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Creators/Authors contains: "Jeevan Chapagain"

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  1. ; ; ; ; ; ; (, Proceedings of 24th International Conference on Artificial Intelligence in Education, AIED 2023, Part 2)
    Self-explanations could increase student’s comprehension in complex domains; however, it works most efficiently with a human tutor who could provide corrections and scaffolding. In this paper, we present our attempt to scale up the use of self-explanations in learning programming by delegating assessment and scaffolding of explanations to an intelligent tutor. To assess our approach, we performed a randomized control trial experiment that measured the impact of automatic assessment and scaffolding of self-explanations on code comprehension and learning. The study results indicate that low-prior knowledge students in the experimental condition learn more compared to high-prior knowledge in the same condition but such difference is not observed in a similar grouping of students based on prior knowledge in the control condition. 
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    Accepted Manuscript Full Text Available
  2. (, The International FLAIRS Conference Proceedings)
    Roman Bartak and Fazel Keshtkar and Michael Franklin (Ed.)
    This paper presents a novel method to automatically assess self-explanations generated by students during code comprehension activities. The self-explanations are produced in the context of an online learning environment that asks students to freely explain Java code examples line-by-line. We explored a number of models consisting of textual features in conjunction with machine learning algorithms such as Support Vector Regression (SVR), Decision Trees (DT), and Random Forests (RF). Support Vector Regression (SVR) performed best having a correlation score with human judgments of 0.7088. The best model used a combination of features such as semantic measures obtained using a Sentence BERT pre-trained model and from previously developed semantic algorithms used in a state-of-the-art intelligent tutoring system. 
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    Full Text Available