EDM – survey of performance factors and algorithms applied

  • Authors

    • Deepali R Vora
    • Kamatchi Iyer
    2018-03-11
    https://doi.org/10.14419/ijet.v7i2.6.10074
  • EDM, Algorithms, Performance Factors, Deep Learning
  • Educational Data Mining (EDM) is a new field of research in the data mining and Knowledge Discovery in Databases (KDD) field. It mainly focuses in mining useful patterns and discovering useful knowledge from the educational information systems from schools, to colleges and universities. Analysing students’ data and information to perform various tasks like classification of students, or to create decision trees or association rules, so as to make better decisions or to enhance student’s performance is an interesting field of research. The paper presents a survey of various tasks performed in EDM and algorithms (methods) used for the same. The paper identifies the lacuna and challenges in Algorithms applied, Performance Factors considered and data used in EDM.

  • References

    1. [1] Karan Sukhija, Dr. Manish Jindal, Dr. Naveen Aggarwal, “The Recent State of Educational Data Mining: A Survey and Future Visionsâ€, IEEE 3rd International Conference on MOOCs,Innovation and Technology in Education, 2015.

      [2] “What is educational data mining†, http://edtechreview.in/dictionary/394whatiseducationaldatamining, Accessed on August 2016.

      [3] Ryan S.J.d. Baker,George Siemens ,â€Educational Data Mining and Learning Analyticsâ€, Cambridge Handbook of the Learning Sciences, 2013

      [4] Wattana Punlumjeak, NachiratRachburee, “A Comparative Study of Feature Selection Techniques for Classify Student Performanceâ€, 7th International Conference on Information Technology and Electrical Engineering (ICITEE), Chiang Mai, Thailand, 2015

      [5] John Jacob, Kavya Jha, Paarth Kotak, Shubha Puthran , “Educational Data Mining Techniques and their Applications,†IEEE International Conference on Green Computing and Internet of Things (ICGCloT), 2015

      [6] NorlidaBuniyamin, Usamah bin Mat, Pauziah Mohd Arshad, “Educational Data Mining for Prediction and Classification of Engineering Students Achievement,†IEEE 7th International Conference on Engineering Education (ICEED),2015

      [7] Camilo Ernesto López Guarín, Elizabeth León Guzmán, and Fabio A. González, “A Model to Predict Low Academic Performance at a Specific Enrollment Using Data Mining,†IEEE Journal of Latin-American Learning Technologies (IEEE-RITA), VOL. 10, NO. 3, AUGUST 2015

      [8] Alana M. de Morais and Joseana M. F. R. Araújo, Evandro B. Costa, “Monitoring Student Performance Using Data Clustering and Predictive Modelling,†IEEE, 2014

      [9] Lu Thi Kim Phung, Vo Thi Ngoc Chau, Nguyen Hua Phung, “Extracting Rule RF in Educational Data Classification from a Random Forest to Interpretable Refined Rules,†IEEE International Conference on Advanced Computing and Applications, 2015

      [10] Asmaa Elbadrawy, AgoritsaPolyzou, Zhiyun Ren, Mackenzie Sweeney, George Karypis, HuzefaRangwala, “Predicting Student Performance Using Personalized Analytics,†IEEE , April 2016

      [11] Anjana Pradeep, Smija Das, Jubilant J Kizhekkethottam, “Students Dropout Factor Prediction Using EDM Techniques,†International Conference on Soft-Computing and Network Security (ICSNS -2015), Coimbatore, INDIA, Feb. 25 – 27, 2015

      [12] Wanli Xing, Rui Guo, Eva Petakovic and Sean Goggins, "Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory", Computers in Human Behavior, vol. 47, pp. 168-181, June 2015.

      [13] Harwati, Ardita Permata Alfiani and FebrianaAyuWulandari, "Mapping Student's Performance Based on Data Mining Approach (A Case Study)", Agriculture and Agricultural Science Procedia, vol.3, pp. 173-177, 2015.

      [14] Wen-Lung Shiau and Patrick Y.K. Chau, "Understanding behavioral intention to use a cloud computing classroom: A multiple model comparison approach", Information & Management, vol. 53, no.3, pp. 355-365, April 2016.

      [15] Sadaf Ashtari and Ali Eydgahi, "Student perceptions of cloud applications effectiveness in higher education", Journal of Computational Science, January 2017.

      [16] Fernando Koch, Marcos D. Assunção, Carlos Cardonha and Marco A.S. Netto, "Optimising resource costs of cloud computing for education", Future Generation Computer Systems, vol.55, pp. 473-479, February 2016.

      [17] Humphrey M. Sabi, Faith-Michael E. Uzoka, KehbumaLangmia and Felix N. Njeh, "Conceptualizing a model for adoption of cloud computing in education", International Journal of Information Management, vol.36, no. 2, pp.183-191, April 2016.

      [18] Janice D. Gobert, Yoon Jeon Kim, Michael A. Sao Pedro, Michael Kennedy and Cameron G. Betts, "Using educational data mining to assess students’ skills at designing and conducting experiments within a complex systems microworld", Thinking Skills and Creativity, vol. 18, pp. 81–90, December 2015.

      [19] Wanli Xing, Rui Guo, Eva Petakovic and Sean Goggins, "Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory", Computers in Human Behavior, vol. 47, pp. 168-181, June 2015.

      [20] Harwati, Ardita Permata Alfiani and FebrianaAyuWulandari, "Mapping Student's Performance Based on Data Mining Approach (A Case Study)", Agriculture and Agricultural Science Procedia, vol.3, pp. 173-177, 2015.

      [21] Pedro J. Munoz Merino, José A. Ruiperez-Valiente, Carlos Alario-Hoyos, Mar Pérez-Sanagustín and Carlos Delgado Kloos, "Precise Effectiveness Strategy for analyzing the effectiveness of students with educational resources and activities in MOOCs," Computers in Human Behavior, vol. 47, pp. 108-118, June 2015.

      [22] “The important role of Non Cognitive Factors in School Performance,†http://singteach.nie.edu.sg/issue25-hottopic/â€, Accessed on 20/08/2017

      [23] Xin Chen, Mihaela Vorvoreanu, and Krishna Madhavan , “Mining Social Media Data for Understanding Students’ Learning Experiences,†IEEE transactions on Learning Technologies, 2013

      [24] Michail N. Giannakos, Trond Aalberg, Monica Divitini, Letizia Jaccheri, Patrick Mikalef, Ilias O. Pappas, GuttormSindre , “Identifying Dropout Factors in Information Technology Education: A Case Study,†IEEE Global Engineering Education Conference (EDUCON), 2017

      [25] Xing Wanli, Guo Rui, PetakovicEva ,Goggins Sean , “Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory,†Elsevier- Computers in Human Behavior, 2014

      [26] Syed Tahir Hijazi1 and S.M.M. Raza Naqvi , “Factors Affecting Students’ Performance,†Bangladesh e-Journal of Sociology. Volume 3. Number 1,2006

      [27] Ali, Shoukat, et al , “Factors Contributing to the Students Academic Performance: A Case Study of Islamia University Sub-Campus,†American Journal of Educational Research 1.8 (2013): 283-289, 2013

      [28] Irfan Mushtaq & Shabana Nawaz Khan , “Factors Affecting Students’ Academic Performanceâ€, Global Journal of Management and Business Research Volume 12 Issue 9 Version 1.0 June 2012

      [29] Omar Augusto Echegaray-Calderon; Dennis Barrios-Aranibar , “Optimal selection of factors using Genetic Algorithms and Neural Networks for the prediction of students' academic performance “, IEEE- Latin America Congress on Computational Intelligence (LA-CCI), 2015

      [30] Angellia Debora Suryawan, ErycoPutra , “Analysis of Determining Factors for Successful Student's GPA Achievementâ€, 11th International Conference on Knowledge, Information and Creativity Support Systems (KICSS), Yogyakarta, Indonesia, 2016

      [31] Jae-Young Park, Heng Luo, Won Ho Kim “Factors affecting students’ completion: A study of an online Master’s program“, International Conference of Educational Innovation through Technology, 2015

      [32] “7 Important Factors that May Affect the Learning Processâ€, http://www.yourarticlelibrary.com/learning/7-important-factors-that-may-affect-the-learning-process/6064/, Accessed on 16/06/2017

      [33] Deepali Vora, Dr. KamatchiIyer , “A Survey of Inferences from Deep Learning Algorithms â€, 7th International Conference on Computing, Engineering and Information Technology (ICCEIT 2017).

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  • How to Cite

    R Vora, D., & Iyer, K. (2018). EDM – survey of performance factors and algorithms applied. International Journal of Engineering & Technology, 7(2.6), 93-97. https://doi.org/10.14419/ijet.v7i2.6.10074