Educational Data Mining in Practice Literature Review
Abstract
Educational data mining (EDM) is an evolving field with a suite of computational and psychological methods for understanding how students learn. Applying Data Mining methods to education data help us to resolve educational investigation issues. The growth of education data offers some unique advantages as well as some new challenges for education study. Some of the challenges are an improvement of student models, identify domain structure model, pedagogical support and extend educational theories. The main objective of this paper is to present the capabilities of data mining in the context of the higher educational system and their applications and progress, through a survey of literature and the classification of articles. We observed the works on investigational situation studies showed in the EDM during the recent past, in addition, we have introduced three data models based on descriptive and predictive data mining techniques. This is oriented towards students in order to recommend learners’ activities, resources, suggest path pruning and shortening or simply links that would favor and improve their learning or to educators in order to get more objective feedback for instruction.
How to cite this article:
Bunkar K, Tanwani S. Educational Data Mining in Practice Literature Review. J Adv Res Embed Sys 2020; 7(1): 1-7.
DOI: https://doi.org/10.24321/2395.3802.202001
References
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[21] Mukesh Kumar, Shankar Shambhu & Punam Aggarwal, “Recognition of Slow Learners Using Classification Data Mining Techniques”, Imperial Journal of Interdisciplinary Research (IJIR) Vol-2, Issue-12, 2016
[22] Abeer Badr El Din Ahmed, Ibrahim Sayed Elaraby, “Data Mining: A prediction for Student's Presentations Using Classification Method”, World Journal of Computer Application and Technology 2(2): 43-47, 2014
[23] Dorina Kabakchieva, “Predicting Student Presentations by Using Data Mining Methods for Classification”, Cybernetics and Information Technologies, Volume 13, No 1, Sofia 2013
[24] Hina Gulati, “Predictive Analytics Using Data Mining Technique”, 978-9-3805-4416-8/15/$31.00 c 2015 IEEE
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[26] Dr.Vijayalakshmi M N, S.Anupama Kumar, Kavyashree BN, “Investigating Interesting Rules Using Association Mining for Educational Data”, International Journal of Advanced Research in Computer Engineering & Technology Volume 3, Issue 2, February 2014
[27] Jayshree Jha and Leena Ragha, “Educational Data Mining using Improved Apriori Algorithm”, International Journal of Information and Computation Technology. Volume 3, Number 5 (2013), pp. 411-418
[28] Rakesh Kumar Arora, Dr. Dharmendra Badal, “Mining Association Rules to Improve Academic Presentations”, International Journal of Computer Science and Mobile Computing, Vol.3 Issue.1, January- 2014, pg. 428-433
[29] Cristobal Romero, Amelia Zafra, Jose Mar ´ ´ıa Luna and Sebastian Ventura, “Association rule mining using genetic programming to provide feedback to instructors from multiple-choice quiz data”, Expert Systems, May 2013, Vol. 30, No. 2
[30] Manolis Chalaris, Stefanos Gritzalis, Manolis Maragoudakis, Cleo Sgouropoulou and Anastasios Tsolakidis, “Improving Quality of Educational Processes Providing New Knowledge using Data Mining Techniques”, Procedia - Social and Behavioral Sciences 147 ( 2014 ) 390 – 397
[31] Ryan S.J.D. Baker, Kalina Yacef, “The State of Educational Data Mining in 2009: A Review and Future Visions”, Journal of Educational Data Mining.
[2] Romero, Cristóbal, Sebastián Ventura, and Enrique García, "Data mining in course management systems: Moodle case study and tutorial." Computers & Education 51.1 (2008): pp. 368-384.
[3] Ahmed, Abeer Badr El Din, and Ibrahim Sayed Elaraby, "Data Mining: A prediction for Student's Presentations Using Classification Method", World Journal of Computer Application and Technology 2.2, 2014, pp. 43-47.
[4] Amjad Abu Saa, “Educational Data Mining & Students’ Presentations Prediction”, International Journal of Advanced Computer Science and Applications, Volume 7, Number 5, 2016
[5] Ritu Gautam and Deepika Pahuja, “A Review on Educational Data Mining”, International Journal of Science and Research (IJSR), Volume 3 Issue 11, November 2014
[6] Brijesh Kumar Bhardwaj and Saurabh Pal, “Data Mining: A prediction for presentations improvement using classification”, Data Mining: A prediction for presentations improvement using classification, Volume 9, Number 4, April 2011
[7] Pelánek, Radek, Jirí Rihák, and Jan Papoušek, "Impact of data collection on interpretation and evaluation of student models", Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, ACM, 2016.
[8] Peña-Ayala, Alejandro, "Educational data mining: A survey and a data mining-based analysis of recent works", Expert systems with applications 41.4 (2014): pp. 1432-1462.
[9] Asif, Raheela and Agathe Merceron, "Analyzing undergraduate students' presentations using educational data mining", Computers & Education (2017).
[10] Ajay Kumar Pal and Saurabh Pal, “Analysis and Mining of Educational Data for Predicting the Presentations of Students”, International Journal of Electronics Communication and Computer Engineering, Volume 4, Issue 5, 2013, pp.1560-1565
[11] Rajni Jindal, and Malaya Dutta Borah. "A survey on educational data mining and research trends." International Journal of Database Management Systems 5.3 (2013): 53.
[12] Rebecca Ferguson, Learning analytics: drivers, progresss and challenges, International Journal of Technology Enhanced Learning, 4(5/6) pp. 304–317.
[13] Gwo-Jen Hwang, Hui-Chun Chu & Chengjiu Yin (2017) Objectives, methodologies and research issues of learning analytics, Interactive Learning Environments, 25:2, 143-146, DOI: 10.1080/10494820.2017.1287338
[14] Nicholas A. Ashford, Professor of Technology and Policy, Massachusetts Institute of Technology, “Major Challenges to Engineering Education for Sustainable Progress: What Has to Change to Make it Creative, Effective, and Acceptable to the Established Disciplines”
[15] Samira ElAtia, Donald Ipperciel, Ahmed Hammad, “Implications and Challenges to Using Data Mining in Educational Research in the Canadian Context”
[16] Shiwani Rana, Roopali Garg, “Evaluation of Student’s Presentations of an Institute Using Clustering Algorithms”, International Journal of Applied Engineering Research, Volume 11, Number 5 (2016) pp 3605-3609
[17] Hanane Ariouat, Awatef Hicheur Cairns, Kamel Barkaoui, Jacky Akoka, Nasser Khelifa, “A two-step clustering approach for improving educational process model discovery”, 25th IEEE International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises, 13 June 2016 - 15 June 2016.
[18] Shinichi Oeda, Genki Hashimoto, “Log-Data Clustering Analysis for Dropout Prediction in Beginner Programming Classes”, Procedia Computer Science 112 (2017) 614–621, 2017 Published by Elsevier B.V
[19] Thaddeus Matundura Ogwoka, Wilson Cheruiyot, George Okeyo, “A Model for Predicting Students’ Academic Presentations using a Hybrid of K-means and Decision tree Algorithms”, International Journal of Computer Applications Technology and Research Volume 4– Issue 9, 693 - 697, 2015
[20] Jiaqu Yi, Sizhe Li, Maomao Wu, H.H. Au Yeung, Wilton W.T Fok, Ying Wang, Fang Liu, “Cloud-based Educational Big Data Application of Apriori algorithm and K-Means Clustering algorithm based on Students' Information”, Fourth International Conference on Big Data and Cloud Computing, 2014 IEEE
[21] Mukesh Kumar, Shankar Shambhu & Punam Aggarwal, “Recognition of Slow Learners Using Classification Data Mining Techniques”, Imperial Journal of Interdisciplinary Research (IJIR) Vol-2, Issue-12, 2016
[22] Abeer Badr El Din Ahmed, Ibrahim Sayed Elaraby, “Data Mining: A prediction for Student's Presentations Using Classification Method”, World Journal of Computer Application and Technology 2(2): 43-47, 2014
[23] Dorina Kabakchieva, “Predicting Student Presentations by Using Data Mining Methods for Classification”, Cybernetics and Information Technologies, Volume 13, No 1, Sofia 2013
[24] Hina Gulati, “Predictive Analytics Using Data Mining Technique”, 978-9-3805-4416-8/15/$31.00 c 2015 IEEE
[25] A. Daud, N. R. Aljohani, R. A. Abbas, M. D. Lytras, F. Abbas, J. S. Alowibdi, “Predicting Student Presentations using Advanced Learning Analytics”, Companion, April 3-7, 2017, Perth, Australia. ACM.
[26] Dr.Vijayalakshmi M N, S.Anupama Kumar, Kavyashree BN, “Investigating Interesting Rules Using Association Mining for Educational Data”, International Journal of Advanced Research in Computer Engineering & Technology Volume 3, Issue 2, February 2014
[27] Jayshree Jha and Leena Ragha, “Educational Data Mining using Improved Apriori Algorithm”, International Journal of Information and Computation Technology. Volume 3, Number 5 (2013), pp. 411-418
[28] Rakesh Kumar Arora, Dr. Dharmendra Badal, “Mining Association Rules to Improve Academic Presentations”, International Journal of Computer Science and Mobile Computing, Vol.3 Issue.1, January- 2014, pg. 428-433
[29] Cristobal Romero, Amelia Zafra, Jose Mar ´ ´ıa Luna and Sebastian Ventura, “Association rule mining using genetic programming to provide feedback to instructors from multiple-choice quiz data”, Expert Systems, May 2013, Vol. 30, No. 2
[30] Manolis Chalaris, Stefanos Gritzalis, Manolis Maragoudakis, Cleo Sgouropoulou and Anastasios Tsolakidis, “Improving Quality of Educational Processes Providing New Knowledge using Data Mining Techniques”, Procedia - Social and Behavioral Sciences 147 ( 2014 ) 390 – 397
[31] Ryan S.J.D. Baker, Kalina Yacef, “The State of Educational Data Mining in 2009: A Review and Future Visions”, Journal of Educational Data Mining.
Published
2020-07-27
How to Cite
BUNKAR, Kamal; TANWANI, Prof. Sanjay.
Educational Data Mining in Practice Literature Review.
Journal of Advanced Research in Embedded System, [S.l.], v. 7, n. 1, p. 1-7, july 2020.
ISSN 2395-3802.
Available at: <http://thejournalshouse.com/index.php/ADR-Journal-Embedded-Systems/article/view/203>. Date accessed: 18 may 2024.
Section
Articles
