Unsupervised machine learning approach for tailoring educational content to individual student weaknesses

Abstract

By analyzing data gathered through Online Learning (OL) systems, data mining can be used to unearth hidden relationships between topics and trends in student performance. Here, in this paper, we show how data mining techniques such as clustering and association rule algorithms can be used on historical data to develop a unique recommendation system module. In our implementation, we utilize historical data to generate association rules specifically for student test marks below a threshold of 60%. By focusing on marks below this threshold, we aim to identify and establish associations based on the patterns of weakness observed in the past data. Additionally, we leverage K-means clustering to provide instructors with visual representations of the generated associations. This strategy aids instructors in better comprehending the information and associations produced by the algorithms. K-means clustering helps visualize and organize the data in a way that makes it easier for instructors to analyze and gain insights, enabling them to support the verification of the relationship between topics. This can be a useful tool to deliver better feedback to students as well as provide better insights to instructors when developing their pedagogy. This paper further shows a prototype implementation of the above-mentioned concepts to gain opinions and insights about the usability and viability of the proposed system.

Keywords

Recommendation system / Clustering / F-P growth / Apriori / Associative pattern / E-learning sphere / Online learning

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

[1] P. Nicholson, A history of E-learning: Echoes of the pioneers, in: Computers and Education: E-Learning, from Theory to Practice, 2007, pp. 1-11.

[2] C. Romero, S. Ventura, Educational data mining: A review of the state of the art, IEEE Trans. Syst. Man Cybern. C 40 (6) (2010) 601-618, http://dx.doi.org/10.1109/TSMCC.2010.2053532.

[3] F.A. Abdurazakov, F.B. Odinaboboev, Pedagogical importance of using module educational technologies in the system of continuous education on the basis of modern approaches, Web Sci.: Int. Sci. Res. J. 3 (1) (2022) 173-180, http://dx.doi.org/10.17605/OSF.IO/N9KSD.

[4] J.A. Cohen, A fit for purpose pedagogy: online learning designing and teaching, Dev. Learn. Organ. 35 (4) (2021) 15-17, http://dx.doi.org/10.1108/DLO-08-2020-0174.

[5] P. Chakraborty, P. Mittal, M.S. Gupta, S. Yadav, A. Arora, Opinion of students on online education during the COVID-19 pandemic,Hum. Behav. Emerg. Technol. 3 (2021) 357-365, http://dx.doi.org/10.1002/hbe2.240.

[6] J.H. Block, R.B. Burns, Mastery learning, Rev. Res. Educ. 4 (1976) 3-49, http://dx.doi.org/10.2307/1167112.

[7] M.C. Urdaneta-Ponte, A. Mendez-Zorrilla, I. Oleagordia-Ruiz, Recommendation systems for education: Systematic review, Electronics 10 (14) (2021) 1611, http://dx.doi.org/10.3390/electronics10141611.

[8] A. Molnar, Computers in education: A brief history, J. (1997) URL https://thejournal.com/articles/1997/06/01/computers-in-education-a-brief-history.aspx.

[9] S. Carliner, An overview of online learning (2nd ed.),Eur. Bus. Rev. 16 (2004) http://dx.doi.org/10.1108/09555340410561723.

[10] D. Hunyadi, Performance comparison of Apriori and FP-Growth algorithms in generating association rules, in:Proceedings of the 5th European Conference on European Computing Conference, 2011, pp. 376-381, URL https://dl.acm.org/doi/abs/10.5555/1991016.1991084.

[11] J.A. Hartigan, M.A. Wong, Algorithm AS 136: A K-means clustering algorithm, J. R. Stat. Soc. Ser. C (Appl. Stat.) 28 (1) (1979) 100-108, http://dx.doi.org/10.2307/2346830.

[12] M. Hillyer, How Has Technology Changed-And Changed Us-In the Past 20Years, Tech. rep., World Economic Forum, 2020, URL https://www.weforum.org/agenda/2020/11/heres-how-technology-has-changed-and-changed-us-over-the-past-20-years/.

[13] M.F. Baris, Future of E-learning: Perspective of European teachers, EURASIA J. Math. Sci. Technol. Educ. 11 (2) (2015) 421-429, http://dx.doi.org/10.12973/EURASIA.2015.1361A.

[14] K. Mahmud, K. Gope, Challenges of implementing E-learning for higher education in least developed countries: A case study on Bangladesh, in: 2009 International Conference on Information and Multimedia Technology, 2009, pp. 155-159, http://dx.doi.org/10.1109/ICIMT.2009.27.

[15] N. Marangunic, A. Granic, Technology acceptance model: A literature review from 1986 to 2013,Univers. Access Inf. Soc. 14 (2015) 81-95, http://dx.doi.org/10.1007/s10209-014-0348-1.

[16] S. Moghavvemi, A. Sulaiman, N.I. Jaafar, N. Kasem, Social media as a complementary learning tool for teaching and learning: The case of Youtube, Int. J. Manag. Educ. 16 (1) (2018) 37-42.

[17] M. Prensky, The role of technology, Educ. Technol. 48 (6) (2008) 1-3, URL https://api.semanticscholar.org/CorpusID:156603367.

[18] D. Jaffee, Asynchronous learning: Technology and pedagogical strategy in a distance learning course, Teach. Sociol. 25 (4) (1997) 262-277, http://dx.doi.org/10.2307/1319295.

[19] M. Higley, Benefits of synchronous and asynchronous e-learning, Retrieved April 8, 2020. URL https://elearningindustry.com/benefits-of-synchronous-and-asynchronous-e-learning.

[20] J. Newman, Using VoIP technology for online courses in higher education, in:Proceedings of SITE 2007-Society for Information Technology Teacher Education International Conference, Association for the Advancement of Computing in Education (AACE), San Antonio, Texas, USA, 2007, pp. 444-447, http://dx.doi.org/10.24059/olj.v12i3.311, URL https://www.learntechlib.org/primary/p/24578/.

[21] J. Lepičnik-Vodopivec, A. Šorgo, The impact of online learning on students’ motivation and self-regulated learning, in: Advances in Intelligent Systems and Computing, Vol. 1233, Springer International Publishing, 2021, pp. 102-111, http://dx.doi.org/10.1007/978-3-030-88520-5_9.

[22] M. Lister, R.E. West, Design of E-learning and online courses: A literature analysis,in: Advanced Methodologies and Technologies in Modern Education, Springer International Publishing, 2016, pp. 216-235, http://dx.doi.org/10.1007/978-3-319-39483-1_9.

[23] C. Rensing, R. Steinmetz, B. Frey, N. Sattes, Adaptive E-learning offers tailored support for learning factually dense content, in: Seamless Learning in the Age of Mobile Connectivity, Springer US, 2012, pp. 425-427, http://dx.doi.org/10.1007/978-1-4614-3185-5_38.

[24] K. Scott, The impact of collaborative writing technologies on student learning, Commun. Inf. Lit. 9 (1) (2015) 43-55, URL https://files.eric.ed.gov/fulltext/EJ1062107.pdf.

[25] G.N. Rayasad, Association rule mining in educational recommender systems, 2017, Unpublished. URL http://ijarse.com/images/fullpdf/1503553093_IETEbanglore589.pdf.

[26] F. Schwenker, E. Trentin, Pattern classification and clustering: A review of partially supervised learning approaches, Pattern Recognit. Lett. 37 (2014) 4-14, http://dx.doi.org/10.1016/j.patrec.2013.10.017.

[27] D. Kučak, V. Juričić, G. Dambić,Machine learning in education - a survey of current research trends, in:Annals of DAAAM Proceedings, Vol. 29, 2018.

[28] L. Averell, A. Heathcote, The form of the forgetting curve and the fate of memories, J. Math. Psychol. 55 (1) (2011) 25-35.

[29] N. Hussain, Forgetting curve - importance of distribution as well as quantity in general practice teaching, 2022, Blog post, British Journal of General Practice. URL https://shorturl.at/tMNX5.

[30] Y. Liang, Z. Liu, X. Li, A conceptual framework for understanding E-learning in a performance simulation context, in: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems,ACM, 2018, p. 443, http://dx.doi.org/10.1145/3178158.3178206.

[31] T. Sitzmann, K. Kraiger, D. Stewart, R. Wisher, The comparative effec- tiveness of web-based and classroom instruction: A meta-analysis, J. Educ. Psychol. 98 (4) (2006) 832-845, http://dx.doi.org/10.1111/j.1744-6570.2006.00049.x.

[32] P.C. Brown, H.L.R. III, M.A. McDaniel, Make It Stick, Harvard University Press, 2014.

[33] J. Mansbach, Y.G. Bachner, Collaborative learning via asynchronous dis- cussion forums: A comparison of academic writing in L2 english and L1hebrew, CALICO J. 27 (2) (2010) 237-256, URL https://www.calico.org/html/article728.pdf.

[34] G. Cheng, J. Chau, Exploring the relationship between learning approaches, self-regulation, and academic achievement of medical students: A structural equation modeling analysis, Adv. Med. Educ. Pract. 7 (2016) 389-396, http://dx.doi.org/10.2147/AMEP.S131638.

[35] G.J. Hwang, P.H. Wu, C.C. Chen, S.H. Huang, Effects of a peer-assessment strategy on online collaborative learning, J. Comput. Assist. Learn. 25 (5) (2009) 438-448, http://dx.doi.org/10.1111/j.1365-2729.2008.00294.x.

[36] M.G. Uddin, A. Isaac, Osama, Impact of the system, information, and service quality of online learning on user satisfaction among public universities students in Bangladesh, in:Proceedings of the 3rd International Conference on Advanced Information and Communication Technology, ICAICT, Vol. 3, 2019, pp. 1-10.

[37] M.S. Rahaman, I.H. Moral, M.M. Rahman, M. Sahabuddin, A.B. Samuel, Online learning in Bangladesh during COVID-19: Perceived effectiveness, challenges, and suggestions, J. Educ. Manag. Dev. Stud. 1 (3) (2021) 35-47, http://dx.doi.org/10.52631/jemds.v1i3.51.

[38] C.A. Twigg, Improving learning and reducing costs: Fifteen Years of course redesign, Change: Mag. High. Learn. 47 (6) (2015) 6-13, http://dx.doi.org/10.1080/00091383.2015.1089753.

[39] Y. Zhang, S. Hao, H. Wang, Detecting incentivized review groups with co-review graph, High-Confid. Comput. 1 (1) (2021) 100006.

[40] Y. He, X. Zheng, R. Xu, L. Tian, Knowledge-based recommendation with contrastive learning, High-Confid. Comput. 3 (4) (2023) 100151.

[41] J. Xiao, M. Wang, L. Wang, X. Zhu, Design and implementation of C-iLearning: A cloud-based intelligent learning system, 11 (3) (2013). http://dx.doi.org/10.4018/JDET.2013070106.

[42] W. Xu, Y. Zhou, Course video recommendation with multimodal information in online learning platforms: A deep learning framework, 51 (5) (2020). http://dx.doi.org/10.1111/BJET.12951.

[43] H. Wang, P. Liu, H. Li, Application of improved association rule algorithm in the courses management, in: 2014 IEEE 5th International Conference on Software Engineering and Service Science, 2014, pp. 804-807, http://dx.doi.org/10.1109/ICSESS.2014.6933688.

[44] F. Liu, S. Zhang, J. Ge, F. Lu, J. Zou, Agricultural major courses recommen-dation using apriori algorithm applied in China open university system, in: 2016 9th International Symposium on Computational Intelligence and Design,ISCID, Vol. 1, 2016, pp. 442-446, http://dx.doi.org/10.1109/ISCID.2016.1109.

[45] S. Panjaitan, Sulindawaty, M. Amin, S. Lindawati, R. Watrianthos, H.T. Sihotang, B. Sinaga, Implementation of Apriori algorithm for analysis of consumer purchase patterns, J. Phys. Conf. Ser. 1255 (1) (2019) 012057, http://dx.doi.org/10.1088/1742-6596/1255/1/012057.

[46] C. Borgelt, An implementation of the FP-growth algorithm, in:Proceedings of the 1st International Workshop on Open Source Data Mining: Frequent Pattern Mining Implementations, OSDM ’05, Association for Computing Machinery, New York, NY, USA, 2005, pp. 1-5, http://dx.doi.org/10.1145/1133905.1133907.

[47] M.M. Rahman, Y. Watanobe, T. Matsumoto, R.U. Kiran, K. Nakamura, Educational data mining to support programming learning using problemsolving data, IEEE Access 10 (2022) 26186-26202, http://dx.doi.org/10.1109/ACCESS.2022.3157288.

[48] R. Xu, D. Wunsch, Survey of clustering algorithms, IEEE Trans. Neural Netw. 16 (3) (2005) 645-678, http://dx.doi.org/10.1109/TNN.2005.845141.

[49] C. Borgelt, R. Kruse, Induction of association rules: Apriori implementation, in: W. Härdle, B. Rönz (Eds.), Compstat, Physica-Verlag HD, Heidelberg, 2002, pp. 395-400.

[50] C. Borgelt,Efficient implementations of apriori and eclat, in:FIMI’03: Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations, Vol. 90, 2003.

[51] C. Borgelt, An implementation of the FP-growth algorithm, in:Proceedings of the 1st International Workshop on Open Source Data Mining: Frequent Pattern Mining Implementations, 2005, pp. 1-5.

[52] H. Gui, Y. Xu, A. Bhasin, J. Han, Network A/B testing: From sampling to estimation,in:Proceedings of the 24th International Conference on World Wide Web, 2015, pp. 399-409.

[53] R. Budde, K. Kautz, K. Kuhlenkamp, H. Züllighoven, What is prototyping? Inf. Technol. People 6 (2/3) (1990) 89-95, http://dx.doi.org/10.1108/eum0000000003546.

[54] Beyond Test Scores, 2017, pp. 14-51, http://dx.doi.org/10.4159/9780674981157-002.

[55] J.M. Duchek, A.F. Stassen, F. Lievens, Do applicants fake on personality questionnaires? A study using implicit response time measures, Appl. Cogn. Psychol. 34 (4) (2020) 794-804, http://dx.doi.org/10.1002/acp.3482.

[56] C.A. Twigg, Models for online learning, Educ. Rev. 38 (2003) 28-38.

[57] G. Chen, Q. Wei, E.E. Kerre, Fuzzy data mining: Discovery of fuzzy generalized association rules+, in: Recent Issues on Fuzzy Databases, 2000, pp. 45-66.

[58] P. Fournier-Viger, R. Nkambou, V.S.M. Tseng, RuleGrowth: Mining sequential rules common to several sequences by pattern-growth,in:Proceedings of the 2011 ACM Symposium on Applied Computing, 2011, pp. 956-961.

[59] E.Z. Guan, X.Y. Chang, Z. Wang, C.G. Zhou, Mining maximal sequential patterns, in: 2005 International Conference on Neural Networks and Brain,Vol. 1, IEEE, 2005, pp. 525-528.

[60] J. Noor, H.I. Akbar, R.A. Sujon, A.A. Al Islam, Secure processing-aware media storage (SPMS), in: 2017 IEEE 36th International Performance Computing and Communications Conference,IPCCC, IEEE, 2017, pp. 1-8.

[61] J. Noor, N.A. Al-Nabhan, A.A. Al Islam, RemOrphan: Object storage sustainability through removing offline-processed orphan garbage data, IEEE Access (2023).

[62] J. Noor, R.H. Ratul, M.S. Basher, J.A. Soumik, S. Sadman, N.J. Rozario, R. Reaz, S. Chellappan, A. Islam,Secure processing-aware media storage and archival (spmsa), Secure Processing-Aware Media Storage and Archival (Spmsa).

[63] J. Noor, M.G. Hossain, M.A. Alam, A. Uddin, S. Chellappan, A.A. Al Islam, SvLoad: An automated test-driven architecture for load testing in cloud systems, in: 2018 IEEE Global Communications Conference,GLOBECOM, IEEE, 2018, pp. 1-7.

[64] K. Lone, S.A. Sofi, A review on offloading in fog-based Internet of Things: Architecture, machine learning approaches, and open issues, High-Confid. Comput. (2023) 100124.