Using Explainable Machine Learning to Automatically Provide Feedback to Students Based on Data Analysis

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  • Kipkirui Keter Light Academy Nairobi
  • Aden Joseph Light Academy Nairobi
  • Jabir Mohamed Light Academy Nairobi



Explainable Machine Learning, Learning Management System


Providing feedback to students is one of the most powerful practices that have enhanced education in the world today. Despite there being useful feedback provided by students’ self-regulation and teachers’ feedback provision, there is still a need for feedback that provides meaningful insights or actionable information about the reasons behind it, which is not provided by the said feedback. This paper explores how we can use explainable machine learning to compute data-driven feedback concerning students’ academic performance and generate actionable recommendations which are beneficial for students and teachers. This method has been developed based on LMS (Learning Management System) data from a university course. The effectiveness of the proposed approach has been evaluated with the results demonstrating 90% accuracy.



Hattie, J., Gan, M., Brooks, C.: Instruction based on feedback. In: Handbook of Research on Learning and Instruction, pp. 249–271 (2011)

Hattie, J., Timperley, H.: The power of feedback. Rev. Educ. Res. 77(1), 88–118(2007)

Choi, S.P., Lam, S.S., Li, K.C., Wong, B.T.: Learning analytics at low cost: at-risk student prediction with clicker data and systematic proactive interventions. J.Educ. Technol. Soc. 21(2), 273–290 (2018)

Howard, E., Meehan, M., Parnell, A.: Contrasting prediction methods for early warning systems at undergraduate level. Internet High. Educ. 37, 66–75 (2018)

Marbouti, F., Diefes-Dux, H.A., Madhavan, K.: Models for early prediction of at-risk students in a course using standards-based grading. Comput. Educ. 103, 1–15(2016)

Baneres, D., Rodr ́ıguez-Gonzalez, M.E., Serra, M.: An early feedback prediction system for learners at-risk within a first-year higher education course. IEEE Trans.Learn. Technol. 12(2), 249–263 (2019)

Bennion, L.D., et al.: Early identification of struggling learners: using prematriculation and early academic performance data. Perspect. Med. Educ. 8(5), 298–304(2019).

Rosenthal, S., et al.: Identifying students at risk of failing the USMLE step 2 clinical skills examination. Fam. Med. 51(6), 483–499 (2019)

Lu, O.H., Huang, A.Y., Huang, J.C., Lin, A.J., Ogata, H., Yang, S.J.: Applying learning analytics for the early prediction of Students’ academic performance in blended learning. J. Educ. Technol. Soc. 21(2), 220–232 (2018)

Gunning, David, Mark Stefik, Jaesik Choi, Timothy Miller, Simone Stumpf, and Guang-Zhong Yang. "XAI—Explainable artificial intelligence." Science robotics 4, no. 37 (2019): eaay7120.




How to Cite

Keter, K., Joseph, A., & Mohamed, J. (2023). Using Explainable Machine Learning to Automatically Provide Feedback to Students Based on Data Analysis. London Journal of Social Sciences, (6), 26–32.