Novel framework for learning performance prediction using pattern identification and deep learning

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Detalles Bibliográficos
Publicado en:	Data Technologies and Applications vol. 59, no. 1 (2025), p. 111-133
Autor principal:	Cheng-Hsiung, Weng
Otros Autores:	Cheng-Kui, Huang
Publicado:	Emerald Group Publishing Limited
Materias:	Teaching Students Deep learning Performance prediction Data mining Depth profiling Educational software Modules Efficiency Performance measurement Prediction models Education Neural networks Online instruction Design Algorithms Identification Learning Frame analysis Information retrieval Academic achievement Learning Analytics Web Based Instruction Academic Failure Prior Learning Learning Processes Computers Educational Technology Cooperative Learning Student Improvement Electronic Learning Adaptive Testing Instructional Material Evaluation
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Descripción
Resumen:	PurposeEducational data mining (EDM) discovers significant patterns from educational data and thus can help understand the relations between learners and their educational settings. However, most previous data mining techniques focus on prediction of learning performance of learners without integrating learning patterns identification techniques.Design/methodology/approachThis study proposes a new framework for identifying learning patterns and predicting learning performance. Two modules, the learning patterns identification module and the deep learning prediction models (DNN), are integrated into this framework to identify the difference of learning performance and predicting learning performance from profiles of students.FindingsExperimental results from survey data indicate that the proposed identifying learning patterns module could facilitate identifying valuable difference (change) patterns from student’s profiles. The proposed learning performance prediction module which adapts DNN also performs better than traditional machine techniques in prediction performance metrics.Originality/valueTo our best knowledge, the framework is the only educational system in the literature for identifying learning patterns and predicting learning performance.
ISSN:	2514-9288 2514-9318 0033-0337 1758-7301
DOI:	10.1108/DTA-09-2023-0539
Fuente:	ABI/INFORM Global