Using AI to Identify Problems in Team Activities in a Programming Course
Abstract
Teaching methodologies such as Problem-Based Learning and Team-Based Learning share the formation of teams and the need for constant teacher monitoring. The submission of written reports by students is an important tool to allow this monitoring. However, reading a large number of these reports can be very time-consuming, making it difficult to identify teams that are facing some kind of problem. This article presents the use of Artificial Intelligence to analyze the reports submitted by students, informing the teacher about the problems that occur in school work teams. The results show that using these techniques can help in the faster identification of problems in teams.
References
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ROGERS, P.; LEA, M. Cohesion in online groups. WIT Transactions on Information and Communication Technologies, v. 31, 2004.
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WATKINS, J.; FABIELLI, M.; MAHMUD, M. SENSE: a Student Performance Quantifier using Sentiment Analysis. 2020 International Joint Conference on Neural Networks (IJCNN). Anais...2020.
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WILLIAMS, E. A.; DURAY, R.; REDDY, V. Teamwork orientation, group cohesiveness, and student learning: A study of the use of teams in online distance education. Journal of Management Education, v. 30, n. 4, p. 592–616, 2006.
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ZAMBRANO, J. et al. Eff ects of prior knowledge on collaborative and individual learning. Learning and Instruction, v. 63, p. 101214, 2019.
ZAMECNIK, A. et al. The cohesion of small groups in technology-mediated learning environments: A systematic literature review. Educational Research Review, p. 100427, 2021.
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ALLEN, J. Natural language understanding. [s.l.] Benjamin-Cummings Publishing Co., Inc., 1995.
ALTRABSHEH, N.; COCEA, M.; FALLAHKHAIR, S. Sentiment analysis: towards a tool for analysing real-time students feedback. 2014 IEEE 26th international conference on tools with artificial intelligence. Anais...IEEE, 2014.
ALVES, H.; JÚNIOR, F. N. Problem Based Learning e Scratch: aliados no ensino de Logica de Programação. 2018.
AUNG, K. Z.; MYO, N. N. Sentiment analysis of students’ comment using lexicon based approach. 2017 IEEE/ACIS 16th international conference on computer and information science (ICIS). Anais...IEEE, 2017.
BALAKRISHNAN, V.; LLOYD-YEMOH, E. Stemming and lemmatization: A comparison of retrieval performances. 2014.
BEHDENNA, S.; BARIGOU, F.; BELALEM, G. Document Level Sentiment Analysis: A survey. EAI Endorsed Transactions on Context-aware Systems and Applications, v. 4, n. 13, p. e2, mar. 2018.
BENNEDSEN, J.; CASPERSEN, M. E. Failure rates in introductory programming. AcM SIGcSE Bulletin, v. 39, n. 2, p. 32–36, 2007.
CAMBRIA, E.; HUSSAIN, A. SenticNet 7: A Commonsense-based Neurosymbolic AI Framework for Explainable Sentiment Analysis, Proceedings of LREC, p. 3829-399, 2022.
CAMBRIA, E. Aff ective computing and sentiment analysis. IEEE Intelligent Systems, n. 2, 2016.
CAROSIA, A. E. O. E.; COELHO, G. P.; DA SILVA, A. E. A. Investment strategies applied to the Brazilian stock market: a methodology based on sentiment analysis with deep learning. Expert Systems with Applications, v. 184, p. 115470, 2021.
CHAI, J.; LI, A. Deep Learning in Natural Language Processing: A State-of-the-Art Survey. 2019 International Conference on Machine Learning and Cybernetics (ICMLC). Anais...2019.
CHAUHAN, G.; AGRAWAL, P.; MEENA, Y. Aspect-Based Sentiment Analysis of Students’ Feedback to Improve Teaching–Learning Process: Proceedings of ICTIS 2018, Volume 2. Em: [s.l: s.n.]. p. 259–266.
CHAWLA, N. V. et al. SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, v. 16, p. 321–357, 2002.
DA CONCEIÇÃO, C. M.; GUEDES, A. M. A. Aprendizagem Baseada em Problemas aplicada à Programação de Computadores: Um Mapeamento Sistemático. RENOTE, v. 18, n. 2, p. 570–580, jan. 2021.
DAKE, D. K.; GYIMAH, E. Using sentiment analysis to evaluate qualitative students’ responses. Education and Information Technologies, v. 28, n. 4, p. 4629–4647, 1 abr. 2023.
DOLIANITI, F. S. et al. Sentiment Analysis Techniques and Applications in Education: A Survey. (M. Tsitouridou, J. A. Diniz, T. A. Mikropoulos, Eds.)Technology and Innovation in Learning, Teaching and Education. Anais...Cham: Springer International Publishing, 2019.
EKMAN, P. Basic emotions. Handbook of cognition and emotion, v. 98, n. 45–60, p. 16, 1999.
FARRA, N. et al. Sentence-Level and Document-Level Sentiment Mining for Arabic Texts. 2010 IEEE International Conference on Data Mining Workshops. Anais...2010.
FAWCETT, T. An introduction to ROC analysis. Pattern recognition letters, v. 27, n. 8, p. 861–874, 2006.
GONÇALVES, L. Imdb pt-br. [link], 2018.
HOLANDA, M. et al. Educational Initiatives to Increase Diversity in CS1 Courses: A Literature Mapping of U.S. eff orts. Proceedings - Frontiers in Education Conference, FIE, 2021. Disponível em: [link]
JURAFSKY, D.; MARTIN, J. H. Speech and Language Processing. [s.l: s.n.].
KASTRATI, Z. et al. Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study. Applied Sciences, v. 11, n. 9, 2021.
LAI, X.; WONG, G. K. -W. Collaborative versus individual problem solving in computational thinking through programming: A meta-analysis. British Journal of Educational Technology, v. 53, n. 1, p. 150–170, 2022.
LI, J. et al. A Survey on Deep Learning for Named Entity Recognition. IEEE Transactions on Knowledge and Data Engineering, v. 34, n. 1, p. 50–70, 2022.
LIDDY, E. D. Natural language processing. 2001.
LUNDQVIST, K.; LIYANAGUNAWARDENA, T.; STARKEY, L. Evaluation of Student Feedback Within a MOOC Using Sentiment Analysis and Target Groups. The International Review of Research in Open and Distributed Learning, v. 21, n. 3, p. 140–156, maio 2020.
MATHEW, L.; BINDU, V. R. A Review of Natural Language Processing Techniques for Sentiment Analysis using Pre-trained Models. 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC). Anais...2020.
MEDEIROS, R.; FALCÃO, T.; RAMALHO, G. Ensino e Aprendizagem de Introdução à Programação no Ensino Superior Brasileiro: Revisão Sistemática da Literatura. Anais do XXVIII Workshop sobre Educação em Computação. Anais...Porto Alegre, RS, Brasil: SBC, 2020. Disponível em: [link]
MIKOLOV, T. Efficient Estimation of Word Representations in Vector Space. ArXiv.org, 7 Sept. 2013, arxiv.org/abs/1301.3781.
MIŁKOWSKI, M. Developing an open-source, rule-based proofreading tool. Software: Practice and Experience, v. 40, n. 7, p. 543–566, 2010.
NADKARNI, P. M.; OHNO-MACHADO, L.; CHAPMAN, W. W. Natural language processing: an introduction. Journal of the American Medical Informatics Association, v. 18, n. 5, p. 544–551, set. 2011.
NAVIGLI, R. Word Sense Disambiguation: A Survey. ACM Comput. Surv., v. 41, n. 2, fev. 2009.
OLIVEIRA, E. et al. Análise de Sentimentos e Emoções em Fóruns Educacionais: Um Estudo de Caso em um Curso de Formação Docente durante a Pandemia. Anais do XXIX Workshop de Informática na Escola. Anais...Porto Alegre, RS, Brasil: SBC, 2023. Disponível em: [link]
PFITSCHER, R. et al. Análise de sentimentos em turmas de programação com vistas ao apoio à permanência estudantil. Anais do XXXIV Simpósio Brasileiro de Informática na Educação. Anais...Porto Alegre, RS, Brasil: SBC, 2023.
RAHARDJA, U.; HARIGUNA, T.; BAIHAQI, W. M. Opinion Mining on E-Commerce Data Using Sentiment Analysis and K-Medoid Clustering. 2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media). Anais...2019.
ROBINS, A.; ROUNTREE, J.; ROUNTREE, N. Learning and teaching programming: A review and discussion. Computer science education, v. 13, n. 2, p. 137–172, 2003.
ROGERS, P.; LEA, M. Cohesion in online groups. WIT Transactions on Information and Communication Technologies, v. 31, 2004.
SALAS, E. et al. Measuring team cohesion: Observations from the science. Human factors, v. 57, n. 3, p. 365–374, 2015.
SANTOS, F.; SILVEIRA, I. F.; LECHUGO, C. Mineração da percepção do aluno: Mineração de Dados Educacionais na geração de indicadores para a avaliação de práticas pedagógicas docentes. Anais dos Workshops do Congresso Brasileiro de Informática na Educação. Anais...2016.
SPARCK-JONES, K. S.; WILLETT, P.; OTHERS. Readings in information retrieval. [s.l.] Morgan Kaufmann, 1997.
WANG, Y. A Comprehensive Survey of Grammatical Error Correction. ACM Trans. Intell. Syst. Technol., v. 12, n. 5, dez. 2021.
WATKINS, J.; FABIELLI, M.; MAHMUD, M. SENSE: a Student Performance Quantifier using Sentiment Analysis. 2020 International Joint Conference on Neural Networks (IJCNN). Anais...2020.
WATSON, C.; LI, F. W. B. Failure rates in introductory programming revisited. Proceedings of the 2014 conference on Innovation & technology in computer science education. Anais...2014.
WILLIAMS, E. A.; DURAY, R.; REDDY, V. Teamwork orientation, group cohesiveness, and student learning: A study of the use of teams in online distance education. Journal of Management Education, v. 30, n. 4, p. 592–616, 2006.
YADOLLAHI, A.; SHAHRAKI, A. G.; ZAIANE, O. R. Current state of text sentiment analysis from opinion to emotion mining. ACM Computing Surveys (CSUR), v. 50, n. 2, p. 1–33, 2017.
ZAMBRANO, J. et al. Eff ects of prior knowledge on collaborative and individual learning. Learning and Instruction, v. 63, p. 101214, 2019.
ZAMECNIK, A. et al. The cohesion of small groups in technology-mediated learning environments: A systematic literature review. Educational Research Review, p. 100427, 2021.
ZHANG, W. et al. Sentiment Analysis in the Era of Large Language Models: A Reality Check. arXiv, , 24 maio 2023. Disponível em: [link]. Acesso em: 17 dez. 2024
Published
2025-07-20
How to Cite
ANDRADE, Havana Diogo Alves; AURELIANO, Viviane Cristina Oliveira; TEDESCO, Patrícia Cabral de Azevedo Restelli.
Using AI to Identify Problems in Team Activities in a Programming Course. In: WORKSHOP ON COMPUTING EDUCATION (WEI), 33. , 2025, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 408-420.
ISSN 2595-6175.
DOI: https://doi.org/10.5753/wei.2025.8109.
