Understanding student cognitive states is essential for assessing human learning. The deep neural networks (DNN)-inspired cognitive state prediction method improved prediction performance significantly; however, the lack of explainability with DNNs and the unitary scoring approach fail to reveal the factors influencing human learning. Identifying…

The article is devoted to learning analytics problems associated with the digital culture development in the university educational space and with the student activity control in the vocational training process. The empirical basis of the study was a series of surveys conducted by the Center for Sociological Research of the Peter the Great…

In order to learn the mappings from words to referents, children must integrate co-occurrence information across individually ambiguous pairs of scenes and utterances, a challenge known as cross-situational word learning. In machine learning, recent multimodal neural networks have been shown to learn meaningful visual-linguistic mappings from…

One of the biggest challenges for EFL (English as Foreign Language) students to learn English is the lack of practicing environments. Although language researchers have attempted to conduct flipped classrooms to increase the practicing time in class, EFL students generally have difficulties interacting with peers and teachers in English in class.…

Aim/Purpose: This research aims to present a brand-new approach for student performance prediction using the Learning Fuzzy Cognitive Map (LFCM) approach. Background: Predicting student academic performance has long been an important research topic in many academic disciplines. Different mathematical models have been employed to predict student…