Skip to main content

Advertisement

SpringerLink
Log in

Student performance analysis and prediction in classroom learning: A review of educational data mining studies

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

Student performance modelling is one of the challenging and popular research topics in educational data mining (EDM). Multiple factors influence the performance in non-linear ways; thus making this field more attractive to the researchers. The widespread availability of e ducational datasets further catalyse this interestingness, especially in online learning. Although several EDM surveys are available in the literature, we could find only a few specific surveys on student performance analysis and prediction. These specific surveys are limited in nature and primarily focus on studies that try to identify possible predictor or model student performance. However, the previous works do not address the temporal aspect of prediction. Moreover, we could not find any such specific survey which focuses only on classroom-based education. In this paper, we present a systematic review of EDM studies on student performance in classroom learning. It focuses on identifying the predictors, methods used for such identification, time and aim of prediction. It is significantly the first systematic survey of EDM studies that consider only classroom learning and focuses on the temporal aspect as well. This paper presents a review of 140 studies in this area. The meta-analysis indicates that the researchers achieve significant prediction efficiency during the tenure of the course. However, performance prediction before course commencement needs special attention.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Abrami, P.C., D’Apollonia, S., & Rosenfield, S. (2007). The dimensionality of student ratings of instruction: what we know and what we do not. In The scholarship of teaching and learning in higher education: an evidence-based perspective (pp. 385–456): Springer.

  • Adjei, S.A., Botelho, A.F., & Heffernan, N.T. (2016). Predicting student performance on post-requisite skills using prerequisite skill data: an alternative method for refining prerequisite skill structures. In Proceedings of the sixth international conference on learning analytics & knowledge (pp. 469–473): ACM.

  • Aghabozorgi, S., Mahroeian, H., Dutt, A., Wah, T.Y., & Herawan, T. (2014). An approachable analytical study on big educational data mining. In International conference on computational science and its applications (pp. 721–737): Springer.

  • Agrawal, R., Imieliṅski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD Record, 22(2), 207–216.

    Google Scholar 

  • Ahmed, N.S., & Sadiq, M.H. (2018). Clarify of the random forest algorithm in an educational field. In 2018 international conference on advanced science and engineering (ICOASE) (pp. 179–184): IEEE.

  • Ahmed, S., Paul, R., Hoque, M.L., & Sayed, A. (2014). Knowledge discovery from academic data using association rule mining. In 2014 17th international conference on computer and information technology (ICCIT) (pp. 314–319): IEEE.

  • Akçapinar, G. (2015). How automated feedback through text mining changes plagiaristic behavior in online assignments. Computers & Education, 87, 123–130.

    Google Scholar 

  • Al-Barrak, M.A., & Al-Razgan, M. (2016). Predicting students final GPA using decision trees: a case study. International Journal of Information and Education Technology, 6(7), 528–533.

    Google Scholar 

  • Al-Obeidat, F., Tubaishat, A., Dillon, A., & Shah, B. (2017). Analyzing students’ performance using multi-criteria classification. Cluster Computing, 21(1), 623–632.

    Google Scholar 

  • Angeli, C., Howard, S., Ma, J., Yang, J., & Kirschner, P.A. (2017). Data mining in educational technology classroom research: can it make a contribution? Computers & Education, 113, 226–242.

    Google Scholar 

  • Asif, R., Merceron, A., Ali, S.A., & Haider, N.G. (2017). Analyzing undergraduate students’ performance using educational data mining. Computers & Education, 113, 177–194.

    Google Scholar 

  • Backenköhler, M., & Wolf, V. (2017). Student performance prediction and optimal course selection: an MDP approach. In International conference on software engineering and formal methods (pp. 40–47): Springer.

  • Bahritidinov, B., & Sánchez, E. (2017). Probabilistic classifiers and statistical dependency: the case for grade prediction. In International work-conference on the interplay between natural and artificial computation (pp. 394–403): Springer.

  • Baker, R.S. (2014). Educational data mining: an advance for intelligent systems in education. IEEE Intelligent Systems, 29(3), 78–82.

    Google Scholar 

  • Baker, R.S., & Inventado, P.S. (2014). Educational data mining and learning analytics. In Learning Analytics (pp. 61–75): Springer.

  • Baker, R.S.J.D., & Yacef, K. (2009). The state of educational data mining in 2009 : a review and future visions. Journal of Educational Data Mining, 1(1), 3–16.

    Google Scholar 

  • Bakhshinategh, B., Zaiane, O.R., ElAtia, S., & Ipperciel, D. (2017). Educational data mining applications and tasks: a survey of the last 10 years. Education and Information Technologies, 23(1), 537–553.

    Google Scholar 

  • Balam, E.M., & Shannon, D.M. (2010). Student ratings of college teaching: a comparison of faculty and their students. Assessment & Evaluation in Higher Education, 35(2), 209–221.

    Google Scholar 

  • Bayer, J., Bydzovská, H., Géryk, J., Obsivac, T., & Popelinsky, L. (2012). Predicting drop-out from social behaviour of students. In International conference on educational data mining (EDM).

  • Beemer, J., Spoon, K., He, L., Fan, J., & Levine, R.A. (2018). Ensemble learning for estimating individualized treatment effects in student success studies. International Journal of Artificial Intelligence in Education, 28 (3), 315–335.

    Google Scholar 

  • Bendikson, L., Hattie, J., & Robinson, V. (2011). Identifying the comparative academic performance of secondary schools. Journal of Educational Administration, 49(4), 433–449.

    Google Scholar 

  • Berkhin, P. (2006). A survey of clustering data mining techniques. In Grouping multidimensional data (pp. 25–71): Springer.

  • Bloom, B.S., Englehard, M., Furst, E., Hill, W., & Krathwohl, D. (1956). Taxonomy of educational objectives: the classification of educational goals. Handbook I: Cognitive Domain.

  • Bodily, R., Ikahihifo, T.K., Mackley, B., & Graham, C.R. (2018). The design, development, and implementation of student-facing learning analytics dashboards. Journal of Computing in Higher Education, 30(3), 572–598.

    Google Scholar 

  • Bogarin, A., Romero, C., Cerezo, R., & Sánchez-Santillan, M. (2014). Clustering for improving educational process mining. In Proceedings of the fourth international conference on learning analytics and knowledge (pp. 11–15): ACM.

  • Bresfelean, V.P., Bresfelean, M., Ghisoiu, N., & Comes, C.A. (2008). Determining students’ academic failure profile founded on data mining methods. In ITI 2008 - 30th international conference on information technology interfaces (pp. 317–322): IEEE.

  • Brocato, B.R., Bonanno, A., & Ulbig, S. (2015). Student perceptions and instructional evaluations: a multivariate analysis of online and face-to-face classroom settings. Education and Information Technologies, 20(1), 37–55.

    Google Scholar 

  • Bucos, M., & Druagulescu, B. (2018). Predicting student success using data generated in traditional educational environments. TEM Journal, 7(3), 617–625.

    Google Scholar 

  • Buldu, A., & Üçgün, K. (2010). Data mining application on students’ data. Procedia-Social and Behavioral Sciences, 2(2), 5251–5259.

    Google Scholar 

  • Buniyamin, N., Mat, U.B., & Arshad, P.M. (2015). Educational data mining for prediction and classification of engineering students achievement. In IEEE 7th international conference on engineering education ICEED 2015 (pp. 49–53).

  • Bydžovská, H. (2016). A comparative analysis of techniques for predicting student performance. In Proceedings of the 9th international conference on educational data mining.

  • Cakmak, A. (2017). Predicting student success in courses via collaborative filtering. International Journal of Intelligent Systems and Applications in Engineering, 5(1), 10–17.

    Google Scholar 

  • Campagni, R., Merlini, D., Sprugnoli, R., & Verri, M.C. (2015). Data mining models for student careers. Expert Systems with Applications, 42(13), 5508–5521.

    Google Scholar 

  • Carter, A.S., Hundhausen, C.D., & Adesope, O. (2017). Blending measures of programming and social behavior into predictive models of student achievement in early computing courses. ACM Transactions on Computing Education, 17 (3), 12.

    Google Scholar 

  • Cen, L., Ruta, D., Powell, L., Hirsch, B., & Ng, J. (2016). Quantitative approach to collaborative learning: Performance prediction, individual assessment, and group composition. International Journal of Computer-Supported Collaborative Learning, 11(2), 187–225.

    Google Scholar 

  • Centra, J.A. (2003). Will teachers receive higher student evaluations by giving higher grades and less course work? Research in Higher Education, 44 (5), 495–518.

    Google Scholar 

  • Chanlekha, H., & Niramitranon, J. (2018). Student performance prediction model for early-identification of at-risk students in traditional classroom settings. In Proceedings of the 10th international conference on management of digital ecosystems - MEDES ’18 (pp. 239–245): ACM.

  • Chatterjee, S., & Hadi, A.S. (2015). Regression analysis by example. New York: Wiley.

    MATH  Google Scholar 

  • Chaturvedi, R., & Ezeife, C. (2013). Mining the impact of course assignments on student performance. In Educational data mining 2013.

  • Chaturvedi, R., & Ezeife, C.I. (2017). Predicting student performance in an ITS using task-driven features. In 2017 IEEE international conference on computer and information technology (CIT) (pp. 168–175): IEEE.

  • Chen, L., Wang, S., Wang, K., & Zhu, J. (2016). Soft subspace clustering of categorical data with probabilistic distance. Pattern Recognition, 51, 322–332.

    Google Scholar 

  • Chen, W., Brinton, C.G., Cao, D., Mason-singh, A., Lu, C., & Chiang, M. (2018). Early detection prediction of learning outcomes in online short-courses via learning behaviors. IEEE Transactions on Learning Technologies, 12 (1), 44–58.

    Google Scholar 

  • Chen, Y., Liu, Q., Huang, Z., Wu, L., Chen, E., Wu, R., & et al. (2017). Tracking knowledge proficiency of students with educational priors. In Conference on information and knowledge management (CIKM) (pp. 989–998).

  • Christian, T.M., & Ayub, M. (2014). Exploration of classification using NBTree for predicting students’ performance. In 2014 international conference on data and software engineering (ICODSE) (pp. 1–6): IEEE.

  • Chrysafiadi, K., & Virvou, M. (2013). Student modeling approaches: a literature review for the last decade. Expert Systems with Applications, 40(11), 4715–4729.

    Google Scholar 

  • Chung, H., & Kim, J. (2016). An ontological approach for semantic modeling of curriculum and syllabus in higher education. International Journal of Information and Education Technology, 6(5), 365.

    Google Scholar 

  • Conijn, R., Snijders, C., Kleingeld, A., & Matzat, U. (2017). Predicting student performance from LMS data: a comparison of 17 blended courses using Moodle LMS. IEEE Transactions on Learning Technologies, 10(1), 17–29.

    Google Scholar 

  • Cooper, H.M. (1988). Organizing knowledge syntheses: a taxonomy of literature reviews. Knowledge, Technology & Policy, 1(1), 104–126.

    Google Scholar 

  • Damaševičius, R. (2010). Analysis of academic results for informatics course improvement using association rule mining. In Information systems development (pp. 357–363). Berlin: Springer.

  • Daud, A., Aljohani, N.R., Abbasi, R.A., Lytras, M.D., Abbas, F., & Alowibdi, J.S. (2017). Predicting student performance using advanced learning analytics. In Proceedings of the 26th international conference on world wide web companion (pp. 415–421): International World Wide Web Conferences Steering Committee.

  • Delen, D., & Crossland, M.D. (2008). Seeding the survey and analysis of research literature with text mining. Expert Systems with Applications, 34(3), 1707–1720.

    Google Scholar 

  • Dutt, A., Ismail, M.A., & Herawan, T. (2017). A systematic review on educational data mining. IEEE Access, 5, 15991–16005.

    Google Scholar 

  • Dvorak, T., & Jia, M. (2016). Do the timeliness, regularity, and intensity of online work habits predict academic performance? Journal of Learning Analytics, 3(3), 318–330.

    Google Scholar 

  • Elbadrawy, A., Polyzou, A., Ren, Z., Sweeney, M., Karypis, G., & Rangwala, H. (2016). Predicting student performance using personalized analytics. Computer, 49(4), 61–69.

    Google Scholar 

  • Elouazizi, N., Birol, G., Jandciu, E., Öberg, G., Welsh, A., Han, A., & et al. (2017). Automated analysis of aspects of written argumentation. In Proceedings of the seventh international learning analytics and knowledge conference on - lak ’17 (pp. 606–607): ACM.

  • Fausett, L.V., & Elwasif, W. (1994). Predicting performance from test scores using backpropagation and counterpropagation. In IEEE international conference on neural networks, (Vol. 5 pp. 3398–3402): IEEE.

  • Feldman, R., & Sanger, J. (2007). The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge: Cambridge University Press.

    Google Scholar 

  • Felisoni, D.D., & Godoi, A.S. (2018). Cell phone usage and academic performance: An experiment. Computers & Education, 117, 175–187.

    Google Scholar 

  • Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4 (5/6), 304–317.

    Google Scholar 

  • Fernandes, E., Carvalho, R., Holanda, M., & Van Erven, G. (2017). Educational data mining: discovery standards of academic performance by students in public high schools in the Federal District of Brazil. In World conference on information systems and technologies (pp. 287–296).

  • Fernandes, E., Holanda, M., Victorino, M., Borges, V., Carvalho, R., & Erven, G.V. (2018). Educational data mining: predictive analysis of academic performance of public school students in the capital of Brazil. Journal of Business Research, 94, 335–343.

    Google Scholar 

  • Figlio, D.N., & Lucas, M.E. (2004). Do high grading standards affect student performance? Journal of Public Economics, 88(9-10), 1815–1834.

    Google Scholar 

  • Foley, J., & Allan, J. (2016). Retrieving hierarchical syllabus items for exam question analysis. In European conference on information retrieval (pp. 575–586). Cham: Springer.

  • Galbraith, C.S., Merrill, G.B., & Kline, D.M. (2012). Are student evaluations of teaching effectiveness valid for measuring student learning outcomes in business related classes? A neural network and Bayesian analyses. Research in Higher Education, 53(3), 353–374.

    Google Scholar 

  • García, E., Romero, C., Ventura, S., & Calders, T. (2007). Drawbacks and solutions of applying association rule mining in learning management systems. In Proceedings of the international workshop on applying data mining in e-learning (ADML 2007), Crete, Greece (pp. 13–22).

  • Garcia, E.P.I., & Mora, P.M. (2011). Model prediction of academic performance for first year students. In 2011 10th Mexican international conference on artificial intelligence (pp. 169–174): IEEE.

  • Gardner, J., & Brooks, C. (2018). Evaluating predictive models of student success: closing the methodological gap. Journal of Learning Analytics, 5(2), 105–125.

    Google Scholar 

  • Gašević, D., Dawson, S., Rogers, T., & Gasevic, D. (2016). Learning analytics should not promote one size fits all: the effects of instructional conditions in predicting academic success. The Internet and Higher Education, 28, 68–84.

    Google Scholar 

  • Gasevic, D., Jovanovic, J., Pardo, A., & Dawson, S. (2017). Detecting learning strategies with analytics: links with self-reported measures and academic performance. Journal of Learning Analytics, 4(2), 113–128.

    Google Scholar 

  • Gedeon, T.D., & Turner, S. (1993). Explaining student grades predicted by a neural network. In Proceedings of 1993 international joint conference on neural networks, 1993. IJCNN’93-Nagoya, (Vol. 1 pp. 609–612): IEEE.

  • Golding, P., & Donaldson, O. (2006). Predicting academic performance. In Frontiers in education conference, 36th Annual (pp. 21–26): IEEE.

  • Goos, M., & Salomons, A. (2016). Measuring teaching quality in higher education: assessing selection bias in course evaluations. Research in Higher Education, 58(4), 341–364.

    Google Scholar 

  • Gowda, S.M., Baker, R.S., Corbett, A.T., & Rossi, L.M. (2013). Towards automatically detecting whether student learning is shallow. International Journal of Artificial Intelligence in Education, 23(1–4), 50–70.

    Google Scholar 

  • Gray, G., McGuinness, C., & Owende, P. (2014). An application of classification models to predict learner progression in tertiary education. In Advance Computing Conference (IACC), 2014 IEEE International (pp. 549–554): IEEE.

  • Grivokostopoulou, F., Perikos, I., & Hatzilygeroudis, I. (2014). Utilizing semantic web technologies and data mining techniques to analyze students learning and predict final performance. In 2014 IEEE international conference on teaching, assessment and learning for engineering (TALE) (pp. 488–494): IEEE.

  • Guarin, C.E.L., Guzman, E.L., & Gonzalez, F.A. (2015). A model to predict low academic performance at a specific enrollment using data mining. Revista Iberoamericana de Tecnologias del Aprendizaje, 10(3), 119–125.

    Google Scholar 

  • Guo, B., Zhang, R., Xu, G., Shi, C., & Yang, L. (2015). Predicting students performance in educational data mining. In International symposium on educational technology, ISET 2015 (pp. 125–128).

  • Guruler, H., Istanbullu, A., & Karahasan, M. (2010). A new student performance analysing system using knowledge discovery in higher educational databases. Computers & Education, 55(1), 247–254.

    Google Scholar 

  • Hämäläinen, W., & Vinni, M. (2006). Comparison of machine learning methods for intelligent tutoring systems. In International conference on intelligent tutoring systems (pp. 525–534): Springer.

  • Hart, S., Daucourt, M., & Ganley, C. (2017). Individual differences related to college students’ course performance in calculus II. Journal of Learning Analytics, 4(2), 129–153.

    Google Scholar 

  • Hasan, R., Palaniappan, S., Raziff, A.R.A., Mahmood, S., & Sarker, K.U. (2018). Student academic performance prediction by using decision tree algorithm. In 2018 4th international conference on computer and information sciences (ICCOINS) (pp. 1–5): IEEE.

  • Hasheminejad, H., & Sarvmili, M. (2018). S3PSO: students’ performance prediction based on particle swarm optimization. Journal of AI and Data Mining, 7(1), 77–96.

    Google Scholar 

  • Hassan, O.R., & Rasiah, R. (2011). Poverty and student performance in Malaysia. International Journal of Institutions and Economies, 3(1), 61–76.

    Google Scholar 

  • Hattie, J., & Clinton, J. (2012). Physical activity is not related to performance at school. Archives of Pediatrics & Adolescent Medicine, 166(7), 678–679.

    Google Scholar 

  • Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112.

    Google Scholar 

  • Helal, S., Li, J., Liu, L., Ebrahimie, E., Dawson, S., & Murray, D.J. (2018). Identifying key factors of student academic performance by subgroup discovery. International Journal of Data Science and Analytics, 7(3), 227–245.

    Google Scholar 

  • Hidayah, I., Permanasari, A.E., & Ratwastuti, N. (2013). Student classification for academic performance prediction using neuro fuzzy in a conventional classroom. In 2013 international conference on information technology and electrical engineering (ICITEE) (pp. 221–225): IEEE.

  • Hong, B., Wei, Z., & Yang, Y. (2017). Online education performance prediction via time-related features. In 2017 IEEE/ACIS 16th international conference on computer and information science (ICIS) (pp. 95–100): IEEE.

  • Hsiao, I.H., & Lin, Y.L. (2017). Enriching programming content semantics: an evaluation of visual analytics approach. Computers in Human Behavior, 72, 771–782.

    Google Scholar 

  • Hsiao, I.H., Pandhalkudi Govindarajan, S.K., & Lin, Y.L. (2016). Semantic visual analytics for today’s programming courses. In Proceedings of the sixth international conference on learning analytics and knowledge (pp. 48–53): ACM.

  • Hu, X., Cheong, C.W.L., Ding, W., & Woo, M. (2017). A systematic review of studies on predicting student learning outcomes using learning analytics. In Proceedings of the seventh international learning analytics & knowledge conference (pp. 528–529): ACM.

  • Huang, S., & Fang, N. (2013). Predicting student academic performance in an engineering dynamics course: a comparison of four types of predictive mathematical models. Computers & Education, 61, 133–145.

    Google Scholar 

  • Ibrahim, Z., & Rusli, D. (2007). Predicting students’ academic performance: comparing artificial neural network, decision tree and linear regression. In 21st Annual SAS Malaysia Forum (pp. 1–6).

  • Ivančević, V., Čeliković, M., & Luković, I. (2010). Analyzing student spatial deployment in a computer laboratory. In Educational data mining (p. 2011).

  • Jara, M., & Mellar, H. (2010). Quality enhancement for e-learning courses: the role of student feedback. Computers & Education, 54(3), 709–714.

    Google Scholar 

  • Jishan, S.T., Rashu, R.I., Haque, N., & Rahman, R.M. (2015). Improving accuracy of students’ final grade prediction model using optimal equal width binning and synthetic minority over-sampling technique. Decision Analytics, 2 (1), 1.

    Google Scholar 

  • Kabakchieva, D. (2012). Student performance prediction by using data mining classification algorithms. International Journal of Computer Science and Management Research, 1(4), 686–690.

    Google Scholar 

  • Kabra, R.R., & Bichkar, R.S. (2011). Performance prediction of engineering students using decision trees. International Journal of Computer Applications, 36(11), 975–8887.

    Google Scholar 

  • Kagdi, H., Collard, M.L., & Maletic, J.I. (2007). A survey and taxonomy of approaches for mining software repositories in the context of software evolution. Journal of Software Maintenance and Evolution: Research and Practice, 19(2), 77–131.

    Google Scholar 

  • Kamley, S., Jaloree, S., & Thakur, R.S. (2016). A review and performance prediction of students’ using association rule mining based approach. Data Mining and Knowledge Engineering, 8(8), 252–259.

    Google Scholar 

  • Kaufman, L., & Rousseeuw, P.J. (2009). Finding groups in data: an introduction to cluster analysis Vol. 344. New York: Wiley.

    Google Scholar 

  • Kaviyarasi, R., & Balasubramanian, T. (2018). Exploring the high potential factors that affects students’ academic performance. International Journal of Education and Management Engineering, 8(6), 15.

    Google Scholar 

  • Kesavaraj, G., & Sukumaran, S. (2013). A study on classification techniques in data mining. In 2013 fourth international conference on computing, communications and networking technologies (ICCCNT) (pp. 1–7): IEEE.

  • Khan, A., & Ghosh, S.K. (2016). Analysing the impact of poor teaching on student performance. In 2016 IEEE international conference on teaching, assessment, and learning for engineering (TALE) (pp. 169–175): IEEE.

  • Khan, A., & Ghosh, S.K. (2018). Data mining based analysis to explore the effect of teaching on student performance. Education and Information Technologies, 23(4), 1677–1697.

    Google Scholar 

  • Khanna, L., Singh, S.N., & Alam, M. (2016). Educational data mining and its role in determining factors affecting students academic performance: a systematic review. In 2016 1st India international conference on information processing (IICIP) (pp. 1–7): IEEE.

  • Kim, B.H., Vizitei, E., & Ganapathi, V. (2018). GritNet: student performance prediction with deep learning. arXiv:1804.07405.

  • Koedinger, K.R., D’Mello, S., McLaughlin, E.A., Pardos, Z.A., & Rosé, C.P. (2015). Data mining and education. Wiley Interdisciplinary Reviews: Cognitive Science, 6(4), 333–353.

    Google Scholar 

  • Kotsiantis, S.B., & Pintelas, P.E. (2005). Predicting students marks in hellenic open university. In Fifth IEEE international conference on advanced learning technologies, 2005. ICALT 2005 (pp. 664–668): IEEE.

  • Koutina, M., & Kermanidis, K.L. (2011). Predicting postgraduate students’ performance using machine learning techniques. In IFIP international conference on artificial intelligence applications and innovations (pp. 159–168): Springer.

  • Kumar, D.A., Selvam, R.P., & Kumar, K.S. (2018). Review on prediction algorithms in educational data mining. International Journal of Pure and Applied Mathematics, 118(8), 531–537.

    Google Scholar 

  • Kumar, M., Singh, A.J., & Handa, D. (2017). Literature survey on student’s performance prediction in education using data mining techniques. International Journal of Education and Management Engineering, 6, 40–49.

    Google Scholar 

  • Li, K.F., Rusk, D., & Song, F. (2013). Predicting student academic performance. In 2013 seventh international conference on complex, intelligent, and software intensive systems (cisis) (pp. 27–33): IEEE.

  • Lin, C.H., Kwon, J.B., & Zhang, Y. (2018). Online self-paced high-school class size and student achievement. Educational Technology Research and Development, pp 1–20.

  • Lipovetsky, S., & Conklin, W.M. (2015). Predictor relative importance and matching regression parameters. Journal of Applied Statistics, 42(5), 1017–1031.

    MathSciNet  MATH  Google Scholar 

  • Liu, Q., Huang, Z., Huang, Z., Liu, C., Chen, E., Su, Y., & et al. (2018a). Finding similar exercises in online education systems. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1821–1830): ACM.

  • Liu, Q., Wu, R., Chen, E., Xu, G., Su, Y., Chen, Z., & et al. (2018b). Fuzzy cognitive diagnosis for modelling examinee performance. ACM Transactions on Intelligent Systems and Technology, 9(4), 48.

  • Livieris, I.E., Drakopoulou, K., Mikropoulos, T.A., Tampakas, V., & Pintelas, P. (2018). An ensemble-based semi-supervised approach for predicting students’ performance. In Research on e-Learning and ICT in Education (pp. 25–42): Springer.

  • Loh, C.S., & Sheng, Y. (2015). Measuring the (dis-)similarity between expert and novice behaviors as serious games analytics. Education and Information Technologies, 20(1), 5–19.

    Google Scholar 

  • Lorenzetti, C., Maguitman, A., Leake, D., Menczer, F., & Reichherzer, T. (2016). Mining for topics to suggest knowledge model extensions. ACM Transactions on Knowledge Discovery from Data, 11(2), 23.

    Google Scholar 

  • Lu, O.H.T., Huang, A.Y.Q., Huang, J.C., Lin, A.J.Q., Ogata, H., & Yang, S.J.H. (2018). Applying learning analytics for the early prediction of students’ academic performance in blended learning. Journal of Educational Technology and Society, 21(2), 220–232.

    Google Scholar 

  • Macfadyen, L.P., Dawson, S., Prest, S., & Gašević, D. (2015). Whose feedback? A multilevel analysis of student completion of end-of-term teaching evaluations. Assessment & Evaluation in Higher Education, 41(6), 821–839.

    Google Scholar 

  • Márquez-Vera, C., Cano, A., Romero, C., & Ventura, S. (2013). Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data. Applied Intelligence, 38(3), 315–330.

    Google Scholar 

  • Marsh, H.W. (1984). Students’ evaluations of university teaching: dimensionality, reliability, validity, potential baises, and utility. Journal of Educational Psychology, 76(5), 707.

    Google Scholar 

  • Marsh, H.W. (2007). Students’ evaluations of university teaching: Dimensionality, reliability, validity, potential biases and usefulness. In The scholarship of teaching and learning in higher education: An evidence-based perspective (pp. 319–383): Springer.

  • Martinez, D. (2001). Predicting Student Outcomes Using Discriminant Function Analysis.

  • Mat, U.B., Buniyamin, N., Arsad, P.M., & Kassim, R. (2013). An overview of using academic analytics to predict and improve students’ achievement: a proposed proactive intelligent intervention. In 2013 IEEE 5th conference on engineering education (ICEED) (pp. 126–130): IEEE.

  • McCarthy, K.S., & Goldman, S.R. (2017). Constructing interpretive inferences about literary text: the role of domain-specific knowledge. Learning and Instruction, 60, 245–251.

    Google Scholar 

  • Meier, Y., Xu, J., Atan, O., & van der Schaar, M. (2016). Predicting grades. IEEE Transactions on Signal Processing, 64(4), 959–972.

    MathSciNet  MATH  Google Scholar 

  • Miguéis, V., Freitas, A., Garcia, P.J., & Silva, A. (2018). Early segmentation of students according to their academic performance: a predictive modelling approach. Decision Support Systems, 115, 36–51.

    Google Scholar 

  • Mimis, M., El Hajji, M., Es-saady, Y., Oueld Guejdi, A., Douzi, H., & Mammass, D. (2018). A framework for smart academic guidance using educational data mining. Education and Information Technologies, 24 (2), 1379–1393.

    Google Scholar 

  • Mishra, T., Kumar, D., & Gupta, S. (2014). Mining students’ data for prediction performance. In International conference on advanced computing and communication technologies, ACCT (pp. 255–262).

  • Moher, D., Liberati, A., Tetzlaff, J., & Altman, D.G. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Annals of Internal Medicine, 151(4), 264–269.

    Google Scholar 

  • Montuschi, P., Lamberti, F., Gatteschi, V., & Demartini, C. (2015). A semantic recommender system for adaptive learning. IT Professional, 17(5), 50–58.

    Google Scholar 

  • Moore, S., & Kuol, N. (2005). Students evaluating teachers: exploring the importance of faculty reaction to feedback on teaching. Teaching in Higher Education, 10(1), 57–73.

    Google Scholar 

  • Moos, D.C., & Azevedo, R. (2008). Self-regulated learning with hypermedia: the role of prior domain knowledge. Contemporary Educational Psychology, 33(2), 270–298.

    Google Scholar 

  • Mueen, A., Zafar, B., & Manzoor, U. (2016). Modeling and predicting students’ academic performance using data mining techniques. International Journal of Modern Education and Computer Science, 8(11), 36.

    Google Scholar 

  • Nakayama, M. (2016). Lexical analysis of syllabi in the area of technology enhanced learning. In 2016 15th international conference on information technology based higher education and training (ITHET) (pp. 1–5): IEEE.

  • Natek, S., & Zwilling, M. (2014). Student data mining solution-knowledge management system related to higher education institutions. Expert Systems with Applications, 41(14), 6400–6407.

    Google Scholar 

  • Nikolic, S., Ritz, C., Vial, P.J., Ros, M., & Stirling, D. (2015). Decoding student satisfaction: How to manage and improve the laboratory experience. IEEE Transactions on Education, 58(3), 151–158.

    Google Scholar 

  • O’Connell, K.A., Wostl, E., Crosslin, M., Berry, T.L., & Grover, J.P. (2018). Student ability best predicts final grade in a college algebra course. Journal of Learning Analytics, 5(3), 167–181.

    Google Scholar 

  • Ogor, E.N. (2007). Student academic performance monitoring and evaluation using data mining techniques. In Electronics, robotics and automotive mechanics conference (pp. 354–359): IEEE.

  • Ornelas, F., & Ordonez, C. (2017). Predicting student success: a naïve Bayesian application to community college data. Technology, Knowledge and Learning, 22(3), 299–315.

    Google Scholar 

  • Osmanbegović, E., & Suljić, M. (2012). Data mining approach for predicting student performance. Economic Review, 10(1), 3–12.

    Google Scholar 

  • Ostrow, K., Donnelly, C., & Heffernan, N. (2015). Optimizing partial credit algorithms to predict student performance. In International conference on educational data mining (EDM).

  • Pal, S., & Chaurasia, V. (2017). Is alcohol affect higher education students performance: searching and predicting pattern using data mining algorithms. International Journal of Innovations & Advancement in Computer Science IJIACS ISSN, 6(4), 2347–8616.

    Google Scholar 

  • Pandey, M., & Taruna, S. (2016). Towards the integration of multiple classifier pertaining to the Student’s performance prediction. Perspectives in Science, 8, 364–366.

    Google Scholar 

  • Pandey, U.K., & Pal, S. (2011). A data mining view on class room teaching language. arXiv:1104.4164.

  • Papamitsiou, Z.K., Terzis, V., & Economides, A.A. (2014). Temporal learning analytics for computer based testing. In Proceedings of the fourth international conference on learning analytics and knowledge (pp. 31–35): ACM.

  • Parack, S., Zahid, Z., & Merchant, F. (2012). Application of data mining in educational databases for predicting academic trends and patterns. In 2012 IEEE international conference on technology enhanced education (ICTEE) (pp. 1–4): IEEE.

  • Pardos, Z.A., Heffernan, N.T., Anderson, B., Heffernan, C.L., & Schools, W.P. (2010). Using fine-grained skill models to fit student performance with Bayesian networks. Handbook of Educational Data Mining, 417.

  • Pen̄a-Ayala, A. (2014). Educational data mining: a survey and a data mining-based analysis of recent works. Expert Systems with Applications, 41(4), 1432–1462.

    Google Scholar 

  • Polyzou, A., & Karypis, G. (2016). Grade prediction with course and student specific models. In Pacific-Asia conference on knowledge discovery and data mining (pp. 89–101). Cham: Springer.

  • Polyzou, A., & Karypis, G. (2019). Feature extraction for next-term prediction of poor student performance. IEEE Transactions on Learning Technologies, 12(2), 237–248.

    Google Scholar 

  • Pong-Inwong, C., & Rungworawut, W. (2012). Teaching evaluation using data mining on moodle LMS forum. In 2012 6th international conference on new trends in information science, service science and data mining (ISSDM2012) (pp. 550–555): IEEE.

  • Price, L., Svensson, I., Borell, J., & Richardson, J.T.E. (2017). The role of gender in students’ ratings of teaching quality in computer science and environmental engineering. IEEE Transactions on Education, 60(4), 281–287.

    Google Scholar 

  • Quadri, M.M.N., & Kalyankar, N.V. (2010). Drop out feature of student data for academic performance using decision tree techniques. Global Journal of Computer Science and Technology, 10(2).

  • Quille, K., & Bergin, S. (2018). Programming: Predicting student success early in CS1. A re-validation and replication study. In Proceedings of the 23rd annual ACM conference on innovation and technology in computer science education (pp. 15–20): ACM.

  • Quinlan, J.R. (1990). Decision trees and decision-making. IEEE Transactions on Systems, Man, and Cybernetics, 20(2), 339–346.

    Google Scholar 

  • Ramesh, V., Parkavi, P., & Ramar, K. (2013). Predicting student performance: a statistical and data mining approach. International Journal of Computer Applications, 63(8), 35–39.

    Google Scholar 

  • Rani, S., & Kumar, P. (2017). A sentiment analysis system to improve teaching and learning. Computer, 50(5), 36–43.

    Google Scholar 

  • Rekha, R., Angadi, A., Pathak, A., Kapur, A., Gosar, H., Ramanathan, M., & et al. (2012). Ontology driven framework for assessing the syllabus fairness of a question paper. In 2012 IEEE international conference on technology enhanced education (ICTEE) (pp. 1–5): IEEE.

  • Romero, C., López, M.I., Luna, J.M., & Ventura, S. (2013). Predicting students’ final performance from participation in on-line discussion forums. Computers and Education, 68, 458–472.

    Google Scholar 

  • Romero, C., & Ventura, S. (2007). Educational data mining: a survey from 1995 to 2005. Expert Systems with Applications, 33(1), 135–146.

    Google Scholar 

  • Romero, C., & Ventura, S. (2010). Educational data mining: a review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 40(6), 601–618.

    Google Scholar 

  • Romero, C., Ventura, S., Espejo, P.G., & Hervás, C. (2008). Data mining algorithms to classify students. In Educational data mining 2008.

  • Saarela, M., & Kärkkäinen, T. (2015). Analysing student performance using sparse data of core bachelor courses. Journal of Educational Data Mining, 7(1), 3–32.

    Google Scholar 

  • Sandoval, A., Gonzalez, C., Alarcon, R., Pichara, K., & Montenegro, M. (2018). Centralized student performance prediction in large courses based on low-cost variables in an institutional context. The Internet and Higher Education, 37, 76–89.

    Google Scholar 

  • Santana, M.A., Costa, E.B., Fonseca, B., Rego, J., & de Araújo, F.F. (2017). Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming courses. Computers in Human Behavior, 73, 247–256.

    Google Scholar 

  • Saxena, P.S., & Govil, M.C. (2009). Prediction of student’s academic performance using clustering. In National conference on cloud computing & big data (pp. 1–6).

  • Shahiri, A.M., Husain, W., & Rashid, A.N. (2015). A review on predicting student’s performance using data mining techniques. Procedia Computer Science, 72, 414–422.

    Google Scholar 

  • She, H.C., Cheng, M.T., Li, T.W., Wang, C.Y., Chiu, H.T., Lee, P.Z., & et al. (2012). Web-based undergraduate chemistry problem-solving: the interplay of task performance, domain knowledge and web-searching strategies. Computers & Education, 59(2), 750–761.

    Google Scholar 

  • Shingari, I., & Kumar, D. (2018). A survey on various aspects of education data mining in predicting student performance. Journal of Applied Science and Computations, 5(6), 38–42.

    Google Scholar 

  • Siemens, G., & Baker, R.S.J.D. (2012). Learning analytics and educational data mining: towards communication and collaboration. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 252–254): ACM.

  • Sivakumar, S., & Selvaraj, R. (2018). Predictive modeling of students performance through the enhanced decision tree. In Advances in electronics, communication and computing (pp. 21–36). Singapore: Springer.

  • Stronge, J.H., Ward, T.J., Tucker, P.D., & Hindman, J.L. (2007). What is the relationship between teacher quality and student achievement? An exploratory study. Journal of Personnel Evaluation in Education, 20(3-4), 165–184.

    Google Scholar 

  • Sullivan, W., Marr, J., & Hu, G. (2017). A predictive model for standardized test performance in michigan schools. In Applied computing and information technology (pp. 31–46): Springer.

  • Superby, J.F., Vandamme, J.P., & Meskens, N. (2006). Determination of factors influencing the achievement of the first-year university students using data mining methods. In Workshop on educational data mining, (Vol. 32 p. 234).

  • Sweeney, M., Rangwala, H., Lester, J., & Johri, A. (2016). Next-term student performance prediction: a recommender systems approach. Journal of Educational Data Mining, 8(1), 22–51.

    Google Scholar 

  • Tair, M.M.A., & El-Halees, A.M. (2012). Mining educational data to improve students’ performance: a case study. International Journal of Information, 2(2), 140–146.

    Google Scholar 

  • Thai-Nghe, N., Busche, A., & Schmidt-Thieme, L. (2009). Improving academic performance prediction by dealing with class imbalance. In Ninth international conference on intelligent systems design and applications (pp. 878–883): IEEE.

  • Uddin, M.F., & Lee, J. (2017). Proposing stochastic probability-based math model and algorithms utilizing social networking and academic data for good fit students prediction. Social Network Analysis and Mining, 7(1), 29.

    Google Scholar 

  • Üstünlüoğlu, E. (2016). Teaching quality matters in higher education: a case study from Turkey and Slovakia. Teachers and Teaching, 23(3), 367–382.

    Google Scholar 

  • Uttl, B., White, C.A., & Gonzalez, D.W. (2017). Meta-analysis of faculty’s teaching effectiveness: Student evaluation of teaching ratings and student learning are not related. Studies in Educational Evaluation, 54, 22–42.

    Google Scholar 

  • Van Inwegen, E., Adjei, S., Wang, Y., & Heffernan, N. (2015). An analysis of the impact of action order on future performance: the fine-grain action model. In Proceedings of the fifth international conference on learning analytics and knowledge (pp. 320–324): ACM.

  • VeeraManickam, M.R.M., Mohanapriya, M., Pandey, B.K., Akhade, S., Kale, S.A., Patil, R., & et al. (2018). Map-reduce framework based cluster architecture for academic student’s performance prediction using cumulative dragonfly based neural network. Cluster Computing, 22(1), 1259–1275.

    Google Scholar 

  • Walters, W.H. (2007). Google scholar coverage of a multidisciplinary field. Information Processing and Management, 43(4), 1121–1132.

    MathSciNet  Google Scholar 

  • Wang, Y., Ostrow, K., Adjei, S., & Heffernan, N. (2016). The opportunity count model: a flexible approach to modeling student performance. In Proceedings of the Third (2016) ACM Conference on Learning@ Scale (pp. 113–116): ACM.

  • Widyahastuti, F., & Tjhin, V.U. (2017). Predicting students performance in final examination using linear regression and multilayer perceptron. In 10th international conference on human system interactions (HSI) (pp. 188–192): IEEE.

  • Willoughby, T., Anderson, S.A., Wood, E., Mueller, J., & Ross, C. (2009). Fast searching for information on the Internet to use in a learning context: the impact of domain knowledge. Computers & Education, 52(3), 640–648.

    Google Scholar 

  • Wook, M., Yusof, Z.M., & Nazri, M.Z.A. (2016). Educational data mining acceptance among undergraduate students. Education and Information Technologies, 22(3), 1195–1216.

    Google Scholar 

  • Xing, W., Guo, R., Petakovic, E., & Goggins, S. (2015). Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory. Computers in Human Behavior, 47, 168–181.

    Google Scholar 

  • Xiong, X., Adjei, S., & Heffernan, N. (2014). Improving retention performance prediction with prerequisite skill features. In Educational data mining.

  • Xu, M., Liang, Y., & Wu, W. (2017). Predicting honors student performance using RBFNN and PCA method. In International Conference on database systems for advanced applications (pp. 364–375): Springer.

  • Yang, S.J.H., Lu, O.H.T., Huang, A.Y.Q., Huang, J.C.H., Ogata, H., & Lin, A.J.Q. (2018). Predicting students’ academic performance using multiple linear regression and principal component analysis. Journal of Information Processing, 26, 170–176.

    Google Scholar 

  • Yim, S., & Warschauer, M. (2017). Web-based collaborative writing in L2 contexts: methodological insights from text mining. Language Learning & Technology, 21(1), 146–165.

    Google Scholar 

  • Yin, H., Wang, W., & Han, J. (2016). Chinese undergraduates’ perceptions of teaching quality and the effects on approaches to studying and course satisfaction. Higher Education, 71(1), 39–57.

    Google Scholar 

  • Yoo, J., & Kim, J. (2014). Can online discussion participation predict group project performance? Investigating the roles of linguistic features and participation patterns. International Journal of Artificial Intelligence in Education, 24(1), 8–32.

    Google Scholar 

  • Yu, L., Lee, C., Pan, H., Chou, C., Chao, P., Chen, Z., & et al. (2018). Improving early prediction of academic failure using sentiment analysis on self evaluated comments. Journal of Computer Assisted Learning, 34(4), 358–365.

    Google Scholar 

  • Zabaleta, F. (2007). The use and misuse of student evaluations of teaching. Teaching in Higher Education, 12(1), 55–76.

    Google Scholar 

  • Zacharis, N.Z. (2015). A multivariate approach to predicting student outcomes in web-enabled blended learning courses. The Internet and Higher Education, 27, 44–53.

    Google Scholar 

  • Zaugg, H., West, R.E., Tateishi, I., & Randall, D.L. (2011). Mendeley: Creating communities of scholarly inquiry through research collaboration. TechTrends, 55(1), 32–36.

    Google Scholar 

  • Zimmermann, J., Brodersen, K.H., Heinimann, H.R., & Buhmann, J.M. (2015). A model-based approach to predicting graduate-level performance using indicators of undergraduate-level performance. Journal of Educational Data Mining, 7(3), 151–176.

    Google Scholar 

  • Zhang, X., Sun, G., Pan, Y., Sun, H., He, Y., & Tan, J. (2018). Students performance modeling based on behavior pattern. Journal of Ambient Intelligence and Humanized Computing, 9(5), 1659–1670.

    Google Scholar 

  • Zollanvari, A., Kizilirmak, R.C., Kho, Y.H., & Hernández-Torrano, D. (2017). Predicting students’ GPA and developing intervention strategies based on self-regulatory learning behaviors. IEEE Access, 5, 23792–23802.

    Google Scholar 

  • Zorilla, M.E., García-Saiz, D., & Balcázar, J.L. (2010). Towards parameter-free data mining: mining educational data with yacaree. In Educational data mining 2011.

  • Zuber, M. (2014). A survey of data mining techniques for social network analysis. International Journal of Research in Computer Engineering & Electronics, 3(6), 1–8.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India

    Anupam Khan & Soumya K. Ghosh

Authors
  1. Anupam Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Soumya K. Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anupam Khan.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khan, A., Ghosh, S.K. Student performance analysis and prediction in classroom learning: A review of educational data mining studies. Educ Inf Technol 26, 205–240 (2021). https://doi.org/10.1007/s10639-020-10230-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-020-10230-3

Keywords

Search

Navigation