Deep Learning para Previsão do Desempenho do Estudante: Um Mapeamento Sistemático da Literatura
Resumo
Não é de hoje que a evasão escolar tem sido um problema, tanto em ambientes presenciais, quanto em ambientes virtuais de aprendizagem. Afetando não apenas a carreira dos estudantes, como também as instituições de ensino. Prever o desempenho do aluno ajuda as instituições a identificar os fatores que levam à evasão, e assim tomar medidas para a retenção do aluno na escola. Este trabalho busca estudar como o Deep Learning tem sido aplicado na previsão do desempenho do estudante, e identificar como ele tem sido implementado, as técnicas, métodos, algoritmos e ferramentas.
Palavras-chave:
Deep Learning, Previsão de Desempenho, Evasão Escolar, Aprendizado de Máquina
Referências
Anantharaman, H., Mubarak, A. e Shobana, B. T. (2018). Modelling an adaptive e-learning system using LSTM and random forest classification. Em 2018 IEEE Conference on e-Learning, e-Management and e-Services (IC3e), páginas 29-34.
Botelho, A. F., Varatharaj, A., Patikorn, T., Doherty, D., Adjei, S. A. e Beck, J. E. (2019). Developing early detectors of student attrition and wheel spinning using deep learning. IEEE Transactions on Learning Technologies, 12(2), 158-170.
Buchinger, D., Cavalcanti, G. e Hounsell, M. (2014). Mecanismos de busca acadêmica: uma análise quantitativa. Revista Brasileira de Computação Aplicada, 6(1), 108-120.
Chen, P., Lu, Y., Zheng, V. W. e Pian, Y. (2018). Prerequisite-driven deep knowledge tracing. Em 2018 IEEE International Conference on Data Mining (ICDM), páginas 39-48.
Coelho, O. B. e Silveira, I. F. (2017). Deep learning applied to learning analytics and educational data mining: A systematic literature review. Em VI Congresso Brasileiro de Informática na Educação (CBIE), página 143.
Devasia, T., Vinushree T e Hegde, V. (2016). Prediction of students' performance using educational data mining. Em 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), páginas 91-95.
Fok, W. W. T., He, Y. S., Yeung, H. H. A., Law, K. Y., Cheung, K., Ai, Y. e Ho, P. (2018). Prediction model for students' future development by deep learning and TensorFlow artificial intelligence engine. Em 2018 4th International Conference on Information Management (ICIM), páginas 103-106.
Fouki, M. E., Aknin, N. e Kadiri, K. E. E. (2019). Multidimensional approach based on deep learning to improve the prediction performance of DNN models. International Journal of Emerging Technologies in Learning (iJET), 14(02), 30-41.
Guo, B., Zhang, R., Xu, G., Shi, C. e Yang, L. (2015). Predicting students' performance in educational data mining. Em 2015 International Symposium on Educational Technology (ISET), páginas 125-128.
Hu, Q. e Rangwala, H. (2019). Reliable deep grade prediction with uncertainty estimation. Em Proceedings of the 9th International Conference on Learning Analytics & Knowledge, LAK19, páginas 76-85, New York, NY, USA, ACM.
Huang, N., Chen, C., Tzeng, J., Fang, T. T. e Lee, C. (2018). Concept assessment system integrated with a knowledge map using deep learning. Em 2018 Learning With MOOCS (LWMOOCS), páginas 113-116.
Hussain, S., Muhsion, Z., Salal, Y., Theodoru, P., Kurto?lu, F. e Hazarika, G. C. (2019). Prediction model on student performance based on internal assessment using deep learning. International Journal of Emerging Technologies in Learning (iJET), 14(08), 4-22.
Jost, I. (2015). Aplicação de deep learning em dados refinados para mineração de opiniões. Dissertação de Mestrado, Universidade do Vale do Rio dos Sinos, São Leopoldo.
Lee, J. e Yeung, D.-Y. (2019). Knowledge query network for knowledge tracing: How knowledge interacts with skills. Em Proceedings of the 9th International Conference on Learning Analytics & Knowledge, LAK19, páginas 491-500, New York, NY, USA, ACM.
Martins, L. C. B., Carvalho, R. N., Carvalho, R. S., Victorino, M. C. e Holanda, M. (2017). Early prediction of college attrition using data mining. Em 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), páginas 1075-1078.
Nagy, M. e Molontay, R. (2018). Predicting dropout in higher education based on secondary school performance. Em 2018 IEEE 22nd International Conference on Intelligent Engineering Systems (INES), páginas 000389-000394.
Patil, A. P., Ganesan, K. e Kanavalli, A. (2017). Effective deep learning model to predict student grade point averages. Em 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), páginas 1-6.
Petersen, K., Feldt, R., Mujtaba, S. e Mattsson, M. (2008). Systematic mapping studies in software engineering. Em Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering, EASE’08, páginas 68-77, Swindon, UK, BCS Learning & Development Ltd.
Romero, C. e Ventura, S. (2013). Data mining in education. Wiley Int. Rev. Data Min. and Knowl. Disc., 3(1), 12-27.
Tanuar, E., Heryadi, Y., Lukas, Abbas, B. S. e Gaol, F. L. (2018). Using machine learning techniques to earlier predict student's performance. Em 2018 Indonesian Association for Pattern Recognition International Conference (INAPR), páginas 85-89.
Wang, W., Yu, H. e Miao, C. (2017). Deep model for dropout prediction in MOOCs. Em Proceedings of the 2Nd International Conference on Crowd Science and Engineering, ICCSE’17, páginas 26-32, New York, NY, USA, ACM.
Wilson, K. H., Xiong, X., Khajah, M., Lindsey, R. V., Zhao, S., Karklin, Y., Inwegen, E. G. V., Han, B., Ekanadham, C. e Beck, J. E. (2016). Estimating student proficiency: Deep learning is not the panacea. In Neural Information Processing Systems, Workshop on Machine Learning for Education.
Xing, W. e Du, D. (2019). Dropout prediction in MOOCs: Using deep learning for personalized intervention. Journal of Educational Computing Research, páginas 547-570.
Botelho, A. F., Varatharaj, A., Patikorn, T., Doherty, D., Adjei, S. A. e Beck, J. E. (2019). Developing early detectors of student attrition and wheel spinning using deep learning. IEEE Transactions on Learning Technologies, 12(2), 158-170.
Buchinger, D., Cavalcanti, G. e Hounsell, M. (2014). Mecanismos de busca acadêmica: uma análise quantitativa. Revista Brasileira de Computação Aplicada, 6(1), 108-120.
Chen, P., Lu, Y., Zheng, V. W. e Pian, Y. (2018). Prerequisite-driven deep knowledge tracing. Em 2018 IEEE International Conference on Data Mining (ICDM), páginas 39-48.
Coelho, O. B. e Silveira, I. F. (2017). Deep learning applied to learning analytics and educational data mining: A systematic literature review. Em VI Congresso Brasileiro de Informática na Educação (CBIE), página 143.
Devasia, T., Vinushree T e Hegde, V. (2016). Prediction of students' performance using educational data mining. Em 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), páginas 91-95.
Fok, W. W. T., He, Y. S., Yeung, H. H. A., Law, K. Y., Cheung, K., Ai, Y. e Ho, P. (2018). Prediction model for students' future development by deep learning and TensorFlow artificial intelligence engine. Em 2018 4th International Conference on Information Management (ICIM), páginas 103-106.
Fouki, M. E., Aknin, N. e Kadiri, K. E. E. (2019). Multidimensional approach based on deep learning to improve the prediction performance of DNN models. International Journal of Emerging Technologies in Learning (iJET), 14(02), 30-41.
Guo, B., Zhang, R., Xu, G., Shi, C. e Yang, L. (2015). Predicting students' performance in educational data mining. Em 2015 International Symposium on Educational Technology (ISET), páginas 125-128.
Hu, Q. e Rangwala, H. (2019). Reliable deep grade prediction with uncertainty estimation. Em Proceedings of the 9th International Conference on Learning Analytics & Knowledge, LAK19, páginas 76-85, New York, NY, USA, ACM.
Huang, N., Chen, C., Tzeng, J., Fang, T. T. e Lee, C. (2018). Concept assessment system integrated with a knowledge map using deep learning. Em 2018 Learning With MOOCS (LWMOOCS), páginas 113-116.
Hussain, S., Muhsion, Z., Salal, Y., Theodoru, P., Kurto?lu, F. e Hazarika, G. C. (2019). Prediction model on student performance based on internal assessment using deep learning. International Journal of Emerging Technologies in Learning (iJET), 14(08), 4-22.
Jost, I. (2015). Aplicação de deep learning em dados refinados para mineração de opiniões. Dissertação de Mestrado, Universidade do Vale do Rio dos Sinos, São Leopoldo.
Lee, J. e Yeung, D.-Y. (2019). Knowledge query network for knowledge tracing: How knowledge interacts with skills. Em Proceedings of the 9th International Conference on Learning Analytics & Knowledge, LAK19, páginas 491-500, New York, NY, USA, ACM.
Martins, L. C. B., Carvalho, R. N., Carvalho, R. S., Victorino, M. C. e Holanda, M. (2017). Early prediction of college attrition using data mining. Em 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), páginas 1075-1078.
Nagy, M. e Molontay, R. (2018). Predicting dropout in higher education based on secondary school performance. Em 2018 IEEE 22nd International Conference on Intelligent Engineering Systems (INES), páginas 000389-000394.
Patil, A. P., Ganesan, K. e Kanavalli, A. (2017). Effective deep learning model to predict student grade point averages. Em 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), páginas 1-6.
Petersen, K., Feldt, R., Mujtaba, S. e Mattsson, M. (2008). Systematic mapping studies in software engineering. Em Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering, EASE’08, páginas 68-77, Swindon, UK, BCS Learning & Development Ltd.
Romero, C. e Ventura, S. (2013). Data mining in education. Wiley Int. Rev. Data Min. and Knowl. Disc., 3(1), 12-27.
Tanuar, E., Heryadi, Y., Lukas, Abbas, B. S. e Gaol, F. L. (2018). Using machine learning techniques to earlier predict student's performance. Em 2018 Indonesian Association for Pattern Recognition International Conference (INAPR), páginas 85-89.
Wang, W., Yu, H. e Miao, C. (2017). Deep model for dropout prediction in MOOCs. Em Proceedings of the 2Nd International Conference on Crowd Science and Engineering, ICCSE’17, páginas 26-32, New York, NY, USA, ACM.
Wilson, K. H., Xiong, X., Khajah, M., Lindsey, R. V., Zhao, S., Karklin, Y., Inwegen, E. G. V., Han, B., Ekanadham, C. e Beck, J. E. (2016). Estimating student proficiency: Deep learning is not the panacea. In Neural Information Processing Systems, Workshop on Machine Learning for Education.
Xing, W. e Du, D. (2019). Dropout prediction in MOOCs: Using deep learning for personalized intervention. Journal of Educational Computing Research, páginas 547-570.
Publicado
11/11/2019
Como Citar
WILL, Nilcimar Neitzel; KEMCZINSKI, Avanilde; PARPINELLI, Rafael Stubs.
Deep Learning para Previsão do Desempenho do Estudante: Um Mapeamento Sistemático da Literatura. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 30. , 2019, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 1798-1807.
DOI: https://doi.org/10.5753/cbie.sbie.2019.1798.
