Over the last decade, much attention has been paid the effects of self-regulated learning (SRL) in massive open online courses (MOOCs). However, a systematic understanding of the topic is rather limited. This study drew a general outline of important factors affecting the SRL in MOOCs. The study summarizes two important SRL modes as the theoretical basis. Then, the study analyses the motivational, metacognitive and cognitive regulation strategies, and behavioural regulation strategies which affect SRL in MOOCs. Finally, suggestions for future research in MOOCs are offered.
<p class="0abstract"><strong>Abstract—</strong>Increasingly, blended learning courses are being offered in schools throughout China. One of the key factors influencing the effectiveness of blended learning is students’ acceptance. The researchers proposed six predictive dimensions that contribute to students’ acceptance for blended learning, namely, perceived usefulness, perceived ease of use, learning climate, attitudes towards online learning, attitudes towards classroom learning and personal charm of the instructor. One existing questionnaire is adapted, together with the interview data with university undergraduates, generating 63 items for the initial questionnaire. The first draft of the scale was distributed to 180 university students in Hubei Province, China. Data collected were analyzed by SPSS 24.0. Eleven items were deleted based on the Exploratory Factor Analysis results. Then the second pilot was done to purify the scale further. 191 questionnaires were distributed, and the data collected were processed by EFA again. One item was removed in the second pilot and 51 items were reserved for the final version of the scale. The final adapted version of Blended Learning Acceptance Scale (BLAS) is reported to possess high levels of reliability and validity. The BLAS can be used to investigate Chinese University students’ acceptance for blended learning so that better opinions can be put forward to improve the effectiveness of blended learning.</p>
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