Journal of Computing in Higher Education最新文献

This study investigated the relations between students' self-reported perceptions of the blended learning environment, their observed online learning strategies, and their academic learning outcomes. The participants were 310 undergraduates enrolled in an introductory course on computer systems in an Australian metropolitan university. A Likert-scale questionnaire was used to examine students' perceptions. The digital traces recorded in a bespoke learning management system were used to detect students' observed online learning strategies. Using the data mining algorithms, including the Hidden Markov Model and an agglomerative hierarchical sequence clustering, four types of online learning strategies were found. The four strategies not only differed in the number of online learning sessions but also showed differences in the proportional distribution with regard to different online learning behaviors. A one-way ANOVA revealed that students adopting different online learning strategies differed significantly on their final course marks. Students who employed intensive theory application strategy achieved the highest whereas those used weak reading and weak theory application scored the lowest. The results of a cross-tabulation showed that the four types of observed online learning strategies were significantly associated with the better and poorer perceptions of the blended learning environment. Specially, amongst students who adopted the intensive theory application strategy, the proportion of students who self-reported better perceptions was significantly higher than those reporting poorer perceptions. In contrast, amongst students using the weak reading and weak theory application strategy, the proportion of students having poorer perceptions was significantly higher than those holding better perceptions.

Higher education may benefit from investigating alternative evidence-based methods of online learning to understand students' learning behaviors while considering students' social cognitive motivational traits. Researchers conducted an in situ design-based research (DBR) study to investigate learner experience design (LXD) methods, deploying approaches of asynchronous video, course dashboards, and enhanced user experience. This mixed-methods study (N = 181) assessed associations of students' social cognitive motivational traits (self-efficacy, task-value, self-regulation) influencing their learning behaviors (engagement, elaboration, critical thinking) resulting from LXD. Social cognitive motivational traits were positively predictive of learning behaviors. As motivational factors increased, students' course engagement, usage of elaboration, and critical thinking skills increased. Self-efficacy, task-value, and self-regulation explained 31% of the variance of engagement, 47% of the explained variance of critical thinking skills, and 57% of the explained variance in the usage of elaboration. As a predictor, task-value beliefs increased the proportion of explained variance in each model significantly, above self-efficacy and self-regulation. Qualitative content analysis corroborated these findings, explaining how LXD efforts contributed to motivations, learning behaviors, and learning experience. Results suggest that mechanisms underpinning LXD and students' learning behaviors are likely the result of dynamically catalyzing social cognitive motivational factors. The discussion concludes with the LXD affordances that explain the positive influences in students' social cognitive motivational traits and learning behaviors, while also considering constraints for future iterations.