Enhance Learner Engagement Research Articles

Edu-Metaverse Design: Perspectives of Undergraduate Learners

Using the metaverse can revolutionize learning and interacting with information. However, despite extensive research on the topic, there is still a lack of understanding of how learners perceive and experience this technology. To design learning experiences, designers must know the current landscape of metaverse implementation and digestion by student users. This article provides an overview of metaverse implementation examples to address this gap and then examines learners' perceptions of using the metaverse in education. Through a qualitative study, we aim to explore the potential advantages and obstacles of applying the metaverse to learning from an informal learning perspective, as perceived by learners. Compared to current implementation strategies, student feedback suggests a diversity of motivations and experiences in valuing the Edu-metaverse. Therefore, our study provides valuable insights into how the metaverse learning design can enhance learner engagement, interaction, and collaboration while identifying potential challenges and limitations. Ultimately, this research helps designers and educators better understand how metaverse technology can impact and improve learning experiences.

Temporal–spatial correlation and graph attention-guided network for micro-expression recognition in English learning livestreams

Micro-expressions, fleeting facial movements lasting 1/25 to 1/3 of a second, offer crucial insights into genuine emotions, particularly valuable in online education settings. The rapid growth of English learning livestreams has heightened the need for accurate, real-time micro-expression recognition to enhance learner engagement and instructional effectiveness. However, existing methods need help with the subtle nature of these expressions, especially in dynamic, low-resolution streaming environments. This paper presents TSG–MER–ELL, a novel end-to-end network for micro-expression recognition in English learning livestreams, integrating temporal–spatial correlation and graph attention mechanisms. The framework addresses the unique challenges of real-time emotion analysis in online language education, where subtle facial cues are crucial in understanding learner engagement and comprehension. The temporal–spatial correlation module employs action units with spatio-temporal graph convolution to aggregate features from diverse facial regions, while transformer encoders construct long-range correlations. The graph attention module builds upon local facial areas to guide self-attention computations, yielding precise local correlation features. These global and local features are fused for the final micro-expression classification. We introduce an adaptive loss function that balances accuracy, efficiency, and relevance to linguistic context. Extensive experiments on SMIC, CASME II, and SAMM datasets, adapted for English learning scenarios, demonstrate TSG–MER–ELL’s superior performance over ten state-of-the-art baselines. The TSG–MER–ELL framework achieves top UF1 and UAR scores across all datasets, significantly improving recognition speed and accuracy. Ablation studies and visualizations of temporal–spatial features and graph attention weights provide insights into the framework’s effectiveness in capturing subtle emotional cues. TSG–MER–ELL’s robust performance in varied online learning conditions highlights its potential to enhance engagement, personalize instruction, and improve overall outcomes in virtual English language education.

Leveraging Classroom Polling and Artificial Intelligence to Enhance Learner Engagement and Metacognition in a First-Year Anatomy and Physiology Course

This paper explores the integration and impact of a polling solution (EchoPoll) enhanced with artificial intelligence (AI) in a first-year university anatomy and physiology course. The primary aim of this study was to evaluate the student and instructor perspectives of integrating this new polling tool into this course. The overall aims for the integration of the tool itself were to increase student engagement, provide real-time feedback, and facilitate deeper understanding of complex concepts in a subject area that has historically been taught in traditional and often didactic face-to-face modalities. Anonymous feedback gathered from two different cohorts of students from 2023 and 2024 regarding their experiences using the polling tool was overwhelmingly positive – particularly in terms of enjoying the interactivity, opportunities to improve comprehension of difficult subject matter in real-time and overall learning. This paper discusses the features of the polling tool itself, its application in different classroom scenarios such as lectures, laboratories and workshops, and how it supported both the students and the instructor to change their approaches to exploring anatomical and physiological concepts based on shared understanding and experiences.

Teacher support in AI-assisted exams: an experimental study to inspect the effects on demotivation, anxiety management in exams, L2 learning experience, and academic success

Although AI technologies show great promise for education, their inclusion into assessment systems has generated debates regarding student motivation, anxiety, learning opportunities, and academic results. This study explored the influence of teacher support in AI-assisted exams on L2 learners’ demotivation, anxiety, L2 learning experience, and academic success. Conducted at a large university in Ethiopia, participants included 92 BA Management students from two intact classes, equally distributed by gender and ranging in age from 18 to 23. The two intact classes were randomly divided into an experimental and a control group. Using a quantitative quasi-experimental pretest–posttest control group design, the study administered an Oxford Quick Placement Test, the Academic Motivation Scale, the Foreign Language Classroom Anxiety Scale, the L2 Learning Experience Scale, and a researcher-made test to assess academic success. The AI tools integrated into the exams included automated assessment and feedback systems to enhance learner engagement. Chi-square analyses and independent samples t-tests revealed significant positive effects of teacher support on reducing demotivation and anxiety, enhancing L2 learning experiences, and improving academic success in the experimental group compared to the control group, highlighting the benefits of combining AI tools with teacher support. These findings suggest that teacher support in AI-assisted exams can substantially benefit L2 learners. Additionally, the findings indicate that AI-assisted exams can considerably improve learning outcomes when paired with effective teacher involvement, highlighting implications for various stakeholders in L2 instruction. Implications of the study, limitations, and suggestions for future research are discussed.

Enhancing software quality assurance in ubiquitous learning environments through mutation testing and diverse test oracles

Ubiquitous learning enables software analysts to acquire knowledge and design test oracles from online resources including developer documentations, forums and collaborative platforms. Test oracles are primarily designed by humans and may contain quality defects. Mutation testing can facilitate the adaptability of ubiquitous learning to enhance software quality assurance. Unfortunately, mutation testing generates a large number of faulty versions known as mutants to analyze the quality of test oracles which is computationally expensive. In this paper, we proposed minimal path selection strategy to select fewer and non-trivial mutants to analyze diverse test oracles realizing ubiquitous learning environments. The proposed selection strategy explores the relationship between faulty conditions and output statements to identify feasible paths. To establish a ubiquitous learning setup, diverse test oracles are selected by incorporating user input, automated scripts and developer insights. This approach aims to enhance learner engagement and effectively design test oracles. The empirical evaluation and state-of-the-art comparison of various Java-built software programs demonstrated the potential of ubiquitous learning in software quality assurance by reducing 75% of the generated mutants. Furthermore, only 64%–85% of adequate test oracles were required to meet the desired quality assurance criteria. The study highlights the significance of personalized and adaptive approaches to assure software quality in ubiquitous learning environments.

Analysing the Impact of Gamification Techniques on Enhancing Learner Engagement, Motivation, and Knowledge Retention: A Structural Equation Modelling Approach

This study investigates the impact of gamification techniques, including Kahoot!, Classcraft, and Badgeville, on learner motivation, engagement, and perceptions of learning effectiveness and enjoyment in online learning environments. Employing a quantitative research approach, the study utilizes Structural Equation Modeling (SEM) to analyze the relationships between gamification elements and learner outcomes, framed by Self-Determination Theory (SDT). Data collected from a survey of 169 academics across varis fields reveal that gamification techniques, such as leaderboards, badges, points systems, and challenges, significantly enhance learner engagement, with an average increase of 25% observed. Rewards, incentives, and competitive challenges boost both intrinsic and extrinsic motivation, leading to a 30% improvement in learner performance. Despite a slight negative perception regarding gamification's impact on learning effectiveness, a 20% increase in perceived enjoyment underscores its overall positive influence. Knowledge retention significantly impacts learner engagement, perceived learning effectiveness, and enjoyment, with a correlation coefficient of 0.65 between retention rates and engagement levels. These findings highlight the importance of balancing competitive elements to optimize motivation, effectiveness, and enjoyment while maintaining a supportive learning environment. The study provides actionable recommendations for designing gamified e-learning environments that effectively integrate gamification elements to enhance engagement, motivation, and knowledge retention, offering evidence-based guidance for educators aiming to create engaging and effective online learning experiences.

Enhancing Engagement in Learning Management Systems using Gamification Framework

The rapidly evolving landscape of higher education demands innovative approaches to engage learners effectively within Learning Management Systems (LMS). This article presents a conceptual framework of gamification explicitly designed for higher education to enhance learner engagement within LMS platforms. Drawing inspiration from game design elements, principles, and motivational theory support, the framework strategically integrates points, badges, and leaderboards to create an interactive and motivating learning environment. The primary objective is to leverage intrinsic motivation, fostering active participation and a deeper understanding of course material. By implementing this framework, higher education institutions can enhance the educational experience for learners, improve retention rates, and cultivate a passion for lifelong learning. This article discusses the gamification framework's theoretical foundation, practical implementation, and potential benefits. It provides insights for educators and administrators seeking to transform their LMS into engaging and effective learning environments in higher education.

The Development and Application of a MCPS (Motivation and Creative Problem Solving) Instructional Model in Computing Liberal Arts for Non-Majors

The purpose of this study is to develop and validate a learning motivation and creative problem-solving MCPS instructional model for non-majors in a computing liberal arts course. This study aimed to propose a specific method for conducting a computing liberal arts class tailored for non-majors. The research was divided into two phases: model development and model application studies. In the model development study, we formulated the MCPS instructional model and lesson design by integrating Papert's constructionism learning principles, addressing the challenges in computing education for non-majors, and incorporating various teaching methods identified from related research. In addition, to improve the completeness of the instructional model, we developed an evaluation rubric that considered both content and evaluation aspects through expert review. In the model application study, we validated the effectiveness of this instructional model by implementing it in a university class. We employed various research analysis methods to derive further insights and implications. Research suggests that the traits of Papert's constructionism learning theory can enhance learner engagement and foster creative problem-solving in computing education when utilizing the MCPS instructional model. This model, as proposed in the study, can effectively serve as a pedagogical approach for delivering semester-long or more extensive computing liberal arts courses tailored for non-majors in college. We anticipate that the outcomes of this study will contribute to the establishment of robust computing education programs in universities, particularly at a time when the demand for computing education among non-majors is increasing, and its significance is growing.

The Impact of AI on Personalized Learning and Educational Analytics

The rapid advancement of artificial intelligence (AI) has revolutionized personalized learning and educational analytics, presenting new opportunities and challenges for adaptive education. This paper explores the impact of AI-driven technologies in creating personalized learning environments by examining how adaptive algorithms and data analytics shape educational experiences. The primary objective of this study is to assess the effectiveness of AI in enhancing learner engagement and outcomes through tailored instructional methods. Utilizing a mixed-method approach, this research gathers quantitative data from learning management systems to analyze engagement metrics, while qualitative insights are derived from interviews with educators and students. The findings indicate that AI-driven personalized learning significantly improves both student motivation and academic performance by adapting content to individual learning needs. Moreover, educational analytics enabled by AI offer educators critical insights into student progress, enabling proactive intervention and support. However, the study also highlights concerns regarding data privacy and the potential over-reliance on AI technologies in educational settings. These findings suggest that while AI holds transformative potential, a balanced approach is necessary to integrate technology with traditional teaching methods to ensure optimal educational outcomes. The study concludes that AI can serve as a powerful tool in enhancing personalized learning and educational analytics, provided that ethical considerations and data security are prioritized.

Code-Switching as a tactical approach to enhance learner engagement with intricate concepts in oral english presentations

The South African English language curriculum states that by the time students reach Grade 7, they should have mastered certain English language skills that enable them to communicate effectively at both personal and educational levels. Therefore, a study was conducted to investigate the potential benefits of code-switching in improving students' oral participation in English classes. The study took place in a primary school in the Malamulele East circuit and involved all fifty Grade 7 students. The students' oral presentations were part of their formative assessment. Researchers used a rigorous methodology, including convenience sampling, to select participants, with the first ten girls and the first ten boys considered for the study. Data was collected during presentations, where each student had an opportunity to present. The qualitative method was used, with audio recordings and observations as data collection tools. Content analysis was employed to analyse the data collected from the Grade 7 students. Based on the students' performance in their oral presentations, code-switching seemed to help them to effectively communicate complex concepts in their primary language when there were no equivalent English words. The findings of this study showed that code-switching enhances students' oral communication skills and makes learning more enjoyable. students achieve their communication goals through code-switching, offering the audience a hopeful outlook on the potential benefits of this approach.

EFL LEARNERS’ PERCEIVED GRAMMAR ACCURACY: AI FEEDBACK TOOLS ASSESSMENT IN FOCUS

This study explores English as a Foreign Language (EFL) learners' perceptions of grammar accuracy and the impact of AI-driven feedback tools on their self-assessment and improvement. Utilizing a mixed-methods approach, data were collected from 54 EFL students majoring in English Language Teaching at Universitas Pancasakti Tegal through quantitative surveys. The findings indicate that a significant majority of participants perceive AI feedback as accurate (57.4% finding it accurate; 29.6% very accurate), useful (53.7% rating it helpful), and satisfying (66.7% satisfied). Furthermore, learners reported that AI tools effectively identify missed errors and enhance their understanding of grammar rules. Despite the positive perceptions, a portion of participants still values traditional feedback, suggesting a potential benefit in a blended approach that combines AI and human interaction. This research underscores the growing importance of integrating AI-driven tools in language education, highlighting their role in enhancing learner engagement and grammar proficiency. Future studies should investigate the long-term effects of AI feedback on language learning outcomes and explore specific features that maximize its effectiveness.

Exploring the Acceptance of Mobile-Assisted Peer Feedback in English Speaking among Chinese EFL Students

Grounded in the Technology Acceptance Model (TAM), this quasi-experimental study examined the use of Mobile-Assisted Peer Feedback (MAPF) tools via the Tencent Mini Programme among Chinese university EFL learners to enhance English speaking proficiency. A mixed-methods approach was employed, using descriptive statistics for questionnaire data and thematic analysis with NVivo for interview data. Sixty non-English major students (CEFR levels A2-B1) were selected through purposive sampling and assigned to three groups: one-way, dialogic, and visualised peer feedback. Results showed positive responses across all modes, with visualised feedback rated highest for usefulness, behavioural intention, and speaking improvement. The study highlights the need to consider technological, individual, and sociocultural factors in mobile-assisted feedback for language learning. Future research should explore long-term effects, application in diverse contexts, and further refinement of mobile-assisted feedback modes to enhance learner engagement. This study contributes to the research on MAPF in English speaking and offers practical insights for educational technology development.

Online Based Learning System (E-Learning) as Distance Learning Method Solutions: A Literature Review

Purpose – This study aims to identify critical success factors influencing the effectiveness of e-learning systems in Indonesia. It examines the advantages of e-learning, such as flexibility and affordability, as well as challenges like the digital divide and varying digital skills. By reviewing literature from diverse sources, the research underscores e-learning's potential as a distance learning solution, emphasizing the need for educational institutions to address challenges to maximize its effectiveness in Indonesian educational settings. Methodology/approach This study employs a literature review method to identify critical success factors affecting the efficacy of e-learning systems in Indonesia. Data is gathered from scientific journals, books, and research reports to analyze the advantages (flexibility, affordability) and challenges (digital divide, varying digital skills) associated with e-learning. Findings The research results show that e-learning has several advantages as a distance learning solution, such as flexibility, accessibility, interactivity and affordability. However, e-learning also has several challenges, such as the digital divide, differences in digital skills, and learning motivation. Novelty/value This study contributes novel insights by specifically examining the factors influencing the effectiveness of e-learning systems within the Indonesian educational context. Unlike broader studies, it focuses on the unique challenges and opportunities present in Indonesia, including the impact of the digital divide, varying levels of digital skills among learners, and specific strategies for enhancing learner engagement and motivation. By conducting a comprehensive literature review drawing from scientific journals, books, and research reports, the study consolidates existing knowledge and identifies gaps that are crucial for informing policy and practice

The Impact of Situational Achievement Goals on Online Learning Behavior: Results from Field Experiments

Given the widespread adoption of online learning, it is crucial to examine how these platforms can effectively maintain learners’ motivation and engagement. This study suggests that the prevalent hands-off, self-directed design approach, which relies heavily on learners’ intrinsic motivation, can potentially neglect crucial aspects like achievement emotions and lead to disengagement. Our findings show that online learning providers can address this issue through motivational interventions based on situational achievement goals. Specifically, our results indicate that among three achievement goals, learning, performance-prove and performance-avoidance, the performance-prove goal is the most effective one in enhancing learner engagement and performance. Additionally, we discuss that a one-size-fits-all course design may not be the most effective strategy for maintaining high engagement. Customizing interventions based on learners’ motivational behavior, prior performance, and social activity can significantly improve engagement. Learners with stronger prior performance benefit more from the performance-prove goal, whereas those with moderate performance levels gain from the performance-avoidance goal, and those with lower prior performance are positively influenced by the learning goal. Socially isolated learners respond best to performance goals.

Designing and Developing an Interactive Syllabus for e-Business Training as Part of Design-Based Research

A syllabus is an arrangement of subjects and a plan for a course of study. It enables visualization of the big picture of the course structure. Syllabi play various valuable functions, including providing information about the communication mechanism between the teacher and students, a planning tool for instructors, a course plan for learners, a source for learning materials or additional readings, a guide that learners can use in their learning both in-class and out-of-class, and it can also serve as a contract or written agreement between learners and instructors or their institutions about learning outcomes. Education today is more interactive, and courses are increasingly multimedia-rich; therefore, syllabi need to be interactive as well. In designing and developing an interactive syllabus for Smart Training for Arabic-speaking Residents on Technology START e-Business Training, learners can visualize the structure of training, search its contents, access frequently asked questions, and explore additional resources. This paper discusses the best strategies used to design START e-Business Training's interactive syllabus and the tools employed to develop it, as a core component of the design-based research DBR methodology. The paper also outlines some challenges in developing an interactive multimedia-rich syllabus, especially with accessibility features, and proposes solutions to overcome these challenges from the perspectives of educators and learning designers. Additionally, the paper explores the iterative process of feedback incorporation and continuous improvement essential to maintaining the syllabus's relevance and effectiveness. By leveraging advanced technological tools and user-centric design principles, the interactive syllabus aims to enhance learner engagement and support diverse learning needs. The discussion includes an evaluation of the pedagogical impacts of the interactive syllabus and its potential to serve as a model for future educational design projects. Furthermore, the implications for broader application in various educational contexts are considered, highlighting the importance of adaptability and innovation in modern educational practices.

The Impact of a Digital Game-Based AI Chatbot on Students’ Academic Performance, Higher-Order Thinking, and Behavioral Patterns in an Information Technology Curriculum

In the age of intelligence, information technology (IT) education has become the focus of attention in the education sector. However, traditional teaching methods fall short in motivating students and fostering higher-order thinking and have difficulty providing a personalized learning experience. Although AI chatbots can provide instant feedback as an innovative teaching tool, it is still challenging to fully enhance learner engagement. Based on this, this study developed a digital game-based AI chatbot system to enhance students’ learning experience through digital game-based learning strategies. This study utilized a quasi-experimental design with the experimental group using a digital game-based AI chatbot and the control group using a traditional AI chatbot. A comparison was made between the two groups concerning student learning performance in IT courses, higher-order thinking (including problem-solving, computational thinking, and creativity), learning motivation, and flow experience. In addition, the behavioral patterns of high-achieving and low-achieving students in the experimental group were analyzed. The results showed that the experimental group was significantly better than the control group in academic performance, problem-solving, computational thinking, learning motivation, and flow experience, but there was no significant difference in creativity tendency. Behavioral pattern analysis showed that high-achieving students in the experimental group showed more systematic learning strategies, while low-achieving students relied more on immediate feedback and external help, but both high- and low-achieving groups were able to actively talk to the AI chatbot and actively explore problem-solving strategies in the digital game. Therefore, AI chatbots based on digital games can be effectively used in IT courses to help students construct knowledge and develop higher-order thinking.

Research on Personalized Teaching Strategies Based on Learner Profiles in a Blended Learning Environment

The effort to implement personalized teaching in blended learning environments faces various challenges, such as the diversity of needs among learners, educators must find ways to identify, understand, and address these differences. This study explores how to construct a learner profile model in blended learning environments, develop personalized teaching strategies based on learner profiles, and evaluate the effectiveness of these strategies. The results demonstrate that personalized teaching strategies significantly enhance learner engagement, autonomy, and academic performance. These strategies have been validated through satisfaction surveys conducted with both teachers and students. The study provides a theoretical framework and practical guidance for personalized teaching while also highlighting challenges in implementation. It is recommended that future research expand sample sizes, integrate quantitative and qualitative research methods, and pay closer attention to learners' personalized needs to further enhance the practicality and effectiveness of personalized teaching strategies.

Unlocking the Potential in a Gamification-Based MOOC: Assessing Autonomous Learning and Self-Directed Learning Behaviors

Massive open online courses (MOOCs) have significantly advanced online education. Yet, MOOC instructors still grapple with the ongoing challenge of low learner engagement while striving to foster autonomous and self-directed learning (SDL). Integrating gamification into these courses could be a promising solution. However, the extent to which gamification shapes autonomous and SDL behaviors within MOOCs remains underexplored. This mixed-methods sequential explanatory study investigates the interplay between MOOC, gamification, autonomous learning, and SDL behaviors to harness the potential of online education. We address this research void by delving into the intricate relationship between gamification and learners’ engagement in autonomous learning and SDL behaviors. Drawing upon self-determination theory (SDT) as a theoretical framework, we scrutinize how gamified MOOCs influence intrinsic motivation and SDL behaviors. Our study utilizes questionnaires and structured interviews to comprehensively examine the users’ experiences within a gamified MOOC. The findings suggest that thoughtfully integrated gamification elements not only enhance learner engagement but also stimulate autonomous learning and SDL behaviors. However, it is crucial to approach the integration of gamification thoughtfully, ensuring a balance that promotes intrinsic motivation without undue reliance on extrinsic motivators. These findings hold significant implications for educators, course designers, and policymakers leveraging gamification to enrich online learning experiences and cultivate self-directed educational pathways. They offer valuable insights for educators, course designers, and policymakers aiming to leverage gamification for enriching online learning experiences and nurturing self-directed educational pathways in MOOCs.

Exploring learner engagement in an Indian ESP classroom

Abstract The lack of interest from learners and their struggles to engage in classroom activities can impede the learning process. In this article, I discuss a framework that has proven effective in my teaching environment, fostering both initial and sustained engagement among learners throughout a course. The CLARA model, adapted from Mercer and Dörnyei (2020), outlines key principles of learner engagement upon which educators can draw to design their teaching approaches, with the aim of enhancing learner engagement and improving learning outcomes. As an illustrative example of this model’s effectiveness, I hope to demonstrate how, as a class, we used CLARA to inform our approach. This involved identifying climate action as a theme for our classroom, challenging ourselves to find ways in which we could work on this topic, and creating climate action comics as an interesting, challenging, and effective output for learner creativity and engagement.

A geriatric game to strengthen older adult healthcare delivery and outcomes.

Serious games enhance learner engagement and knowledge, yet few medical education games target older adults' healthcare. Addressing this gap, we developed Geri-POP (Geriatrics Population Health), focusing on the Age-Friendly Health System (AFHS) framework and Geriatric 4Ms. Geri-POP, a healthcare game is aimed at educating health profession learners about the AFHS framework. Geri-POP employs plan-do-study-act cycles of rapid improvement to apply AFHS principles and explore evidence-based geriatric practices within the game environment while garnering points for insight, trust and outcomes. Faculty and medical students were surveyed for feedback on an alpha version of Geri-POP. Players manage patient panels across three age groups (65-74, 75-84, and 85 years and older) while engaging in plan-do-study-act (PDSA) cycles, applying Geriatric 4Ms (What Matters, Medications, Mentation, and Mobility), and refining strategies based on resource utilization, health outcomes, and real-time feedback. Alpha testing of the game received mixed perceptions on graphics, with faculty endorsing the game for training and integration into the curriculum, while students prioritize academic commitments. Suggestions include enhancing graphics and refining dialogue for a more professional tone. Geri-POP demonstrates the potential of gamifying older adult population health and quality improvement around AFHS. Feedback on a prototype game revealed different attitudes between faculty and students, thus emphasizing the importance of game development as an iterative process that accounts for educator and learner-centric needs. A future consideration is whether the game informs user's clinical practices and changes healthcare outcomes for older adults.