Electronic Journal of e-Learning

Quantile-based e-Learning Student Engagement Classification

2026-05-24T15:37:54+00:00

Classifying student engagement accurately is critical for timely academic intervention; however, most existing approaches rely on arbitrarily defined thresholds that lack statistical grounding and are difficult to transfer across institutional contexts. This limitation reduces the practical applicability of engagement analytics in diverse educational settings. This study evaluates a quantile-based engagement classification framework across two contrasting datasets to assess its validity, transferability, and consistency of predictive features. Unlike threshold-based approaches, the proposed framework derives engagement categories directly from dataset-specific interaction distributions. The Open University Learning Analytics Dataset (OULAD) represents large-scale fully online learning, while the Unistudium dataset reflects a smaller blended learning context. The two datasets differ substantially in size and delivery mode, with a student ratio of approximately 17.4 to 1. This contrast provides a rigorous basis for assessing method transferability. Engagement categories (passive, moderate, and active) are derived using dataset-specific quartile thresholds (Q1 and Q3). This strategy adapts automatically to local interaction distributions and avoids manual parameter tuning. Five temporal behavioural features were extracted, including active days, unique actions, and learning consistency. Random Forest was employed as the proposed model, while a Decision Tree classifier was included as a baseline for comparative evaluation. The results indicate that the proposed framework remains effective across different educational contexts. In the OULAD dataset, the model achieved an accuracy of 92.04% with a Cohen’s κ of 0.87. In the Unistudium dataset, accuracy reached 72.50% with a Cohen’s κ of 0.59. Although performance differed between datasets, variance remained low. Feature importance analysis further revealed strong consistency across contexts, with a Spearman correlation of 0.90. Active days and unique actions were the most influential predictors in both cases. The baseline comparison further confirmed the superiority of Random Forest over the Decision Tree baseline across both datasets. These findings support e-learning practice by offering institutions a statistically grounded and automated method for engagement classification. The approach removes the need for arbitrary thresholds and reduces operational overhead in analytics deployment. From a research perspective, the study establishes realistic performance benchmarks for engagement analytics at different institutional scales, demonstrates the applicability of quantile-based engagement classification across heterogeneous datasets, and confirms that key behavioural engagement indicators transfer reliably across online and blended learning environments.

Designing a Gamified Retirement Planning Application: Evidence from a Workplace Study of Millennial Employees

2026-02-21T11:34:02+00:00

The phenomenon of a global retirement crisis has gained increasing attention in recent years. A growing number of individuals struggle to secure sufficient financial resources to sustain themselves during retirement. Financial knowledge not only increases the tendency to save for retirement, but it also affects an individual’s everyday money practices, including borrowing, saving, and investing decisions. Millennials are currently the largest cohort in the workforce and should be constantly made aware of the urgent need to save from the earliest age possible. The development and implementation of a unique gamified retirement planning application aimed at improving retirement preparedness was critically examined for its design rationale, user experiences, and observed successes and shortcomings. A randomised control experiment was conducted to observe any changes the gamified tool might have on the players’ retirement preparedness. Participants, millennial employees at a higher education institution, aged between 24 and 43 were randomly divided into one of three groups (gamification group, education group, and control group), ranging between 29 and 34 participants. The gamified application created a platform where the gamification group could experience the long-term effects of their decisions in a low-risk setting by modelling real-life financial decisions, which helped them develop a more accurate grasp of retirement planning. The education group was only exposed to infographics on retirement planning, and the control group was not exposed to any intervention. Data were analysed and tested using a nonparametric ANCOVA. The gamified application tends to be more effective at improving basic financial literacy, promoting interest, fostering awareness, and building confidence. However, a decrease was observed in respondents’ perceptions of how well they are prepared for retirement and whether they will achieve the income they need in retirement, which may reflect a “reality check” effect. The infographics appeared to be more effective at improving financial retirement literacy and resulted in a more positive perception of how well they are prepared for retirement, which may reflect overconfidence in their knowledge, leading them to believe they are better prepared than they actually are. Despite some content and technical limitations of the gamified application, it exhibits promise as a financial education tool, motivating individuals to take proactive steps toward retirement planning. Collectively, the evidence suggests that gamification enhances financial literacy and interest in retirement planning, prompts self-reflection, and serves as a “reality check” on respondents’ perceived confidence in retirement preparedness. Consequently, e-learning environments for financial education interventions should not only consider gamification for motivation, but also balance engagement, knowledge development, and behavioural awareness, ensuring a meaningful and realistic development of financial proficiency and behaviour. The limitations of the study suggest that the sample size limits statistical power and restricts the generalisability of the findings, the self-reported measures may be subject to social desirability bias or overconfidence, particularly among participants exposed to educational material, and, lastly, due to the time limit of the study, long-term knowledge retention and sustained behavioural change could not be assessed.

EsyGrade: An AI-Based Essay Assessment System for Economics Education

2026-02-11T15:44:45+00:00

This study aims to design, develop, and evaluate EsyGrade, a web-based essay grading system integrated with ChatGPT to support the assessment of conceptual understanding in economics education. This study is motivated by the prevalence of multiple-choice questions in Indonesian high schools due to efficiency considerations, while essay assessments better suited to measuring conceptual reasoning are still rarely used because of high assessment load. The study employed a simplified Research and Development (R&D) approach consisting of seven stages. Data were collected through expert validation using Aiken’s V, a user response questionnaire, and pretest–post-test, and were later analysed using N-Gain and effect size (Cohen’s d). The research findings indicate that EsyGrade has a high level of validity based on the expert evaluation and an excellent acceptance rate in the initial pilot test. In the main field trial, the results indicated a moderate improvement in conceptual understanding based on N-Gain, with a “large” effect size. These findings suggest that the overall change in learning outcomes indicates a fairly strong impact, although the degree of improvement varied among students. In particular, the system’s key value lies in its ability to generate structured, reflective feedback, so that it serves not only as a summative assessment tool but also as a means for formative one. This study contributes to the development of e-learning by demonstrating that AI-based essay grading systems can be designed to improve efficiency while supporting deeper learning through the integration of pedagogical principles and technology.

Evaluating an AI-Supported Revision Module for Project-Based Research in Teacher Education

2026-03-15T16:41:03+00:00

While Generative Artificial Intelligence (GenAI) allows new methods to support students in writing processes, its incorporation into university teaching needs well-designed pedagogy to ensure academic integrity and autonomy of learners, especially in Project-Based Research (PBR) courses in teacher education, where iterative feedback is important but restricted by the size of groups and limited resources. The current study aims to address the scalability problem in the feedback process by evaluating the AI-Supported Revision Module (AIRM). It is implemented as a scaffold designed according to a rubric and embedded into Moodle, using a local version of the LLaMA-2-7B model for generation of prompts based on instructor comments for revision in four modes: polishing, restructuring, justifying, and synthesizing. The purpose of the module is to provide targeted guidance to students rather than full-text generation and support revision while preserving authorship. A mixed-methods case study approach was used involving 158 primary education student teachers, out of which 132 submitted draft and revision versions, assessed using a six-criterion rubric. A mixed-design repeated measures ANOVA test revealed interaction effects for both Literature Review and Methodology at the level of p < .01, with effect sizes of d = 0.58 and d = 0.52. The results suggest more improvement in structure and synthesis for the AI group than the conventional group, while no significant differences were found for Data Justification and Interpretation, suggesting a boundary between procedural support and higher-order analytical reasoning. Process data analysis revealed active involvement of learners, evidenced by the fact that on average 14.2% of draft content was changed and 39.7% of AI suggestions were rejected. Qualitative data analysis revealed that learners utilized this AI-powered module mostly to increase text coherence and clarity while staying in control of making sense of the content, whereas teachers indicated a shift from superficial to more methodological feedback practices. Thus, the findings demonstrate that GenAI may be successfully implemented in the feedback process as an additional scaffold for revision while still respecting the agency of learners.

Explaining Online Learning Attitudes: Community of Inquiry Presences, Learning Outcomes, and Learner Characteristics

2026-03-22T09:49:45+00:00

Online learning has become a core mode of higher education, intensifying questions about what constitutes high-quality teaching and learning in digital environments and how students form evaluations of their online learning experiences. The Community of Inquiry (CoI) framework highlights teaching, social, and cognitive presence as key pedagogical conditions, yet less is known about how these conditions operate alongside learner characteristics and students’ perceived learning outcomes to shape attitudes toward online learning. This quantitative study surveyed 316 undergraduates enrolled in 22 fully online undergraduate courses. Students reported demographic characteristics (including age, gender, ethnicity, faculty affiliation, learning-disability status) and self-reported cumulative Grade Point Average (GPA) range. Perceptions of CoI presences were measured using a validated CoI instrument, and perceived learning outcomes were assessed as multidimensional gains (cognitive, metacognitive, and social). Analyses included group comparisons and hierarchical regression models predicting attitudes toward online learning. Group comparisons indicated significant differences in perceived presence across demographic groups, although most effects were small. Perceptions of cognitive and social presence also varied across self-reported GPA ranges, whereas teaching presence was relatively stable. In hierarchical regression, demographic variables explained a modest portion of variance in attitudes. Adding CoI presences substantially improved prediction, with cognitive presence emerging as the primary presence-related predictor. When perceived learning outcomes were added, perceived cognitive and metacognitive gains were the strongest predictors of more positive attitudes, and the unique contribution of CoI presences was reduced, suggesting that perceived learning gains may help explain the presence–attitude link. The findings therefore point to a more integrative account of online learning quality, in which students’ attitudes appear to depend less on fixed demographic differences and more on whether online courses are experienced as cognitively meaningful and supportive of reflective growth. Findings underscore the centrality of cognitive engagement and perceived learning gains for shaping students’ attitudes toward online learning and point to actionable design priorities: inquiry-oriented activities, structured reflection and metacognitive scaffolds, and consistent course organization and support that promote equity across diverse learners. These results also inform institutional policy by emphasizing shared online-course quality standards and professional learning focused on evidence-informed design practices.

Transforming the Ethnochemistry Technology Lecture Model: Integrating Digital Literacy and Character Education

2026-02-02T18:07:45+00:00

Papua, Indonesia, has a rich ethnochemical heritage, including plant-based dyeing and starch processing, that is rarely represented in tertiary chemistry curricula. At the same time, uneven internet connectivity constrains students’ opportunities to develop robust digital literacy. These dual challenges highlight a need for e-learning models that are culturally grounded, technologically adaptive, and pedagogically transformative. This study redesigned an Ethnochemistry Technology lecture into a low-bandwidth, culturally embedded, and virtue-infused e-learning model to improve students’ digital literacy and character outcomes measurably. Using a semester-long Design-Based Research approach at a public university in Jayapura, the course integrated Papuan case vignettes, flipped and HyFlex delivery, downloadable H5P interactives, short micro-lectures, an augmented-reality dye-extraction lab with offline alternatives, communal reflection journals, peer mentoring, and a service-learning partnership with a village craft cooperative. Cultural consultants, including community elders and a dye artisan, ensured epistemic authenticity. Learning tasks were aligned with Kurikulum Merdeka virtues, respect for local wisdom, collaboration, and environmental stewardship, and mapped to UNESCO digital-literacy domains. Findings indicate substantial and educationally meaningful gains in digital literacy and character mastery. Students demonstrated great improvement across all digital-literacy domains, alongside marked increases in demonstrated respect for local wisdom, collaborative engagement, and environmental responsibility. Engagement indicators also rose consistently throughout the semester, particularly during immersive and community-linked activities. Qualitative feedback revealed heightened confidence in evaluating digital sources and a deeper recognition of chemistry within local cultural practices. Beyond local impact, this study contributes to e-learning practice by presenting a scalable blueprint for culturally responsive, low-bandwidth digital pedagogy in resource-constrained contexts. It advances the field of e-learning in Papua by validating indigenous knowledge as a central epistemic resource in technology-enhanced higher education. The model demonstrates that digital transformation need not marginalize local culture; instead, it can amplify it. Future research should explore multi-campus replication, long-term retention effects, and adaptation for additional Indigenous language communities.

Simulation-based Learning and Project Management Certification Outcomes: Evidence from IPMA Level D Training

2026-04-16T07:35:26+00:00

The development and assessment of project management competencies remain a persistent challenge in professional education. While competence frameworks emphasise behavioural and contextual judgement, assessment practices in entry-level certification contexts continue to rely predominantly on standardised knowledge-based examinations. At the same time, simulation-based and game-based learning approaches are increasingly used to support experiential learning, yet evidence linking such interventions to externally validated assessment outcomes remains limited. This study examines whether the inclusion of a simulation-based learning intervention in a preparatory course for the International Project Management Association (IPMA) Level D certification is associated with differences in certification examination performance. Using a quasi-experimental design, examination results for participants who completed simulation-supported training (n = 178) were compared with those of a reference cohort who completed the same course without the simulation component (n = 455). Certification exam scores were analysed across competence areas defined in the IPMA Individual Competence Baseline (ICB 4.0). The results indicate that participants in the simulation-supported group achieved higher overall examination scores, with statistically significant differences concentrated mainly in selected People and Practice competence elements. Mean scores in the Perspective area were also higher in the simulation-supported group, but the differences were not statistically significant. While the non-randomised design does not allow causal conclusions, the findings suggest an association between competence-oriented simulation-based learning and higher performance in a standardised, externally administered certification examination. The analysis focused not only on overall examination performance but also on the distribution of differences across individual competence elements. This made it possible to examine whether observed differences corresponded to the areas most directly activated by the simulation scenario. The study therefore provides evidence on the alignment between simulation design, competence frameworks, and externally administered assessment outcomes. The study contributes to e-learning research by demonstrating how simulation-based learning can be examined in relation to formal assessment outcomes within a professional certification context. It highlights the importance of aligning experiential learning design with competence frameworks while maintaining independence from existing assessment formats.

Early Childhood Computational Thinking through Tangible Floor-Robot Programming in an eTwinning Community of Practice

2026-04-16T07:26:47+00:00

This study explores how early-years teachers evaluate and implement Computational Thinking (CT) through tangible floor-robot activities within an eTwinning Community of Practice (CoP), with attention to gender-related participation patterns. Although CT is increasingly recognised as an essential dimension of Early Childhood Education (ECE - referring to ages 4-6 according to the Greek Education System), there is still limited empirical evidence on how teachers transform CT concepts into developmentally appropriate practice and how gender may shape children’s engagement in such activities. Addressing these gaps, the study focuses on teachers’ evaluations and classroom implementation of CT, their experiences with tangible programming tasks, and their perceptions of gender-related patterns in participation, support needs, and CT performance. The research was conducted over a 24-week asynchronous professional-learning programme hosted on Moodle and involved one national cohort of Greek early-years educators (N = 473). During the professional-learning programme, participants engaged in weekly STEAM-oriented CT activities and collaboratively designed classroom learning scenarios using floor robots (e.g., Bee-Bot). Survey instruments captured teachers’ perceptions of CT, educational robotics, STEAM pedagogy, and gender-related classroom observations, while focus-group discussions provided complementary insights into classroom enactment. Following a DBR-informed exploratory mixed-methods approach, the research combined iterative engagement in the CoP with descriptive analysis of survey and focus-group data to identify patterns in teachers’ experiences and reported classroom practices. Findings indicate consistently high levels of participation for both girls and boys, with only small gender differences. Boys appeared slightly more often in the highest engagement band but were also more likely to require ongoing support, whereas girls were more frequently described as working independently and showed a modest descriptive advantage in problem solving, sequencing, and simple algorithm design. In addition, the learning scenarios were rated as useful or very useful for cultivating CT and for fostering collaboration, communication, and problem solving in early-years classrooms. Qualitative findings identified three design features as particularly effective in supporting children’s engagement: explicit sequencing supports, structured testing and debugging cycles, and the use of cooperative roles. Taken together, these findings underpin a set of practical learning-design principles for implementing CT through floor-robot activities in early childhood. More broadly, the research illustrates how an online CoP can support the adaptation of CT-focused designs into everyday classroom practice. It also contributes to e-learning research by illustrating how developmentally appropriate robotics activities within a Community of Practice may support equitable opportunities for young children to engage with foundational CT practices from the earliest years of schooling.