How are my Students going? A Tool to Analyse Students' Interactions on Capstone Courses

• Márcia Lima Universidade Federal do Amazonas (UFAM) / Universidade do Estado do Amazonas (UEA)
• Awdren Fontão Universidade Federal do Amazonas (UFAM) / Instituto de Ciência e Tecnologia (SIDIA)
• David Fernandes Universidade Federal do Amazonas (UFAM)
• Tayana Conte Universidade Federal do Amazonas (UFAM)
• Bruno Gadelha Universidade Federal do Amazonas (UFAM)

DOI: https://doi.org/10.5753/cbie.sbie.2019.1611

Resumo

Computing-related undergraduate students are encouraged to participate in Project-based Learning (PBL) courses through capstone courses in order to bridge the gap between software engineering (SE) educational and industrial worlds. In these courses, students improve their skills on industrial tools and processes and engage in real-world projects. One of the challenges of this kind of courses is how to monitor students' progress. In this work, we propose a software tool based on statistical analysis and data-mining algorithms to investigate the usefulness of students' communication logs to support professors' pedagogical activities during a capstone course involving three different SE disciplines. Our results indicate the feasibility of using textual content and metadata content extracted from Slack logs to identify opportunities for the professor's intervention. A quantitative analysis reveals an average precision of 81% at identifying the top-5 relevant sentences registered in the log.

Palavras-chave: Project-based Learning, Capstone Course, Software Engineering, Statistical Analysis, Data Mining Algorithms

Referências

Ahadi, A., Lister, R., Haapala, H., and Vihavainen, A. (2015). Exploring machine learning methods to automatically identify students in need of assistance. In Proceedings of the Eleventh Annual International Conference on International Computing Education Research, ICER ’15, pages 121–130, New York, NY, USA. ACM.

Alkadhi, R., Nonnenmacher, M., Guzman, E., and Bruegge, B. (2018). How do developers discuss rationale? In 2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER), pages 357–369. IEEE.

Bastarrica, M. C., Perovich, D., and Samary, M. M. (2017). What can students get from a software engineering capstone course? In 2017 IEEE/ACM 39th International Conference on Software Engineering: Software Engineering Education and Training Track (ICSE-SEET), pages 137–145. IEEE.

Felix, I., Ambrósio, A. P., LIMA, P. D. S., and Brancher, J. D. (2018). Data mining for student outcome prediction on moodle: a systematic mapping. In Brazilian Symposium on Computers in Education - SBIE, volume 29, page 1393.

Gambhir, M. and Gupta, V. (2017). Recent automatic text summarization techniques: a survey. Artificial Intelligence Review, 47(1):1–66.

Garousi, V., Petersen, K., and Ozkan, B. (2016). Challenges and best practices in industry-academia collaborations in software engineering: A systematic literature review. Information and Software Technology, 79:106–127.

Grant, M. M. (2002). Getting a grip on pbl: theory, cases and recommendations. Meridian: A Middle School Computer Technologies Journal A Service of NC State University, Raleigh, 5 (1).

Haiduc, S., Aponte, J., and Marcus, A. (2010). Supporting program comprehension with source code summarization. In Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering-Volume 2, pages 223–226. ACM.

Liu, T.-Y. (2011). Learning to rank for information retrieval. Springer Science & Business Media.

Marques, M. R., Quispe, A., and Ochoa, S. F. (2014). A systematic mapping study on practical approaches to teaching software engineering. In 2014 IEEE Frontiers in Education Conference (FIE) Proceedings, pages 1–8. IEEE.

Mushtaq, H., Malik, B. H., Shah, S. A., Bin Siddique, U., Shahzad, M., and Siddique, I. (2018). Implicit and explicit knowledge mining of crowdsourced communities: Architectural and technology verdicts. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 9(1):105–111.

Neyem, A., Diaz-Mosquera, J., Munoz-Gama, J., and Navon, J. (2017). Understanding student interactions in capstone courses to improve learning experiences. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education, SIGCSE ’17, pages 423–428, New York, NY, USA. ACM.

Özdağoğlu, G., Öztaş, G. Z., and Çağliyangil, M. (2018). An application framework for mining online learning processes through event-logs. Business Process Management Journal.

Porfirio, A., Pereira, R., and Maschio, E. (2018). Inferência de conhecimento a partir da detecção automática de evidências no domínio da programação de computadores. In Brazilian Symposium on Computers in Education - SBIE), volume 29, page 1553.

Schwaber, K. (1997). Scrum development process. In Business object design and implementation, pages 117–134. Springer.