Exploring the interaction of cognition and emotion in small group collaborative discourse by Heuristic Mining Algorithm (HMA) and Inductive Miner Algorithm (IMA).

This study explored the interaction between cognition and emotion in blended collaborative learning. The participants (n = 30) of this study were undergraduate students enrolled in a 16-week course on information technology teaching. These students were divided into six groups of five people each. The behavior modes of the participants were analyzed using a heuristic mining algorithm and inductive miner algorithm. Compared with the groups with low task scores, the high-scoring groups exhibited more reflection phases and cycles in the interaction process and thus more frequent self-evaluation and regulation behavior for forethought and performance. Moreover, the frequency of emotion events unrelated with cognition was higher for the high-scoring groups than for the low-scoring groups. On the basis of the research results, this paper presents suggestions for developing online and offline blended courses.

Improving the Behavioral Intention of Continuous Online Learning Among Learners in Higher Education During COVID-19.

The COVID-19 pandemic caused colleges and universities to rely heavily on online learning to continue knowledge dissemination to learners. This study used the second-generation model of unified theory of acceptance and use of technology (UTAUT2) to comprehensively analyze the mediating effects of self-efficacy, which affects learners' effective use of online tools for learning, and capability of metacognition and self-regulation, which can independently adjust learning progress into the UTAUT2 model, on the learner's willingness to continue online learning [i.e., their behavioral intention (BI)] by constructing a UTAUT2-based e-learning model. This study administered questionnaires to undergraduates in universities in East China to collect data. The effects of performance expectancy, effort expectancy (EE), social influence (SI), and facilitating conditions (FCs), hedonic motivation (HM), price value (PV), and habits on BI (directly or through mediators) were analyzed through data analysis and structural equation modeling, and the UTAUT2-based e-learning model was accordingly modified. The results indicated that the self-efficacy enhanced the effects of EE, SI, FCs, HM, and PV on learners' BI; that metacognition and self-regulation (MS) capabilities enhanced the effects of EE on learners' BI; and that habits had a direct and strong effect on BI. This study also provided some suggestions to enhance higher education learners' willingness to continue online learning, such as improving social recognition and support, careful design of teaching content, easy-to-use technology, financial support. These results and suggestions may guide colleges and universities in conducting, continuing, or enhancing online education, particularly as the pandemic continues.