Adaptive learning path recommendation model forexamination-oriented education

Adaptive learning paths provide individual learning objectives that best match a learner's characteristics. This isespecially 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 attractedsignificant interest. However, most current research only focuses on providing learners with adaptive objects andsequences 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 timepossible, which is a typical scenario in exanimation-oriented education systems. This study aims to solve thisproblem by introducing a new approach that builds on existing methods. First, the eight properties in Gardner'smultiple intelligence theory are introduced into the present knowledge and learner models to define the relationshipbetween learning objects (LOs) and learners, thereby improving recommendation accuracy rates. Then, a noveladaptive learning path recommendation model is presented where viable knowledge topologies, knowledge bases andthe previously-established properties relating to a learner's ability are combined by Dempster-Shafer (D-S) evidencetheory. A series of practical experiments were performed to assess the approach's adaptability, the appropriatenessof the selected evidence and the effectiveness of the recommendations. In the results, it was found that the proposedlearning path recommendation model helped learners learn the most important elements and obtain superior testgrades when confronted with limited time for learning.