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Arabian Journal for Science and Engineering
Published in: Arabian Journal for Science and Engineering 2/2022

13-09-2021 | Research Article-Computer Engineering and Computer Science

Back to Basics: An Interpretable Multi-Class Grade Prediction Framework

Author: Basma Alharbi

Published in: Arabian Journal for Science and Engineering | Issue 2/2022

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Abstract

Next-term grade prediction is a challenging problem. The objective of this problem is to predict students grades in new courses, given their grades in courses they have previously taken. Adopting various machine learning algorithms is a very common and straightforward approach to tackling this problem. However, such models are very difficult to interpret. That is, it is difficult to explain to a student (or a teacher) why the model predicted grade B for a given student for example. In this work, we shed light on the importance of building interpretable models for educational data mining tasks. Specifically, we propose a novel interpretable framework for multi-class grade prediction that is based on an optimal rule-list mining algorithm. Additionally, we evaluate our proposed framework on two private datasets and compare our results with baseline models. Our findings show that our proposed framework is capable of achieving higher prediction and interpretability values when compared to black-box models.
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Footnotes
1
In the literature, interpretability and explainability are sometimes used interchangeably. However, in this work, we emphasize the distinction between the two terms and use the same definitions as [13]. In [26], on the other hand, the term ‘simulatability’ is used to indicate the same concept as interpretability in this work and Rudin [13] and Baker [14]. In some work, such as Freitas [12], the notion of interpretability is referred to as comprehensibility.
 
2
The datasets cannot be shared with the public for privacy reasons.
 
3
Note that in GPD2, due to the utilized data collection mechanism, these course-oriented features cannot be computed from previous course data, but rather from the current one.
 
Literature
1.
Sweeney, M.; Lester, J.; Rangwala, H.: Next-term student grade prediction. In: 2015 IEEE International Conference on Big Data (Big Data), pp. 970–975. IEEE (2015)
2.
Sandoval, A.; Gonzalez, C.; Alarcon, R.; Pichara, K.; Montenegro, M.: Centralized student performance prediction in large courses based on low-cost variables in an institutional context. Internet Higher Edu. 37, 76–89 (2018)CrossRef
3.
Akçapınar, G.; Hasnine, M.N.; Majumdar, R.; Flanagan, B.; Ogata, H.: Developing an early-warning system for spotting at-risk students by using ebook interaction logs. Smart Learn. Environ. 6(1), 4 (2019)CrossRef
4.
5.
Bratko, I.: Machine learning: between accuracy and interpretability. In: Learning, networks and statistics, pp. 163–177. Springer (1997)
6.
Dawes, R.M.: The robust beauty of improper linear models in decision making. Am. Psychol. 34(7), 571 (1979)CrossRef
7.
Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Mach. Learn. 11(1), 63–90 (1993)CrossRef
8.
Giraud-Carrier, C.: Beyond predictive accuracy: what. In: Proceedings of the ECML-98 Workshop on Upgrading Learning to Meta-Level: Model Selection and Data Transformation, pp. 78–85 (1998)
9.
Rüping, S.: Learning interpretable models. Ph.D. Thesis (2006)
10.
11.
Vellido, A., Martín-Guerrero, J.D., Lisboa, P.J.G.: Making machine learning models interpretable. In: ESANN, vol. 12, pp. 163–172. Citeseer (2012)
12.
Freitas, A.A.: Comprehensible classification models: a position paper. ACM SIGKDD Exp. Newsl. 15(1), 1–10 (2014)CrossRef
13.
Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1(5), 206–215 (2019)CrossRef
14.
Baker, R.S.: Challenges for the future of educational data mining: the baker learning analytics prizes. JEDM|J. Edu. Data Min. 11(1), 1–17 (2019)
15.
Alamri, R.; Alharbi, B.: Explainable student performance prediction models: a systematic review. IEEE Access (2021)
16.
Miller, G.A.: The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63(2), 81 (1956)CrossRef
17.
Cowan, N.: The magical mystery four: how is working memory capacity limited, and why? Curr. Dir. Psychol. Sci. 19(1), 51–57 (2010)MathSciNetCrossRef
18.
Angelino, E.; Larus-Stone, N.; Alabi, D.; Seltzer, M.; Rudin, C.: Learning certifiably optimal rule lists for categorical data. J. Mach. Learn. Res. 18(1), 8753–8830 (2017)MathSciNetMATH
19.
Aldowah, H.; Al-Samarraie, H.; Fauzy, W.M.: Educational data mining and learning analytics for 21st century higher education: a review and synthesis. Telemat. Inform. 37, 13–49 (2019)CrossRef
20.
Papamitsiou, Z.; Economides, A.A.: Learning analytics and educational data mining in practice: a systematic literature review of empirical evidence. J. Edu. Technol. Soc. 17(4), 49–64 (2014)
21.
Peña-Ayala, A.: Educational data mining: a survey and a data mining-based analysis of recent works. Expert Syst. Appl. 41(4), 1432–1462 (2014)CrossRef
22.
Kamal, P.; Ahuja, S.: Academic performance prediction using data mining techniques: identification of influential factors effecting the academic performance in undergrad professional course. In: Harmony Search and Nature Inspired Optimization Algorithms, pp. 835–843. Springer (2019)
23.
Su, Y.; Liu, Q.; Liu, Q.,;Huang, Z.; Yin, Y.; Chen, E.; Ding, C.; Wei, S.; Hu, G.: Exercise-enhanced sequential modeling for student performance prediction. In: 32nd AAAI Conference on Artificial Intelligence (2018)
24.
Macfadyen, L.P.; Dawson, S.: Mining LMS data to develop an “early warning system” for educators: a proof of concept. Comput. Edu. 54(2), 588–599 (2010)
25.
Murdoch, W.J.; Singh, C.; Kumbier, K.; Abbasi-Asl, R.; Yu, B.: Definitions, methods, and applications in interpretable machine learning. Proc. Natl. Acad. Sci. 116(44), 22071–22080 (2019)MathSciNetCrossRef
26.
Murdoch, W.J.; Singh, C.; Kumbier, K.; Abbasi-Asl, R.; Yu, B.: Interpretable machine learning: definitions, methods, and applications. arXiv:​1901.​04592 (2019)
27.
Arrieta, A.B.; Díaz-Rodríguez, N.; Del Ser, J.; Bennetot, A.; Tabik, S.; Barbado, A.; García, S.; Gil-López, S.; Molina, D.; Benjamins, R.; Chatila, R.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fus. 58, 82–115 (2020)CrossRef
28.
Bhatt, U.; Xiang, A.; Sharma, S.; Weller, A.; Taly, A.; Jia, Y.; Ghosh, J.; Puri, R.; Moura, J.M.; Eckersley, P.: Explainable machine learning in deployment. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 648–657 (2020)
29.
Lakkaraju, H.; Bastani, O.: “how do i fool you?” manipulating user trust via misleading black box explanations. In: Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 79–85 (2020)
30.
Proença, H.M.; van Leeuwen, M.: Interpretable multiclass classification by mdl-based rule lists. Inf. Sci. 512, 1372–1393 (2020)CrossRef
31.
Yang, H.; Rudin, C.; Seltzer, M.: Scalable Bayesian rule lists. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 3921–3930. JMLR. org (2017)
32.
Rudin, C.; Ertekin, Ş.: Learning customized and optimized lists of rules with mathematical programming. Math. Progr. Comput. 10(4), 659–702 (2018)
33.
Letham, B.; Rudin, C.; McCormick, T.H.; Madigan, D.; et al.: Interpretable classifiers using rules and Bayesian analysis: building a better stroke prediction model. Ann. Appl. Stat. 9(3), 1350–1371 (2015)MathSciNetCrossRef
34.
Cano, A.; Zafra, A.; Ventura, S.N.: An interpretable classification rule mining algorithm. Inf. Sci. 240, 1–20 (2013)CrossRef
35.
Molnar, C.: Interpretable machine learning. Lulu.com (2019)
36.
Zhang, W.; Zhou, Y.; Yi, B.: An interpretable online learner’s performance prediction model based on learning analytics. In: Proceedings of the 2019 11th International Conference on Education Technology and Computers, pp. 148–154 (2019)
37.
Meca, I.; Mollá-Campello, N.; Rabasa, A.: A new methodology for early warning of critical academic performance, based on discrete predictive models. In: Proceedings of the 7th International Conference on Technological Ecosystems for Enhancing Multiculturality, pp. 680–685 (2019)
38.
Helal, S.; Li, J.; Liu, L.; Ebrahimie, E.; Dawson, S.; Murray, D.J.; Long, Q.: Predicting academic performance by considering student heterogeneity. Knowl.-Based Syst. 161, 134–146 (2018)
39.
Villagrá-Arnedo, C.J.; Gallego-Durán, F.J.; Llorens-Largo, F.; Compañ-Rosique, P.; Satorre-Cuerda, R.; Molina-Carmona, R.: Improving the expressiveness of black-box models for predicting student performance. Comput. Hum. Behav. 72, 621–631 (2017)CrossRef
40.
Xing, W.; Guo, R.; Petakovic, E.; Goggins, S.: Participation-based student final performance prediction model through interpretable genetic programming: integrating learning analytics, educational data mining and theory. Comput. Hum. Behav. 47, 168–181 (2015)CrossRef
41.
Ya-Han, H.; Lo, C.-L.; Shih, S.-P.: Developing early warning systems to predict students’ online learning performance. Comput. Hum. Behav. 36, 469–478 (2014)
42.
Polyzou, A.; Karypis, G.: Feature extraction for classifying students based on their academic performance. International Educational Data Mining Society (2018)
43.
Fayyad, U.; Irani, K.: Multi-interval discretization of continuous-valued attributes for classification learning (1993)
44.
Flach, P.: Machine learning: the art and science of algorithms that make sense of data. Cambridge University Press (2012)
45.
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)
46.
García, S.; Fernández, A.; Luengo, J.; Herrera, F.: A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability. Soft. Comput. 13(10), 959 (2009)CrossRef
47.
Nauck, D.D: Measuring interpretability in rule-based classification systems. In: The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ’03, vol. 1, pp. 196–201. IEEE (2003)
48.
Fan, R.-E.; Chang, K.-W.; Hsieh, C.-J.; Lin, C.-J.: Liblinear: a library for large linear classification. J. Mach. Learn. Rese. 9, 1871–1874 (2008)MATH
49.
Liaw, A.; Wiener, M.; et al.: Classification and regression by randomforest. R News 2(3), 18–22 (2002)
50.
Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; Vanderplas, J.; Passos, A.; Cournapeau, D.; Brucher, M.; Perrot, M.; Duchesnay, E.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetMATH
Metadata
Title
Back to Basics: An Interpretable Multi-Class Grade Prediction Framework
Author
Basma Alharbi
Publication date
13-09-2021
Publisher
Springer Berlin Heidelberg
Published in
Arabian Journal for Science and Engineering / Issue 2/2022
Print ISSN: 2193-567X
Electronic ISSN: 2191-4281
DOI
https://doi.org/10.1007/s13369-021-06153-x

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