Emotion-Semantic-Aware Dual Contrastive Learning for Epistemic Emotion Identification of Learner-Generated Reviews in MOOCs.

Identifying the epistemic emotions of learner-generated reviews in massive open online courses (MOOCs) can help instructors provide adaptive guidance and interventions for learners. The epistemic emotion identification task is a fine-grained identification task that contains multiple categories of emotions arising during the learning process. Previous studies only consider emotional or semantic information within the review texts alone, which leads to insufficient feature representation. In addition, some categories of epistemic emotions are ambiguously distributed in feature space, making them hard to be distinguished. In this article, we present an emotion-semantic-aware dual contrastive learning (ES-DCL) approach to tackle these issues. In order to learn sufficient feature representation, implicit semantic features and human-interpretable emotional features are, respectively, extracted from two different views to form complementary emotional-semantic features. On this basis, by leveraging the experience of domain experts and the input emotional-semantic features, two types of contrastive losses (label contrastive loss and feature contrastive loss) are formulated. They are designed to train the discriminative distribution of emotional-semantic features in the sample space and to solve the anisotropy problem between different categories of epistemic emotions. The proposed ES-DCL is compared with 11 other baseline models on four different disciplinary MOOCs review datasets. Extensive experimental results show that our approach improves the performance of epistemic emotion identification, and significantly outperforms state-of-the-art deep learning-based methods in learning more discriminative sentence representations.

Looking at MOOC discussion data to uncover the relationship between discussion pacings, learners' cognitive presence and learning achievements.

The MOOCs (Massive Open Online Courses) forum carries rich discussion data that contains multi-level cognition-related behavior patterns, which brings the potential for an in-depth investigation into the development trend of the group and individual cognitive presence in discourse interaction. This paper describes a study conducted in the context of an introductory astronomy course on the Chinese MOOCs platform, examining the relationship between discussion pacings (i.e., instructor-paced or learner-paced discussion), cognitive presence, and learning achievements. Using content analysis, lag sequential analysis, logistic regression, and grouped regression approaches, the study analysed the online discussion data collected from the Astronomy Talk course involving 2603 participants who contributed 24,018 posts. The findings of the study demonstrated the significant cognitive sequential patterns, and revealed the significant differences in the distribution of cognitive presence with different discussion pacings and learning achievement groups, respectively. Moreover, we found that the high-achieving learners were mostly in the exploration, integration, and resolution phase, and learner-paced discussion had a greater moderating effect on the relationship between cognitive presence and learning achievements. Based on the findings and discussion, suggestions for improving the learners' cognitive presence and learning achievements in the MOOC environment are discussed.

Autobalanced Multitask Node Embedding Framework for Intelligent Education.

Recently, online education has become popular. Many e-learning platforms have been launched with various intelligent services aimed at improving the learning efficiency and effectiveness of learners. Graphs are used to describe the pairwise relations between entities, and the node embedding technique is the foundation of many intelligent services, which have received increasing attention from researchers. However, the graph in the intelligent education scenario has three noteworthy properties, namely, heterogeneity, evolution, and lopsidedness, which makes it challenging to implement ecumenical node embedding methods on it. In this article, an autobalanced multitask node embedding model is proposed, named MNE, and applied to the interaction graph, settling a few actual tasks in intelligent education. More specifically, MNE builds two purpose-built self-supervised node embedding learning tasks for heterogeneous evolutive graphs. Edge-specific reconstruction tasks are built according to the semantic information and properties of the heterogeneous edges, and an evolutive weight regression task is designed, aiding the model to perceive the evolution of learners' implicit cognitive states. Then, both aleatoric and epistemic uncertainty quantification techniques are introduced, achieving both task-and node-level weight estimation and instructing subtask autobalancing. Experimental results on real-world datasets indicate that the proposed model outperforms the state-of-the-art graph embedding methods on two assessment tasks and demonstrates the validity of the proposed multitask framework and subtask balancing mechanism. Our implementations are available at https://github.com/ccnu-mathits/MNE4HEN.