We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

A Research Travelogue Towards Educational Data Mining

by Bernard Ugalde, R. Venkateswaran
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 42
Year of Publication: 2018
Authors: Bernard Ugalde, R. Venkateswaran
10.5120/ijca2018917005

Bernard Ugalde, R. Venkateswaran . A Research Travelogue Towards Educational Data Mining. International Journal of Computer Applications. 179, 42 ( May 2018), 39-48. DOI=10.5120/ijca2018917005

@article{ 10.5120/ijca2018917005,
author = { Bernard Ugalde, R. Venkateswaran },
title = { A Research Travelogue Towards Educational Data Mining },
journal = { International Journal of Computer Applications },
issue_date = { May 2018 },
volume = { 179 },
number = { 42 },
month = { May },
year = { 2018 },
issn = { 0975-8887 },
pages = { 39-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number42/29366-2018917005/ },
doi = { 10.5120/ijca2018917005 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:58:12.043764+05:30
%A Bernard Ugalde
%A R. Venkateswaran
%T A Research Travelogue Towards Educational Data Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 42
%P 39-48
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the recent era of technology, educational institutions innovate itself to find new ways to serve its educational community efficiently and effectively. Information systems have been there for quite some time as the backbone of education institutions to support its daily operations. At this point, educational databases have much information but remain utilized. In order to make benefit from such big data, a power tool is required like data mining for analysis and prediction. Data mining has been proven useful in various aspects of our lives like in advertising, marketing, loans and now a new frontier in the field of education. It has been noted that there is no unified approach among researchers in educational data mining and a considerable amount of work is required towards this field. This research presents a comprehensive travelogue (2010- 2017) in educational data mining with respect to related international journals available from various sources, and secondary data collected from the organization in the form of survey reports.

References
  1. "educationaldatamining.org", Educationaldatamining.org, 2017. [Online]. Available: http://educationaldatamining.org/. [Accessed: 13- Dec- 2017].
  2. "Mining educational data to analyze learning and teaching methods, the case of medicine | Persyval-Lab", Persyval-lab.org, 2017. [Online]. Available: https://persyval-lab.org/en/exploratory-project/Med_LT_med. [Accessed: 13- Dec- 2017].
  3. A. Francisco, "Realizing the Opportunity for Big Data in Education - Digital Promise", Digital Promise, 2017. [Online]. Available: http://digitalpromise.org/2014/04/17/realizing-the-opportunity-for-big-data-in-education/. [Accessed: 13- Dec- 2017].
  4. "Data Mining Classification & Prediction", www.tutorialspoint.com, 2017. [Online]. Available: https://www.tutorialspoint.com/data_mining/dm_classification_prediction.htm. [Accessed: 14- Dec- 2017].
  5. "What is Clustering in Data Mining?", Big Data Made Simple - One source. Many perspectives., 2017. [Online]. Available: http://bigdata-madesimple.com/what-is-clustering-in-data-mining/. [Accessed: 14- Dec- 2017].
  6. C. Rygielski, J. Wang and D. Yen, "Data mining techniques for customer relationship management", 2017. .
  7. "Educational data mining - EduTech Wiki", Edutechwiki.unige.ch, 2017. [Online]. Available: http://edutechwiki.unige.ch/en/Educational_data_mining. [Accessed: 14- Dec- 2017].
  8. D. Thanamani, "An Overview of Knowledge Discovery Databaseand Data mining Techniques", Rroij.com, 2017. [Online]. Available: http://www.rroij.com/open-access/an-overview-of-knowledge-discovery-databaseand-data-mining-techniques.php?aid=48833. [Accessed: 14- Dec- 2017].
  9. "MS SQL Server Tutorial", www.tutorialspoint.com, 2017. [Online]. Available: https://www.tutorialspoint.com/ms_sql_server/. [Accessed: 14- Dec- 2017].
  10. "Oracle Data Mining", Oracle.com, 2017. [Online]. Available: http://www.oracle.com/technetwork/database/options/advanced-analytics/odm/overview/index.html. [Accessed: 14- Dec- 2017].
  11. D. science and P. analytics, "IBM SPSS Software | IBM Analytics", Ibm.com, 2017. [Online]. Available: https://www.ibm.com/analytics/data-science/predictive-analytics/spss-statistical-software. [Accessed: 14- Dec- 2017].
  12. "Weka 3 - Data Mining with Open Source Machine Learning Software in Java", Cs.waikato.ac.nz, 2017. [Online]. Available: https://www.cs.waikato.ac.nz/ml/weka/. [Accessed: 14- Dec- 2017].
  13. "Orange – Data Mining Fruitful & Fun", Orange.biolab.si, 2017. [Online]. Available: https://orange.biolab.si/. [Accessed: 14- Dec- 2017].
  14. D. Weiss, "Carrot2 - Open Source Search Results Clustering Engine", Project.carrot2.org, 2017. [Online]. Available: https://project.carrot2.org/. [Accessed: 14- Dec- 2017].
  15. G. Kavitha and L. Raj, "Educational Data Mining and Learning Analytics Educational Assistance for Teaching and Learning", International Journal of Computer & organization Trends, vol. 41, no. 1, pp. 21-25, 2017.
  16. J. Rˇ ihák and R. Pelánek, "Measuring Similarity of Educational Items Using Data on Learners’ Performance", 10th International Conference on Educational Data Mining, China, 2017.
  17. J. Rˇ ihák and R. Pelánek, "Measuring Similarity of Educational Items Using Data on Learners’ Performance", 10th International Conference on Educational Data Mining, China, 2017.
  18. Z. Ren, X. Ning and H. Rangwala, "Grade Prediction with Temporal Course-wise Influence", in 10th International Conference on Educational Data Mining, China, 2017.
  19. A. Stewart, N. Bosch and S. D’Mello, "Generalizability of Face-Based Mind Wandering Detection Across Task Contexts", 10th International Conference on Educational Data Mining, China, 2017.
  20. A. Olney, D. Bakhtiari, D. Greenberg and A. Graesser, "Assessing Computer Literacy of Adults with Low Literacy Skills", in 10th International Conference on Educational Data Mining, China, 2017.
  21. R. Balyan, K. McCarthy and D. McNamara, "Combining Machine Learning and Natural Language Processing to Assess Literary Text Comprehension", in 10th International Conference on Educational Data Mining, China, 2017.
  22. A. Katare and S. Dubey, "A Study of various Techniques for Predicting student Performance under Educational Data Mining", International Journal of Electrical, Electronics and Computer Engineering, 2016.
  23. A. Abu, "Educational Data Mining & Students’ Performance Prediction", International Journal of Advanced Computer Science and Applications, vol. 7, no. 5, 2016.
  24. A. Vail, J. Wiggins, J. Grafsgaard, K. Boyer, E. Wiebe and J. Lester, "The Affective Impact of Tutor Questions: Predicting Frustration and Engagement", 9th International Conference on Educational Data Mining, 2016.
  25. M. Stapel, Z. Zheng and N. Pinkwart, "An Ensemble Method to Predict Student Performance in an Online Math Learning Environment", in 9th International Conference on Educational Data Mining, 2016.
  26. M. Saarela and T. K ̈arkk ̈ainen, "Analysing Student Performance using Sparse Data of Core Bachelor Courses", Journal of Educational Data Mining, vol. 7, no. 1, 2016.
  27. A. Sharma, A. Biswas, A. Gandhi, S. Patil and O. Deshmukh, "LIVELINET: A Multimodal Deep Recurrent Neural Network to Predict Liveliness in Educational Videos", in 9th International Conference on Educational Data Mining, 2016.
  28. K. Niu, Z. Niu, X. Zhao, C. Wang, K. Kang and M. Ye, "A Coupled User Clustering Algorithm for Web-based Learning Systems", in 9th International Conference on Educational Data Mining, 2016.
  29. E. Bumbacher, S. Salehi, M. Wierzchula and P. Blikstein, "Learning Environments and Inquiry Behaviors in Science Inquiry Learning: How their Interplay Affects the Development of Conceptual Understanding in Physics", in 8th International Conference on Educational Data Mining, Spain, 2015.
  30. J. Nižnan, R. Pelánek and J. Rˇ ihák, "Student Models for Prior Knowledge Estimation", in 8th International Conference on Educational Data Mining, Spain, 2015.
  31. Y. Chen, P. Wuillemin and J. Labat, "Discovering Prerequisite Structure of Skills through Probabilistic Association Rules Mining", in 8th International Conference on Educational Data Mining, Spain, 2015.
  32. A. Ezen-Can and K. Boyer, "Choosing to Interact: Exploring the Relationship Between Learner Personality, Attitudes, and Tutorial Dialogue Participation", in 8th International Conference on Educational Data Mining, Spain, 2015.
  33. M. Saarela and T. Kärkkäinen, "Do Country Stereotypes Exist in PISA? A Clustering Approach for Large, Sparse, and Weighted Data.", in 8th International Conference on Educational Data Mining, Spain, 2015.
  34. Peña-Ayala, Alejandro. "Educational data mining: A survey and a data mining-based analysis of recent works." Expert systems with applications 41.4 (2014): 1432-1462.
  35. Saranya, S., R. Ayyappan, and N. Kumar. "Student Progress Analysis and Educational Institutional Growth Prognosis Using Data Mining." International Journal Of Engineering Sciences & Research Technology, 2014
  36. Archer, Elizabeth, Yuraisha Bianca Chetty, and Paul Prinsloo. "Benchmarking the habits and behaviors of successful students: A case study of academic-business collaboration." The International Review of Research in Open and Distance Learning 15.1 (2014).
  37. Hicheur Cairns, Awatef, et al. "Towards Custom-Designed Professional Training Contents and Curriculums through Educational Process Mining." IMMM 2014, The Fourth International Conference on Advances in Information Mining and Management. 2014.
  38. Arora, Rakesh Kumar, and Dharmendra Badal. "Mining Association Rules to Improve Academic Performance." (2014).
  39. S. Fancsali, T. Nixon and S. Ritter, "Optimal and Worst-Case Performance of Mastery Learning Assessment with Bayesian Knowledge Tracing", in International Conference on Educational Data Mining, USA, 2013.
  40. J. Grafsgaard, J. Wiggins, K. Boyer, E. Wiebe and J. Lester, "Automatically Recognizing Facial Expression: Predicting Engagement and Frustration", in International Conference on Educational Data Mining, USA, 2013.
  41. W. Hawkins, N. Heffernan, Y. Wang and R. Baker, "Extending the Assistance Model: Analyzing the Use of Assistance over Time", in International Conference on Educational Data Mining, USA, 2013.
  42. Osmanbegović, Edin, and Mirza Suljić. "Data mining approach for predicting student performance." Economic Review 10.1 (2012).
  43. Sukanya, M., S. Biruntha, Dr S. Karthik, and T. Kalaikumaran. "Data mining: Performance improvement in education sector using classification and clustering algorithm." In International conference on computing and control engineering,(ICCCE 2012), vol. 12. 2012.
  44. Torenbeek, M., E. P. W. A. Jansen, and W. H. A. Hofman. "Predicting first-year achievement by pedagogy and skill development in the first weeks at university." Teaching in Higher Education 16.6 (2011): 655-668.
  45. Yongqiang, He, and Zhang Shunli. "Application of Data Mining on Students' Quality Evaluation." Intelligent Systems and Applications (ISA), 2011 3rd International Workshop on. IEEE, 2011.
  46. Sakurai, Yoshitaka, Setsuo Tsuruta, and Rainer Knauf. "Success Chances Estimation of University Curricula Based on Educational History, Self-Estimated Intellectual Traits and Vocational Ambitions." Advanced Learning Technologies (ICALT), 2011 11th IEEE International Conference on. IEEE, 2011.
  47. Aher, Sunita B., and L. M. R. J. Lobo. "Data mining in educational system using Weka." IJCA Proceedings on International Conference on Emerging Technology Trends (ICETT). Vol. 3. 2011.
  48. Sharma, Mamta, and Monali Mavani. "Accuracy Comparison of Predictive Algorithms of Data Mining: Application in Education Sector." Advances in Computing, Communication and Control. Springer Berlin Heidelberg, 2011. 189-194.
  49. Ayesha, Shaeela, Tasleem Mustafa, Ahsan Raza Sattar, and M. Inayat Khan. "Data mining model for higher education system." Europen Journal of Scientific Research 43, no. 1 (2010): 24-29.
  50. Kovacic, Zlatko. "Early prediction of student success: Mining students' enrolment data." (2010).
  51. Al-shargabi, Asma A., and Ali N. Nusari. "Discovering vital patterns from UST students data by applying data mining techniques." Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on. Vol. 2. IEEE, 2010.
  52. Yan, Zhi-min, Qing Shen, and Bin Shao. "The analysis of student's grade based on Rough Sets." Ubi-media Computing (U-Media), 2010 3rd IEEE International Conference on. IEEE, 2010.
  53. Ningning, Gao. "Proposing Data Warehouse and Data Mining in Teaching Management Research." Information Technology and Applications (IFITA), 2010 International Forum on. Vol. 1. IEEE, 2010.
  54. Knauf, Rainer, Yoshitaka Sakurai, Setsuo Tsuruta, and Kouhei Takada. "Empirical evaluation of a data mining method for success chance estimation of university curricula." In Systems Man and Cybernetics (SMC), 2010 IEEE International Conference on, pp. 1127-1133. IEEE, 2010.
  55. Wu, X., Zhang, H., & Zhang, H. (2010, October). Study of comprehensive evaluation method of undergraduates based on data mining. In Intelligent Computing and Integrated Systems (ICISS), 2010 International Conference on(pp. 541-543). IEEE.
  56. Youping, Bian Xiangjuan Gong. "The application of data mining technology in analysis of college student's performance." Information Science (2010).
  57. Liu, Zhiwu, and Xiuzhi Zhang. "Prediction and Analysis for Students' Marks Based on Decision Tree Algorithm." Intelligent Networks and Intelligent Systems (ICINIS), 2010 3rd International Conference on. IEEE, 2010.
Index Terms

Computer Science
Information Sciences

Keywords

Educational Data Mining Trends in EMD Future vision of EDM