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

• URL: http://arxiv.org/abs/2512.18383v1
• Date: Sat, 20 Dec 2025 14:51:54 GMT
• Title: Adaptive Learning Mechanisms for Learning Management Systems: A Scoping Review and Practical Considerations
• Authors: Sebastian Kucharski, Iris Braun, Gregor Damnik, Matthias Wählisch,
• Abstract summary: We conducted a systematic review of the literature addressing the following research questions.<n>How are adaptive learning mechanisms integrated into LMSs system-independently?<n>We identified 61 relevant approaches and extracted eight variables for them through in-depth reading.
• Score: 1.0616273526777913
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background: Traditional Learning Management Systems (LMS) usually offer a one-size-fits-all solution that cannot be customized to meet specific learner needs. To address this issue, adaptive learning mechanisms are integrated either by LMS-specific approaches into individual LMSs or by system-independent mechanisms into various existing LMSs to increase reusability. Objective: We conducted a systematic review of the literature addressing the following research questions. How are adaptive learning mechanisms integrated into LMSs system-independently? How are they provided, how are they specified, and on which database do they operate? A priori, we proposed three hypotheses. First, the focused adaptive learning mechanisms, 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 for gray literature to identify 3370 papers published between 2003 and 2023 for screening, and conducted a snowball search. Results: We identified 61 relevant approaches and extracted eight variables for them through in-depth reading. The results support the proposed hypotheses. Conclusion: Based on the challenges raised by the proposed hypotheses with regard to the relevant user groups, we defined two future research directions - developing a conceptual model for the system-independent specification of adaptive learning mechanisms and a corresponding architecture for the provision, and supporting the authoring of these mechanisms by users with low technical expertise.

Related papers

• Part I: Tricks or Traps? A Deep Dive into RL for LLM Reasoning [53.85659415230589]
  This paper systematically reviews widely adoptedReinforcement learning techniques.<n>We present clear guidelines for selecting RL techniques tailored to specific setups.<n>We also reveal that a minimalist combination of two techniques can unlock the learning capability of critic-free policies.
  arXiv  Detail & Related papers  (2025-08-11T17:39:45Z)
• Between Randomness and Arbitrariness: Some Lessons for Reliable Machine Learning at Scale [2.50194939587674]
  dissertation: quantifying and mitigating sources of arbitiness in ML, randomness in uncertainty estimation and optimization algorithms, in order to achieve scalability without sacrificing reliability. dissertation serves as an empirical proof by example that research on reliable measurement for machine learning is intimately bound up with research in law and policy.
  arXiv  Detail & Related papers  (2024-06-13T19:29:37Z)
• Classification, Challenges, and Automated Approaches to Handle Non-Functional Requirements in ML-Enabled Systems: A Systematic Literature Review [10.09767622002672]
  We propose a systematic literature review targeting two key aspects: the classification of the non-functional requirements investigated so far, and the challenges to be faced when developing models in ML-enabled systems. We report that current research identified 30 different non-functional requirements, which can be grouped into six main classes. We also compiled a catalog of more than 23 software engineering challenges, based on which further research should consider the nonfunctional requirements of machine learning-enabled systems.
  arXiv  Detail & Related papers  (2023-11-29T09:45:41Z)
• Resilience of Deep Learning applications: a systematic literature review of analysis and hardening techniques [3.265458968159693]
  The review is based on 220 scientific articles published between January 2019 and March 2024. The authors adopt a classifying framework to interpret and highlight research similarities and peculiarities.
  arXiv  Detail & Related papers  (2023-09-27T19:22:19Z)
• Bias and Fairness in Large Language Models: A Survey [73.87651986156006]
  We present a comprehensive survey of bias evaluation and mitigation techniques for large language models (LLMs) We first consolidate, formalize, and expand notions of social bias and fairness in natural language processing. We then unify the literature by proposing three intuitive, two for bias evaluation, and one for mitigation.
  arXiv  Detail & Related papers  (2023-09-02T00:32:55Z)
• Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach [77.61131357420201]
  We conduct a thorough and rigorous study on fairness disparities in peer review with the help of large language models (LMs) We collect, assemble, and maintain a comprehensive relational database for the International Conference on Learning Representations (ICLR) conference from 2017 to date. We postulate and study fairness disparities on multiple protective attributes of interest, including author gender, geography, author, and institutional prestige.
  arXiv  Detail & Related papers  (2022-11-07T16:19:42Z)
• Learning Physical Concepts in Cyber-Physical Systems: A Case Study [72.74318982275052]
  We provide an overview of the current state of research regarding methods for learning physical concepts in time series data. We also analyze the most important methods from the current state of the art using the example of a three-tank system.
  arXiv  Detail & Related papers  (2021-11-28T14:24:52Z)
• Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning [16.998805882711864]
  Two established approaches to engineer adaptive systems are architecture-based adaptation that uses a Monitor-Analysis-Planning-Executing loop, and control-based adaptation that relies on principles of control theory (CT) to realize adaptation. We are concerned with the question of how these approaches are related with one another and whether combining them and supporting them with machine learning can produce better adaptive systems. We motivate the combined use of different adaptation approaches using a scenario of a cloud-based enterprise system and illustrate the analysis when combining the different approaches.
  arXiv  Detail & Related papers  (2021-03-19T15:00:08Z)
• Applying Machine Learning in Self-Adaptive Systems: A Systematic Literature Review [15.953995937484176]
  There is currently no systematic overview of the use of machine learning in self-adaptive systems. We focus on self-adaptive systems that are based on a traditional Monitor-Analyze-Plan-Execute feedback loop (MAPE) The research questions are centred on the problems that motivate the use of machine learning in self-adaptive systems, the key engineering aspects of learning in self-adaptation, and open challenges.
  arXiv  Detail & Related papers  (2021-03-06T13:45:59Z)
• Leveraging Expert Consistency to Improve Algorithmic Decision Support [62.61153549123407]
  We explore the use of historical expert decisions as a rich source of information that can be combined with observed outcomes to narrow the construct gap. We propose an influence function-based methodology to estimate expert consistency indirectly when each case in the data is assessed by a single expert. Our empirical evaluation, using simulations in a clinical setting and real-world data from the child welfare domain, indicates that the proposed approach successfully narrows the construct gap.
  arXiv  Detail & Related papers  (2021-01-24T05:40:29Z)
• Self-organizing Democratized Learning: Towards Large-scale Distributed Learning Systems [71.14339738190202]
  democratized learning (Dem-AI) lays out a holistic philosophy with underlying principles for building large-scale distributed and democratized machine learning systems. Inspired by Dem-AI philosophy, a novel distributed learning approach is proposed in this paper. The proposed algorithms demonstrate better results in the generalization performance of learning models in agents compared to the conventional FL algorithms.
  arXiv  Detail & Related papers  (2020-07-07T08:34:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.