An adaptive learning path planning system based on a recommender system and knowledge space

Abstract

This study aims to apply the subject area knowledge space knowledge state and recommender system to predict the learning situation of students in mathematics knowledge units and plan adaptive learning paths to replace the traditional single path teaching method; thus the learning paths can correspond and be adaptive to the differences of students The knowledge space is jointly defined by the teachers from a girls’ senior high school and a boys’ senior high school in Tainan Using Mathematics knowledge space a set of learning paths with prerequisite can be generated This study administers a pretest including 10 different knowledge units as the learning state and then provides the recommender system to predict the performance of other knowledge units of students The recommender system selects a user-based collaborative filtering (UBCF) algorithm that predicts student performance based on students’ learning state and other students’ learning data in the past to plan suitable learning paths corresponding to the knowledge space In this study a total of 108 students from the remedial classes of two high schools were recruited They were individually divided into two classes implementing the traditional learning path and the adaptive learning path The high school mathematics knowledge spaces of 1st and 2nd semesters were developed All participants used online learning platform had the same learning time and took the pretest and posttest The research result indicated that the posttest scores are both significantly higher than the pretest scores in both traditional learning path and adaptive learning path And the posttest scores of the adaptive learning path class in the boys’ senior high school are significantly higher than those in the traditional learning path class Besides this study also used the survey of technology acceptance model the learning motivation questionnaire and the interview to collect student feedback The result found that the changes in learning motivation are significantly positively correlated with the posttest scores If the student’s preferred learning strategy is consistent with the adaptive learning path planning his/her learning motivation and academic achievement are enhanced Future research can expand the number of students using the platform collect more learning data used by students for analysis and make the predictions of the recommendation system more accurate and achieve the efficiency and effectiveness of personalized knowledge

Date of Award	2019
Original language	English
Supervisor	Jeen-Shing Wang (Supervisor)