Enhancement of student learning performance using personalized diagnosis and remedial learning system

Although conventional student assessments are extremely convenient for calculating student scores, they do not conceptualize how students organize their knowledge. Therefore, teachers and students rarely understand how to improve their future learning progress. The limitations of conventional testing methods indicate the importance of accurately assessing and representing student knowledge structures. The personalized diagnosis and remedial learning system (PDRLS) proposed in this study enhances the effectiveness of the Pathfinder network by providing remedial learning paths for individual learners based on their knowledge structure. The sample was 145 students enrolled in introductory JAVA programming language courses at a Central Taiwan technology university. The experimental results demonstrate that learners who received personalized remedial learning guidance via PDRLS achieved improved learning performance, self-efficacy, and PDRLS use intention. The experimental results also indicated that students with lower knowledge level gain more benefits from the PDRLS than those with higher level of knowledge and that field dependence (FD) students obtain a greater benefit from PDRLS than field independence (FI) students do.

Research highlights
► This research provided an alternative assessment technique to locate specific knowledge status of individual learners for larger groups of students.
► This study also developed a Web-based intelligent personalized diagnosis and remedial learning system (PDRLS) that can assess student knowledge structure and diagnose student misconceptions, and finally, to provide individual learners with remedial learning paths. The PDRLS provides more precise and detailed information representing the conceptual properties of student misconceptions and more guidance for optimizing their future learning progress.
► Besides knowledge structure, this study also discovered that two human factors, knowledge level and cognitive style, may influence learning effectiveness. Further analyses indicated that students with low knowledge levels obtain a greater benefit from PDRLS than students with high knowledge levels do and that PDRLS is more beneficial for FD students than for FI students.
► This study demonstrated that multiple human factors should be considered when developing personalized Web-based learning systems. These experimental results can be used to construct robust user models for customized Web-based learning systems that accommodate individual preferences and abilities.