DATA MINING ALGORITHMS FOR PREDICTION OF STUDENT TEACHERS’ PERFORMANCE IN ICT: A SYSTEMATIC LITERATURE REVIEW
PDF

Keywords

data mining algorithms
educational data mining
student teachers
teacher training colleges

How to Cite

[1]
J. H. Shindo, M. M. Mjahidi, and M. D. Waziri, “ DATA MINING ALGORITHMS FOR PREDICTION OF STUDENT TEACHERS’ PERFORMANCE IN ICT: A SYSTEMATIC LITERATURE REVIEW”, ITLT, vol. 96, no. 4, pp. 29–45, Sep. 2023, doi: 10.33407/itlt.v96i4.5246.

Abstract

Poor ICT performance in teacher training colleges makes it more difficult for the majority of teachers to successfully use ICT resources in their teaching and learning. When teachers can efficiently utilize ICT resources, it empowers them to update their knowledge through online learning, consequently enhancing the overall quality of teaching and learning. This positive outcome can be observed through improved ICT performance. The aim of this article is to identify the appropriate Data Mining algorithms for predicting student teachers’ performance in ICT. The systematic literature review that was guided by the PRISMA statement 2020 served as the study methodology. It makes for clear reporting and offers a detailed checklist and flow diagram that direct the review procedure. On November 6, 2022, about 196 scholarly articles were downloaded from three digital libraries: Science Direct (38), ACM Digital Library (72), IEEE Xplore (51), and 35 from the Google Scholar search engine. After screening and eligibility checking, 28 scholarly articles were selected and analysed through content analysis in terms of the most commonly used algorithms, the year of publication, the study purposes, and the accuracy performance metrics. Considering the specific study findings represented quantitatively, Decision Trees and Naive Bayes were found to be the most commonly used Data Mining algorithms, with a count of 20.6% each. The most recently identified articles were published between 2014 and 2022. In terms of study purposes, a large number of studies focused on predicting student performance. Furthermore, about 6 out of 8 algorithms used in previous studies were found to score 80% or above in the average percentage of the highest and lowest accuracy metrics. Therefore, considering the general findings, the study identified five Data Mining algorithms as appropriate and most commonly used for prediction of student teachers’ performance in ICT. They are Naive Bayes, K-Nearest Neighbour, Support Vector Machine, Random Forest, and Decision Tree. The findings of this study would assist the government, college tutors, and student teachers in making better decisions to improve ICT performance for pre-service and in-service teachers.

PDF

References

REFERENCES (TRANSLATED AND TRANSLITERATED)

D. Kabakchieva, “Student performance prediction by using data mining classification algorithms,” Int. J. Comput. Sci. Manag. Res., vol. 1, no. 4, pp. 686–690, 2012.

B. Andersson, E. N. Nfuka, S. Sumra, P. Uimonen, and A. Pain, “Evaluation of implementation of ICT in teachers’ colleges project in Tanzania,” Stockholm, Sweden, 2014. [Online]. Available: http://www.sida.se/publication

M. Chirwa, “Access and use of internet in teaching and learning at two selected teachers’ colleges in Tanzania,” Int. J. Educ. Dev. using Inf. Commun. Technol., vol. 14, no. 2, pp. 4–16, 2018.

A. R. Kimaro and M. Mohamed, “Access and Use of ICT among Tutors in Public Teachers Training Colleges: A Case of Moshi Rural District Tanzania,” JAET No. 22, 2019, p. 46, 2019.

Y. K. Saheed, T. O. Oladele, A. O. Akanni, and W. M. Ibrahim, “Student performance prediction based on data mining classification techniques,” Niger. J. Technol., vol. 37, no. 4, p. 1087, 2018, doi: 10.4314/njt.v37i4.31.

F. Joel and H. Mungwabi, “Factors constraining effective application of ICTs in teachers’ training colleges in Tanzania,” African Journals Online (AJOL), vol. 11, no. 1, pp. 53–70, 2015, [Online]. Available: https://www.ajol.info/index.php/udslj/article/view/162190

M. Selemani, V. A. Ndume, and D. H. Kisanga, “Integrating ICT in Tanzania secondary schools : Experience of Tanzania as it grows to second world economy,” Int. Acad. J. Educ. Lit., vol. 2, no. 5, pp. 81–95, 2021, doi: 10.47310/iajel.2021.v02i05.010.

A. Rajab and R. Ramadhan, “Application of data mining techniques in students’ performance prediction and analysis big data analytics view project artificial intelligent field view project,” Int. J. Acad. Inf. Syst. Res. ISSN, vol. 3, no. 3, pp. 23–36, 2020, [Online]. Available: www.ijeais.org/ijaisr

K. Govindasamy and T. Velmurugan, “A Study on classification and clustering data mining algorithms based on students Academic performance prediction,” Int. J. Control theory Appl., vol. 10, no. 23, pp. 147–160, 2017.

N. Louis, “ICT teaching and learning environment in teachers training : The case of diploma student-teachers in Tanzania,” The University of Dodoma, 2015. [Online]. Available: http://hdl.handle.net/20.500.12661/796

K. Masalu, “Challenges preventing student teachers and tutors from exploiting ICT in their learning and teaching in teachers Colleges,” Open University of Tanzania, 2018. [Online]. Available: http://repository.out.ac.tz/id/eprint/2185

B. Albreiki, N. Zaki, and H. Alashwal, “A systematic literature review of student’ performance prediction using machine learning techniques,” Educ. Sci., vol. 11, no. 9, 2021, doi: 10.3390/educsci11090552.

I. Shingari, D. Kumar, and M. Khetan, “A review of applications of data mining techniques for prediction of students’ performance in higher education,” J. Stat. Manag. Syst., vol. 20, no. 4, pp. 713–722, 2017, doi: 10.1080/09720510.2017.1395191.

K. Haruehansapong and S. Rungraungsilp, “Educational data mining applied for predicting students‘ ICT literacy,” Int. J. Sci. Technol. Res., vol. 10, no. 02, pp. 339–344, 2021, [Online]. Available: www.ijstr.org

P. Kavishe, “Performance prediction in mathematics using educational data mining techniques: A case of Mzumbe University in Tanzania,” University of Dodoma, 2020. [Online]. Available: http://hdl.handle.net/20.500.12661/2696

S. K. Yadav and S. Pal, “Data mining: A prediction for performance improvement of engineering students using classification,” vol. 2, no. 2, pp. 51–56, 2012, [Online]. Available: http://arxiv.org/abs/1203.3832

C. Angeli, S. K. Howard, J. Ma, J. Yang, and P. A. Kirschner, “Data mining in educational technology classroom research: Can it make a contribution?,” Comput. Educ., vol. 113, pp. 226–242, 2017, doi: 10.1016/j.compedu.2017.05.021.

I. Shingari and D. Kumar, “Predicting student performance using classification data mining techniques,” Int. J. Comput. Sci. Eng., vol. 6, no. 7, pp. 43–48, 2018, doi: 10.26438/ijcse/v6i7.4348.

R. Hasan, S. Palaniappan, A. R. A. Raziff, S. Mahmood, and K. U. Sarker, “Student academic performance prediction by using decision tree algorithm,” in 2018 4th International Conference on Computer and Information Sciences: Revolutionising Digital Landscape for Sustainable Smart Society, ICCOINS, 2018, pp. 1–5. doi: 10.1109/ICCOINS.2018.8510600.

P. Cortez and A. Silva, “Using data mining to predict secondary school student performance,” 15th Eur. Concurr. Eng. Conf. 2008, ECEC 2008 - 5th Futur. Bus. Technol. Conf. FUBUTEC 2008, no. January 2008, pp. 5–12, 2008.

E. B. Michael and O. Gold, “Prediction of students’ performance in general mathematics at Wassce using decision tree,” Am. J. Eng. Res., vol. 7, no. 6, pp. 336–343, 2018, [Online]. Available: www.ajer.org

M. A. Yehuala, “Application of data mining techniques for student success and failure frediction ( The case of Debre _ Markos University ),” Int. J. Sci. Technol. Res., vol. 4, no. 4, pp. 91–94, 2015, [Online]. Available: www.ijstr.org

M. J. Page et al., “The PRISMA 2020 statement: An updated guideline for reporting systematic reviews,” Int. J. Surg., vol. 88, no. 1, 2021, doi: 10.1016/j.ijsu.2021.105906.

R. Alamri and B. Alharbi, “Explainable student performance prediction models: A systematic review,” IEEE Access, vol. 9, pp. 33132–33143, 2021, doi: 10.1109/ACCESS.2021.3061368.

A. Namoun and A. Alshanqiti, “Predicting student performance using data mining and learning analytics techniques: A systematic literature review,” Appl. Sci., vol. 11, no. 1, p. 237, 2020, doi: 10.3390/app11010237.

M. V. Amazona and A. A. Hernandez, “Modelling student performance using data mining techniques: Inputs for academic program development,” ACM Int. Conf. Proceeding Ser., pp. 36–40, 2019, doi: 10.1145/3330530.3330544.

C. Ma, B. Yao, F. Ge, Y. Pan, and Y. Guo, “Improving prediction of student performance based on multiple feature selection approaches,” ACM Int. Conf. Proceeding Ser., vol. Part F1319, pp. 36–41, 2017, doi: 10.1145/3141151.3141160.

H. Chanlekha and J. Niramitranon, “Student performance prediction model for early-identification of at-risk students in traditional classroom settings,” MEDES 2018 - 10th Int. Conf. Manag. Digit. Ecosyst., pp. 239–245, 2018, doi: 10.1145/3281375.3281403.

X. Wu, Z. Shi, Y. Zhou, and H. Xing, “The application of three machine learning algorithms in student performance evaluation,” in In Proceedings of the 4th International Conference on Computer Science and Application Engineering, 2020, pp. 1–5. doi: 10.1145/3424978.3425072.

B. Sekeroglu, K. Dimililer, and K. Tuncal, “Student performance prediction and classification using machine learning algorithms,” in ACM International Conference Proceeding Series, 2019, pp. 7–11. doi: 10.1145/3318396.3318419.

S. Aydogdu, “Educational data mining studies in Turkey: A systematic review,” Turkish Online J. Distance Educ., vol. 21, no. 3, pp. 170–185, 2020, doi: 10.17718/TOJDE.762046.

A. M. Shahiri, W. Husain, and N. A. Rashid, “A review on predicting student’s performance using data mining techniques,” Procedia Comput. Sci., vol. 72, no. 1, pp. 414–422, 2015, doi: 10.1016/j.procs.2015.12.157.

A. Khan and S. K. Ghosh, “Student performance analysis and prediction in classroom learning: A review of educational data mining studies,” Educ. Inf. Technol., vol. 26, no. 1, pp. 205–240, 2021, doi: 10.1007/s10639-020-10230-3.

A. Ashraf, S. Anwer, and M. Gufran, “A comparative study of predicting student’s performance by use of data mining techniques,” pp. 122–136, 2018.

M. H. bin Roslan and C. J. Chen, “Educational data mining for student performance prediction: A systematic literature review (2015-2021),” Int. J. Emerg. Technol. Learn., vol. 17, no. 5, pp. 147–179, 2022, doi: 10.3991/ijet.v17i05.27685.

A. Yusuf and A. Lawan, “Prediction of students’ academic performance using educational datamining technique: Literature review,” 2018, [Online]. Available: https://www.academia.edu/34009565/prediction_of_students_academic_performance_using_education al_datamining_technique_literature_review.

T. Thilagaraj and N. Sengottaiyan, “A Review of educational data mining in higher education system,” in Proceedings of the Second International Conference on Research in Intelligent and Computing in Engineering, 2017, vol. 10, pp. 349–358. doi: 10.15439/2017r87.

M. Kumar, S. Shambhu, and P. Aggarwal, “Recognition of slow learners using classification data mining techniques,” Imp. J. Interdiscip. Res., vol. 2, no. 12, pp. 1–6, 2016, [Online]. Available: http://www.onlinejournal.in

S. Hussain, N. A. Dahan, F. M. Ba-Alwib, and N. Ribata, “Educational data mining and analysis of students’ academic performance using WEKA,” Indones. J. Electr. Eng. Comput. Sci., vol. 9, no. 2, pp. 447–459, 2018, doi: 10.11591/ijeecs.v9.i2.pp447-459.

V. Vijayalakshmi, K. Venkatachalapathy, and V. Ohmprakash, “A comparison of classification techniques on prediction of student performance in educational data mining,” in 2017 International Conference On Intelligent Computing And Control(I2c2), 2017, no. 1, pp. 132–136.

S. Batool, J. Rashid, M. Wasif, and N. Jungeun, Educational data mining to predict students ’ academic performance : A survey study, no. 0123456789. Springer US, 2022. doi: 10.1007/s10639-022-11152-y.

A. Saa, “Educational data mining and students’ performance rediction,” Int. J. Adv. Comput. Sci. Appl., vol. 7, no. 5, pp. 212–220, 2016, doi: 10.14569/ijacsa.2016.070531.

G. Ramaswami, T. Susnjak, and A. Mathrani, “On Developing Generic Models for Predicting Student Outcomes in Educational Data Mining,” Big Data Cogn. Comput., vol. 6, no. 1, 2022, doi: 10.3390/bdcc6010006.

R. Raju, N. Kalaiselvi, M. Aathiqa Sulthana, I. Divya, and A. Selvarani, “Educational data mining: A comprehensive study,” in 2020 International Conference on System, Computation, Automation and Networking, ICSCAN 2020, 2020, pp. 1–5. doi: 10.1109/ICSCAN49426.2020.9262399.

S. S. Athani, S. A. Kodli, M. N. Banavasi, and P. G. S. Hiremath, “Student academic performance and social behavior predictor using data mining techniques,” Proceeding - IEEE Int. Conf. Comput. Commun. Autom. ICCCA 2017, vol. 2017-Janua, pp. 170–174, 2017, doi: 10.1109/CCAA.2017.8229794.

N. Ndou, R. Ajoodha, and A. Jadhav, “Educational data-mining to determine student success at higher education institutions,” in 2020 2nd International Multidisciplinary Information Technology and Engineering Conference, IMITEC 2020, 2020, pp. 1–8. doi: 10.1109/IMITEC50163.2020.9334139.

J. López-Zambrano, J. A. L. Torralbo, and C. Romero, “Early prediction of student learning performance through data mining: A systematic review,” Psicothema, vol. 33, no. 3, pp. 456–465, 2021, doi: 10.7334/psicothema2021.62.

L. Indi, P. Aji, U. A. Yogyakarta, A. Sunyoto, and U. A. Yogyakarta, “An implementation of C4 . 5 classification algorithm to analyze student’s performance,” in Published in: 2020 3rd International Conference on Information and Communications Technology (ICOIACT), 2021, pp. 5–9. doi: 10.1109/ICOIACT50329.2020.9332088.

K. Parmar, D. Vaghela, and P. Sharma, “Performance prediction of students using distributed data mining,” ICIIECS 2015 - 2015 IEEE Int. Conf. Innov. Information, Embed. Commun. Syst., no. Ddm, 2015, doi: 10.1109/ICIIECS.2015.7192860.

H. Pallathadka, A. Wenda, E. Ramirez-Asís, M. Asís-López, J. Flores-Albornoz, and K. Phasinam, “Classification and prediction of student performance data using various machine learning algorithms,” Mater. Today Proc., 2021, doi: 10.1016/j.matpr.2021.07.382.

R. Asif, A. Merceron, S. A. Ali, and N. G. Haider, “Analyzing undergraduate students’ performance using educational data mining,” Comput. Educ., vol. 113, pp. 177–194, 2017, doi: 10.1016/j.compedu.2017.05.007.

N. M. Masanja and H. Mkumbo, “The Application of Open Source Artificial Intelligence as an Approach to Frugal Innovation in Tanzania,” Int. J. Res. Innov. Appl. Sci. |, vol. 5, no. 3, pp. 2454–6194, 2020, [Online]. Available: www.rsisinternational.org

A. Khan, S. Ghosh, and S. K. Ghosh, “Measuring domain knowledge for early prediction of student performance: A semantic approach,” Proc. 2020 IEEE Int. Conf. Teaching, Assessment, Learn. Eng. TALE 2020, pp. 444–451, 2020, doi: 10.1109/TALE48869.2020.9368439.

R. Jidagam and N. Rizk, “Evaluation of predictive data mining algorithms in student academic performance,” in INTED2016 Proceedings, 2016, vol. 1, no. 1, pp. 6314–6324. doi: 10.21125/inted.2016.0487.

G. Ramaswami, T. Susnjak, A. Mathrani, J. Lim, and P. Garcia, “Using educational data mining techniques to increase the prediction accuracy of student academic performance,” Inf. Learn. Sci., vol. 120, no. 7–8, pp. 451–467, 2019, doi: 10.1108/ILS-03-2019-0017.

S. Lemm, B. Blankertz, T. Dickhaus, and K. R. Müller, “Introduction to machine learning for brain imaging,” Neuroimage, vol. 56, no. 2, pp. 387–399, 2011, doi: 10.1016/j.neuroimage.2010.11.004.

A. Hellas et al., “Predicting academic performance: A systematic literature review,” in Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE, 2018, no. 1, pp. 175–199. doi: 10.1145/3293881.3295783.

C. Chaka, “Educational data mining, student academic performance prediction, prediction methods, algorithms and tools: An overview of reviews,” pp. 1–17, 2021, doi: 10.20944/preprints202108.0345.v1.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Copyright (c) 2023 Juma Habibu Shindo, Mohamedi Mohamedi Mjahidi, Mohamed Dewa Waziri

Downloads

Download data is not yet available.