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Hyperparameter Tuning Using Automated Methods to Improve Models for Predicting Student Success

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Book cover Information and Software Technologies (ICIST 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1283))

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Abstract

Predicting student failure is an important task for educators and a popular application in Educational Data Mining. However, building prediction models is not an easy task and requires time and expertise for feature engineering, model selection, and hyperparameters tuning. In this paper, a strategy of automatic machine learning is used to assess the impact on the performance of prediction models. A previous experiment was modified to include hyperparameter tuning with an autoML method for hyperparameters tuning. The data cleaning, preprocessing, feature engineering and time segmentation approach part of the experiment remained unchanged. With this approach, the correct impact on model performance by hyperparameter tuning can be measured on models that were carefully built. The results show improved performance especially for Decision Tree, Extra Tree, Random Forest Classifiers. This study shows that even carefully planned educational prediction models can benefit for the use of autoML methods and could help non-expert users in the field of EDM to achieve accurate results.

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References

  1. Peña-Ayala, A.: Educational data mining: a survey and a data mining-based analysis of recent works. Exp. Syst. Appl. 41, 1432–1462 (2014)

    Article  Google Scholar 

  2. Romero, C., Ventura, S.: Educational data mining: a review of the state of the art. Syst. Man Cybern. Part C Appl. Rev. IEEE Trans. 40, 601–618 (2010)

    Google Scholar 

  3. Hämäläinen, W., Vinni, M.: Comparison of machine learning methods for intelligent tutoring systems. In: Ikeda, M., Ashley, K.D., Chan, T.-W. (eds.) ITS 2006. LNCS, vol. 4053, pp. 525–534. Springer, Heidelberg (2006). https://doi.org/10.1007/11774303_52

    Chapter  Google Scholar 

  4. Mueen, A., Zafar, B., Manzoor, U.: Modeling and predicting students’ academic performance using data mining techniques. Int. J. Mod. Educ. Comput. Sci. 8, 36–42 (2016). King Abdulaziz University, Saudi Arabia, Jeddah https://doi.org/10.5815/ijmecs.2016.11.05

  5. Sweeney, M., Rangwala, H., Lester, J., Johri, A.: Next-term student performance prediction: a recommender systems approach. J. Educ. Data Min. 8, 22–50 (2016)

    Google Scholar 

  6. Okubo, F., Yamashita, T., Shimada, A., Ogata, H.: A neural network approach for students’ performance prediction. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, pp. 598–599. Association for Computing Machinery, Vancouver (2017). https://doi.org/10.1145/3027385.3029479

  7. Delavari, N., Phon-Amnuaisuk, S., Beikzadeh, M.R.: Data mining application in higher learning institutions. Inform. Educ. Int. J. 7, 31–54 (2008)

    Google Scholar 

  8. Feurer, M., Klein, A., Eggensperger, K., Springenberg, J.T., Blum, M., Hutter, F.: Auto-sklearn: efficient and robust automated machine learning. In: Hutter, F., Kotthoff, L., Vanschoren, J. (eds.) Automated Machine Learning. TSSCML, pp. 113–134. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05318-5_6

    Chapter  Google Scholar 

  9. Bucos, M., Drăgulescu, B.: Predicting student success using data generated in traditional educational environments. TEM J. 7, 617–625 (2018). https://doi.org/10.18421/TEM73-19

    Article  Google Scholar 

  10. Tsiakmaki, M., Kostopoulos, G., Kotsiantis, S., Ragos, O.: Implementing AutoML in educational data mining for prediction tasks. Appl. Sci. 10, 90 (2020)

    Article  Google Scholar 

  11. Feyyad, U.M.: Data mining and knowledge discovery: making sense out of data. IEEE Exp. 11, 20–25 (1996)

    Article  Google Scholar 

  12. Kurgan, L.A., Musilek, P.: A survey of knowledge discovery and data mining process models. Knowl. Eng. Rev. 21, 1–24 (2006)

    Article  Google Scholar 

  13. Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  14. Lee, N., Kim, J.-M.: Conversion of categorical variables into numerical variables via Bayesian network classifiers for binary classifications. Comput. Stat. Data Anal. 54, 1247–1265 (2010)

    Article  MathSciNet  Google Scholar 

  15. Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2011)

    MATH  Google Scholar 

  16. Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and Regression Trees. CRC Press, Boca Raton (1984)

    MATH  Google Scholar 

  17. Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63, 3–42 (2006)

    Article  Google Scholar 

  18. Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)

    Article  Google Scholar 

  19. Yu, H.-F., Huang, F.-L., Lin, C.-J.: Dual coordinate descent methods for logistic regression and maximum entropy models. Mach. Learn. 85, 41–75 (2011)

    Article  MathSciNet  Google Scholar 

  20. Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. TIST 2, 27 (2011)

    Google Scholar 

  21. Van Hulse, J., Khoshgoftaar, T.M., Napolitano, A.: Experimental perspectives on learning from imbalanced data. In: Proceedings of the 24th International Conference on Machine learning, pp. 935–942 (2007)

    Google Scholar 

  22. Arlot, S., Celisse, A.: A survey of cross-validation procedures for model selection. Stat. Surv. 4, 40–79 (2010)

    Article  MathSciNet  Google Scholar 

  23. Hens, A.B., Tiwari, M.K.: Computational time reduction for credit scoring: an integrated approach based on support vector machine and stratified sampling method. Exp. Syst. Appl. 39, 6774–6781 (2012)

    Article  Google Scholar 

  24. Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., Hutter, F.: Efficient and robust automated machine learning. In: Advances in Neural Information Processing Systems, pp. 2962–2970 (2015)

    Google Scholar 

  25. Brodersen, K.H., Ong, C.S., Stephan, K.E., Buhmann, J.M.: The balanced accuracy and its posterior distribution. In: 2010 20th International Conference on Pattern Recognition, pp. 3121–3124. IEEE (2010)

    Google Scholar 

  26. Márquez-Vera, C., Cano, A., Romero, C., Ventura, S.: Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data. Appl. Intell. 38, 315–330 (2013)

    Article  Google Scholar 

  27. Aguiar, E., Chawla, N.V., Brockman, J., Ambrose, G.A., Goodrich, V.: Engagement vs performance: using electronic portfolios to predict first semester engineering student retention. In: Proceedings of the Fourth International Conference on Learning Analytics and Knowledge, pp. 103–112 (2014)

    Google Scholar 

  28. Lopez, M.I., Luna, J.M., Romero, C., Ventura, S.: Classification via clustering for predicting final marks based on student participation in forums. In: Proceedings of the 5th International Conference on Educational Data Mining, pp. 148–151. International Educational Data Mining Society, Chania (2012)

    Google Scholar 

  29. Romero, C., Espejo, P.G., Zafra, A., Romero, J.R., Ventura, S.: Web usage mining for predicting final marks of students that use Moodle courses. Comput. Appl. Eng. Educ. 21, 135–146 (2013)

    Article  Google Scholar 

  30. Tan, M., Shao, P.: Prediction of student dropout in e-learning program through the use of machine learning method. Int. J. Emerg. Technol. Learn. IJET 10, 11–17 (2015)

    Article  Google Scholar 

  31. González-Brenes, J.P., Mostow, J., Duan, W.: How to classify tutorial dialogue? Comparing feature vectors vs. sequences. In: Proceedings of the 4th International Conference on Educational Data Mining, pp. 169–178 (2011)

    Google Scholar 

  32. Drăgulescu, B., Bucos, M., Vasiu, R.: Predicting assignment submissions in a multiclass classification problem. TEM J. 4, 244–254 (2015)

    Google Scholar 

  33. Wolff, A., Zdrahal, Z., Herrmannova, D., Knoth, P.: Predicting student performance from combined data sources. In: Peña-Ayala, A. (ed.) Educational Data Mining. SCI, vol. 524, pp. 175–202. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-02738-8_7

    Chapter  Google Scholar 

  34. Monllaó Olivé, D., Huynh, D.Q., Reynolds, M., Dougiamas, M., Wiese, D.: A supervised learning framework: using assessment to identify students at risk of dropping out of a MOOC. J. Comput. High. Educ. 32(1), 9–26 (2019). https://doi.org/10.1007/s12528-019-09230-1

    Article  Google Scholar 

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Authors and Affiliations

  1. Politehnica University Timișoara, Timișoara, Romania

    Bogdan Drăgulescu & Marian Bucos

Authors
  1. Bogdan Drăgulescu
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  2. Marian Bucos
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Corresponding author

Correspondence to Bogdan Drăgulescu .

Editor information

Editors and Affiliations

  1. Kaunas University of Technology, Kaunas, Lithuania

    Audrius Lopata

  2. Kaunas University of Technology, Kaunas, Lithuania

    Rita Butkienė

  3. Kaunas University of Technology, Kaunas, Lithuania

    Daina Gudonienė

  4. Kaunas University of Technology, Kaunas, Lithuania

    Vilma Sukackė

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Drăgulescu, B., Bucos, M. (2020). Hyperparameter Tuning Using Automated Methods to Improve Models for Predicting Student Success. In: Lopata, A., Butkienė, R., Gudonienė, D., Sukackė, V. (eds) Information and Software Technologies. ICIST 2020. Communications in Computer and Information Science, vol 1283. Springer, Cham. https://doi.org/10.1007/978-3-030-59506-7_25

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  • DOI: https://doi.org/10.1007/978-3-030-59506-7_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59505-0

  • Online ISBN: 978-3-030-59506-7

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