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Using multimodal learning analytics to model students' learning behavior in animated programming classroom
By Abdullahi Yusuf
Studies examining students' learning behavior predominantly employed rich video data as their main source of information due to the limited knowledge of computer vision and deep learning algorithms. However, one of the challenges faced during such observation is the strenuous task of coding large amounts of video data through repeated viewings. In this research, we confirm the possibilities of classifying students' learning behavior using data obtained from multimodal distribution. We employed computer algorithms to classify students' learning behavior in animated programming classrooms and used information from this classification to predict learning outcomes. Specifically, our study indicates the presence of three clusters of students in the domain of stay active, stay passive, and to-passive. We also found a relationship between these profiles and learning outcomes. We discussed our findings in accordance with the engagement and instructional quality models and believed that our statistical approach will support the ongoing refinement of the models in the context of behavioral profiling and classroom interaction. We recommend that further studies should identify different epistemological frames in diverse classroom settings to provide sufficient explanations of students' learning processes.
Revising the computer programming attitude scale in the context of attitude ambivalence
By Abdullahi Yusuf
Background: Several attitude scales have been developed to measure students' attitudes toward computer programming, including the prominent one developed by Cetin and Ozden. The development of these scales stemmed from the elusive nature of attitude and the lack of specific constructs to measure attitude. These instruments measure students' attitudes based on one-dimensional perspective, thus, making it difficult to interpret the meaning of some attitude evaluations such as the meaning of neutral points in a 10-point scale (for example).Objectives: The computer programming attitude scale was modified to measure ambivalence. The study also investigate attitude differences across demographic variables and used these variables to predict ambivalence.Methods: The study was conducted in two phases. In the first phase, the instrument was validated using exploratory factor analysis and confirmatory factor analysis. In the second phase, the revised scale was administered to another 547 students in four research universities for empirical investigation.Results: Results show that the instrument is valid and suitable for measuring students' programming attitudes. Participants' attitudes skewed toward the negative attitude dimension. Lastly, we found that both attitude and ambivalence are factors of programming experience.Conclusions: We discussed the findings, recommend the instrument to programming tutors, and strongly emphasise the evaluation of students' ambivalent attitudes.
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