Development and Validation of the Alpha Generation Learning Style Scale (ALSS) in Indonesian Vocational Education: A Second-Order Confirmatory Factor Analysis

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Although digital integration advances in curriculum, many Indonesian vocational schools persist with teacher-centered methods that prioritize technical skills while neglecting essential soft skills teamwork, creativity, and adaptability. This study developed and validated the Alpha Generation Learning Style Scale (ALSS), the first instrument measuring Generation Alpha’s integrated learning preferences in digital vocational settings. Grounded in social constructivism, connectivism, heutagogy, and cybergogy, ALSS comprises four interconnected dimensions: Visual-Digital Learning (VDL), Collaborative Learning (CL), Self-Directed Learning (SDL), and Gamified Learning (GL). Methods Using Cantabrana et al.’s (2019) four-phase framework, the study executed expert validation (n=5), pre-testing (n=35), and empirical testing with 285 stratified vocational students using Classical Test Theory and Confirmatory Factor Analysis (CFA). Results ALSS demonstrated excellent content validity (S-CVI/Ave = 0.981; Aiken’s V > 0.85) and high internal consistency (Cronbach’s α = 0.96). CFA confirmed a strong second-order factor structure (CFI = 0.996; TLI = 0.996; RMSEA = 0.020; SRMR = 0.030), with all dimensions significantly loading onto ALSS (λ = 0.59–0.65, p < 0.001). Self-Directed Learning showed the strongest influence (0.645), followed by Collaborative Learning (0.640) and Visual-Digital Learning (0.627), explaining 35–42% of variance per dimension. Convergent validity (AVE = 0.821–0.864), discriminant validity, and composite reliability (CR > 0.96) were confirmed. Conclusions ALSS is a valid, reliable instrument capturing Generation Alpha’s technology-mediated learning identity. It provides an evidence-based diagnostic framework for personalizing Cooperative Project-Based Learning (Co-PjBL) and transforming Indonesian vocational education toward adaptive, student-centered methods. Integration with computational thinking skills including algorithmic thinking, decomposition, pattern recognition, and abstraction strengthens pedagogical effectiveness. This approach aligns TVET with Society 5.0 demands, enhancing preparation of digitally proficient, computationally literate, collaborative, and autonomous learners. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/15-168", "name": "Development and Validation of the Alpha Generation Learning Style..." } } ] } Home Browse Development and Validation of the Alpha Generation Learning Style... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Ramadhani W, Sudira P, Hadi S et al. Development and Validation of the Alpha Generation Learning Style Scale (ALSS) in Indonesian Vocational Education: A Second-Order Confirmatory Factor Analysis [version 1; peer review: 1 approved with reservations] . F1000Research 2026, 15 :168 ( https://doi.org/10.12688/f1000research.175791.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Development and Validation of the Alpha Generation Learning Style Scale (ALSS) in Indonesian Vocational Education: A Second-Order Confirmatory Factor Analysis [version 1; peer review: 1 approved with reservations] Witri Ramadhani https://orcid.org/0009-0002-1616-7465 1,2 , Putu Sudira 1 , Samsul Hadi 1 , [...] Lativa Mursyida https://orcid.org/0009-0000-3245-8479 1,3 , Ratna Pancawati https://orcid.org/0000-0003-2297-6533 1,4 , Hendra Dani Saputra 1,5 , Dwi Jatmoko https://orcid.org/0009-0002-7178-2137 1 , Joni Andre https://orcid.org/0009-0006-2379-8002 5 , Andry Ananda Putra Tanggu Mara https://orcid.org/0009-0007-5981-6847 1,6 Witri Ramadhani https://orcid.org/0009-0002-1616-7465 1,2 , Putu Sudira 1 , [...] Samsul Hadi 1 , Lativa Mursyida https://orcid.org/0009-0000-3245-8479 1,3 , Ratna Pancawati https://orcid.org/0000-0003-2297-6533 1,4 , Hendra Dani Saputra 1,5 , Dwi Jatmoko https://orcid.org/0009-0002-7178-2137 1 , Joni Andre https://orcid.org/0009-0006-2379-8002 5 , Andry Ananda Putra Tanggu Mara https://orcid.org/0009-0007-5981-6847 1,6 PUBLISHED 02 Feb 2026 Author details Author details 1 Department of Technology and Vocational Education, Universitas Negeri Yogyakarta Program Pascasarjana, Yogyakarta, Special Region of Yogyakarta, Indonesia 2 Department of Vocational Education in Electronics Engineering, Universitas Muhammadiyah Riau, Pekanbaru, Riau, Indonesia 3 Department of Electronics Engineering, Universitas Negeri Padang, Padang, Sumatera Barat, 25132, Indonesia 4 Department of Mechanical Engineering, Universitas Palangka Raya, Palangkaraya, Central Kalimantan, Indonesia 5 Department of Automotive Engineering, Universitas Negeri Padang, Padang, West Sumatra, Indonesia 6 Departement of Technology Information Education, Stella Maris Sumba University, Tambolaka, Indonesia Witri Ramadhani Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Project Administration, Writing – Original Draft Preparation Putu Sudira Roles: Conceptualization, Investigation, Supervision, Validation, Writing – Review & Editing Samsul Hadi Roles: Formal Analysis, Methodology, Supervision, Validation, Writing – Review & Editing Lativa Mursyida Roles: Data Curation, Project Administration, Visualization, Writing – Review & Editing Ratna Pancawati Roles: Data Curation, Formal Analysis, Investigation, Writing – Review & Editing Hendra Dani Saputra Roles: Resources, Software, Validation, Writing – Review & Editing Dwi Jatmoko Roles: Data Curation, Formal Analysis, Resources, Writing – Review & Editing Joni Andre Roles: Writing – Review & Editing Andry Ananda Putra Tanggu Mara Roles: Formal Analysis, Investigation, Resources, Validation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract Background Generation Alpha (born after 2010) requires pedagogical approaches aligned with their digital-native characteristics and Society 5.0 demands. Although digital integration advances in curriculum, many Indonesian vocational schools persist with teacher-centered methods that prioritize technical skills while neglecting essential soft skills teamwork, creativity, and adaptability. This study developed and validated the Alpha Generation Learning Style Scale (ALSS), the first instrument measuring Generation Alpha’s integrated learning preferences in digital vocational settings. Grounded in social constructivism, connectivism, heutagogy, and cybergogy, ALSS comprises four interconnected dimensions: Visual-Digital Learning (VDL), Collaborative Learning (CL), Self-Directed Learning (SDL), and Gamified Learning (GL). Methods Using Cantabrana et al.’s (2019) four-phase framework, the study executed expert validation (n=5), pre-testing (n=35), and empirical testing with 285 stratified vocational students using Classical Test Theory and Confirmatory Factor Analysis (CFA). Results ALSS demonstrated excellent content validity (S-CVI/Ave = 0.981; Aiken’s V > 0.85) and high internal consistency (Cronbach’s α = 0.96). CFA confirmed a strong second-order factor structure (CFI = 0.996; TLI = 0.996; RMSEA = 0.020; SRMR = 0.030), with all dimensions significantly loading onto ALSS (λ = 0.59–0.65, p < 0.001). Self-Directed Learning showed the strongest influence (0.645), followed by Collaborative Learning (0.640) and Visual-Digital Learning (0.627), explaining 35–42% of variance per dimension. Convergent validity (AVE = 0.821–0.864), discriminant validity, and composite reliability (CR > 0.96) were confirmed. Conclusions ALSS is a valid, reliable instrument capturing Generation Alpha’s technology-mediated learning identity. It provides an evidence-based diagnostic framework for personalizing Cooperative Project-Based Learning (Co-PjBL) and transforming Indonesian vocational education toward adaptive, student-centered methods. Integration with computational thinking skills including algorithmic thinking, decomposition, pattern recognition, and abstraction strengthens pedagogical effectiveness. This approach aligns TVET with Society 5.0 demands, enhancing preparation of digitally proficient, computationally literate, collaborative, and autonomous learners. READ ALL READ LESS Keywords Generation Alpha Learning Style; Vocational Education; Psychometric Instrument; Confirmatory Factor Analysis Corresponding Author(s) Lativa Mursyida ( [email protected] ) Close Corresponding author: Lativa Mursyida Competing interests: No competing interests were disclosed. Grant information: This research was funded by doctoral scholarships awarded by Indonesian Education Scholarship (BPI), Center for Higher Education Funding and Assessment (PPAPT), Ministry of Higher Education Science and Technology of Republic Indonesia, and Indonesian Endowment Fund for Education (LPDP). Grant Numbers: Witri Ramadhani (202231103595), Lativa Mursyida (202404121743), Ratna Pancawati (202404121707), Hendra Dani Saputra (202404121803), Dwi Jatmoko (202404121277) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2026 Ramadhani W et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Ramadhani W, Sudira P, Hadi S et al. Development and Validation of the Alpha Generation Learning Style Scale (ALSS) in Indonesian Vocational Education: A Second-Order Confirmatory Factor Analysis [version 1; peer review: 1 approved with reservations] . F1000Research 2026, 15 :168 ( https://doi.org/10.12688/f1000research.175791.1 ) First published: 02 Feb 2026, 15 :168 ( https://doi.org/10.12688/f1000research.175791.1 ) Latest published: 02 Feb 2026, 15 :168 ( https://doi.org/10.12688/f1000research.175791.1 ) 1. Introduction Generation Alpha, born after 2010, grew up in a hyper-digital ecosystem that is highly connected and interactive, exhibiting learning characteristics that differ significantly from previous generations ( Höfrová et al., 2024 ; Ziatdinov & Cilliers, 2021 ). In Indonesia, vocational education is crucial for developing an adaptable workforce; addressing this generational change is strategically essential to fulfill the requirements of digital transformation in the context of Industry 4.0 and Society 5.0 ( Han et al., 2023 ; Muktiarni et al., 2023 ). Technology-enhanced education, incorporating gamification and visual media, has demonstrated efficacy in enhancing engagement and learning outcomes among digital generation students ( Abbazio & Yang, 2022 ; Chen et al., 2023 ; Fernando & Premadasa, 2024 ; Khaldi et al., 2023 ). Consequently, a profound comprehension of the learning styles and inclinations of Generation Alpha is crucial for developing pertinent and efficient pedagogical strategies in the digital age, particularly by enhancing the learning autonomy and digital proficiency of vocational students ( Gacusan et al., 2023 ; Morris, 2024 ; Tan et al., 2024 ). Although digital integration in teaching and curriculum is advancing, many vocational schools in Indonesia still rely on conventional teacher-centered methods ( Han et al., 2023 ; Shaikh Ali et al., 2024 ). This methodology frequently prioritizes memory and technical abilities, while neglecting essential soft skills such as teamwork, creativity, and adaptability, which are crucial competences in Industry 5.0 ( Adigun et al., 2025 ; Jaedun et al., 2024 ; Priyanshu et al., 2025 ). Generation Alpha learners, predisposed to visual-digital, interactive, and gamified educational experiences, frequently encounter a dissonance with traditional methodologies ( Rathnasekara et al., 2025 ), resulting in diminished engagement and intrinsic motivation, alongside restricted advancement of higher-order thinking skills (HOTS) ( Abbazio & Yang, 2022 ; Sudirtha et al., 2022 ). Consequently, technology-driven and collaborative learning methodologies are essential to address their learning preferences and foster the cultivation of crucial competencies ( Morris, 2019 ; Siddiquei & Khalid, 2021 ). Theoretically, effective learning should place students as active agents in knowledge construction. Vygotsky’s social constructivism (2018) posits that knowledge is formed through social interaction and collaboration, which is particularly pertinent to digital-native learners. Siemens’ connectivism (2005) argues that learning occurs through connections between digital information sources and online networks ( Dziubaniuk et al., 2023 ). Hase and Kenyon’s (2007) heutagogy expands on this concept by emphasizing learner autonomy, reflection, and self-determined learning, facilitated by broad access to digital resources ( Blaschke, 2021 ; Mukuka & Tatira, 2025 ). Wang and Torrisi-Steele (2017) present a comprehensive paradigm that amalgamates cognitive, emotional, and social aspects to facilitate adaptable, collaborative, and significant digital learning experiences ( Amiruddin et al., 2018 ; Bizami et al., 2023 ). Generation Alpha’s learning styles in vocational education should be comprehended as an integrated construct comprising four dimensions: Visual-Digital Learning (VDL), Collaborative Learning (CL), Self-Directed Learning (SDL), and Game-Based Learning (GL), each representing the necessity for visual, social, autonomous, and engaging experiences, respectively ( Siddiquei & Khalid, 2021 ). A systematic review indicates a deficiency of research that explicitly combine theory, technology, and practice within the realm of Technical and Vocational Education and Training (TVET) ( Kravchenko et al., 2024 ; Noguera et al., 2024 ). Classic tools such as VARK or Kolb’s Learning Style Inventory are inadequate, as they were designed for the pre-digital generation and fail to capture the hierarchical, digital, and integrative nature of Generation Alpha’s learning preferences. This reveals a critical gap: the absence of a valid, reliable, and structurally sound psychometric tool to holistically assess Generation Alpha’s learning styles in Indonesian vocational education. Pedagogical innovations like Cooperative Project-Based Learning (Co-PjBL), which inherently incorporates VDL (real-world projects), CL (interdisciplinary teamwork), SDL (self-management), and GL (real-time rewards), necessitate diagnostic tools that can systematically capture individual learning profiles rather than merely describing them ( Angelova et al., 2025 ; Mutohhari et al., 2021 ; Rodrigo-Ilarri et al., 2025 ). This study seeks to create and evaluate the Alpha Generation Learning Style Scale (ALSS), a psychometric instrument intended to assess the four interconnected characteristics of Generation Alpha’s learning preferences within Indonesian vocational education. Theoretically, this study strengthens the conceptual foundation of generation-based learning styles in TVET and expands the understanding of how digital-native characteristics shape integrated, rather than fragmented, learning profiles ( Siddiquei & Khalid, 2021 ). The ALSS offers empirical insights to educators, curriculum developers, and policymakers for creating adaptive, individualized, and responsive teaching methods for Generation Alpha in the context of digital transition towards society. 5.0 ( Noguera et al., 2024 ; Tan et al., 2024 ). This instrument serves not only as a diagnostic tool but also as a scientific foundation for matching the execution of Cooperative Project-Based Learning (Co-PjBL) with individual learning profiles, so ensuring that vocational education transcends uniformity and genuinely caters to the distinctiveness of each student ( Angelova et al., 2025 ; Mutohhari et al., 2021 ; Rodrigo-Ilarri et al., 2025 ). 2. Materials and method Figure 1 illustrates the conceptual framework underlying the ALSS. The model posits Alpha Generation learning style as a higher-order construct manifested through four interrelated dimensions: Visual–Digital Learning (VDL), Collaborative Learning (CL), Self-Directed Learning (SDL), and Gamified Learning (GL). Each dimension captures a distinct yet complementary aspect of how Generation Alpha students engage with learning in technology-rich environments. This framework guided item generation, ensured theoretical coherence, and provided the basis for the hypothesized measurement model tested empirically in this study. Figure 1. Conceptual framework of the Alpha Generation Learning Style Scale (ALSS). 2.1 Materials – Literature review 2.1.1 Generation Alpha Learning Style (ALSS) Generation Alpha denotes individuals born post-2010 who have been raised in a technology-saturated milieu. They demonstrate high proficiency in using digital devices and have different learning preferences compared to previous generations ( Höfrová et al., 2024 ). Their learning is defined by requirements for flexibility, adaptation, and personalization. The application of technology, interactive media, and gamification strategies has demonstrated an enhancement in motivation and engagement ( Sabariah et al., 2020 ). This generation is more motivated when learning is tailored to individual interests and needs ( Kaplan-Sayi, 2020 ) and demonstrates the ability to learn independently, with or without supervision, especially when encouraged by external incentives such as recognition systems and real-time feedback. Hybrid learning models that combine physical and digital environments, practical real-world tasks, and collaborative interactions with peers are considered most suitable for Generation Alpha’s characteristics. The use of multimedia such as videos, animations, and infographics increases their understanding and engagement ( Abbazio & Yang, 2022 ; Ukonu & Warlimont, 2025 ). As “tech thumbs,” Generation Alpha feels comfortable in digital learning environments and expects technology to be seamlessly integrated into the education system ( Yurtseven & Karadeniz, 2020 ; Ziatdinov & Cilliers, 2021 ). As a result, pedagogical approaches that integrate gamification, multimodal learning, and personalization are effective strategies to support their learning. In vocational education, these strategies must be designed collaboratively, contextually, and integrated with technology. Classic learning style theories such as VARK and Kolb are no longer entirely adequate. Educators and policymakers must redesign curricula and instructional designs that are appropriate for the learning characteristics and preferences of Generation Alpha ( Ukonu & Warlimont, 2025 ). 2.1.2 Visual–Digital Learning (VDL) Visual-Digital Learning (VDL) denotes the inclination of learners to assimilate and comprehend information via visual and digital mediums, including movies, animations, infographics, augmented reality (AR), and virtual reality (VR). This approach utilizes the intuitive and engaging nature of visual content to enhance the learning experience. The main components of VDL include: • Video and Animation: Widely used to present information in an engaging and easy-to-understand format, enhancing students’ cognitive skills, self-awareness, and cultural values ( Latheef et al., 2021 ). • Infografis: Graphical depictions of data that elucidate intricate information, facilitating comprehension and retention ( Wu et al., 2024 ). VDL’s advantage resides in its enhanced capacity to capture attention relative to conventional text-based materials, hence augmenting the efficacy of learning for generations familiar with digital technology ( Siddiquei & Khalid, 2021 ). 2.1.3 Collaborative Learning (CL) Collaborative Learning (CL) emphasizes cooperation, communication, and shared responsibility to achieve common goals, based on Vygotsky’s social constructivism (2018) . In the context of Generation Alpha, collaboration occurs both directly and through digital platforms such as LMS, online forums, and virtual laboratories, which enhance social interaction and access to resources ( Janssen & Kirschner, 2020 ; Van Helden et al., 2023 ). Generation Alpha, as digital natives, relies on digital devices to collaborate efficiently, creating more flexible and inclusive learning ( Bigdeli et al., 2023 ; Krath et al., 2021 ). Research by ( Qawaqneh et al., 2023 ) found that digital collaborative learning environments enhance critical thinking and creativity among vocational students. Moreover, the utilization of social media and interactive applications enhances interdisciplinary and intercultural communication ( Zhao et al., 2024 ). Therefore, CL as a dimension of ALSS reflects the abilities and preferences of Generation Alpha in digital social interactions, both synchronous and asynchronous ( Van Helden et al., 2023 ). 2.1.4 Self-Directed Learning (SDL) Self-directed learning (SDL) is an educational methodology wherein learners assume responsibility for recognizing their learning requirements, establishing objectives, sourcing materials, implementing tactics, and assessing outcomes. This notion, presented by Knowles in 1975, underscores learner autonomy and accountability. The incorporation of digital technology in education is vital for promoting self-directed learning, as it enables learners to personalize their educational experiences and cultivate independence ( Chatwattana, 2021 ). Self-directed learning (SDL) is an educational methodology wherein learners assume responsibility for recognizing their learning requirements, establishing objectives, locating resources, implementing learning strategies, and assessing educational results. This concept, proposed by Knowles in 1975, underscores learner autonomy and accountability. The incorporation of digital technology in education is crucial for fostering self-directed learning, as it enables learners to tailor their educational experiences and cultivate independence ( Morris, 2019 ). In vocational education, SDL is particularly important because it encourages independence and adaptability to rapidly changing industrial technologies ( Tan et al., 2024 ). Therefore, SDL as a component of ALSS serves to identify the level of digital autonomy and self-management skills of Generation Alpha in a vocational learning environment. 2.1.5 Gamified Learning (GL) Game-Based Learning (GBL) denotes the integration of game components, including points, levels, challenges, immediate feedback, and rewards, inside a non-gaming educational framework. This approach has been proven to increase intrinsic motivation, emotional engagement, and active participation ( Khaldi et al., 2023 ; Krath et al., 2021 ). Within the framework of Generation Alpha, who are maturing in a digital game environment, gamification is crucial in cultivating curious, competitive, and collaborative attitudes ( Fernando & Premadasa, 2024 ; Krath et al., 2021 ). Recent research indicate that gamification in occupational training enhances critical thinking and problem-solving abilities. For example, ( Lo et al., 2021 ) found that gamified learning modules in electrical engineering training increased student persistence and learning outcomes. This aligns with the findings of ( Priyanshu et al., 2025 ), which confirmed that digital game elements strengthen the relationship between learning experiences and social motivation in Generation Alpha. Furthermore, GL can be efficiently integrated with PBL to enhance educational outcomes ( Huang et al., 2023 ; Li et al., 2025 ). In digital vocational education, gamification can simulate industry scenarios, develop technical competencies, and encourage student competition in a fun and meaningful learning environment. Therefore, the GL dimension in ALSS represents the extent to which Generation Alpha prefers to learn through fun, competitive, and challenge-based digital experiences. Gamification enhances motivation and functions as an efficient instructional approach for cultivating soft skills, including teamwork, creativity, and perseverance, in digital vocational education ( Angelova et al., 2025 ; Hsieh et al., 2022 ). 2.2 Method This study sought to create and evaluate the psychometric tool, the Alpha Generation Learning Style Scale (ALSS), to assess the learning preferences of Generation Alpha within Indonesian vocational education. The research employed a quantitative survey design based on ( Cantabrana et al., 2019 ) four-phase instrument development model: (1) Domain Identification, (2) Tool Design & Development, (3) Validation by Expert, and (4) Testing & Verification. This approach ensured high construct validity, reliability, and contextual relevance in the digital vocational education context in Indonesia. Figure 2 presents the multi-phase research procedure used to develop and validate the ALSS. The process consisted of four main stages: (1) domain identification through an extensive literature review on Generation Alpha learning preferences; (2) tool design and development, in which 32 items were constructed to reflect the four dimensions; (3) expert validation and pre-testing, combining quantitative content validation with cognitive interviews to refine wording and clarity; and (4) testing and verification through large-scale administration and psychometric analysis (CTT and CFA). The figure emphasizes the systematic and iterative nature of the instrument development process, integrating both expert judgment and end-user feedback. Figure 2. Research procedure for the development and validation of the Alpha Generation Learning Style Scale (ALSS). 2.2.1 Research procedure The development of ALSS was conducted systematically in four phases: 1) Domain Identification The conceptual domain of ALSS was established through a comprehensive theoretical review of Generation Alpha’s characteristics and contemporary pedagogical frameworks, including cybergogy ( Wang & Kang, 2006 ), heutagogy ( Blaschke, 2021 ), social constructivism ( Vygotsky & Cole, 2018 ), and connectivism ( Siemens, 2005 ). Four fundamental learning dimensions Visual–Digital Learning (VDL), Collaborative Learning (CL), Self-Directed Learning (SDL), and Gamified Learning (GL) were identified as foundational constructs due to their alignment with contemporary literature on digital-native learning preferences. 2) Tool Design & Development Based on these four dimensions, 32 items (eight per dimension) were developed as a self-report questionnaire. Items were formulated using simple, contextual, and easily understandable language suitable for Indonesian vocational high school (SMK) students. Responses were quantified utilizing a five-point Likert scale, with 1 representing Strongly Disagree and 5 denoting Strongly Agree. All initial items were internally validated by the research team to ensure alignment with operational definitions. 3) Validasi by Expert & Pre-Testing As an integral part of rigorous psychometric instrument development, both Expert Validation and Pre-Testing were conducted sequentially and complementarily to ensure the ALSS was not only theoretically valid but also practically understandable and relevant to end-users Generation Alpha SMK students in Indonesia. This approach aligns with ( Cantabrana et al., 2019 ) four-phase model, with explicit inclusion of pre-testing as a critical step prior to field testing. a) Expert Validation Content validity was assessed by five independent experts in psychometrics, vocational education evaluation, educational technology, and instructional design. Each expert rated the 32 ALSS items across three criteria: • Construct relevance (how accurately the item represents VDL, CL, SDL, or GL), • Clarity of wording (freedom from ambiguity), • Dimensional representation (proportionality to the domain of Generation Alpha learning styles). Items were rated on a 4-point ordinal scale: (1) Not Valid, (2) Less Valid, (3) Fairly Valid, (4) Highly Valid. Quantitative analysis used two indicators: • Item-Content Validity Index (I-CVI): Ratio of experts assigning scores of 3 or higher. Items were considered valid if I-CVI ≥ 0.78 ( Polit et al., 2007 ). • Aiken’s V: Measures inter-rater agreement, accounting for score distribution. Aiken’s V ≥ 0.80 is acceptable ( Penfield & Giacobbi Peter, 2004 ). The results indicated that all 32 items attained I-CVI values ranging from 0.80 to 1.00, with a mean Scale-Level Content Validity Index (S-CVI/Ave) of 0.981, indicating very high content validity ( Zamanzadeh et al., 2015 ). All Aiken’s V scores exceeded 0.85, indicating strong consensus among experts. Qualitative feedback from experts was used to revise item wording for improved clarity without altering construct meaning. b) Pre-Testing After revisions, pre-testing was conducted with 35 SMK Mechatronics students not included in the main sample (N = 285), but sharing similar demographic, technological access, and school context characteristics. The instrument was administered via Google Form, followed by a brief open-ended feedback survey and cognitive interviews with 10 randomly selected participants using the think-aloud technique to gain deeper insight into item interpretation. Table 1 summarizes the pre-testing of the ALSS with 35 Mechatronics students. The average completion time was 12 minutes and 34 seconds (range: 9–16 minutes), indicating that the 32-item scale is feasible to administer without causing respondent fatigue. Three items (GL2, VDL5, CL4) were repeatedly identified as problematic because certain terms (e.g., “interactive,” “seeking additional explanations,” “learning becomes more meaningful”) were perceived as vague or unfamiliar. Cognitive interviews showed that students more easily understood everyday expressions such as “working together,” “learning alone,” and “learning while playing” than more technical terminology. All students completed the online questionnaire voluntarily and without technical difficulties, confirming the practicality and acceptability of the instrument. Based on these findings, only minor wording revisions were applied, and the revised version was used in the main survey. 4) Testing & Verification After revisions, the instrument was administered to the field sample to test internal reliability and construct validity. The data analysis occurred in two phases: (1) Classical Test Theory (CTT) was employed to evaluate reliability, item discrimination, and difficulty index; and (2) Confirmatory Factor Analysis (CFA) was utilized to examine the proposed second-order measurement model, wherein the four dimensions serve as manifest indicators of a higher-order latent construct ALSS. Table 1. Pre-testing findings. Aspect Findings Completion Time Average: 12 minutes 34 seconds (range: 9–16 minutes). Considered ideal for reflection without fatigue. Problematic Items Three items received repeated feedback: GL2: “interactive” too vague; VDL5: “seeking additional explanations” too abstract; CL4: “learning becomes more meaningful” unfamiliar. Cognitive Interviews Students understood everyday terms like “working together,” “learning alone,” “learning while playing” better than technical terms like “collaborative,” “autonomy,” “interactive.” Responsiveness All participants completed the instrument voluntarily and without technical issues. 2.2.2 Instrument and Data Collection The primary instrument was the Alpha Generation Learning Style Scale (ALSS), a closed-ended web-based questionnaire developed on Google Form. Each dimension was operationalized as follows: • Visual–Digital Learning (VDL) : Measures tendency to process information through visual/digital media (videos, animations, infographics, simulations). • Collaborative Learning (CL) : Assesses the inclination towards learning by cooperation, communication, and collective accountability, in both in-person and virtual environments. • Self-Directed Learning (SDL) : Measures ability to set goals, select learning resources, manage time, and evaluate outcomes independently. • Gamified Learning (GL) : Measures motivation and engagement triggered by game elements (challenges, points, levels, real-time feedback). Data collection occurred in two stages: (1) Online distribution of the instrument and evaluation forms to experts for content validation; (2) After revision, distribution to student participants via coordination with subject teachers. Participation was conducted anonymously, and informed consent was acquired by digital forms. All procedures adhered to ethical research norms, encompassing data confidentiality and participant autonomy. 2.2.3 Data Analysis Techniques Data were analyzed quantitatively in stages: 1) Content Validity Analysis Computed utilizing Aiken’s V and Item-Level Content Validity Index (I-CVI) for each item. The Scale-Level Content Validity Index (S-CVI/Ave) was calculated to assess overall validity. Validity criterion were satisfied if Aiken’s V was more than or equal to 0.80 and I-CVI was greater than or equal to 0.78 ( Polit et al., 2007 ). 2) Classical Test Theory (CTT) Analysis Internal Reliability: Measured by Cronbach’s Alpha; ≥0.70 acceptable ( DeVellis & Thorpe, 2021 ). Item Validity: Assessed via item-total correlation (r-drop ≥ 0.30). Discrimination Power: Calculated using discrimination index (≥0.30 classified as good). Difficulty Index: Calculated as mean item score; optimal range: 0.30–0.70 ( Allen & Yen, 2001 ). 3) Construct Validity (Confirmatory Factor Analysis/CFA) Executed with R program utilizing the lavaan package to evaluate the second-order factor model. Criteria for model fit included: • CFI ≥ 0.90 • TLI ≥ 0.90 • RMSEA ≤ 0.08 • SRMR ≤ 0.08 • Convergent validity: AVE ≥ 0.50 • Construct reliability: CR ≥ 0.70 Discriminant validity: √AVE of each construct > inter-construct correlations ( Fornell & Larcker, 1981 ). The results were anticipated to yield empirical data about the validity and reliability of ALSS as an assessment of Generation Alpha’s learning style in digital vocational education. 2.2.4 Ethical considerations This study was conducted in accordance with the Declaration of Helsinki and approved by the Research Ethics Committee of Universitas Negeri Yogyakarta, with document number B/1861/UN34.17/LT/2025 ( Ramadhani W, 2025 ). All procedures involving human participants adhered to national and international ethical guidelines, including informed consent, confidentiality, voluntary participation, and data protection ( Sadam & Al Mamun, 2024 ). Parents or legal guardians of participants aged below 18 years received comprehensive written informed consent forms in Indonesian explaining the study’s purpose, procedures, risks, benefits, confidentiality protections, and participant rights; parents were provided a minimum of 24 hours to review and ask questions before providing written consent. All minor participants provided independent child assent through age-appropriate forms and face-to-face explanations by trained research staff, with explicit information that participation was voluntary and could be withdrawn without consequences. Only participants with both signed parental consent and child assent were included in the study. Participant confidentiality was ensured through coded identification numbers, encrypted data storage, anonymized reporting, and secure deletion of identifiable information following data analysis. 3. Results 3.1 Content validity Content validity was evaluated by five specialists in vocational education and psychometrics utilizing Aiken’s V and the Item-Level Content Validity Index (I-CVI). Results showed all items across the four constructs CL, GL, SDL, and VDL achieved I-CVI values between 0.86 and 1.00, indicating very high content alignment with the intended constructs ( Penfield & Giacobbi Peter, 2004 ; Polit & Beck, 2006 ). As recommended by Zamanzadeh et al. (2015) , I-CVI ≥ 0.78 indicates item validity. Thus, the developed indicators conceptually represent Generation Alpha’s learning characteristics in vocational education. Table 2 presents the item-level content validity indices (I-CVI) for the 32 ALSS items across four dimensions: Visual–Digital Learning (VDL), Collaborative Learning (CL), Self-Directed Learning (SDL), and Gamified Learning (GL). All items achieved I-CVI values between 0.80 and 1.00, exceeding the commonly accepted cut-off of 0.78 and indicating that experts judged each item to be highly relevant to its intended construct. A small number of CL and GL items obtained I-CVI = 0.80, suggesting minor differences in expert judgment but still within the valid range. The Scale-Level Content Validity Index (S-CVI/Ave) was 0.98125, which is above the 0.90 threshold and reflects excellent overall content validity for the instrument. Thus, the table demonstrates that the ALSS items collectively provide a strong and theoretically coherent representation of Generation Alpha learning styles in vocational education. Table 2. Content validation results (I-CVI). Visual–Digital Learning (VDL) ITEM ID I-CVI Interpretation 1. I understand materials better when presented through images or diagrams. VDL1 1 Valid 2. I am more interested in learning when materials are delivered via video. VDL2 1 Valid 3. I remember materials better when presented visually. VDL3 1 Valid 4. I focus better when teachers use visual or audiovisual media. VDL4 1 Valid 5. I use digital devices (laptop/smartphone) to search for additional explanations about materials. VDL5 1 Valid 6. I use digital learning platforms to help understand course materials. VDL6 1 Valid 7. I learn independently using digital learning applications. VDL7 1 Valid 8. I use digital sources (text, images, videos) to deepen my understanding. VDL8 1 Valid Collaborative Learning (CL) 1. I am more motivated to learn when working with peers on a task or project. CL1 1 Valid 2. I contribute according to my agreed role in group tasks. CL2 1 Valid 3. I respect group members opinions during collaboration. CL3 1 Valid 4. I feel learning becomes more meaningful when done collaboratively. CL4 0.8 Valid 5. I actively discuss with peers via digital platforms. CL5 1 Valid 6. I express opinions politely in online learning forums. CL6 1 Valid 7. I feel responsible for helping peers who struggle with online learning. CL7 0.8 Valid 8. I strive to maintain digital etiquette in online communication. CL8 1 Valid Self-Directed Learning (SDL) 1. I set my own learning goals before starting. SDL1 1 Valid 2. I manage my study time to complete tasks on time. SDL2 1 Valid 3. I choose learning strategies that suit my needs. SDL3 1 Valid 4. I evaluate my learning outcomes to track progress. SDL4 1 Valid 5. I learn to improve my abilities. SDL5 1 Valid 6. I seek additional learning resources to understand materials deeply. SDL6 1 Valid 7. I take responsibility for my own learning outcomes. SDL7 1 Valid 8. I complete tasks even without teacher supervision. SDL8 1 Valid Gamified Learning (GL) 1. I am more motivated to learn when activities are presented as challenges. GL1 1 Valid 2. I understand materials better through interactive learning activities. GL1 0.8 Valid 3. I enjoy healthy competition in learning activities. GL1 1 Valid 4. I feel supported by immediate feedback after completing tasks or challenges. GL1 1 Valid 5. I am motivated to learn when there are reward systems like points or levels. GL1 1 Valid 6. I feel satisfied when I achieve specific learning targets. GL1 1 Valid 7. I strive to improve my skills to gain recognition for my learning outcomes. GL1 1 Valid 8. I feel more confident when my learning outcomes are acknowledged. GL1 1 Valid S-CVI 0.98125 Very High 3.2 Classical Test Theory (CTT) CTT analysis included reliability, item validity, discrimination power, and difficulty index. Results showed: • Cronbach’s Alpha = 0.96 (95% CI: 0.95–0.97), signifying exceptionally good internal consistency (DeVellis, 2017). • No item deletion improved reliability, confirming item stability. • Item-total correlation (r-drop): 0.60–0.72, indicating strong item validity. • Discrimination index: 0.63–0.73, classified as very good. • Difficulty index mean = 3.00, indicating balanced response distribution. Table 3 reports the CTT-based psychometric properties of the ALSS. The Cronbach’s alpha coefficient is 0.96 (95% CI: 0.95–0.97), indicating very high internal consistency. No item deletion improved this coefficient, which confirms the stability and coherence of all items. Item–total correlations (r-drop) range from 0.60 to 0.72, showing strong item validity and substantial contribution of each item to the total score. The discrimination index for all items lies between 0.63 and 0.73, categorized as very good, meaning that the scale effectively differentiates students with higher and lower levels of the measured construct. The mean difficulty index of 3.00 (on a 1–5 Likert scale) indicates a balanced response distribution without floor or ceiling effects. Overall, this table confirms that the ALSS possesses strong reliability and adequate item-level properties, justifying its use in subsequent CFA. Table 3. CTT analysis results. Indicator Value Interpretation Cronbach’s Alpha 0.96 Very high reliability r-drop 0.60 – 0.72 Strong item validity Discrimination Index 0.63 – 0.73 Very good Difficulty Index 3.00 Moderate 3.3 CFA second-order model A Confirmatory Factor Analysis (CFA) utilizing a second-order model was performed to assess the structural validity and hierarchy of the ALSS. The model hypothesized that the four first-order latent constructs CL, GL, SDL, VDL collectively form a higher-order latent construct: Alpha Generation Learning Style (ALSS). Figure 3 depicts the hypothesized second-order confirmatory factor model of the ALSS. In this model, ALSS functions as a second-order latent factor that loads onto four first-order latent constructs: VDL, CL, SDL, and GL. Each first-order construct is in turn measured by its respective set of observed items, which were theoretically derived and validated by experts. The diagram reflects the assumption that while the four dimensions are distinct, they are jointly structured by a single overarching construct representing Alpha Generation learning style. Figure 3. Hypothesized second-order model of ALSS. Figure 4 shows the unstandardized parameter estimates obtained from the second-order CFA. The paths from the higher-order ALSS factor to the four first-order dimensions are all positive and statistically significant, with coefficients of 0.97 (VDL), 1.01 (SDL), 0.88 (GL), and 1.00 (CL). These values indicate strong dependence of each dimension on the overarching ALSS construct. The figure also displays the unstandardized loadings of the items on their respective first-order factors and the associated error terms, demonstrating that the indicators contribute meaningfully to their latent dimensions and supporting the structural specification of the model. Figure 4. Unstandardized parameter estimates (CFA Second-Order). Figure 5 presents the standardized parameter estimates of the second-order CFA model. The standardized loadings from ALSS to the four dimensions range from 0.59 to 0.64 (VDL = 0.63; SDL = 0.64; GL = 0.59; CL = 0.64), all exceeding the conventional 0.50 threshold and indicating substantial contributions of each dimension to the higher-order construct. Standardized item loadings on the first-order factors are high (approximately 0.85–0.93), with corresponding error variances in the low-to-moderate range, providing strong evidence of convergent validity. Together with excellent fit indices (χ 2 (463) = 515.635, p = 0.046; CFI = 0.996; TLI = 0.996; RMSEA = 0.020), this figure supports the conclusion that the second-order ALSS model is both statistically well-fitting and theoretically robust. a. Discriminant Validity Table 4 presents the discriminant validity of the four first-order constructs: CL, GL, SDL, and VDL. The diagonal entries display the square root of the Average Variance Extracted (√AVE), which ranges from 0.906 to 0.929, indicating strong convergent validity for each construct. The off-diagonal cells show inter-construct correlations, which are moderate (approximately 0.34–0.42) and consistently lower than the corresponding √AVE values. This pattern satisfies the Fornell–Larcker criterion, confirming that each construct is empirically distinct from the others. Complementary indicators reported together with this table show high construct reliability (CR) for the first-order factors (0.965–0.993) and acceptable reliability for the second-order ALSS factor (CR = 0.714). The standardized second-order loadings (0.593–0.645) and R 2 values (0.351–0.415) further indicate that ALSS explains 35–42% of the variance in each dimension. Collectively, Table 4 supports the conclusion that ALSS is a valid and reliable second-order model in which four interrelated, yet distinct, dimensions form a holistic construct of Generation Alpha learning style. b. Construct Reliability (CR) • CL: 0.982 • GL: 0.965 • SDL: 0.993 • VDL: 0.967 • ALSS (second-order): 0.714 All CR > 0.70 → High reliability for first-order constructs. ALSS CR (0.714) is slightly below 0.70 but acceptable for higher-order constructs with excellent fit indices. c. Second-Order Factor Loadings (Standardized) • CL → ALSS: 0.640 • GL → ALSS: 0.593 • SDL → ALSS: 0.645 • VDL → ALSS: 0.627 All significant (p < 0.001). SDL showed the strongest influence, followed by CL, VDL, and GL. d. R 2 (Indicators of Determination) • CL: 0.410 • GL: 0.351 • SDL: 0.415 • VDL: 0.394 ALSS explains 35–42% of variance in each dimension → Significant contribution Figure 5. Standardized parameter estimates (CFA Second-Order). Table 4. Discriminant validity matrix. Construct √AVE CL GL SDL VDL CL 0.923 – 0.362 0.411 0.422 GL 0.906 – 0.413 0.337 SDL 0.929 – 0.394 VDL 0.910 – Conclusion of CFA: The ALSS model is valid and reliable. The second-order structure confirms that Generation Alpha’s learning style in vocational education is best represented as a unified, holistic construct (ALSS) composed of four interrelated dimensions. 4. Discussion This study successfully developed and validated the ALSS, the first psychometric instrument measuring Generation Alpha’s integrated learning style in Indonesian vocational education. Unlike traditional instruments (VARK, Kolb), ALSS captures the digital-native, multidimensional, and hierarchical nature of this generation’s learning preferences through a theoretically grounded second-order model. The elevated S-CVI/Ave (0.981), Cronbach’s α (0.96), and outstanding CFA fit indices (CFI = 0.996, RMSEA = 0.020) validate the superior psychometric quality of ALSS. The second-order structure validates the theoretical assumption that VDL, CL, SDL, and GL are not isolated preferences but manifestations of a unified learning identity shaped by digital immersion. The finding that SDL and CL had the highest standardized loadings on ALSS suggests that autonomy and collaboration are the most dominant drivers of Generation Alpha’s learning identity in vocational settings. This aligns with findings from ( Mukuka & Tatira, 2025 ) and ( Flavian, 2024 ), who argue that digital natives prioritize agency and social connection over passive consumption. The ALSS provides educators and policymakers with a diagnostic tool to personalize Co-PjBL instruction. For example, a student with high SDL and low GL may benefit from self-paced digital projects with minimal gamification, while one with high GL and CL may thrive in team-based challenge simulations with point systems. This instrument bridges a critical gap in TVET research: the lack of validated, theory-driven scales for Generation Alpha. It enables data-driven curriculum design, teacher training, and policy development aligned with the actual learning profiles of Indonesia’s digital-native youth. 5. Conclusion This study developed and validated the Alpha Generation Learning Style Scale (ALSS), a psychometrically robust instrument to measure the integrated learning preferences of Generation Alpha in Indonesian vocational education. The second-order CFA model confirmed that Visual–Digital Learning, Collaborative Learning, Self-Directed Learning, and Gamified Learning collectively form a higher-order construct ALSS representing a holistic, digitally mediated learning identity. With exceptional content validity (S-CVI/Ave = 0.981), reliability (Cronbach’s α = 0.96), and structural fit (CFI = 0.996, RMSEA = 0.020), ALSS offers educators, curriculum designers, and policymakers a scientifically grounded diagnostic tool to implement personalized, Co-PjBL-informed instruction. By aligning pedagogy with students’ authentic learning profiles, ALSS supports the transition from uniform to individualized vocational education in the digital age. Future research should explore cross-cultural validation and longitudinal impacts of ALSS-informed instruction on learning outcomes. 5.1 Implications This study provides substantial theoretical, practical, and policy implications for vocational education. The validation of ALSS through a second-order CFA model reinforces that Generation Alpha’s learning style is an integrated construct rather than separate preferences, reflecting the combined influence of social constructivism, connectivism, heutagogy, and cybergogy. This contributes to modernizing learning style theory by demonstrating the need for a contemporary psychometric model suited to digital-native learners. Practically, ALSS serves as a diagnostic tool that enables teachers to design personalized, data-driven learning strategies, adapt instructional resources, and align Cooperative Project-Based Learning (Co-PjBL) with learners’ strengths to improve engagement, motivation, and higher-order thinking. The instrument also informs teacher professional development by guiding the adoption of digital pedagogy and differentiated instruction. At the policy level, ALSS offers an evidence-based mechanism to support digital transformation in SMK, informing curriculum development, resource allocation, and standards for technology-integrated learning, ultimately strengthening workforce preparation for Industry 4.0 and Society 5.0. 5.2 Limitations and future research This study has several limitations that should be considered when interpreting the findings. The sample was restricted to Mechatronics students, limiting the generalizability of ALSS to other vocational fields with different learning environments. The use of self-report questionnaires introduces potential response bias, especially among younger learners. The study focused primarily on validating the instrument’s structure and reliability without examining its direct impact on learning performance, engagement, or project-based learning outcomes. Additionally, validation was conducted solely within the Indonesian context, leaving cross-cultural applicability uncertain. Future research should extend validation across diverse vocational areas and cultural settings, conduct longitudinal studies to observe changes in learning styles over time, and examine whether ALSS predicts autonomy, collaboration, or digital engagement. Investigating the effects of ALSS-informed instruction on academic outcomes and integrating behavioral data from learning analytics could improve objectivity. Developing adaptive digital systems based on ALSS may further enhance personalized vocational learning. Ethical approval and consent statement The Research Ethics Committee of Universitas Negeri Yogyakarta granted ethical permission for this investigation (Approval No. B/1861/UN34.17/LT/2025). All procedures adhered to international ethical standards for research involving human subjects, encompassing the concepts of respect, beneficence, and fairness ( Association, 2002 ). Data were obtained through an online questionnaire administered to vocational high school students who participated voluntarily. Before participation, students were apprised of the research objectives, anonymity, confidentiality, and their freedom to withdraw at any moment ( Creswell & Creswell, 2017 ). Digital informed consent was obtained through an online form, and permissions for underage students were coordinated through school authorities in accordance with institutional regulations ( BERA, 2018 ). All replies were gathered anonymously, devoid of identifying information, and securely stored with access limited to the research team, so ensuring confidentiality and minimizing risk to participants ( Flick, 2017 ). AI disclosure statement In preparing this manuscript, the author utilized DeepL AI to enhance grammatical accuracy and improve readability. Following the use of this tool, the author conducted a thorough review and made necessary revisions, taking full responsibility for the final content of the publication. Data availability statement Zenodo. Research Dataset on Development and Validation of the Alpha Generation Learning Style Scale (ALSS) in Indonesian Vocational Education: A Second-Order Confirmatory Factor Analysis. https://doi.org/10.5281/zenodo.18182632 ( Ramadhani W, 2025 ) This project contains the following underlying data: • Appendix Content Validation Results (I-CVI).xlsx • Dataset.xlsx • Figure 1 - Conceptual Framework of the Alpha Generation Learning Style Scale (ALSS).jpg • Figure 4 - Unstandardized Parameter Estimates (CFA Second-Order).jpg • Figure-2 - Research Procedure for the Development and Validation of the Alpha Generation Learning Style Scale (ALSS).jpg • Figure-3 - Hypothesized Second-Order Model of ALSS.jpg • Figure-5 - Standardized Parameter Estimates (CFA Second-Order).jpg • Questionnaire (Indonesian and English) Data is available under the terms of the Creative Commons Attribution 4.0 International . Acknowledgements The authors gratefully acknowledge the financial and institutional support provided by the Indonesian Education Scholarship (BPI), Center for Higher Education Funding and Assessment (PPAPT), Ministry of Higher Education, Science and Technology of Republic Indonesia and Indonesian Endowment Fund for Education (LPDP). The authors also express their appreciation to the academic mentors, reviewers and institutional partners who contributed valuable insights to the development of this study. References Abbazio JM, Yang ZS: Are Infographics Worth It?: An Assessment of Information Retention in Relation to Information Embedded in Infographics. Music. Ref. Serv. Q. 2022; 25 (4): 99–130. Publisher Full Text Adigun OT, Mpofu N, Maphalala MC: Fostering self-directed learning in blended learning environments: A constructivist perspective in Higher Education. High. Educ. Q. 2025; 79 (1): 1–16. Publisher Full Text Allen MJ, Yen WM: Introduction to measurement theory. 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Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 02 Feb 2026 ADD YOUR COMMENT Comment Author details Author details 1 Department of Technology and Vocational Education, Universitas Negeri Yogyakarta Program Pascasarjana, Yogyakarta, Special Region of Yogyakarta, Indonesia 2 Department of Vocational Education in Electronics Engineering, Universitas Muhammadiyah Riau, Pekanbaru, Riau, Indonesia 3 Department of Electronics Engineering, Universitas Negeri Padang, Padang, Sumatera Barat, 25132, Indonesia 4 Department of Mechanical Engineering, Universitas Palangka Raya, Palangkaraya, Central Kalimantan, Indonesia 5 Department of Automotive Engineering, Universitas Negeri Padang, Padang, West Sumatra, Indonesia 6 Departement of Technology Information Education, Stella Maris Sumba University, Tambolaka, Indonesia Witri Ramadhani Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Project Administration, Writing – Original Draft Preparation Putu Sudira Roles: Conceptualization, Investigation, Supervision, Validation, Writing – Review & Editing Samsul Hadi Roles: Formal Analysis, Methodology, Supervision, Validation, Writing – Review & Editing Lativa Mursyida Roles: Data Curation, Project Administration, Visualization, Writing – Review & Editing Ratna Pancawati Roles: Data Curation, Formal Analysis, Investigation, Writing – Review & Editing Hendra Dani Saputra Roles: Resources, Software, Validation, Writing – Review & Editing Dwi Jatmoko Roles: Data Curation, Formal Analysis, Resources, Writing – Review & Editing Joni Andre Roles: Writing – Review & Editing Andry Ananda Putra Tanggu Mara Roles: Formal Analysis, Investigation, Resources, Validation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information This research was funded by doctoral scholarships awarded by Indonesian Education Scholarship (BPI), Center for Higher Education Funding and Assessment (PPAPT), Ministry of Higher Education Science and Technology of Republic Indonesia, and Indonesian Endowment Fund for Education (LPDP). Grant Numbers: Witri Ramadhani (202231103595), Lativa Mursyida (202404121743), Ratna Pancawati (202404121707), Hendra Dani Saputra (202404121803), Dwi Jatmoko (202404121277) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (1) version 1 Published: 02 Feb 2026, 15:168 https://doi.org/10.12688/f1000research.175791.1 Copyright © 2026 Ramadhani W et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 02 Feb 2026 Views 0 Cite How to cite this report: Bonilla HAM. Reviewer Report For: Development and Validation of the Alpha Generation Learning Style Scale (ALSS) in Indonesian Vocational Education: A Second-Order Confirmatory Factor Analysis [version 1; peer review: 1 approved with reservations] . F1000Research 2026, 15 :168 ( https://doi.org/10.5256/f1000research.193800.r466839 ) The direct URL for this report is: https://f1000research.com/articles/15-168/v1#referee-response-466839 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 18 Mar 2026 Hugo Alejandro Muñoz Bonilla , Corporación Universitaria Minuto de Dios - UNIMINUTO, Cali, Colombia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.193800.r466839 The study addresses a highly relevant and timely topic: the development of a learning style scale for "Generation Alpha" in the context of vocational education. The theoretical framework integrating connectivism and heutagogy is well-conceived. However, there are significant statistical ... Continue reading READ ALL The study addresses a highly relevant and timely topic: the development of a learning style scale for "Generation Alpha" in the context of vocational education. The theoretical framework integrating connectivism and heutagogy is well-conceived. However, there are significant statistical inconsistencies and methodological choices regarding the psychometric validation that must be addressed to ensure the validity of the instrument. 1. Mathematical Inconsistency: Factor Loadings vs. AVE There is a severe discrepancy in the reported validity metrics in Table 5 . The authors report an AVE (Average Variance Extracted) between 0.821 and 0.864 . However, the standardized factor loadings ($\lambda$) are reported in the range of 0.59 to 0.65 . Mathematically, the AVE is the average of the squared loadings. If the loadings are $\approx 0.60$, the AVE should be approximately 0.36 . An AVE $> 0.80$ would require loadings consistently above 0.90 . The authors must clarify this calculation and check if there was a transcription error or a fundamental mistake in the software output interpretation. 2. Inappropriate Estimator for Ordinal Data (Scale of 4 points) The ALSS uses a 4-point Likert scale . According to psychometric literature (e.g., Finney & DiStefano), a scale with fewer than 5 categories is strictly ordinal and does not meet the assumptions of multivariate normality required for Maximum Likelihood (ML) estimation. The authors appear to have used ML and Pearson correlations. This typically leads to biased fit indices and underestimated standard errors. The analysis should be re-run using a robust estimator such as DWLS (Diagonally Weighted Least Squares) or ULSMV based on a polychoric correlation matrix , which is the standard for ordinal data. 3. Reliability Measures: Cronbach’s Alpha vs. Ordinal Nature The reported Cronbach’s Alpha of 0.96 is exceptionally high for an instrument with moderate factor loadings. Alpha assumes continuous data and tau-equivalence. For a 4-point scale, Alpha is known to be biased. The authors should report McDonald’s Omega ($\omega$) or Ordinal Alpha based on polychoric correlations to provide a more realistic estimate of internal consistency. 4. Concerns Regarding Model Overfitting The fit indices ($CFI=0.996, TLI=0.996, RMSEA=0.020$) are "too perfect" for a $n=285$ sample in social sciences. Such values often indicate that the model was over-fitted using modification indices (correlating error terms) without a theoretical justification. Please disclose if any modification indices were applied and provide the Chi-square ($\chi^2$) and degrees of freedom ($df$) to evaluate the model's parsimony. Minor Revisions. The study is based on students from only two vocational schools in Yogyakarta. The title and conclusions should be adjusted to reflect that this is a local validation study rather than a representative nationwide scale for the entire "Alpha Generation." With an Alpha of 0.96 and 20 items, there is a possibility of item redundancy. A brief discussion on the uniqueness of each item would be beneficial. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Project Management, Business Sciences, and Advanced Quantitative Research Methods. Expert in Psychometric Validation, Structural Equation Modeling (SEM), and the adoption of Artificial Intelligence in organizational contexts. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Bonilla HAM. Reviewer Report For: Development and Validation of the Alpha Generation Learning Style Scale (ALSS) in Indonesian Vocational Education: A Second-Order Confirmatory Factor Analysis [version 1; peer review: 1 approved with reservations] . F1000Research 2026, 15 :168 ( https://doi.org/10.5256/f1000research.193800.r466839 ) The direct URL for this report is: https://f1000research.com/articles/15-168/v1#referee-response-466839 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 02 Feb 2026 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 Version 1 02 Feb 26 read Hugo Alejandro Muñoz Bonilla , Corporación Universitaria Minuto de Dios - UNIMINUTO, Cali, Colombia Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Bonilla H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 18 Mar 2026 | for Version 1 Hugo Alejandro Muñoz Bonilla , Corporación Universitaria Minuto de Dios - UNIMINUTO, Cali, Colombia 0 Views copyright © 2026 Bonilla H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The study addresses a highly relevant and timely topic: the development of a learning style scale for "Generation Alpha" in the context of vocational education. The theoretical framework integrating connectivism and heutagogy is well-conceived. However, there are significant statistical inconsistencies and methodological choices regarding the psychometric validation that must be addressed to ensure the validity of the instrument. 1. Mathematical Inconsistency: Factor Loadings vs. AVE There is a severe discrepancy in the reported validity metrics in Table 5 . The authors report an AVE (Average Variance Extracted) between 0.821 and 0.864 . However, the standardized factor loadings ($\lambda$) are reported in the range of 0.59 to 0.65 . Mathematically, the AVE is the average of the squared loadings. If the loadings are $\approx 0.60$, the AVE should be approximately 0.36 . An AVE $> 0.80$ would require loadings consistently above 0.90 . The authors must clarify this calculation and check if there was a transcription error or a fundamental mistake in the software output interpretation. 2. Inappropriate Estimator for Ordinal Data (Scale of 4 points) The ALSS uses a 4-point Likert scale . According to psychometric literature (e.g., Finney & DiStefano), a scale with fewer than 5 categories is strictly ordinal and does not meet the assumptions of multivariate normality required for Maximum Likelihood (ML) estimation. The authors appear to have used ML and Pearson correlations. This typically leads to biased fit indices and underestimated standard errors. The analysis should be re-run using a robust estimator such as DWLS (Diagonally Weighted Least Squares) or ULSMV based on a polychoric correlation matrix , which is the standard for ordinal data. 3. Reliability Measures: Cronbach’s Alpha vs. Ordinal Nature The reported Cronbach’s Alpha of 0.96 is exceptionally high for an instrument with moderate factor loadings. Alpha assumes continuous data and tau-equivalence. For a 4-point scale, Alpha is known to be biased. The authors should report McDonald’s Omega ($\omega$) or Ordinal Alpha based on polychoric correlations to provide a more realistic estimate of internal consistency. 4. Concerns Regarding Model Overfitting The fit indices ($CFI=0.996, TLI=0.996, RMSEA=0.020$) are "too perfect" for a $n=285$ sample in social sciences. Such values often indicate that the model was over-fitted using modification indices (correlating error terms) without a theoretical justification. Please disclose if any modification indices were applied and provide the Chi-square ($\chi^2$) and degrees of freedom ($df$) to evaluate the model's parsimony. Minor Revisions. The study is based on students from only two vocational schools in Yogyakarta. The title and conclusions should be adjusted to reflect that this is a local validation study rather than a representative nationwide scale for the entire "Alpha Generation." With an Alpha of 0.96 and 20 items, there is a possibility of item redundancy. A brief discussion on the uniqueness of each item would be beneficial. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Project Management, Business Sciences, and Advanced Quantitative Research Methods. Expert in Psychometric Validation, Structural Equation Modeling (SEM), and the adoption of Artificial Intelligence in organizational contexts. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Bonilla HAM. Peer Review Report For: Development and Validation of the Alpha Generation Learning Style Scale (ALSS) in Indonesian Vocational Education: A Second-Order Confirmatory Factor Analysis [version 1; peer review: 1 approved with reservations] . F1000Research 2026, 15 :168 ( https://doi.org/10.5256/f1000research.193800.r466839) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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