Reseach Article
Predicting Level of Computer Science Education Students' Engagement in Programming Activities and Hackathon using Random Forest Supervised Machine Learning
| Journal of Advanced Artificial Intelligence |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 1 - Number 1 |
| Year of Publication: 2024 |
| Authors: Ajayi Olufunke Esther |
10.5120/jaai202401
|
Ajayi Olufunke Esther . Predicting Level of Computer Science Education Students' Engagement in Programming Activities and Hackathon using Random Forest Supervised Machine Learning. Journal of Advanced Artificial Intelligence. 1, 1 ( Oct 2024), 1-13. DOI=10.5120/jaai202401
Abstract
The study uses a Random Forest supervised machine learning model to predict computer science education students' level of participation in programming activities and hackathons. To categorize student engagement, 310 students' data were reviewed. Variables such as the amount of time spent programming, participation in online forums, test results, and hackathon attendance were all considered. With an R-squared value of -0.0014 and a Mean Squared Error (MSE) of 8.64, the model's prediction accuracy was found to be lacking, indicating the need for more varied data sources. The main conclusions revealed a moderate association between online engagement and programming time, and poor correlations between test results, programming time, and hackathon participation. The study underlines the need of adding psychological and social elements into future models and advocates broader integration of hackathons into the curriculum to boost student participation. The findings draw attention to the shortcomings of the current predictive methodology and suggest investigating more factors in order to raise the accuracy of the model.
References
- Baek, Y., Lee, S., & Yoon, S. (2021). Machine learning approaches to predict student engagement in online learning environments. Sustainability, 13 (14), 7833.
- Breiman, L. (2001). Random forests. Machine Learning, 45 (1), 5-32.
- Briscoe, G., & Mulligan, C. (2014). Digital innovation: The hackathon phenomenon. Creativeworks London.
- Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. ,. Review of Educational Research, 74 (1), 59-109.
- Fredricks, J. A., Filsecker, M., & Lawson, M. A. (2016). Student engagement, context, and adjustment: Addressing definitional, measurement, and methodological issues. Learning and Instruction, 43, 1-4. doi:https://doi.org/10.1016/j.learninstruc.2016.02.00
- Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42 (1), 38-43.
- Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015). Measuring student engagement in technology-mediated learning: A review. . Computers & Education, 90, 36-53. doi:https://doi.org/10.1016/j.compedu.2015.09.005
- Hidi, S., Renninger, K. A., & Krapp, A. (2019). The role of interest in learning and development. Cambridge: Cambridge University Press. doi:https://doi.org/10.10
- Hsu, T. C., Chang, S. C., & Hung, Y. T. (2018). How to learn and how to teach computational thinking: Suggestions based on a review of the literature. Computers & Education, 126, 296-310.
- Kahu, E. R., & Nelson, K. (2018). Student engagement in the educational interface: Understanding the mechanisms of student success. Higher Education Research & Development, 37 (1), 58-71. doi:https://doi.org/10.1080/07294360.2017.1344197
- Kaliisa, R., & Picard, M. (2017). A systematic review on mobile learning in higher education: The African perspective. The Turkish Online Journal of Educational Technology, 16 (1), 1-18.
- Komssi, M., Pichlis, D., Raatikainen, M., Kindstrom, K., & Jarvinen, J. (2015). What are hackathons for? IEEE Software, 32(5), 60-67.
- Kuh, G. D. (2008). High-Impact Educational Practices: What They Are, Who Has Access to Them, and Why They Matter. Washington, D.C: Association of American Colleges and Universities.
- Lawson, M. A., & Lawson, H. A. (2013). New conceptual frameworks for student engagement research, policy, and practice. Review of Educational Research, 83 (3), 432-479. doi:https://doi.org/10.3102/0034654313480891
- Lawson, M. A., & Lawson, H. A. (2013). New conceptual frameworks for student engagement research, policy, and practice. . Review of Educational Research, 83 (3), 432-479. doi:https://doi.org/10.3102/0034654313480891
- Norris, C., & Coutas, R. (2014). Building student engagement through Learning Management Systems. Journal of Educational Technology, 25 (2), 127-142.
- Picton, I., Clark, C., & Judge, C. (2018). Teachers' and learners' perceptions of engagement and motivation in literacy learning. Journal of Educational Research, 61 (3), 156-169.
- Resnick, M. (2017). Lifelong Kindergarten: Cultivating Creativity through Projects, Passion, Peers, and Play. MIT Press.
- Schindler, L. A., Burkholder, G. J., Morad, O. A., & Marsh, C. (2017). Computer-based technology and student engagement: A critical review of the literature. International Journal of Educational Technology in Higher Education, 14 (1), 1-28. doi:https://doi.org/10.1186/s41239-017-0063-0
- Webb, M., Gibson, D., & Forkosh-Baruch, A. (2017). Challenges for IT-supported formative assessment of complex 21st-century skills. Technology, Pedagogy and Education, 26 (2), 135-146. doi:https://doi.org/10.1080/1475939X.2016.1164007
- Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49 (3), 33-35.
Index Terms
Keywords