Adaptive Learning Mechanisms for Learning Management Systems: A Scoping Review and Practical Considerations

Abstract

Background: Traditional Learning Management Systems (LMSs) usually offer one-size-fits-all solutions that cannot be customized to meet specific learner needs. To address this issue, adaptive learning mechanisms (ALMs) are integrated either by LMS-specific approaches into individual LMSs or by system-independent approaches into various existing LMSs. Objective: We conducted a scoping review of the literature addressing the following research questions. How are ALMs integrated into LMSs system-independently? How are they provided, how are they specified, and what data do they process as the basis for adaptation? What are the educational surrounding conditions and outcomes of their application? A priori, we proposed three hypotheses. First, the focused ALMs rarely consider existing data. Second, they usually support a limited number of data processing mechanisms. Third, the users intended to provide them, are rarely given the ability to adapt how they work. Furthermore, to investigate the differences between system-independent and LMS-specific approaches, we also included the latter. Design: We used Scopus, Web of Science, and Google Scholar to identify 3370 papers, excluding duplicates, that had been published between 2003 and 2023 for screening, and conducted a snowball search. Results: We identified 61 relevant approaches and extracted eleven variables for them through in-depth reading. The results support the proposed hypotheses. Conclusion: Based on the challenges raised by these hypotheses with regard to the relevant target user groups, we defined two future research directions - developing a conceptual model for the system-independent specification of ALMs and a corresponding architecture for the provision, and supporting the authoring of these mechanisms.