Supporting Decisions Using Educational Data Analysis
Resumo
We present the Machine Teaching, an online learning environment with two main goals: (1) supporting student practicing and exercise marking; and most important, (2) collecting data on students’ knowledge while they progress. Machine teaching was key to bringing programming courses to online learning during the 2020 pandemic, helping educators provide a safe and smooth online practice environment for students and helping them to master programming skills in early stages of their bachelor’s degree studies, a skill that increases the possibilities for immediate job placement. In addition, the educational data collected are mined and used to support short- and long-term pedagogical decision-making, allowing for a quick feedback and enabling material adaptations for the classes offered in the remote mode.
Palavras-chave:
educational data mining, computer science education, learning analytics
Referências
Benotti, L. et al. 2018. The Eff ect of a Web-based Coding Tool with Automatic Feedback on Students’ Performance and Perceptions. In Proc. 49th ACM Tech. Symp. Comp. Sci. Educ. (SIGCSE ’18). Baltimore, Maryland, USA, 2–7. https://doi.org/10.1145/3159450.3159579 http://doi.acm.org/10.1145/3159450.3159579.
Delgado, C. et al. 2016. The teaching of functions as the first step to learn imperative programming. In Anais do Workshop sobre Educação em Computação (WEI). Sociedade Brasileira de Computação - SBC, 388–397. https://doi.org/10.5753/wei.2016.9683 doi: 10.5753/wei.2016.9683.
Ihantola, P. et al. 2015. Educational Data Mining and Learning Analytics in Programming: Literature Review and Case Studies. In Proc. 2015 ITiCSE Work. Group Rep. (ITICSE-WGR ’15). Vilnius, Lithuania, 41–63. https://doi.org/10.1145/2858796.2858798 doi: 10.1145/2858796.2858798.
Jelena Jovanović, Shane Dawson, Srećko Joksimović, and George Siemens. 2020. Supporting actionable intelligence: reframing the analysis of observed study strategies. In Proceedings of the 10th International Conference on Learning Analytics & Knowledge (LAK ’20). Association for Computing Machinery, New York, NY, USA, 161–170. https://doi.org/10.1145/3375462.3375474
Amruth N. Kumar. 2008. The Effect of Using Problem-Solving Software Tutors on the Self-Confidence of Female Students. In Proc. 39th SIGCSE Tech. Symp. Comput. Sci. Educ. (SIGCSE ’08) . Portland, OR, USA, 523–527. https://doi.org/10.1145/1352135.1352309 https://doi.org/10.1145/1352135.1352309.
Latham, A. et al. 2012. A conversational intelligent tutoring system to automatically predict learning styles. Comput. & Educ. 59, 1 (Aug. 2012), 95– 109. http://www.sciencedirect.com/science/article/pii/S0360131511002648 https://doi.org/10.1016/j.compedu.2011.11.001.
Richard Lobb and Jenny Harlow. 2016. Coderunner: A Tool for Assessing Computer Programming Skills. ACM Inroads 7, 1 (Feb. 2016), 47–51. https://doi.org/10.1145/2810041 https://doi.org/10.1145/2810041.
Laura O. Moraes and Carlos Eduardo Pedreira. 2020. Designing an Intelligent Tutoring System Across Multiple Classes. In 4th Educational Data Mining in Computer Science Education Workshop (Virtual).
Laura O. Moraes and Carlos Eduardo Pedreira. 2021. Clustering Introductory Computer Science Exercises Using Topic Modeling Methods. IEEE Trans. Learn. Technol. (2021). https://doi.org/10.1109/TLT.2021.3056907
Abelardo Pardo, Kathryn Bartimote, Simon Buckingham Shum, Shane Dawson, Jing Gao, Dragan Gašević, Steve Leichtweis, Danny Liu, Roberto Martínez-Maldonado, Negin Mirriahi, Adon Christian Michael Moskal, Jurgen Schulte, George Siemens, and Lorenzo Vigentini. 2018. OnTask: Delivering Data-Informed, Personalized Learning Support Actions. Learning Analytics 5, 3 (Dec. 2018), 235–249. https://doi.org/10.18608/jla.2018.53.15
Carolyn P. Rosé, Elizabeth A. McLaughlin, Ran Liu, and Kenneth R. Koedinger. 2019. Explanatory learner models: Why machine learning (alone) is not the answer. British Journal of Educational Technology 50, 6 (Nov. 2019), 2943–2958. https://doi.org/10.1111/bjet.12858
Chunpai Wang, Shaghayegh Sahebi, Siqian Zhao, Peter Brusilovsky, and Laura O. Moraes. 2021. Knowledge Tracing for Complex Problem Solving: Granular Rank-Based Tensor Factorization. In Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (Utrecht, Netherlands) (UMAP ’21) . Association for Computing Machinery, New York, NY, USA, 179–188. https://doi.org/10.1145/3450613.3456831
Delgado, C. et al. 2016. The teaching of functions as the first step to learn imperative programming. In Anais do Workshop sobre Educação em Computação (WEI). Sociedade Brasileira de Computação - SBC, 388–397. https://doi.org/10.5753/wei.2016.9683 doi: 10.5753/wei.2016.9683.
Ihantola, P. et al. 2015. Educational Data Mining and Learning Analytics in Programming: Literature Review and Case Studies. In Proc. 2015 ITiCSE Work. Group Rep. (ITICSE-WGR ’15). Vilnius, Lithuania, 41–63. https://doi.org/10.1145/2858796.2858798 doi: 10.1145/2858796.2858798.
Jelena Jovanović, Shane Dawson, Srećko Joksimović, and George Siemens. 2020. Supporting actionable intelligence: reframing the analysis of observed study strategies. In Proceedings of the 10th International Conference on Learning Analytics & Knowledge (LAK ’20). Association for Computing Machinery, New York, NY, USA, 161–170. https://doi.org/10.1145/3375462.3375474
Amruth N. Kumar. 2008. The Effect of Using Problem-Solving Software Tutors on the Self-Confidence of Female Students. In Proc. 39th SIGCSE Tech. Symp. Comput. Sci. Educ. (SIGCSE ’08) . Portland, OR, USA, 523–527. https://doi.org/10.1145/1352135.1352309 https://doi.org/10.1145/1352135.1352309.
Latham, A. et al. 2012. A conversational intelligent tutoring system to automatically predict learning styles. Comput. & Educ. 59, 1 (Aug. 2012), 95– 109. http://www.sciencedirect.com/science/article/pii/S0360131511002648 https://doi.org/10.1016/j.compedu.2011.11.001.
Richard Lobb and Jenny Harlow. 2016. Coderunner: A Tool for Assessing Computer Programming Skills. ACM Inroads 7, 1 (Feb. 2016), 47–51. https://doi.org/10.1145/2810041 https://doi.org/10.1145/2810041.
Laura O. Moraes and Carlos Eduardo Pedreira. 2020. Designing an Intelligent Tutoring System Across Multiple Classes. In 4th Educational Data Mining in Computer Science Education Workshop (Virtual).
Laura O. Moraes and Carlos Eduardo Pedreira. 2021. Clustering Introductory Computer Science Exercises Using Topic Modeling Methods. IEEE Trans. Learn. Technol. (2021). https://doi.org/10.1109/TLT.2021.3056907
Abelardo Pardo, Kathryn Bartimote, Simon Buckingham Shum, Shane Dawson, Jing Gao, Dragan Gašević, Steve Leichtweis, Danny Liu, Roberto Martínez-Maldonado, Negin Mirriahi, Adon Christian Michael Moskal, Jurgen Schulte, George Siemens, and Lorenzo Vigentini. 2018. OnTask: Delivering Data-Informed, Personalized Learning Support Actions. Learning Analytics 5, 3 (Dec. 2018), 235–249. https://doi.org/10.18608/jla.2018.53.15
Carolyn P. Rosé, Elizabeth A. McLaughlin, Ran Liu, and Kenneth R. Koedinger. 2019. Explanatory learner models: Why machine learning (alone) is not the answer. British Journal of Educational Technology 50, 6 (Nov. 2019), 2943–2958. https://doi.org/10.1111/bjet.12858
Chunpai Wang, Shaghayegh Sahebi, Siqian Zhao, Peter Brusilovsky, and Laura O. Moraes. 2021. Knowledge Tracing for Complex Problem Solving: Granular Rank-Based Tensor Factorization. In Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (Utrecht, Netherlands) (UMAP ’21) . Association for Computing Machinery, New York, NY, USA, 179–188. https://doi.org/10.1145/3450613.3456831
Publicado
05/11/2021
Como Citar
MORAES, Laura O.; PEDREIRA, Carlos Eduardo; DELGADO, Carla; FREIRE, João Pedro.
Supporting Decisions Using Educational Data Analysis. In: WORKSHOP DE FERRAMENTAS E APLICAÇÕES - SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 27. , 2021, Minas Gerais.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 99-102.
ISSN 2596-1683.
DOI: https://doi.org/10.5753/webmedia_estendido.2021.17622.
