In e-learning, extracting suitable learning objects (LOs) from a vast resource pool and organizing them into high-quality learning paths is crucial for helping e-learners achieve their goals. Numerous approaches have been proposed to recommend optimal learning paths for e-learners. However, it is essential to emphasize that e-learning systems typically consist of a wide range of LOs with varying levels of granularity, ranging from fine-grained to coarse-grained. Unfortunately, current research has not adequately considered the underlying granularity structure of LOs when optimizing learning paths. Existing methods primarily focus on organizing LOs at a single granularity level, limiting their applicability in real-world e-learning systems. To address the limitations, we propose a multigranularity learning path recommendation (MGLPR) framework that aims to flexibly and effectively integrate the diverse granularity levels of LOs into high-quality learning paths. In this framework, a two-layer [knowledge point (KP) and LO layers] model is developed to formulate the MGLPR problem as a constrained optimization problem and an improved ant colony optimization algorithm (IACO) is introduced to solve it to identify optimal learning paths for e-learners. To evaluate the effectiveness of the proposed IACO, we conducted extensive computational experiments using 30 simulation datasets with varying problem sizes and complexities. The results demonstrate that our proposed IACO achieves superior performance and robustness compared with other competitors. Additionally, an empirical study was conducted to investigate the efficacy of the proposed approach in an authentic learning context, with results indicating that the proposed method outperforms the traditional self-organized ones.

In the context of e-learning, learners often struggle to make informed decisions about what and how to learn when they have access to a vast array of learning resources. To address this issue, several approaches have been proposed from different perspectives, aiming to generate personalized learning paths for e-learners. These approaches include learner-based, knowledge-based, and hybrid recommendation methods. Among them, hybrid methods have emerged as a promising solution for personalized learning path recommendations, as they combine the strengths of both learner-based and knowledge-based approaches. However, existing hybrid methods typically employ exhaustive techniques to determine optimal paths by extracting all possible learning paths. This approach can be time-consuming and computationally expensive. To overcome this challenge, we propose a novel two-stage personalized learning path recommendation approach that integrates concept map and an improved genetic algorithm. We conducted computational experiments using diverse simulation datasets to assess the effectiveness of our proposed method. The experimental results indicate that our approach surpasses other competing methods in terms of both performance and robustness.

This study introduces an innovative cross‐modal coattention network (CMCAN) framework, specifically designed to tackle the challenges of temporal misalignment and modality‐specific feature preservation in multimodal depression detection. The architecture comprises two fundamental components: (1) guided multihead attention, which facilitates context‐aware interactions across modalities, and (2) dedicated self‐attention pathways that ensure the preservation of key unimodal features. To enhance the estimation of depression severity, a cascaded fusion strategy is employed, combining feature superposition with hierarchical stacking. When evaluated on a Chinese localized depression dataset, CMCAN demonstrates exceptional performance, achieving optimal results (accuracy: 0.839; precision: 0.829; recall: 0.863) with its audiovisual guided coattention module (AVGA (SA (V), SA (A))) comprising three cascaded layers. The framework consistently surpasses unimodal baselines across various emotional valence stimuli (positive, neutral, and negative) and achieves state‐of‐the‐art performance on AVEC 2014 (MAE: 5.38). Comprehensive ablation studies validate the effectiveness of individual components, whereas comparative analyses demonstrate significant improvements over existing multimodal fusion approaches. These findings underscore the robustness and generalizability of CMCAN, validating its effectiveness in harmonizing cross‐modal synergy while preserving modality‐specific features, thereby advancing practical solutions for automated depression detection.

Emotions can significantly affect the learning effect. The affective states of students can be automatically recognized through behavioral cues using machine learning techniques. However, in the field of education, researchers mainly focus on the detection and analysis of basic emotions and pay little attention to learning-centered emotions. The reason for this phenomenon is the lack of datasets labeled with academic emotions. To address this shortcoming, a spontaneous facial dataset was constructed in this study through actual online learning activities. Then, the convolutional neural networks were used to train the academic emotion recognition models based on the dataset. Finally, the optimal model was selected to recognize and analyze the academic emotions of two groups of students with different academic achievements, and the emotional changes of two of them were observed. The results show that the average accuracy of academic emotion recognition is 79%. When analyzing the affective states of the two groups of students, it is found that the high-achiever group has a higher proportion of positive emotions and less neutral emotions, while the proportion of negative emotions is basically the same in the two groups of learners. In addition, the emotional evolution process of different achievement learners showed obvious differences. This study provides technical support for applications such as supporting accurate online intervention, constructing perfect learner portraits, and mining online learning behaviors.