Abstract
This study aimed to automatically construct knowledge graphs for online collaborative programming. We proposed several models and developed a system to construct knowledge graphs based on online discussion texts and the target knowledge graph for the C programming language. Our system included two main modules, namely, entity recognition and relation extraction. We proposed an innovative approach for recognizing knowledge entities, which included sequence tagging, text classification, and keyword matching. The extraction of relationships among knowledge entities was performed through queries of the target knowledge graph. The six kinds of knowledge graphs could be automatically generated through our method, including the activated and unactivated knowledge graphs of each student, each group, and each class. The accuracy of entity recognition reached 87.27%. The accuracies of relation extraction for students, groups, and the class achieved 89.7%, 90.4%, and 90.2%, respectively. This study is very promising and significant for both teachers and practitioners to provide interventions and personalized learning services based on the constructed knowledge graphs.
Highlights
Online collaborative learning has been widely used in the field of education, especially during Corona Virus Disease 2019 (COVID-19)
Online collaborative learning is known as telecollaboration, which is defined as an educational endeavor that engages learners in different locations through networks and resources to learn together [1]
To close this research gap, this study aimed to automatically construct knowledge graphs based on online discussion texts during online collaborative programming
Summary
Online collaborative learning has been widely used in the field of education, especially during Corona Virus Disease 2019 (COVID-19). Online collaborative learning is known as telecollaboration, which is defined as an educational endeavor that engages learners in different locations through networks and resources to learn together [1]. Learners often generate a large volume of discussion texts during online collaborative learning [4]. It has been pointed out that online discussion texts should be analyzed in real-time to detect the learners’ perceptions, problems or difficulties [5]. It is necessary to provide real-time analytics results to demonstrate the latest knowledge-building progress. In this way can teachers and instructors provide real-time and personalized support for learners to improve their learning performance
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