research-article

Exercises Recommendation in Adaptive Learning System

Authors:

Zuoxi Jin,

Kun Ma,

Kun Liu,

Ke JiAuthors Info & Claims

ICBDT '19: Proceedings of the 2nd International Conference on Big Data Technologies

Pages 97 - 100

https://doi.org/10.1145/3358528.3358556

Published: 28 August 2019 Publication History

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Abstract

The adaptive learning system develops gradually, but most attention is paid to the construction of student model and domain model. In this paper, a recommendation algorithm based on students' current knowledge level is proposed to match suitable exercises and avoid homogenization of learning content for all students, for the purpose of achieving so-called "adaptative". It is worth noting that the learning system recommendation is different from the general recommendation. Not only the method, the evaluation standard of recommendation result is also different. We should not simply recommend to students the exercises they must or must not mastered, but recommend to them the learning resources they should have within the range of their abilities according to the theory of proximal development zone. We also use the bayesian knowledge tracing model to judge students' mastery of knowledge as the evaluation standard of this algorithm.

References

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Cited By

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Aouifi HHajji MEs-Saady YDouzi H(2024)Video-Based Learning Recommender Systems: A Systematic Literature ReviewIEEE Transactions on Learning Technologies10.1109/TLT.2023.331339117(485-497)Online publication date: 2024
https://doi.org/10.1109/TLT.2023.3313391
Lin LWang F(2023)Adaptive Learning System Based on Knowledge GraphProceedings of the 9th International Conference on Education and Training Technologies10.1145/3599640.3599647(1-7)Online publication date: 21-Apr-2023
https://dl.acm.org/doi/10.1145/3599640.3599647
Li ZXu F(2022)A personalized exercise recommendation method for teaching objectivesInternational Conference on Computer Application and Information Security (ICCAIS 2021)10.1117/12.2637387(20)Online publication date: 25-May-2022
https://doi.org/10.1117/12.2637387
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Index Terms

Exercises Recommendation in Adaptive Learning System
1. Applied computing
  1. Education
    1. E-learning

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Published In

ICBDT '19: Proceedings of the 2nd International Conference on Big Data Technologies

August 2019

382 pages

ISBN:9781450371926

DOI:10.1145/3358528

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Shandong Univ.: Shandong University

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Association for Computing Machinery

New York, NY, United States

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Published: 28 August 2019

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ICBDT2019

ICBDT2019: 2019 2nd International Conference on Big Data Technologies

August 28 - 30, 2019

Jinan, China

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Aouifi HHajji MEs-Saady YDouzi H(2024)Video-Based Learning Recommender Systems: A Systematic Literature ReviewIEEE Transactions on Learning Technologies10.1109/TLT.2023.331339117(485-497)Online publication date: 2024
https://doi.org/10.1109/TLT.2023.3313391
Lin LWang F(2023)Adaptive Learning System Based on Knowledge GraphProceedings of the 9th International Conference on Education and Training Technologies10.1145/3599640.3599647(1-7)Online publication date: 21-Apr-2023
https://dl.acm.org/doi/10.1145/3599640.3599647
Li ZXu F(2022)A personalized exercise recommendation method for teaching objectivesInternational Conference on Computer Application and Information Security (ICCAIS 2021)10.1117/12.2637387(20)Online publication date: 25-May-2022
https://doi.org/10.1117/12.2637387
Chettaoui NAtia ABouhlel M(2021)Predicting Student Performance in an Embodied Learning Environment2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC)10.1109/MIUCC52538.2021.9447603(1-7)Online publication date: 26-May-2021
https://doi.org/10.1109/MIUCC52538.2021.9447603

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