Construction of Evaluation Index System for the Effectiveness of Deep Learning among College Students in Blended Learning Mode

Abstract

Abstract: Deep learning has become the core content of the education informatization 2.0 era. Blended learning fully integrates the advantages of online and offline learning, providing comprehensive and full process support for deep learning, including technology, context, resources, interaction, and learning evaluation. This study draws on classic deep learning performance evaluation indicators from both domestic and foreign sources, fully considers the characteristics of blended learning mode, and constructs an evaluation index system for deep learning performance of college students under blended learning mode. Through expert consultation questionnaires, the importance comparison scores of primary indicators and the importance ranking results of secondary indicators are obtained. Then, the Analytic Hierarchy Process (AHP) is used to determine the weights of each indicator. This indicator system can provide reference for the practice of evaluating the effectiveness of deep learning for college students.

Key words: Blended learning mode, Deep learning, AHP method, Evaluation indicator system, Judgment matrix, Consistency check

CLC Number:

G642

LIU Kefang. Construction of Evaluation Index System for the Effectiveness of Deep Learning among College Students in Blended Learning Mode[J]. The Theory and Practice of Innovation and Enntrepreneurship, 2024, 7(16): 5-7.

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