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).

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).

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.

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.

2.2.2 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).

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).

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.

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.

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).

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.

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 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 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.

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.

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.

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.