Emostudent: a dataset for complex student behaviour evaluation.
DOI:
https://doi.org/10.15276/opu.1.67.2023.07Keywords:
computer vision, neural networks, dataset, action understanding, video understanding, emotion understanding, artificial intelligence-based proctoring systemsAbstract
This article discusses artificial intelligence based automated online proctoring systems. The practical implementation of the task of analysing students’ behaviour in the process of working with these systems and the datasets used to solve this task were considered. A general model for processing data on human activity in the process of online learning has been created, which is aimed at analysing and describing the activity and emotional state of the student. For this purpose, various features that affect the assessment of student behaviour during independent work on test tasks or exams were identified. Based on the analysis of existing datasets and the problems of modern implementations of automated online proctoring systems, a classification of features used specifically for analysing video sequences in the context of solving the problem of analysing student behaviour was made. In accordance with the developed requirements, a dataset was proposed. The data source for the developed dataset was the YouTube platform: videos with a Creative Commons licence were used. Amazon SageMaker platform was utilized to organise the process of data labelling and dataset formation. The generated dataset was added to the Kaggle and Hugging Face platforms. This allows us to utilized the work among other scientists and software developers and to test the developed dataset in practice in training of various implemented models of artificial neural networks.
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