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.

In the field of recommender systems, self-supervised learning has become an effective framework. In response to the noisy interaction behaviors in realworld scenarios, as well as the skewed distribution influenced by data sparsity and popularity bias, graph contrastive learning has been introduced as a powerful self-supervised method in collaborative filtering (CF) to learn enhanced user and item representations. Despite their success, neither heuristic manual enhancement methods nor the use of final node representations to construct contrastive pairs are sufficient to provide effective and rich self-supervised signals to regulate the training process. Therefore, the learned representations of users and items are either fragile or lack heuristic guidance. In light of this, we propose the Hierarchical multiview graph contrastive learning framework HMCF, which leverages the message passing mechanism at the layer level to introduce different granularity levels of view augmentation using supervised signals, thus better enhancing the CF paradigm. HMCF leverages rich, high-quality self-supervised signals from different granularity views for accurate contrastive optimization, helping to alleviate data sparsity and noise issues. It also explains the hierarchical topology and relative distances between nodes in the original graph. Comprehensive experiments on three public datasets shows that our model significantly outperforms the state-of-the-art baselines.

Digital maps have become important teaching and learning tools in education. However, limited research has examined the factors influencing learners’ acceptance of map-based online learning systems. This study proposes and validates an extended Technology Acceptance Model (TAM) that integrates two psychological constructs—habit and self-efficacy—into the original TAM framework to better explain students’ behavioural intention to use a map-based online learning system (Map-OLS). Structural equation modelling (SEM) was employed to analyse data from 812 participants with prior online learning experience. The results indicated that perceived ease of use (PEoU) and perceived usefulness (PU) had direct positive effects on the behavioural intention to use Map-OLS. PEoU positively affected PU and indirectly influenced behavioural intention to use Map-OLS via PU. Both habit and self-efficacy had significantly positive influences on PEoU and PU. Self-efficacy also directly influenced the behavioural intention to use Map-OLS. This study makes a theoretical contribution by extending and empirically validating TAM in the context of map-based learning environments, while also offering practical insights for designing more engaging and effective online learning systems.