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Condensed Discriminative Question Set for Reliable Exam Score Prediction

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Artificial Intelligence in Education (AIED 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12749))

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The inevitable shift towards online learning due to the emergence of the COVID-19 pandemic triggered a strong need to assess students using shorter exams whilst ensuring reliability. This study explores a data-centric approach that utilizes feature importance to select a discriminative subset of questions from the original exam. Furthermore, the discriminative question subset’s ability to approximate the students exam scores is evaluated by measuring the prediction accuracy and by quantifying the error interval of the prediction. The approach was evaluated using two real-world exam datasets of the Scholastic Aptitude Test (SAT) and Exame Nacional do Ensino Médio (ENEM) exams, which consist of student response data and the corresponding the exam scores. The evaluation was conducted against randomized question subsets of sizes 10, 20, 30 and 50. The results show that our method estimates the full scores more accurately than a baseline model in most question sizes while maintaining a reasonable error interval. The encouraging evidence found in this paper provides support for the strong potential of the on-going study to provide a data-centric approach for exam size reduction.

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  1. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  Google Scholar 

  2. Choi, Y., et al: Assessment modeling: fundamental pre-training tasks for interactive educational systems. arXiv preprint arXiv:2002.05505 (2020)

  3. Hellas, A., et al.: Predicting academic performance: a systematic literature review. In: Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education, pp. 175–199 (2018)

    Google Scholar 

  4. James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning, vol. 112. Springer, Heidelberg (2013).

    Book  MATH  Google Scholar 

  5. Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Advances in Neural Information Processing Systems, pp. 6402–6413 (2017)

    Google Scholar 

  6. Meier, Y., Xu, J., Atan, O., Van der Schaar, M.: Predicting grades. IEEE Trans. Signal Process. 64(4), 959–972 (2015)

    Article  MathSciNet  Google Scholar 

  7. Mouta, A., Sánchez, E.T., Llorente, A.P.: Blending machines, learning, and ethics. In: Proceedings of the Seventh International Conference on Technological Ecosystems for Enhancing Multiculturality, pp. 993–998 (2019)

    Google Scholar 

  8. Sani, S.M., Bichi, A.B., Ayuba, S.: Artificial intelligence approaches in student modeling: half decade review (2010–2015). IJCSN-Int. J. Comput. Scie. Netw. 5(5) (2016)

    Google Scholar 

  9. Sweeney, M., Rangwala, H., Lester, J., Johri, A.: Next-term student performance prediction: a recommender systems approach. arXiv preprint arXiv:1604.01840 (2016)

  10. Vie, J.J., Popineau, F., Bruillard, É., Bourda, Y.: A review of recent advances in adaptive assessment. In: Peña-Ayala, A. (ed.) Learning Analytics: Fundaments, Applications, and Trends, vol. 94, pp. 113–142. Springer, Cham (2017).

  11. Zhang, S., Chang, H.H.: From smart testing to smart learning: how testing technology can assist the new generation of education. Int. J. Smart Technol. Learn. 1(1), 67–92 (2016)

    Article  Google Scholar 

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Correspondence to Juneyoung Park .

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Kim, J.H., Baek, J., Hwang, C., Bae, C., Park, J. (2021). Condensed Discriminative Question Set for Reliable Exam Score Prediction. In: Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science(), vol 12749. Springer, Cham.

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  • Print ISBN: 978-3-030-78269-6

  • Online ISBN: 978-3-030-78270-2

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