Applying Petri-Net to Construct Knowledge Graphs for Adaptive Learning Diagnostics and Learning Recommendations

• Published in: Jiao yu ke xue yan jiu qi kan Vol. 66; no. 3; p. 61
• Main Authors: Jiann-Yun Dai, Kuo-Liang, Yeh, Man-Ting Kao, Yu-Hsi, Yuan, Min-Wen, Chang
• Format: Journal Article
• Language: Chinese
• Published: Taipei National Taiwan Normal University, Dept of Education 01.09.2021
• Subjects: Academic Achievement; Adaptive learning; Between-subjects design; Case Studies; Cognition & reasoning; Cognitive Structures; Constructivism (Learning); Content Validity; Correlation; Data Analysis; Delphi method; Delphi Technique; Electricity; Graphs; Inferences; Instructional Effectiveness; Knowledge representation; Learning Theories; Manufacturing Industry; Novices; Opinions; Personalized learning; Recommender systems; Remedial Instruction; Students; Validity; Taiwan
• ISSN: 2073-753X
• DOI: 10.6209/JORIES.202109_66(3).0003

Because of the increasing heterogeneity among students in classes and schools, determining a student’s basic learning status and ability in each subject and tailoring instruction or adapting remedial teaching to a student’s needs and characteristics have become challenging, especially for those students with learning disadvantages. According to Skinner’s behavioral learning theory (as cited in Gregory, 1987), differences in a student’s learning experiences (such as in understanding concepts) lead to considerable disparities in future learning. Drastic differences in internal cognition and concept structure may exist even among students with the same traditional learning achievements (i.e., scores) (Yu & Yu, 2006). Furthermore, the differences in concept cognition structure between experts and novices may be discoverable by analyzing similarities in students’ conceptual understandings, relationships, or psychological metrics (Brand-Gruwel et al., 2005; Hsu et al., 2012). Cognitive diagnostic models, such as th