Total views : 175

Enhanced Elective Subject Selection for ICSE School Students using Machine Learning Algorithms

Affiliations

  • Department of Computer Science, Christ University, Hosur Road, Bengaluru – 560029, Karnataka, India

Abstract


Objective: Academic advising requires a lot of expertise, time and responsibility. To assist the human advisors in an efficient way, upcoming of computerized advising system is a necessity Methods/Statistical Analysis: Course Advisory System has been implemented using WEKA tool to recommend subjects for 8th class students of ICSE board. Machine learning algorithms – Naïve Bayes, J48, PART, Random Forest and KNN have been modeled and tested on the data set. The performance of each classifier has been compared and analyzed Findings: It is inferred that no advising system has been developed to assist school students in subject selection. Research work based on Indian students’ requirements is minimal. Research work based on students’ data caters more on binary class problems whereas the addressing of multi class problems is minimal. This work proposes an advising system for the school students of 8th standard of ICSE board to choose their electives. Application/Improvements: This work focuses on Indian educational system of school students. The approach takes care of the school students which will add its advantage to the existing systems. As school students are more vulnerable by taking wrong decisions, the course Advisory system will assist them in analyzing their academic history and help them choose their electives wisely. The classification algorithms might give a better accuracy with increasing instances. The Course advisory system can be enhanced using ensemble approach.

Keywords

Course Advisory System, Feature Selection Algorithms, ICSE School Students, Machine Learning Algorithms, Subject Selection

Full Text:

 |  (PDF views: 132)

References


  • Daramola O, Emebo O, Afolabi I, Ayo C. Implementation of an intelligent course advisory expert system: cased-based course advisory expert system. International Journal of Advanced Research in Artificial Intelligence (IJARAI). 2014; 3(5):6–12.
  • Zocco D. Risk theory and student course selection. Research in Higher Education Journal. 2009 Jan; 3:1–29.
  • Abdulwahhab RS, Makhmari HSA, Battashi SNA. An educational web application for academic advising. In the Proceedings of the Institute of Electrical and Electronics Engineers (IEEE) 8th GCC Conference and Exhibition; 2015 Feb 1–4. p. 1–6.
  • El–Bishouty MM, Chang T–W, Graf S, Kinshuk, Chen N–S.Smart e-course recommender based on learning styles.Journal of Computers in Education. 2014 Mar; 1(1):99– 111. Crossref
  • Matte N, Dodson T, Guerin JT, Goldsmith J, Mazur M. Lessons learned from development of a software tool to support academic advising. In the Proceedings of the Institute of Electrical and Electronics Engineers (IEEE) Zone 1 Conference of the American Society for Engineering Education; 2014 Apr 3–5. p. 1–8. Crossref
  • Roushan T, Chaki D, Hasdak O, Chowdhury MS, Rasel AA, Rahman MA, Arif H. University course advising: overcoming the challenges using decision support system. In the Proceedings of the Institute of Electrical and Electronics Engineers (IEEE) 16th International Conference on Computer and Information Technology (ICCIT), Khulna, Bangladesh; 2014 Mar 8–10. p. 13–8.
  • Taha K. Automatic academic advisor. In the Proceedings of the Institute of Electrical and Electronics Engineers (IEEE) 8th International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom); 2012 Oct 14–17. p. 262–8.
  • Singh S, Lal SP. Educational courseware evaluation using machine learning techniques. In the Proceedings of the Institute of Electrical and Electronics Engineers (IEEE) Conference on e-Learning, e-Management and e-Services, Kucing, Malaysia; 2013 Dec 2–4. p. 73–8.
  • Pathan AA, Hasan M, Ahmed MF, Farid DM. Educational data mining, A mining model for developing students’ programming skills. In the Proceedings of the Institute of Electrical and Electronics Engineers (IEEE) 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), Dhaka, Bangladesh; 2014 Dec 18–20. p. 1–5. Crossref
  • Manne S. Moodle system using datamining techniques.2015 Mar; 5:1– 5.
  • Kolekar SV, Sanjeevi SG, Bormane DS. Learning style recognition using artificial neural network for adaptive user interface in e-learning. In the Proceedings of the Institute of Electrical and Electronics Engineers (IEEE) International Conference on Computational Intelligence and Computing Research, Coimbatore, India; 2010 Dec 28–29. p. 1–5.Crossref
  • Guo B, Zhang B, Xu G, Shi C, Yang L. Predicting students performance in educational data mining. In the Proceedings of the Institute of Electrical and Electronics Engineers (IEEE) International Symposium on Educational Technology (ISET), Wuhan, China; 2015 Jul 27–29. p. 125–8.
  • Gray G, McGuinness C, Owende P. An application of classification models to predict learner progression in tertiary education. In the Proceedings of the Institute of Electrical and Electronics Engineers (IEEE) International Advance Computing Conference (IACC), Gurgaon, India; 2014 Feb 21–22. p. 549–54. Crossref
  • Goker H, Bulbul HL. Improving an early warning system to prediction of student examination achievement. In the Proceedings of the Institute of Electrical and Electronics Engineers (IEEE) 13th International Conference on Machine Learning and Applications, USA; 2014 Dec 3–6. p.568–73. Crossref
  • Gamulin J, Gamulin O, Kermek D. Comparing classification models in the final exam performance prediction. In the Proceedings of the Institute of Electrical and Electronics Engineers (IEEE) 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia; 2014 May 26–30. p. 663–8. Crossref

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.