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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]

• High-carbon silty dolomite is the most favorable exploration target for shale oil in Lucaogou Formation of Jimsar Sag. • TOC, microscopic pore-throat structure, and pore types are the key factors controlling the movable oil content of different lithofacies. • A new Conceptual model is given to illustrate multi-scale linkage between lithofacies, pore structure, and fluid occurrence. The heterogeneity of lithofacies is a pivotal factor influencing the shale oil potential. A comprehensive understanding of the intricate influence of rock complexity on hydrocarbon generation properties, microscopic pore-throat structure, and fluid occurrences is crucial for shale oil exploration and development. The Lucaogou Formation in the Jimsar Sag is a type of saline lacustrine shale oil, which was chosen as a research example in this paper. To unravel the causal linkage and confirm the coupling relationship between lithofacies, pores, and fluid mobility in the sedimentary sequence of saline lacustrine basins, a series of experiments were conducted. These included total organic carbon (TOC), X-ray diffraction (XRD), vitrinite reflectance (R o), Rock-Eval pyrolysis, field emission-scanning electron microscopy (FE-SEM), low-field nuclear magnetic resonance (LF-NMR), mercury intrusion porosimetry (MIP), and low-temperature nitrogen adsorption (LTN 2 A). The results revealed significant differences in pores, oiliness, and fluid mobility among the four dominant lithofacies. Employing an innovative comprehensive evaluation method, high-carbon silty dolomite manifests significant potential for hydrocarbon generation, substantial storage capacity, effective oiliness and mobility , heightened brittle mineral content, and facile fracture production. Consequently, it was the most favorable lithofacies. Furthermore, TOC, pore size, and pore types emerged as key factors influencing the movable oil content of different lithofacies. More importantly, a detailed multiscale model illustrated potential linkages across the three different scales of lithofacies associations, pores, and fluid mobility. The study's outcomes can offer enhanced guidance for accurately assessing shale oil resources, particularly in the identification of crucial "sweet spots". [ABSTRACT FROM AUTHOR]