Programming skills have gained increasing attention in recent years because digital technologies have become an indispensable part of life. However, little is known about the roles of fade-in and fade-out scaffolding in online collaborative programming settings. To close this research gap, the present study aims to examine the roles of fade-in and fade-out scaffolding for novice programmers in online collaborative programming. A total of 90 undergraduate students participated in the exploratory study and were assigned to 15 fade-in groups and 15 fade-out groups. All of the participants completed the same programming task. The findings reveal that fade-in scaffolding can significantly improve collaborative knowledge building, programming skills, metacognitive behaviors, emotions, and collective efficacy. Goal setting, planning, monitoring and control, enacting strategies, and evaluation and reflection are identified as the crucial metacognitive behaviors. The main contribution of this exploratory study is to shed light on how to design and implement scaffolding for novice programmers.

Prior studies have shown the importance of classroom dialogue in academic performance, through which knowledge construction and social interaction among students take place. However, most of them were based on small scale or qualitative data, and few has explored the availability and potential of big data collected from online classrooms. To address this issue, this paper analyzes dialogues in live classrooms of a large online learning platform in China based on natural language processing techniques. The features of interactive types and emotional expression are extracted from classroom dialogues. We then develop neural network models based on these features to predict high-and low-academic performing students, and employ interpretable AI (artificial intelligence) techniques to determine the most important predictors in the prediction models. In both STEM (science, technology, engineering, mathematics) and non-STEM courses, it is found that high-performing students consistently exhibit more positive emotion, cognition and off-topic dialogues in all stages of the lesson than low-performing students. However, while the metacognitive dialogue illustrates its importance in non-STEM courses, this effect cannot be found in STEM courses.While high-performing students in non-STEM courses show negative emotion in the last stage of lessons, STEM students show positive emotion.

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.

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