Learning Optimal and Personalized Knowledge Component Sequencing Policies
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 13356 LNCS, Page: 338-342
2022
- 1Citations
- 7Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
One of the goals of adaptive learning systems is to realize adaptive learning sequencing by optimizing the order of learning materials to be presented to different learners. This paper proposes a novel approach to recommending optimal and personalized learning sequences for learners taking an online course based on the contextual bandit framework where the background knowledge of the learners is the context. To improve learning efficiency and performance of learners, the adaption engine of such an adaptive learning system can select an optimal learning path for a learner by continually evaluating the learners’ progress as the course advances. To overcome the complexity of learning path recommendation due to the large number of knowledge components, we use the ‘divide-and-conquer’ approach to modeling the domain and designing the sequence adaptation algorithm. Also, the adaptation engine can dynamically replan the learning path for a learner if her/his performance is worse than expected. Finally, our approach can improve over time by learning from the experience of previous learners who adopted recommended sequences.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85135905873&origin=inward; http://dx.doi.org/10.1007/978-3-031-11647-6_65; https://link.springer.com/10.1007/978-3-031-11647-6_65; https://dx.doi.org/10.1007/978-3-031-11647-6_65; https://link.springer.com/chapter/10.1007/978-3-031-11647-6_65
Springer Science and Business Media LLC
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