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Open research questions in Online Learning and Analytics

1,106 unresolved questions extracted from the limitations and future-work sections of 3,369 Online Learning and Analytics papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • Students' misuse of AI tools. The potential erosion of students' critical thinking skills. The lack of professional development and clear guidelines for teachers.

    Opportunities for learning amidst concerns of misuse: Secondary teachers' uses and perceptions of artificial intelligence · 2026 · DOI

  • the study does not model formal differential privacy or cryptographic anonymization mechanisms, - the computational configuration was intentionally designed to remain lightweight

    Privacy-aware learning analytics for exploratory behavior through federated and explainable AI · 2026 · DOI

  • exploring the application of the framework to other educational platforms, - investigating the use of other machine learning models for federated learning

    Privacy-aware learning analytics for exploratory behavior through federated and explainable AI · 2026 · DOI

  • Further research could explore the impact of lecturer personality on learner engagement, - Research could investigate the effects of different video formats on cognitive engagement, - Studies could examine the role of social cues in shaping learner perceptions

    MOOC Videos in the Age of Streaming: Learners’ Preferences and Perceptions · 2026 · DOI

  • The study identifies a gap in understanding how learners perceive affect, cognition, and social cues in MOOC video lectures. The study aims to address this gap by analyzing 2,239 reviews across 25 courses using a qualitative approach.

    MOOC Videos in the Age of Streaming: Learners’ Preferences and Perceptions · 2026 · DOI

  • The need to prepare students for the inevitable disruptions AI will cause in all education professions. The challenge of developing awareness of ethical issues around the use of GenAI. The need to support faculty in developing their knowledge and skills to teach in an AI-rich environment.

    Educating the Educators About AI: Strategic Initiatives in a Graduate School of Education · 2026 · DOI

  • The need for schools of education to prepare students to manage AI in their future careers. The lack of guidance on how to respond to GenAI in education. The need for exploratory research to identify the potential and risks of GenAI applications in education.

    Educating the Educators About AI: Strategic Initiatives in a Graduate School of Education · 2026 · DOI

  • limited availability of relevant e-learning datasets, - participation decreased across stages due to the voluntary nature of the MOOC, - small sample sizes in some measurements

    Reducing learner disorientation in MOOC learning paths: Evidence from a hybrid recommender system · 2026 · DOI

  • exploring the application of the recommender system in various learning contexts, - investigating the impact of the system on learner behavior and outcomes in different domains

    Reducing learner disorientation in MOOC learning paths: Evidence from a hybrid recommender system · 2026 · DOI

  • Future research should incorporate objective learning analytics - Future research should use longitudinal approaches to examine how the relationships evolve over time

    Student-content interaction and learning outcomes in MOOCs: The moderating role of self-efficacy · 2026 · DOI

  • Prior MOOC research has recognized the importance of learning experiences, but has not fully explored the moderating role of self-efficacy in the relationship between student-content interaction and learning outcomes. The study addresses this gap by using structural equation modeling to test main and interaction effects.

    Student-content interaction and learning outcomes in MOOCs: The moderating role of self-efficacy · 2026 · DOI

  • The challenge of analyzing longitudinal educational data with correlated predictors. The difficulty of selecting appropriate penalties for ridge regression. The need to address distribution shift and transport evaluation in applying models to new datasets.

    Benchmarking Observable Platform Dynamics Beyond Persistence in Three Open Educational Datasets · 2026 · DOI

  • The data are observational and deidentified. Unmeasured learner characteristics, instructional events, and selection processes remain. The study did not test generative-AI effects or causal educational mechanisms. Formal measurement invariance was neither assessed nor established.

    Benchmarking Observable Platform Dynamics Beyond Persistence in Three Open Educational Datasets · 2026 · DOI

  • High school students, particularly those learning English as a Foreign Language, often encounter significant challenges when participating in English-medium instruction MOOCs. The study highlights the need for effective strategies to develop students' intercultural communication competencies. The study also notes the importance of designing online lessons that are accessible and engaging for high school students.

    A Tale of Two Platforms: Decoding the Digital Footprints of High School Students in Bilingual Scaffolded MOOCs · 2025 · DOI

  • The application and effectiveness of MOOCs for high school students, especially EFL learners, remain underexplored. There is a lack of understanding of how high school students' attitudes and perceptions influence course completion outcomes in bilingual scaffolded MOOCs. The study aims to address this gap by investigating the impact of bilingual scaffolded MOOCs on high school students' learning outcomes.

    A Tale of Two Platforms: Decoding the Digital Footprints of High School Students in Bilingual Scaffolded MOOCs · 2025 · DOI

  • The paper identifies the challenge of determining whether AI is an enabler of deep learning or inadvertently induces cognitive dependency. It highlights the risk that AI may stifle more judgmental and analytical skills (higher-order thinking). The study also emphasizes the need to balance the integration of AI in education to avoid overreliance on AI suggestions and promote effortful engagement.

    The cognitive paradox of AI in education: between enhancement and erosion · 2025 · DOI

  • over-reliance on AI could compromise autonomy, - excessive dependence on AI could impair the skill to develop independent critical thinking skills, - the need for human interaction for enhanced concept formation and engagement

    The cognitive paradox of AI in education: between enhancement and erosion · 2025 · DOI

  • The psychological mechanisms through which AI-driven adaptive learning platforms enhance educational outcomes remain insufficiently understood. There is a need to examine the sequential relationships between self-regulated learning and learning engagement from a psychological perspective.

    Self-regulated learning and engagement as serial mediators between AI-driven adaptive learning platform characteristics and educational quality: a psychological mechanism analysis · 2025 · DOI

  • The study identifies the challenge of designing user-friendly AI technologies that can be easily adopted by lifelong learners. The research highlights the need to promote the practical application of AI-generated knowledge to enhance individual impact and usage. The study notes the challenge of building users' confidence in their ability to learn with AI, which is crucial for maximizing the benefits of AI-powered e-learning.

    AI-Powered E-Learning for Lifelong Learners: Impact on Performance and Knowledge Application · 2024 · DOI

  • The study identifies a gap in the understanding of the factors that influence the adoption and effectiveness of AI-powered e-learning among lifelong learners. There is a need to investigate the relationships between technicality, knowledge application, self-efficacy in AI learning, individual impact, and usage.

    AI-Powered E-Learning for Lifelong Learners: Impact on Performance and Knowledge Application · 2024 · DOI

  • The challenge of making observations at scale and in a regular manner. The need for methods to measure and report on teachers' digital maturity.

    Contribution to Teaching Analytics: Measuring Teachers' Digital Maturity With Large‐Scale VLE Logs Dataset · 2026 · DOI

  • Abstract Evaluating teaching quality in higher education remains a critical but complex task due to the prevalence of subjective assessment methods, inconsistent data standards, and limited real-time feedback mechanisms.

    SDCS-ALBPNN model for data-driven teaching quality evaluation in higher education · 2026 · DOI

  • However, systematic assessments of the global research landscape and development trends in the field remain insufficient.

    A bibliometric analysis of artificial intelligence based predictive analytics in education · 2026 · DOI

  • Limited sample size may affect the depth and breadth of the findings; therefore, future research is needed to increase the population and expand the findings by involving different stakeholders.

    Exploring teachers’ perceptions of integrating artificial intelligence (AI) in STEM education using the TPACK framework: an exploratory case study · 2025 · DOI

  • However, the impact of cognitive load on learners' satisfaction has not been thoroughly explored.

    Investigation of the impact of cognitive load on EFL learners’ satisfaction with MOOCs: the mediating role of expectation confirmation and perceived usefulness · 2025 · DOI

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1,106 open questions have been extracted from the limitations and future-work passages of 3,369 Online Learning and Analytics papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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