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Background: Investigating emotion sequence patterns in the posts of discussion forums in massive open online courses (MOOCs) holds a vital role in shaping online interactions and impacting learning achievement. While the majority of research focuses on the relationship between emotions and interactions in MOOC forum discussions, research on identifying the crucial difference in emotion sequence patterns among different interaction groups remains in its infancy. Objectives: This research utilizes deep learning and sequential pattern mining to investigate whether there are differences in emotion sequence patterns across different groups of learners who exhibit various types of interactions in online discussion forums. Methods: Data from a comprehensive array of sources, including log files, discussion texts and scores from 498 learners in online discussion forums, were collected for this study. The agglomerative hierarchical algorithm is used to classify learners into groups with different levels of interactions. Additionally, we implement and evaluate multiple deep learning models for detecting different emotions from online discussions. Relevant emotion sequence patterns were identified using sequence pattern analysis and the identified emotion sequence patterns were compared across different groups with different levels of interactions. Results and Conclusions: Using an agglomerative hierarchical algorithm, we classified learners into three distinct groups characterized by different levels of interactions: high, average and low level. Leveraging the bi‐directional long short‐term memory model for emotion detection yielded the highest predictive performance, with an impressive F‐measure of 94.01%, a recall rate of 93.83% and an accuracy score of 95.01%. The results also revealed that learners in the low‐level interaction group experienced more emotion transition from boredom to frustration than the other two groups. Therefore, the aggregation of students into groups and the utilization of their MOOC log data offer educators the capability to provide adaptive emotional feedback, customize assessments and offer more personalized attention as needed. Lay Description: What is currently known about the subject matter: Emotions are dynamic over time when learners experience cognitive disequilibrium/equilibrium.Online interactions are critical components, which influence learners' emotional state, cognitive processes and learning achievement.It is not clear what are differences in emotion sequence patterns across the groups with different interaction types. What the paper adds: An agglomerative hierarchical algorithm was implemented to cluster learners into three groups by analysing behavioural data.We explore possibilities for automated classification of emotions using deep learning approaches.Learners in the low‐level interaction group experienced more emotion transition from boredom to frustration. Implications for practitioners: A considerable amount of effort should be expended to identify and respond to learners who experience boredom and frustration emotions.Designing interventions or scaffolding to facilitate learners' interaction and promote favourable emotions.Educators could provide more personalized support based on learners' online interaction cluster. [ABSTRACT FROM AUTHOR]

This paper explores the components of Chinese college counsellors' mental models in their daily work based on a rich content analysis of the story accounts of 152 exemplary college counsellors over the past 8 years. On the basis of a rigid three‐step coding procedure, this study reveals that Chinese college counsellors' mental models generally comprise four common constituents, namely a love‐filled faith system, a rule‐based thinking system, a professionalism‐based knowledge system and action‐based performance. These components jointly illustrate the general landscape of Chinese college counsellors' internal well‐being and fill a research gap regarding the sparse attention given to counsellors in the global literature on faculty professionalism. [ABSTRACT FROM AUTHOR]

As a healthcare ICT4D solution, mobile health (mHealth) can potentially improve users' well‐being during pandemics, especially in developing countries with limited healthcare resources. Recent ICT4D research reveals that providing end‐users with access to ICT is insufficient for improving well‐being and, thus, understanding how mHealth empowers end‐users to enhance well‐being against stressful events is important. However, prior research has rarely discussed the issue of empowerment in the domain of mHealth or the context of major disruptive events. This paper contributes to the literature by conceptualizing the psychological empowerment of mHealth users (PEMU) and investigating its nomological network during pandemics. Drawing upon theories of psychological empowerment and event characteristics, we developed a research model and tested it through a mixed‐methods investigation, containing a quantitative study with 602 Chinese mHealth users during COVID‐19 and a follow‐up qualitative study of 326 online articles and reviews. We found that PEMU, driven by three technological characteristics (perceived response efficacy, ease of use, and mHealth quality), affects well‐being through both (a) a stress‐buffering effect, which counterbalances the detrimental, stress‐increasing effects of event criticality and disruption, and (b) a vitality‐stimulating effect, which is intensified by event criticality. These findings have important implications for ICT4D research and practice. [ABSTRACT FROM AUTHOR]