The field of emotion understanding is replete with measures, yet lacks an integrated conceptual organizing structure. To identify and organize skills associated with the recognition and knowledge of emotions, and to highlight the focus of emotion understanding as localized in the self, in specific others, and in generalized others, we introduce the conceptual framework of Emotion Understanding in Recognition and Knowledge Abilities. We then categorize 56 existing methods of emotion understanding within this framework to highlight current gaps and future opportunities (...) in assessing emotion understanding across the lifespan. We hope the EUReKA model provides a systematic and integrated framework for conceptualizing and measuring emotion understanding for future research. (shrink)

The vigorous development of online education has produced massive amounts of education data. How to mine and analyze education big data has become an urgent problem in the field of education and big data knowledge engineering. As for the dynamic learning data, knowledge tracing aims to track learners’ knowledge status over time by analyzing the learners’ exercise data, so as to predict their performance in the next time step. Deep learning knowledge tracking performs well, but they mainly model the knowledge (...) components while ignoring the personalized information of questions and learners, and provide limited interpretability in the interaction between learners’ knowledge status and questions. A context-aware attentive knowledge query network model is proposed in this paper, which combines flexible neural network models with interpretable model components inspired by psychometric theory. We use the Rasch model to regularize the embedding of questions and learners’ interaction tuples, and obtain personalized representations from them. In addition, the long-term short-term memory network and monotonic attention mechanism are used to mine the contextual information of learner interaction sequences and question sequences. It can not only retain the ability to model sequences, but also use the monotonic attention mechanism with exponential decay term to extract the hidden forgetting behavior and other characteristics of learners in the learning process. Finally, the vector dot product is used to simulate the interaction between the learners’ knowledge state and questions to improve the interpretability. A series of experimental results on 4 real-world online learning datasets show that CAKQN has the best performance, and its AUC value is improved by an average of 2.945% compared with the existing optimal model. Furthermore, the CAKQN proposed in this paper can not only track learners’ knowledge status like other models but also model learners’ forgetting behavior. In the future, our research will have high application value in the realization of personalized learning strategies, teaching interventions, and resource recommendations for intelligent online education platforms. (shrink)

In this paper, according to the perspective of customers and products, by using clustering analysis and association rule technology, this paper proposes a cross-marketing model based on an improved sequential pattern mining algorithm, where an improved algorithm AP is applied. The algorithm can reduce the time cost of constructing a projection database and the influence of the increase of support on the algorithm efficiency. The improved idea is that when the first partition is used to generate the projection database, the (...) number of itemsets in the projection database is sorted from small to large, and when the second partition is used, the sequence patterns are generated directly from the mined sequence patterns, so as to reduce the construction of the database. The experimental results show that this method can quickly mine the effective information in complex data sets, improve the accuracy and efficiency of data mining, and occupy less memory consumption, which has good theoretical value and application value. (shrink)

This study investigated eye-movement patterns during emotion perception for children with hearing aids and hearing children. Seventy-eight participants aged from 3 to 7 were asked to watch videos with a facial expression followed by an oral statement, and these two cues were either consistent or inconsistent in emotional valence. Results showed that while normal-hearing children paid more attention to the upper part of the face, children with hearing aids paid more attention to the lower face after the oral statement was (...) presented. When there was an inconsistency between the visual and auditory cues, children with hearing aids were likely to increase attention to the upper face. The results revealed the underlying mechanism of deficits in emotion perception for hearing-impaired children. (shrink)