) compared to neutral narration. The large motivation effect size (d\u0026thinsp;=\u0026thinsp;2.32) aligns with studies on expressive narration (Beege et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) but may be inflated due to the small sample size (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;67), warranting replication.\u003c/p\u003e\u003cp\u003eTo control for individual differences, pupil size was baseline-corrected using the average within 500 ms post-stimulus onset (Math\u0026ocirc;t et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). An independent samples t-test showed significantly greater pupil dilation in the emotional narration condition (\u003cem\u003et\u003c/em\u003e(65)\u0026thinsp;=\u0026thinsp;8.57, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.10), consistent with heightened emotional arousal (Bradley et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Blink rate, calculated as blinks per 6.5-minute video, was lower in the emotional narration condition (\u003cem\u003et\u003c/em\u003e(65)\u0026thinsp;=\u0026thinsp;2.88, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.70). These large effect sizes (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.10, 0.70) align with prior findings in aesthetic contexts (Laeng et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) but may reflect sample size limitations. Descriptive Statistics of eye tracking metrics for Experiment 2 were displayed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\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\u003eDescriptive Statistics of eye tracking metrics for Experiment 2.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEye movement indicators\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeutral narration (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEmotional narration (n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePupil dilation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-72.67 (74.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58.89 (49.87)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlink rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30.13 (14.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.24 (13.15)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\n\u003ch3\u003eDiscussion\u003c/h3\u003e\n\u003cp\u003eExperiment 2 extended experiment 1 by incorporating emotional narration into poetry learning material to examine the effects of combined emotional cues on Chinese poetry learning. The results showed that the emotional narration has no effect on learners\u0026rsquo; emotions but promotes learning motivation and comprehension performance. Regarding eye movement indicators, under the emotional narration condition, learners had a stronger emotional response to the poetry.\u003c/p\u003e\u003cp\u003eOn behavioral indicators, the lack of significant effects on emotional valence and arousal (H2a) may reflect the poems\u0026rsquo; complex emotional tone, blending negative (e.g., longing) and positive (e.g., aesthetic liking) elements, which could dilute self-reported emotional changes (Johnson-Laird \u0026amp; Oatley, 2022). This contrasts with Experiment 1, where emotion laden cues elicited a modest valence effect, suggesting visual cues may be more effective for static emotional priming. Second, Emotional narration positively influenced learning motivation compared to neutral narration, fully supporting H2c. This aligns with the emotion as facilitator hypothesis (Beege et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Emotional narration with mixed emotional tones elicited social responses, making learners perceive the narrator as engaging and experienced, thereby increasing their motivation (Zhao \u0026amp; Mayer, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Third, emotional narration improved comprehension (H2d, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.56), likely because expressive vocal cues highlighted the poems\u0026rsquo; affective content, facilitating deeper processing of emotional and semantic elements (Levine et al., 2014). This alignment of narration with the poem\u0026rsquo;s emotional tone may have enhanced learners\u0026rsquo; ability to interpret imagery and themes, consistent with the emotional design hypothesis (Beege et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEye-tracking metrics revealed greater pupil dilation (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.10) and reduced blink rate (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.70) in the emotional narration condition, supporting H2e and indicating heightened emotional arousal and engagement (Bradley et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Maffei \u0026amp; Angrilli, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These findings align with studies linking pupil dilation to emotional processing in aesthetic contexts (Laeng et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and reduced blink rate to immersive emotional engagement during narrative videos (Maffei \u0026amp; Angrilli, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The discrepancy with null self-reported emotional effects (H2a) may reflect peak arousal occurring mid-video, captured by eye-tracking but dissipated by posttest self-reports, as physiological measures are more sensitive to transient emotional changes (Hitsuwari \u0026amp; Nomura, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The large effect sizes may be inflated due to the small sample size (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;67), necessitating replication. Given the relatively small sample size, these unusually large effects may partly reflect sample-specific variance or Type I error. The findings should therefore be considered preliminary until replicated with larger and more diverse cohorts.\u003c/p\u003e"},{"header":"General Discussion","content":"\u003cp\u003eThis study investigated the effects of emotional visual and auditory cues on Chinese classical poetry learning, leveraging the cueing principle and emotional design hypothesis to enhance emotional engagement and comprehension (Alpizar et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Beege et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Across two experiments, we examined how visual cues (emotion label words, emotion laden words, or both) and auditory cues (emotional narration) influence learners\u0026rsquo; emotional responses, cognitive load, motivation, and comprehension. The findings provide nuanced insights into the role of emotional cues in literary learning, particularly for culturally complex texts like Chinese classical poetry.\u003c/p\u003e\u003cp\u003eIn Experiment 1, emotion laden word cues modestly increased negative emotional valence (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.49), partially supporting H1a, but the non-significant omnibus test (\u003cem\u003eF\u003c/em\u003e(3, 135)\u0026thinsp;=\u0026thinsp;1.64, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.18, \u003cem\u003eη\u0026sup2;\u003c/em\u003e = 0.04) suggests limited practical impact. No effects were observed on cognitive load, motivation, or comprehension (H1b\u0026ndash;H1d, \u003cem\u003eη\u0026sup2;\u003c/em\u003e \u0026le; 0.02), indicating that static visual cues alone may not sufficiently engage learners with poetry\u0026rsquo; s intricate emotional and semantic content. This aligns with prior research suggesting that visual cues are less effective when cognitive demands are high, as in interpreting poetry\u0026rsquo; s condensed imagery (Sweller et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In contrast, Experiment 2 demonstrated that emotional narration significantly enhanced motivation (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.32), comprehension (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.56), and eye-tracking indicators of emotional arousal (pupil dilation, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.10; blink rate, \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.70), supporting H2c, H2d, and H2e, but not H2a or H2b (emotional valence, cognitive load). These findings suggest that dynamic auditory cues, which align with the poem\u0026rsquo; s affective tone through expressive vocal delivery, are more effective than static visual cues in facilitating emotional and cognitive processing (Beege et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe discrepancy between the experiments highlights the importance of cue modality. Emotional narration likely outperformed visual cues because its dynamic, temporal nature mirrors the rhythmic and emotive qualities of poetry, fostering deeper engagement through mood-affect congruency (Beege et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The null effects on self-reported emotional valence and arousal in Experiment 2, despite significant eye-tracking results, may reflect the transient nature of emotional arousal, captured in real-time by pupil dilation but dissipated by posttest self-reports (Hitsuwari \u0026amp; Nomura, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This suggests that physiological measures are more sensitive to immediate affective responses in literary contexts, consistent with prior aesthetic research (Laeng et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTheoretically, these findings extend the cueing principle by demonstrating that emotional cues enhance engagement when aligned with a text's inherent affective content, particularly in literary learning where emotions are central (Mayer \u0026amp; Estrella, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). They also support the emotional design hypothesis by showing that emotionally salient cues (e.g., narration) improve motivation and comprehension by facilitating germane cognitive load, though visual cues alone may be insufficient in complex tasks (Stark et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Practically, emotional narration offers a promising tool for poetry instruction, but its implementation requires teacher training and multimedia resources, which may be challenging in resource-limited settings.\u003c/p\u003e\u003cp\u003eLimitations include the passive learning mode, which likely constrained engagement, as active learning enhances outcomes per the ICAP framework (Chi \u0026amp; Wylie, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Future studies should test interactive methods, such as AI-supported annotations or generative drawing (Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xie et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The small sample size in Experiment 2 (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;67) may have inflated effect sizes, necessitating replication with larger, diverse samples. The moderate comprehension test reliability (Cronbach\u0026rsquo;s \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.65) suggests scoring challenges, which could be addressed with refined rubrics, expanded item pools, or more intensive rater training. An alternative would be to incorporate objective items (e.g., multiple-choice imagery identification) alongside open-ended analysis to increase reliability. Longitudinal designs are also needed to assess the durability of emotional cue effects on retention and appreciation of Chinese poetry\u0026rsquo;s cultural nuances (Xia, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, both samples were Chinese high school and university students. While appropriate for studying classical Chinese poetry, this restricts generalizability. Future studies should investigate whether emotional cueing effects extend to learners from other cultural or linguistic backgrounds who approach Chinese poetry as second-language or world literature.\u003c/p\u003e\u003cp\u003eIn conclusion, this study underscores the value of emotional auditory cues in enhancing poetry learning, offering a foundation for future research into multimodal instructional strategies that use poetry\u0026rsquo;s affective power to foster deeper learning experiences.\u003c/p\u003e\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003eImplications and limitations\u003c/h2\u003e\u003cp\u003eThis study provides novel insights into the role of emotional visual and auditory cues in enhancing engagement and comprehension in Chinese classical poetry learning. The findings from Experiment 1 suggest that emotion laden word cues can modestly enhance emotional valence, while Experiment 2 demonstrates that emotional narration significantly improves motivation and comprehension, with eye-tracking metrics (pupil dilation, blink rate) indicating heightened emotional arousal (Bradley et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Maffei \u0026amp; Angrilli, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These results extend the cueing principle by showing that emotional cues, when aligned with poetry\u0026rsquo;s affective content, enhance engagement and learning outcomes, supporting the emotional design hypothesis in a literary context (Beege et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mayer \u0026amp; Estrella, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Practically, integrating emotional cues into poetry instruction shows promise for fostering deeper emotional and cognitive processing, particularly through dynamic auditory narration. However, implementation requires teacher training to design and deliver emotionally aligned cues and access to multimedia tools, which may limit scalability in resource-constrained settings, such as rural schools or underfunded districts.\u003c/p\u003e\u003cp\u003eDespite these contributions, the study has limitations. First, the passive learning mode, where participants viewed videos without interactive tasks, likely restricted engagement, as predicted by the ICAP framework, which emphasizes active and constructive learning for deeper processing (Chi \u0026amp; Wylie, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Future studies should incorporate interactive elements, such as generative drawing of poetic imagery (Xie et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) or AI-supported annotations to highlight emotional content (Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), to enhance active learning and motivation. Second, the small sample size in Experiment 2 (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;67) may have inflated effect sizes (e.g., \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.32 for motivation), necessitating replication with larger, more diverse samples to confirm generalizability. Third, the study focused on short-term effects, with posttests administered immediately after learning. Longitudinal designs are needed to assess the durability of emotional cue effects on comprehension and retention, particularly for novice learners navigating the complex emotional and cultural nuances of Chinese poetry (Xia, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Finally, the moderate reliability of the comprehension test (Cronbach\u0026rsquo;s \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.65) suggests challenges in scoring subjective responses, which could be addressed by refining the rubric or incorporating objective measures, such as multiple-choice questions on imagery identification.\u003c/p\u003e\u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmad S, Asghar MZ, Alotaibi FM, Khan S (2020) Classification of poetry text into the emotional states using deep learning technique. IEEE Access 8:73865\u0026ndash;73878\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlemdag E, Cagiltay K (2018) A systematic review of eye tracking research on multimedia learning. Comput Educ 125:413\u0026ndash;428\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlpizar D, Adesope OO, Wong RM (2020) A meta-analysis of signaling principle in multimedia learning environments. Education Tech Research Dev 68(5):2095\u0026ndash;2119\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeege M, Schneider S (2023) Emotional design of pedagogical agents: The influence of enthusiasm and model observer similarity. Education Tech Research Dev 71(3):859\u0026ndash;880\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeege M, Schneider S, Nebel S, H\u0026auml;\u0026szlig;ler A, Rey GD (2018) Mood-affect congruency. Exploring the relation between learners\u0026rsquo; mood and the affective charge of educational videos. Comput Educ 123:8596\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeege M, Schneider S, Nebel S, Rey GD (2020) Does the effect of enthusiasm in a pedagogical Agent's voice depend on mental load in the Learner's working memory? Comput Hum Behav 112:106483\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBradley MM, Miccoli L, Escrig MA, Lang PJ (2008) The pupil as a measure of emotional arousal and autonomic activation. Psychophysiology 45(4):602\u0026ndash;607\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChan KY, Lyons C, Kon LL, Stine K, Manley M, Crossley A (2020) Effect of on-screen text on multimedia learning with native and foreign-accented narration. Learn Instruction 67:101305\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChang CC, Chen TC (2024) Emotion, cognitive load and learning achievement of students using e textbooks with/without emotional design and paper textbooks. Interact Learn Environ 32(2):674\u0026ndash;692\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChang HS, Lee CY, Wang X, Young ST, Li CH, Chu WC (2023) Emotional tones of voice affect the acoustics and perception of Mandarin tones. PLoS ONE, 18(4), e0283635\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen Y, Zhang X, Hu L (2024) A progressive prompt-based image generative AI approach to promoting students\u0026rsquo; achievement and perceptions in learning ancient Chinese poetry. Educational Technol Soc 27(2):284\u0026ndash;305\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChi MT, Wylie R (2014) The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychol 49(4):219\u0026ndash;243\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCohen J (1988) Statistical power analysis for the behavioral sciences, 2nd edn. Lawrence Erlbaum Associates\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCreely E (2018) What's poetry got to do with it? The importance of poetry for enhancing literacy and fostering student engagement. Lit Learning: Middle Years 26(3):64\u0026ndash;70\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDe Koning BB, Tabbers HK, Rikers RM, Paas F (2009) Towards a framework for attention cueing in instructional animations: Guidelines for research and design. Educational Psychol Rev 21:113\u0026ndash;140\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEva-Wood AL (2004) Thinking and feeling poetry: Exploring meanings aloud. J Educ Psychol 96(1):182\u0026ndash;191\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHitsuwari J, Nomura M (2025) Effects of emotional and cognitive changes on aesthetic evaluation of poetry based on subjective and physiological continuous responses with pupil diameter measurements. Perceptual and Motor Skills Advance online publication\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHorovitz T, Mayer RE (2021) Learning with human and virtual instructors who display happy or bored emotions in video lectures. Comput Hum Behav 119:106724\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eH\u0026ouml;ffler TN, Leutner D (2007) Instructional animation versus static pictures: A meta\u0026ndash;analysis. Learn Instruction 17(6):722\u0026ndash;738\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIsen AM, Reeve J (2005) The influence of positive affect on intrinsic and extrinsic motivation: Facilitating enjoyment of play, responsible work behavior, and self-control. Motivation Emot 29(4):295\u0026ndash;323\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohnson Laird PN, Oatley K (2022) How poetry evokes emotions. Acta Psychol 224:103506\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLei Y, He X (2025) The effect of imagery on the comprehension and aesthetic appreciation of Chinese ancient poetry. Psychology of Aesthetics, Creativity, and the Arts. Advance online publication\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLaeng B, Eidet LM, Sulutvedt U, Panksepp J (2016) Music chills: The eye pupil as a mirror to music\u0026rsquo;s soul. Conscious Cogn 44:161\u0026ndash;178\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi W, Qian L, Feng Q, Luo H (2024) Using olfactory cues in text materials benefits delayed retention and schemata construction. Sci Rep 14(1):17819\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLevine S (2014) Making interpretation visible with an affect-based strategy. Reading Res Q 49(3):283\u0026ndash;303\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLevine S, Horton WS (2013) Using affective appraisal to help readers construct literary interpretations. Sci Study Literature 3(1):105\u0026ndash;136\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLevine S, Trepper K, Chung RH, Coelho R (2021) How feeling supports students\u0026rsquo; interpretive discussions about literature. J Lit Res 53(4):491\u0026ndash;515\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaffei A, Angrilli A (2019) Spontaneous blink rate as an index of attention and emotion during film clips viewing. Physiol Behav 204:256\u0026ndash;263\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMath\u0026ocirc;t S, Fabius J, van Heusden E, van Der Stigchel S (2018) Safe and sensible preprocessing and baseline correction of pupil size data. Behav Res Methods 50(1):94\u0026ndash;106\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOsowiecka M, Kolańczyk A (2018) Let\u0026rsquo;s read a poem! What type of poetry boosts creativity? Front Psychol 9:1781\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePaas F, Van Merrienboer JJG, Adam JJ (1994) Measurement of cognitive load in instructional research. Percept Motor Skills 79:419\u0026ndash;430\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePan Y, Cheng X, Hu Y (2023) Three heads are better than one: cooperative learning brains wire together when a consensus is reached. Cereb Cortex 33(4):1155\u0026ndash;1169\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePark B, Kn\u0026ouml;rzer L, Plass JL, Br\u0026uuml;nken R (2015) Emotional design and positive emotions in multimedia learning: An eyetracking study on the use of anthropomorphisms. Comput Educ 86:30\u0026ndash;42\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePi Z, Huang X, Wen Y, Wang Q, Zhao X, Li X (2025) Happy facial expressions and mouse pointing enhance EFL vocabulary learning from instructional videos. Br J Edu Technol 56(1):388\u0026ndash;409\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePi Z, Liu C, Wang L, Yang J, Li X (2023) Cues facilitate foreign language vocabulary learning from instructional videos: Behavioral and neural evidence. Lang Teach Res, 13621688231164724\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePlass JL, Heidig S, Hayward EO, Homer BD, Um E (2014) Emotional design in multimedia learning: Effects of shape and color on affect and learning. Learn Instruction 29:128\u0026ndash;140\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePlass JL, Kalyuga S (2019) Four ways of considering emotion in cognitive load theory. Educational Psychol Rev 31:339\u0026ndash;359\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStark L, Br\u0026uuml;nken R, Park B (2018) Emotional text design in multimedia learning: A mixed-methods study using eye tracking. Comput Educ 120:185\u0026ndash;196\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSweller J, van Merri\u0026euml;nboer JJ, Paas F (2019) Cognitive architecture and instructional design: 20 years later. Educational Psychol Rev 31(2):261\u0026ndash;292\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTeng MF (2023) Language learning strategies. Cogn Individual Differences Second Lang Acquisition: Theor Assess Pedagogy 19:147\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVan der Wel P, Van Steenbergen H (2018) Pupil dilation as an index of effort in cognitive control tasks: A review. Psychon Bull Rev 25:2005\u0026ndash;2015\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReynaert V, Possik J, Demarey C, Kieken D, Abert B, De Witte B (2024, November) Immersive poetry learning: a field study with middle school students. Front Educ 9:1463635\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eScheepers C, Mohr S, Fischer MH, Roberts AM (2013) Listening to limericks: a pupillometry investigation of perceivers\u0026rsquo; expectancy. PLoS ONE, 8(9), e74986\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evan Gog T (2022) The signaling (or cueing) principle in multimedia learning. In Mayer, R.E., \u0026amp; L. Fiorella (Eds.), \u003cem\u003eThe Cambridge Handbook of Multimedia Learning\u003c/em\u003e (pp. 221\u0026ndash;230). 3rd edition. Cambridge University Press\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang X, Mayer RE, Han M, Zhang L (2023) Two emotional design features are more effective than one in multimedia learning. J Educational Comput Res 60(8):1991\u0026ndash;2014\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang X, Lin L, Han M, Spector JM (2020) Impacts of cues on learning: Using eye tracking technologies to examine the functions and designs of added cues in short instructional videos. Comput Hum Behav 107:106279\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y, Zhang M, Yu Q, Zhou Z, Paas F (2025) Effects of an onscreen instructor\u0026rsquo;s emotions and picture types on poetry appreciation. J Experimental Educ, 1\u0026ndash;23\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Y, Zhou Z, Paas F (2023) Effects of instruction colour and learner empathy on aesthetic appreciation of Chinese poetry. Instr Sci 51(4):617\u0026ndash;637\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang Z, Gong SY, Xu S, Hu XE (2019) Elaborated feedback and learning: Examining cognitive and motivational influences. Comput Educ 136:130\u0026ndash;140\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMayer RE, Estrella G (2014) Benefits of emotional design in multimedia instruction. Learn Instruction 33:12\u0026ndash;18\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXia C (2021) Poetry and emotion in classical Chinese literature. The Routledge Handbook of Chinese Studies. Routledge, pp 289\u0026ndash;303\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXie H, Lin D, He W, Chen Q (2024) The aesthetics at a pencil tip: The effects of drawing on learning poems. Learn Instruction 91:101881\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXie H, Wang F, Hao Y, Chen J, An J, Wang Y, Liu H (2017) The more total cognitive load is reduced by cues, the better retention and transfer of multimedia learning: A meta-analysis and two meta regression analyses. PLoS ONE 12(8):e0183884\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Q, Chen C, Lu J, Zhang P (2016) The mechanism of emotional contagion. Acta Physiol Sinica 48(11):1423\u0026ndash;1436\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang J, Wu C, Meng Y, Yuan Z (2017) Different neural correlates of emotion label words and emotion laden words: An ERP study. Front Hum Neurosci 11:455\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang J, B\u0026uuml;rkner PC, Kiesel A, Dignath D (2023) How emotional stimuli modulate cognitive control: A meta-analytic review of studies with conflict tasks. Psychol Bull 149(12):25\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao F, Mayer RE (2023) Benefits of turning the illustrations in a narrated slideshow into cartoons: An extension of the positivity principle. Learn Instruction 86:101779\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Declarations","content":"\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e\u003ch2\u003eEthics Statement\u003c/h2\u003e\u003cp\u003eThe studies involving human participants were reviewed and approved by the local ethics committee at the School of Psychology, Central China Normal University.\u003c/p\u003e\u003c/div\u003e\u003cp\u003eThe legal guardians of the participants in experiment 1 consented to participate in the study.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Central China Normal University","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":"Emotional design cues, Emotion label words, Emotion laden words, Emotional narration, Chinese classical poetry","lastPublishedDoi":"10.21203/rs.3.rs-7731687/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7731687/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eChinese classical poetry, known for its concise and evocative language, often challenges learners due to its subtle emotional expressions. Affective scaffolds, such as emotional visual and auditory cues, may enhance emotional engagement and comprehension.\u003c/p\u003e\u003ch2\u003eAim\u003c/h2\u003e\u003cp\u003eThis study investigated whether emotional visual and auditory cues facilitate learners\u0026rsquo; emotional engagement and understanding of Chinese classical poetry.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e\u003cp\u003eTwo experiments were conducted. In Experiment 1, a between-subjects design with 139 high school students examined the effects of emotional visual cues (no cues, emotion label words, emotion laden words, and combined cues). In Experiment 2, a single-factor between-subjects design with 67 undergraduates explored emotional narration versus neutral narration, using eye-tracking to measure emotional engagement via pupil dilation and blink rate.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIn Experiment 1, emotion laden word cues increased negative emotional valence (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.49) but did not affect comprehension. In Experiment 2, emotional narration improved poetic comprehension (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.56) and motivation (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.32), with greater pupil dilation (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.10) and reduced blink rate (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.70) indicating enhanced emotional engagement.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eEmotional visual cues alone foster emotional engagement without improving comprehension, while combining them with emotional narration significantly enhances both motivation and understanding. These findings extend the cueing principle by demonstrating that emotional visual and auditory cues enhance engagement with affective content and support the emotional design hypothesis by showing that cues aligned with inherent emotional content improve comprehension and motivation in literary learning.\u003c/p\u003e","manuscriptTitle":"Effects of emotional visual and auditory cues on Chinese poetry learning: An eye-tracking study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-02 14:06:01","doi":"10.21203/rs.3.rs-7731687/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":"75edba9f-16a0-43d9-a238-f1d9660b6d35","owner":[],"postedDate":"October 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-02T14:06:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-02 14:06:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7731687","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7731687","identity":"rs-7731687","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}