A Dynamic Knowledge Diagnosis Approach Integrating Cognitive Features

Abstract

The rapid development of data analytic technologies has advanced personalized learning and increased its popularity in K-12 education significantly. Specifically, one fundamental step in personalized learning is knowledge proficiency diagnosis which reveals blind spots in students' knowledge. However, existing approaches to diagnosis either exploit data from a one-time assessment for the cognitive diagnosis (ignoring the previous historical interactions) or trace the knowledge state using recurrent neural networks to predict students' future performance (ignoring the cognitive features). To this end, this study proposes a dynamic approach to knowledge diagnosis integrating cognitive features with a key-value memory network to store latent exercise information and capture long-term temporal features based on cognitive psychology. Specifically, given the characteristics of assessment data in China, our approach mainly aims to model sequence data with cognitive features, including forgetting and learning. Two corresponding gates are used to weaken the knowledge memory and strengthen the repeated knowledge memory over time, respectively, in the memory updating process. Finally, to evaluate our approach, we conducted extensive experiments on four real-world datasets collected from K-12 education. The results show that the approach can effectively process the time sequence in education, whose prediction results are better and more stable than other existing baseline models. We also conducted experiments for parameter sensitivity, different feature integration methods, and the effectiveness of cognitive features to ensure that the models achieved the best results. The application visualization further confirms the practicability of our approach in dealing with problems of dynamic knowledge diagnosis.INDEX TERMS Cognitive features, dynamic knowledge diagnosis, key-value memory, performance prediction.

A deepening study of philosophical cognitive logic based on intelligent interaction

“…Where, () sn is the speech signal sequence, () wn is the window function, commonly used window functions are rectangular window, formula (6), Haining window formula (7), Hamming window formula (8), etc.. In this paper, the Hamming window is used as the window function in the subject, which can improve the spectral leakage problem.…”

“…In addition to the traditional problem of logical omnipotence, there are other philosophical aspects [4][5]. Despite the problems faced by cognitive logic, it has made great progress, gradually moving from static to dynamic and from single-subject to multi-subject directions while diversifying the research methods [6]. Cognitive logic has developed from single-subject class modality research to multi-subjects, from static cognitive logic to dynamic cognitive logic, and the portrayal of knowledge has gradually involved group knowledge, including universal knowledge, public knowledge, and distributed knowledge, under the joint promotion of multiple fields [7][8].…”