Adaptive learning path recommendation model for examination‒oriented education

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. © 2022, Beijing University of Posts and Telecommunications. All rights reserved.