Abstract

Autonomous learning is crucial for students’ academic achievements, however, there lacks a structure-validated questionnaire to measure the optimal time for autonomous learning with different learning contents. Based on the previous investigations, we have designed a matrix of 49 items based on seven learning contents and seven time periods of a day to measure the optimal time for autonomous learning and invited 305 Chinese university students to answer the matrix.  Through both exploratory and confirmatory factor analyses, we have developed an Optimal Time for Learning Questionnaire with a satisfactory model structure of five factors (7, 6, 6, 6, 5 items per factor respectively) namely Noon, Late Night, Nightfall, Morning and Afternoon.  The internal reliabilities of these factors were acceptable, and their inter-correlations were significant, albeit in low or medium levels.  The Optimal time for Learning Questionnaire may help students find their optimal learning efficiency individually.

Keywords: Confirmatory factor analysis; Optimum time for learning; Principal component analysis; Structure-validated questionnaire