Electronic Journal of e-Learning

Machine Learning in Art Teacher Education: A Comparative Analysis and Student Perceptions

2025-10-23T10:15:11+00:00

Amid the global push for digital transformation in higher education, there is a critical need for objective, scalable assessment tools in subjective disciplines like visual arts. Modern teacher education increasingly integrates intelligent technologies, yet the application of machine learning (ML) for formative assessment in art education remains underexplored. While ML offers scalable feedback, its capacity to evaluate subjective creativity remains contested. The study aims to examine the technical accuracy of a CNN-based model trained on a local dataset of 300 archived projects, compared to instructor evaluations, and to analyze how future teachers (N = 180) perceive algorithmic feedback in assessment contexts. A mixed-methods design was employed using a highly reliable survey instrument (Cronbach’s α = .925) and comparative scoring analysis across four key dimensions: Technique, Composition, Color, and Creativity. Results indicate that the model aligns strongly with human assessments on technical execution (r = .426, p < .001), and moderate alignment for Composition (r = .430, p < .001) and weaker alignment for Color (r = .327, p < .001), while correlations for Creativity were notably weaker (r = .181, p = .015), indicating persistent limitations in modeling abstract artistic intent. ANOVA results revealed that students’ digital literacy significantly predicts their trust in the system (F = 3.547, p = .031) and willingness to use it (F = 8.476, p < .001). Furthermore, discrepancy analysis indicated systematic divergence across proficiency levels, with the model exhibiting increasing underestimation for highly proficient students, particularly in cases involving stylistic deviation or non-standard cultural expression. The findings suggest that while the algorithm provides consistent, transparent scoring that enhances assessment literacy, it lacks the sensitivity to evaluate high-level originality due to standardization bias. This study contributes to the field by empirically demonstrating the "accuracy–creativity trade-off" in ML-based art assessment and by validating a hybrid assessment framework that balances algorithmic precision with pedagogical intuition. The study concludes that ML tools should function as "human-in-the-loop" support systems rather than autonomous graders, fostering critical reflection and digital competence in future educators.

From Intention to Reflection: Understanding Self-Directed Learning in the Use of Generative AI in Vietnam

2025-11-17T18:41:18+00:00

This study extends the Theory of Planned Behavior (TPB) to explore how students’ behavioral intentions toward using generative artificial intelligence (GenAI) are associated with their reflective engagement and self-directed learning (SDL) in higher education. As GenAI tools such as ChatGPT increasingly mediate learning, understanding how learners’ intentions are linked to autonomous and reflective learning behaviors becomes essential. Data were collected from 149 first-year university students (predominantly female) in Vietnam who had prior experience with GenAI for academic purposes. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the study examined relationships among attitudes, subjective norms, perceived behavioral control, behavioral intention, actual use, reflection, and two dimensions of SDL, including intentional learning and self-management. The results reveal that attitudes and perceived behavioral control significantly predict students’ intentions and actual use of GenAI, whereas subjective norms have no significant effect. Behavioral engagement is positively associated with reflection and both dimensions of SDL, while reflection is positively related to intentional learning and self-management, confirming its mediating role within the proposed model linking motivation-related constructs with autonomous learning outcomes. These findings highlight reflection as a metacognitive mechanism that links students’ behavioral engagement with GenAI and their SDL-related outcomes. Theoretically, the study advances TPB by positioning reflection and SDL as outcome constructs within the proposed model, rather than fixed learner traits. Practically, it suggests that educators and institutions working with first-year university students or similar learner populations should integrate reflective activities and AI literacy into curricula to promote critical, ethical, and autonomous engagement with GenAI. Designing learning environments that position AI as a reflective partner, rather than merely a content generator, supports learners’ self-regulation and reflective engagement. Overall, this research contributes to understanding how intentional and reflective interaction with GenAI is associated with deeper and more autonomous learning of students among first-year university students in a GenAI-supported learning context.

The Impact of AI Literacy on Undergraduate Autonomous Learning

2025-07-25T13:04:41+00:00

This study explores the factors driving autonomous learning (AU) among undergraduate students in AI-enhanced education. It specifically examines the role of AI literacy (AI-L), critical thinking (CT), self-regulation (SR), and self-efficacy (SE). Data collected from Thai university students were analyzed using Structural Equation Modeling (SEM). The results show that AI-L demonstrated a strong and significant positive influence on all three mediating variables—SE (β = 0.99, t = 20.00), SR (β = 0.93, t = 18.53), and CT (β = 0.70, t = 7.30). SE exerted as the most powerful predictor of AU (β = 0.52, t = 6.38), while critical thinking had a smaller direct impact. The findings suggest that AI-L is a foundational competency that requires metacognitive support. Consequently, educators should utilize strategies like blended learning and reflective practice. These insights encourage a learner-centered approach to digital education, fostering future-ready, autonomous learners.

Ecological Predictors of AI Literacy in Chinese K-12 Teachers: A Structural Equation Modeling Study

2025-08-18T09:00:55+00:00

Although AI is being rapidly developed and applied in education, gaps remain in factors affect teachers’ AI literacy. A cross-sectional survey of 1,680 teachers was conducted to explore relationships between school environment, social environment, teacher self-efficacy, and AI literacy via structural equation modeling (CFI = 0.986; RMSEA = 0.03). The results showed that teachers’ AI literacy was 3.89 ± 1 (out of 5) in total, and the theory-practice gap was significant: stronger performance in awareness (β = 0.75) and ethics (β = 0.76), but weaker performance in application literacy (β = 0.72) and evaluation literacy (β = 0.81). School environment had the strongest direct effect on AI literacy (β = 0.270, p < 0.001), followed by teacher self-efficacy, which served as an important mediator (β = 0.259, p < 0.001). Social environment had no direct effect on teachers’ AI literacy (β = 0.060, p = 0.362), implying that distal effects need to be mediated by school. Demographic analysis showed urban–rural differences, decline after age 40, and subject differences (science > liberal arts). Therefore, we suggest that policymakers should transfer to supporting school-level interventions with targeted resources allocation. School leaders should create supportive technological environments and self-efficacy programs. In addition, teachers should participate in hands-on training with a focus on practical skills. This study provides useful references for integrating AI into K-12 education in China.

Learning Design and Learning Analytics to Improve Higher Education: A Systematic Literature Review

2025-10-31T19:06:36+00:00

In recent years, higher education has increasingly emphasized the integration of Learning Design and Learning Analytics to foster more engaging, personalized, and effective learning environments. This systematic literature review investigates how these two domains interact to enhance teaching learning processes and improve educational outcomes. The review identifies key benefits and opportunities associated with this integration across three stakeholder groups: students, lecturers, and educational institutions by analyzing 55 peer-reviewed publications. The results show that learning effectiveness can be significantly enhanced through the visualization of students’ learning interactions using straightforward and user-friendly analytical approaches. Furthermore, successful implementation requires the development of lecturers’ data literacy and programming competencies, as well as the incorporation of sociocultural, psychological, and physical data to achieve a more holistic understanding of learners. The review also identifies four major research directions to guide future efforts in bridging Learning Analytics and Learning Design. Finally, the paper underscores the importance of establishing clear ethical and privacy frameworks to ensure the responsible application of Learning Analytics in higher education.

From Chalkboards to Smart Classrooms: Faculty Perceptions of IoT Integration in Jordanian Universities

2025-12-26T17:17:05+00:00

Digital transformation in higher education has increased interest in faculty adoption of emerging technologies such as the Internet of Things (IoT). This study investigates faculty perceptions of IoT integration in Jordanian private universities, with particular attention to gender and academic rank. Grounded in the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), the study examines how key acceptance constructs shape IoT adoption in teaching. A quantitative, descriptive survey design was employed using a validated 21-item questionnaire administered to 350 full-time faculty members at Al-Zaytoonah University of Jordan. The instrument demonstrated strong reliability (Cronbach’s α = 0.91) and sound construct validity confirmed through confirmatory factor analysis (CFI = 0.95, RMSEA = 0.06). Results indicated a high overall level of acceptance of IoT applications in teaching (M = 4.12, SD = 0.88). No statistically significant differences were found by gender, while small but statistically significant differences emerged by academic rank, with assistant and associate professors reporting more positive perceptions than full professors (η² = 0.024). The findings suggest that IoT acceptance is broadly shared among faculty, with academic rank functioning as a modest, context-dependent moderator. The study contributes empirical evidence on IoT-enabled e-learning practices in Middle Eastern private higher education and highlights the need for targeted professional development and institutional support strategies.

Digital Pedagogy in Indian Higher Education: Faculty Perspectives

2026-01-13T19:58:18+00:00

The rapid digitalization of higher education has significantly reshaped teaching and learning practices worldwide; however, the adoption of digital pedagogy among university teachers remains uneven, particularly in developing contexts such as India. This study examines the lived experiences of Indian university teachers in adopting digital pedagogy and explores the factors influencing this process within higher education institutions. Using a qualitative research design, the study employs Interpretative Phenomenological Analysis to develop an in depth understanding of how teachers perceive, experience, and make sense of digitally mediated teaching practices. Data were collected through semi structured interviews with university teachers representing diverse disciplinary backgrounds and institutional settings. The analysis followed a systematic and iterative IPA approach to identify emergent themes grounded in participants’ narratives. The findings indicate that digital pedagogy adoption is shaped by a dynamic interplay of institutional, technological, and personal factors. Institutional support structures, availability of digital infrastructure, access to professional development opportunities, and collaborative peer environments emerged as key enablers of adoption. In contrast, challenges such as inadequate training, inconsistent technical support, increased workload, infrastructural disparities between institutions, and varying levels of digital confidence among teachers were identified as persistent barriers. The study further highlights the central role of teachers’ beliefs, attitudes, and perceived pedagogical value of digital tools in determining the depth and sustainability of digital pedagogy integration. By foregrounding faculty perspectives, this research contributes to the limited qualitative literature on digital pedagogy adoption in Indian higher education and extends existing scholarship beyond technology acceptance oriented explanations. The study supports e learning practice by offering context specific recommendations related to faculty training, institutional policy, and digital readiness. By emphasizing teachers’ lived experiences, the findings advance understanding of digital pedagogy as a socially situated and contextually embedded practice, providing a foundation for inclusive and sustainable digital transformation in higher education. The insights generated offer important implications for higher education leaders and policymakers seeking to strengthen digital capacity and enhance teaching quality in evolving educational environments.

Strategic Leverage Points in Blended Learning: A Systems Science Approach Using Grey-DEMATEL-ISM-MICMAC Framework in Higher Education

2026-02-21T13:10:37+00:00

E-learning has emerged as a cornerstone of contemporary higher education, offering flexible and technology-mediated environments that accommodate modern learning needs. Among its various modalities, blended learning (BL), which strategically integrates face-to-face and online instruction, has become a pivotal approach in higher education for enhancing learning outcomes and fostering talent cultivation. However, its successful implementation depends on the coordinated interaction of individual, technological, environmental, and course dimensions, constituting a complex network of interdependent factors that often remain fragmented in practice. Existing studies typically examine these factors in isolation and commonly rely on linear analytical approaches, providing limited insights into the systematic, comprehensive, and hierarchical understanding of the interrelationships among them. Understanding these structural interrelationships is therefore essential for identifying strategic leverage points that can optimise system performance and ensure the sustainable success of BL initiatives. To address this gap, this study proposes a systems science-based analytical framework that integrates the Grey Decision-Making Trial and Evaluation Laboratory (Grey-DEMATEL), Interpretive Structural Modelling (ISM), and Matrix Impact Cross Multiplication Applied to Classification (MICMAC). This integrated approach enables comprehensive and data-driven modelling of the causal parameters, hierarchical structure, and driving-dependence relationships among critical success factors of BL. First, ten critical success factors were identified through a systematic literature review and were then pairwise evaluated by twelve experts from a higher education institution in Thailand. Grey-DEMATEL was subsequently employed to quantify the causal properties and relative significance of these factors, while ISM was applied to construct a multi-layer hierarchical structure. MICMAC analysis further categorised the factors according to their driving and dependence powers. The results reveal a three-layer hierarchical structure of BL critical success factors, where policy support (R − C = 2.78), system quality (R – C = 1.73), and technical support (R – C = 1.62) serve as key causal drivers, forming the institutional and technological foundation of the BL system. Course design and technology experience act as mediating linkages connecting institutional mechanisms with learning outcomes, while attitude, perceived usefulness, and interaction represent outcome-level indicators of system performance. Among these factors, course design exhibits the highest level of centrality value (R + C = 18.6) with the causal structure. The findings extend the understanding of the causal hierarchy and strategic leverage points for achieving BL success, illustrate how institutional and technological investment are realised through course design to improve individual experience. The study offers actionable insights for policymakers and instructional designers to inform data-driven decision-making and strategic planning in higher education, as well as how this is implemented at the level of the individual academic.

Generative AI and Knowledge Mapping in Programming Education: Student Learning and Engagement

2026-03-15T12:06:13+00:00

This study examines how Generative AI and knowledge-mapping tools support student learning and engagement in programming education. A quasi-experimental design was conducted with 30 undergraduate students enrolled in an object-oriented programming course, where participants used both tools across a four-week intervention. Data were collected through task performance and learner perception surveys. The results indicate that students reported higher ease of use and immediate support when using Generative AI, while knowledge mapping was associated with stronger support for conceptual understanding and reflective learning in later stages. These findings suggest that the two approaches support different aspects of learning, with Generative AI facilitating rapid clarification and knowledge-mapping tools encouraging structured conceptual engagement. The study contributes to the e-learning field by providing empirical insight into how different forms of learning support function within the same instructional context. Rather than positioning the tools as direct alternatives, the findings highlight their complementary pedagogical roles and offer guidance for integrating adaptive AI support with structured learning approaches in programming education.

Apriori-based Analysis of Learned Helplessness in Mathematics Tutoring: Behavioral Patterns by Level, Intervention, and Outcome

2026-02-21T06:37:33+00:00

This study applied the Apriori algorithm to analyze behavioral interaction patterns associated with learned helplessness (LH) in mathematics tutoring system logs. Interaction data were examined across three dimensions: LH level (low vs. high), system-based intervention (with vs. without), and problem-solving outcomes (solved vs. unsolved). The analysis of the complete dataset showed that skipping problems without using hints was the most frequent pattern linked to unsolved outcomes, while persistence behaviors such as not skipping were less dominant overall. Comparisons by LH level showed that low-LH students had stronger links between problem solving and not skipping, as well as positive associations between hint use and solved outcomes. High-LH students showed more avoidance patterns, with skipping strongly tied to unsolved outcomes. In the comparison of system-based intervention conditions, students without intervention had the highest lift for persistence–success links, while the with-intervention group had stronger patterns involving skipping behaviors leading to unsolved outcomes. Outcome-specific analysis showed that not skipping was consistently associated with solved problems across all groups, while skipping without hints predicted unsolved outcomes. Practical implications and recommendations are discussed.