Currently, the increasing information overload on Massive Open Online Courses(MOOCs) inhibits the appropriate choice of learning objects by learners, leading to low efficiency and high dropout rates. However, in MOOC platforms, recommendation network structures that can selectively extract implicit features such as heterogeneous learning preference and knowledge organization of learning objects are still not comprehensively studied. To this end, we propose a learning object recommendation model namely ACGCN based on heterogeneous learning behavior and knowledge graph. By introducing an attention mechanism, information is amplified when updating the representation of the heterogeneous graph, which eliminates the impact of noise and improves the robustness of ACGCN. Experimental results using a real-world dataset revealed that our proposed model has the best performance compared to those of several existing baselines.

Current evidence indicates that the semantic representation of question and answer sentences is better generated by deep neural network-based sentence models than traditional methods in community answer selection tasks. In particular, as a widely recognized language model, the self-attention model computes the similarity between the specific word and the whole sets of words in the same sentence and generates new semantic representation through the similarity-weighted summation of semantic representations of the whole words. However, the self-attention operation entirely considers all the signals with a weighted sum operation, which disperses the distribution of attention, which may result in overlooking the relation of neighboring signals. This issue becomes serious when applying the self-attention model to online community question answering platforms because of the varied length of the user-generated questions and answers. To address this problem, we introduce an attention mechanism enhanced local self-attention (LSA), which restricts the range of original self-attention by a local window mechanism, thereby scaling linearly when increasing the sequence length. Furthermore, we propose stacking multiple LSA layers to model the relationship of multiscale$n$-gram features. It captures the word-to-word relationship in the first layer and then captures the chunk-to-chunk (such as lexical$n$-gram phrases) relationship in its deeper layers. We also test the effectiveness of the proposed model by applying the learned representation through the LSA model to a Siamese and a classification network in community question answer selection tasks. Experiments on the public datasets show that the proposed LSA achieves a good performance.

Massive Open Online Courses (MOOCs) have received unprecedented attention, in which learners can obtain a large number of learning objects anytime and anywhere. However, the increasing information overload on MOOCs inhibits the appropriate choice of learning objects by learners, leading to a low efficiency and high dropout rates in the learning process of this human-computer interaction scenario. E-learning recommendation systems have been studied to present learning objects directly to learners, thereby relieving such problem. However, in MOOC platforms, recommendation network structures which can selectively extract implicit feature such as heterogeneous learning preference and knowledge organization of learning objects are still not comprehensively studied. To this end, we propose a learning object recommendation model based on heterogeneous learning behavior and knowledge graph. To generate a unified representation of each entity and relation, we first propose an Attentive Composition based Graph Convolutional Network (ACGCN). By introducing an attention mechanism, information is amplified when updating the representation of the heterogeneous graph, which eliminates the impact of noise and improves the robustness of the model. Then, a Dense Feature based Operation-Aware Network (DFOAN) is utilized to capture implicit and complex learners’ interactive behaviors, and to further provide a recommendation. Experimental results using two real-world datasets revealed that our proposed model has the best precision, recall, F1, and accuracy scores compared to those of several existing models.

With the development of computer science and multimedia technology, Web-based learning becomes increasingly popular. User evaluation plays a significant role in the process of guided learning. In recent years, there is great progress in the development of evaluation technology. However, few evaluation methods fully take online learning activity analysis and individual differences into account. This paper proposes a personalized user evaluation model for Web-based learning systems. The model is utilized to record and analyze various learning activities throughout the entire learning process. Considering individual differences, learners are clustered and specific evaluation standards are set for different clusters. Comprehensive evaluation is achieved by combining Analytic Hierarchy Process, Fuzzy C-Means clustering and normalization algorithm. Through the comparison with several other common evaluation methods, experimental results show that the proposed method outperforms existing ones on the accuracy of learner evaluation.