IJSTR

International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
0.2
2019CiteScore
 
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020

CALL FOR PAPERS
AUTHORS
DOWNLOADS
CONTACT

IJSTR >> Volume 10 - Issue 2, February 2021 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Educational Data Mining Applied For Predicting Students’ ICT Literacy

[Full Text]

 

AUTHOR(S)

Kanjana Haruehansapong, Suppat Rungraungsilp

 

KEYWORDS

Classification, Data mining, Decision tree, Educational data mining, ICT literacy, Prediction

 

ABSTRACT

ICT literacy is essentially regarded as one of six strategies in the digital development plan for the Thai economy and society. The identification of students who are ICT literate and those that are not is therefore crucial. Educational institutions normally provide capability testing to classify ICT literacy of students; however, it is inconvenient to examine large groups by testing. This research proposed data mining techniques from historical student information for classification based on a decision tree, to build a model for the ICT literacy classification of the new students. In this way, the results of the ICT capabilities of students will be recognized with no need for knowledge examination by testing all students. If the result of prediction shows that students have low ICT literacy, they are required to attend an online course to improve their ICT literacy skills. As this research created a decision rule using the C4.5 algorithms and tested the predictive efficiency, the accuracy is 86.12%.

 

REFERENCES

[1] Educational Testing Service Policy Information Center & International ICT Literacy Panel. Digital Transformation: A Framework for ICT Literacy. Available online: http://www.ets.org/Media/Research/pdf/ICTREPORT.pdf (accessed on 16 June 2020).
[2] Ministry of Information and Communication Technology. Digital Thailand. Available online: https://www.ega.or.th/upload/download/file_9fa5ae40143e13a659403388d226efd.8pdf (accessed on 16 June 2020).
[3] The Office of the National Digital Economy and Society Commission, Ministry of Digital Economy and Society. Thailand Digital Economy Society Development Plan. Available online: https://file.onde.go.th/assets/portals/1/ebookcategory/23_Digital_Thailand_pocket_book_EN/ (accessed on 16 July 2020).
[4] X. Hu, Y. Gong, C. Lai, FKS. Leung, “The relationship between ICT and student literacy in mathematics, reading, and science across 44 countries: A multilevel analysis,” Computers & Education, vol. 125, pp. 1-13, 2018.
[5] L.S.J. Farmer, “ICT literacy integration: Issues and sample efforts,” 10.4018/978-1-5225-2000-9.ch004, 2016.
[6] M.H. Baturay, “An overview of the world of MOOCs,” Procedia - Social and Behavioral Sciences, vol. 174, pp. 427-433, 2015.
[7] B. Liu and H. Chen, “A study on the role of MOOCs in computer basic teaching in universities,” Proceeding of the 15th International Conference on Computer Science and Education (ICCSE 2020), Delft, Netherlands, 18-22 August 2020, pp 235-238.
[8] J.M. Amala, S.I. Elizabeth, "Role of educational data mining in student learning processes with sentiment analysis: A survey,” International Journal of Knowledge and Systems Science, vol. 11, no. 4, pp. 31-44, 2020.
[9] I.D. Sudirman and I.D. Utama, “Predicting GPA in Entrepreneurship Study Program by Using Data Mining Technique,” Universal Journal of Educational Research, vol. 8, no. 7, pp. 3259-3273, 2020.
[10] A.D. Wulansari, H.S. Kumaidi, M. Saleh, Friyatmi, “Detection of Students’ Interest with the Logistics Model,” TEM Journal., vol. 8, no. 2, pp. 564-571, 2019.
[11] O.E. Hatlevik, I. Throndsenb, M. Loi, G.B. Gudmundsdottir, “Students’ ICT self-efficacy and computer and information literacy: Determinants and relationships,” Computers & Education, vol. 118, pp. 107-119, 2018.
[12] Y. Hou, J. Xu,Y. Huang, X. Ma, “A big data application to predict depression in the university based on the reading habits,” Proceeding of the 3rd International Conference on Systems and Informatics (ICSAI), Shanghai, China, 19-21 November 2016; pp. 1085-1089.
[13] G. Mihai, “Recommendation System Based On Association Rules For Distributed E-Learning Management Systems,” ACTA Universitatis Cibiniensis, vol. 67, no. 1, pp. 99-104, 2015.
[14] S. Sawangarreerak, P Thanathamathee, “Random Forest with Sampling Techniques for Handling Imbalanced Prediction of University Student Depression,” Information, vol. 11, no. 11, pp. 519, 2020.
[15] G. Badr, A. Afnan, A. Hanadi, A. Manal, “Predicting Students’ Performance in University Courses: A Case Study and Tool in KSU Mathematics Department,” Procedia Computer Science, pp. 80-89, 2016.
[16] M. Apolinar-Gotardo, “classification algorithm analysis of students’ ict competency level using data mining technique,” International Journal of Scientific and Technology Research, vol. 9, no. 3, pp. 3256-3258, 2020.
[17] L.N.M. Bezerra and M.T. Silva, “Educational data mining applied to a massive course,” International Journal of Distance Education Technologies, vol. 18, No. 4, pp. 17-30, 2020. doi:10.4018/IJDET.2020100102
[18] P. Eakasit, An Introduction to Data Mining Techniques. Asia Publisher: Bangkok, Thailand, pp. 50-75, 2015.
[19] H.W. Ian, F. Eibe, A.H. Mark, Data Mining: Practical machine learning tools and techniques, 3rd Ed., Morgan Kaufmann Publishers: San Francisco, USA, pp. 4-76, 2011.
[20] T.L. Daniel, Discovering Knowledge in Data: An Introduction to Data Mining. John Wiley & Sons: New Jersey, USA, pp.1-39, 2014.
[21] J. Han, M. Kamber, J. Pei, Data Mining Concepts and Techniques, 3rd Ed.; Morgan Kaufmann Publishers: San Francisco, USA, pp. 327-391, 2012.
[22] S. Vikram and N. Midha, “A Survey on Classification Techniques in Data Mining,” International Journal of Knowledge Management Studies, vol. 16, no. 1, ISSN (Online) 2231-5268, 2015.
[23] J.R. Quinlan, “Induction of decision tree,” Journal of Machine Learning Research, vol. 1, no. 1, pp. 81-106, 1986.
[24] C.S. Dhir, N. Iqbal, S. Lee, “Efficient feature selection based on information gain criterion for face recognition,” Proceedings of the International Conference on Information Acquisition, Jeju, Korea, 8–11 July 2007, pp. 523–527.
[25] T.A. Alhaj, M.M. Siraj, A. Zainal, H.T. Elshoush, F. Elhaj, “Feature Selection Using Information Gain for Improved Structural-Based Alert Correlation,” PLoS ONE, vol. 11, no. 11, 2016. https://doi.org/10.1371/journal.pone.0166017.
[26] P.N. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining. Pearson: Boston, USA, pp. 25-43, 2005.
[27] J.R. Quinlan, C4.5: Programs for Machine Learning. Kaufmann Publishers: San Francisco, USA, pp. 5-13, 1993.
[28] P. Eakasit, Advanced Predictive Modeling with R & RapidMiner Studio 7. Asia Publisher: Bangkok, Thailand, 2018.