The Journal of China Universities of Posts and Telecommunications ›› 2022, Vol. 29 ›› Issue (4): 77-88.doi: 10.19682/j.cnki.1005-8885.2022.2021

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Adaptive learning path recommendation model for examination-oriented education

Wang Jian, Qiao Kuoyuan, Yuan Yanlei, Liu Xiaole, Yang Jian   

  1. 1. School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
    2. Research and Development Department, Tongfang Knowledge Network Technology Company Limited,Beijing 100192, China
    3. The Teaching Affaires Office, Zhengzhou Hi-Tech Zone Langyuehui Foreign Language Middle School, Zhengzhou 450001, China
    4. Yunnan Key Laboratory of Smart City in Cyberspace Security, Yuxi Normal University, Yuxi 653100, China
  • Received:2021-07-06 Revised:2021-08-31 Accepted:2022-04-01 Online:2022-08-31 Published:2022-08-31
  • Contact: Wang Jian, E-mail:
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61972133), Plan for “1125" Innovation
    Leading Talent of Zhengzhou City (2019), the Opening Foundation of Yunnan Key Laboratory of Smart City in
    Cyberspace Security (202105AG070010).

Abstract: Adaptive learning paths provide individual learning objectives that best match a learner's characteristics. This is especially helpful when learners need to balance limited available learning time and multiple learning objectives. The automatic generation of personalized learning paths to improve learning efficiency has therefore attracted significant interest. However, most current research only focuses on providing learners with adaptive objects and sequences according to their own interests or learning goals given a normal amount of time or ordinary conditions. There is little research that can help learners to obtain the most important knowledge for a test in the shortest time possible, which is a typical scenario in exanimation-oriented education systems. This study aims to solve this problem by introducing a new approach that builds on existing methods. First, the eight properties in Gardner's multiple intelligence theory are introduced into the present knowledge and learner models to define the relationship between learning objects (LOs) and learners, thereby improving recommendation accuracy rates. Then, a novel adaptive learning path recommendation model is presented where viable knowledge topologies, knowledge bases and the previously-established properties relating to a learner's ability are combined by Dempster-Shafer (D-S) evidence theory. A series of practical experiments were performed to assess the approach's adaptability, the appropriateness of the selected evidence and the effectiveness of the recommendations. In the results, it was found that the proposed learning path recommendation model helped learners learn the most important elements and obtain superior test grades when confronted with limited time for learning.

Key words: adaptive learning, learning path recommendation, Dempster-Shaferevidence theory, knowledge model, learner model

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