Self-regulated learning in an e-learning environment in a Malaysian University
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This study aimed to conceptualise, design and validate an instrument for measuring self-regulated learning in the e-learning environment. It examined how students at Univerisiti Sains Malaysia (USM) self-regulate their learning in an e-learning environment. It investigated how learners monitor their reflections, learning strategies, metacognitive awareness, intrinsic motivation, extrinsic motivation and amotivation in their learning activities.A conceptual model of self-regulated learning in an e-learning environment was developed from a review of pertinent literature. This model was then used to develop a student self-report rating scale instrument, the data from which were scrutinised by the Statistical Package for the Social Sciences (IBM -SPSS), and Rasch Unidimensional Measurement Models (RUMM2030).Quantitative research methodology was adopted based on deductive approach. Thus, convenience sampling was employed for university students who volunteered to participate anonymously.Factor analysis identified 28 factors and after data reduction, eight „natural‟ groupings were found. The factors were Ability and Effort Beliefs, Reflection, Introjected Regulation, Task Character, Strategic Use, Value of Task, Stimulus Response and Recognition. Data from the respective items comprising the eight factors were then analysed using RUMM20303 to ascertain whether the factors could be measured. This showed that measures had been constructed. Data were also examined for the effects of categorical variables such as student gender, age, year of study, ethnicity and school.The findings of this study provide useful information for university instructional technologists, software developers, students, facilitators, administrators and researchers who are interested in self-regulated learning and ways in which information and communication e-learning technology can enhance and facilitate learning. The study is also significant because it used a highly contemporary method for instrument development and data analysis – the Rasch model.
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