Abstract
In today's educational climate, developing tools to support students and learning in a traditional or online context is a crucial responsibility. The first stages in employing machine learning techniques to enable such technology centered on forecasting a student's success in terms of marks earned. The disadvantage of these methods is that they are not as effective at predicting low-achieving students. The goal of our efforts is twofold. To begin, we investigate whether badly performing students may be more accurately predicted by recasting the task as a binary classification problem. Second, in order to learn more about the reasons that contribute to bad performance, we created a set of human-interpretable attributes that quantify these aspects. We conduct a study based on these characteristics to identify distinct student groups of interest while also determining their value.
Keywords: Binary classification, Performance, Human Interpretable Attributes
References
[1] L. Breiman. Random forests. Machine learning, 45(1):5–32, 2001.
[2] L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen. Classification and regression trees. CRC press, 1984.
[3] C. Cortes and V. Vapnik. Support-vector networks. Machine learning, 20(3):273–297, 1995.
[4] J. H. Friedman. Greedy function approximation: a gradient boosting machine. Annals of statistics, pages 1189–1232, 2001.
[5] Z. Iqbal, J. Qadir, A. N. Mian, and F. Kamiran. Machine learning based student grade prediction: A case study. arXiv preprint arXiv:1708.08744, 2017.
[6] J. E. Knowles. Of needles and haystacks: Building an accurate statewide dropout early warning system in wisconsin. Journal of Educational Data Mining, 7(3):18–67, 2015.
[7] S. Kotsiantis, C. Pierrakeas, and P. Pintelas. Predicting students’performance in distance learning using machine learning techniques. Applied Artificial Intelligence, 18(5):411–426, 2004.
[8] J. McFarland, B. Hussar, C. de Brey, T. Snyder, X. Wang, S. Wilkinson-Flicker, S. Gebrekristos,
J. Zhang, A. Rathbun, A. Barmer, et al.
Undergraduate retention and graduation rates. In The
Condition of Education 2017. NCES 2017-144. ERIC, 2017.
[9] B. Minaei-Bidgoli, D. A. Kashy, G. Kortemeyer, and W. F. Punch. Predicting student performance: an application of data mining methods with an educational web-based system. In Frontiers in education, 2003. FIE 2003 33rd annual, volume 1, pages T2A–13. IEEE, 2003.
[10] S. Morsy and G. Karypis. Cumulative knowledge-based regression models for nextterm grade prediction. In Proceedings of the 2017 SIAM International Conference on Data Mining, pages 552–560. SIAM, 2017.
[11] E. Osmanbegovi´c and M. Sulji´c. Data mining approach for predicting student performance. Economic Review, 10(1):3–12, 2012.
[12] A. Pardo, N. Mirriahi, R. Martinez-Maldonado, J.
Jovanovic, S. Dawson, and D. Gaˇsevi´c. Generating actionable predictive models of academic performance. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, pages 474–478. ACM, 2016.
[13] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel,
B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer,
R. Weiss, V. Dubourg, J. Vanderplas, A. Passos,
D. Cournapeau, M. Brucher, M. Perrot, and
E. Duchesnay. Scikit-learn: Machine learning inPython. Journal of Machine Learning Research, 12:2825–2830, 2011.
[14] A. Polyzou and G. Karypis. Grade prediction with models specific to students and courses. International Journal of Data Science and Analytics, 2(3-4):159–171, 2016.
[15] Z. Ren, X. Ning, and H. Rangwala. Grade prediction with temporal course-wise influence. In Proceedings of the 10th International
Conference on Educational
Data Mining, pages 48–55, 2017.
[16] C. Romero, S. Ventura, P. G. Espejo, and C. Herva´s. Data mining algorithms to classify students. In Educational Data Mining 2008, 2008.
[17] A. A. Saa. Educational data mining & students’ performance prediction. International Journal of Advanced Computer Science & Applications, 1:212–220, 2016.
[18] M. Sweeney, J. Lester, and H. Rangwala. Nextterm student grade prediction. In Big Data (Big Data), 2015 IEEE International Conference on, pages 970–975. IEEE, 2015.
[19] M. Sweeney, H. Rangwala, J. Lester, and A. Johri. Next-term student performance prediction: A recommender systems approach. arXiv preprint arXiv:1604.01840, 2016. [20] J.Vignesh, E. Gajendran, Sameeruddin Khan, N. Balakumar and S. R. Boselin Prabhu, “Design and implementation of hybrid cascaded energy Efficient Kogge Stone Adder” ARPN Journal of Engineering and Applied Sciences Vol 12 Issue no 21 2017.
[21] Jaganathan, M., Sabari, A. An heuristic cloud based segmentation technique using edge and texture based two dimensional entropy. Cluster Computing Vol 22, PP 12767–12776(2019). https://doi.org/10.1007/s10586-018-1757-3
[22] Senthil kumar, V., Prasanth, K. Weighted Rendezvous Planning on Q-Learning Based Adaptive Zone Partition with PSO Based Optimal Path Selection. Wireless Personal Communication 110, 153–167 (2020). https://doi-org.libproxy.viko.lt /10.1007/s11277-019-06717-z
[23] Vignesh Janarthanan, A.Viswanathan, M. Umamaheswari “Neural Network and Cuckoo Optimization Algorithm for Remote Sensing Image Classification” in the International Journal of Recent Technology and Engineering, (IJRTE) Volume-8, Issue 4, November 2019 ISSN 2777-3878.
[24] Dr.Vignesh Janarthanan, Dr.Venkata Reddy Medikonda,.Er.Dr.G.Manoj Someswar Proposal of a Novel Approach for Stabilization of the Image from Omni-Directional System in the case of Human Detection & Tracking “American Journal of Engineering Research (AJER)” vol 6 issue 11 2017
[25] Dr. V. Senthil kumar, Mr. P. Jeevanantham, Dr. A. Viswanathan, Dr. Vignesh Janarthanan, Dr. M. Umamaheswari, Dr. S. Sivaprakash Emperor Journal of Applied Scientific Research “Improve Design and Analysis of Friend-to-Friend Content Dissemination System ”Volume - 3 Issue - 3 2021
[26] V.Senthilkumar , K. Prashanth” A Survey of Rendezvous planning Algorithms for Wireless Sensor Networks International Journal of communication and computer Technologies, Vol 4 Issue No 1 (2016)
[27] Sowmitha, V., and Mr V. Senthilkumar. "A Cluster Based Weighted Rendezvous Planning for Efficient Mobile-Sink Path Selection in WSN." International Journal for Scientific Research & Development Vol 2 Issue 11 2015
[28] Viswanathan, A., Arunachalam, V. P., & Karthik, S. (2012). Geographical division traceback for distributed denial of service. Journal of Computer Science, 8(2), 216.
[29] Anurekha, R., K. Duraiswamy, A. Viswanathan, V.P. Arunachalam and K.G. Kumar et al., 2012. Dynamic approach to defend against distributed denial of service attacks using an adaptive spin lock rate control mechanism. J. Comput. Sci., 8: 632-636.
[30] Umamaheswari, M., & Rengarajan, N. (2020). Intelligent exhaustion rate and stability control on underwater wsn with fuzzy based clustering for efficient cost management strategies. Information Systems and e-Business Management, 18(3), 283-294.
[31] Babu, G., & Maheswari, M. U. (2014). Bandwidth Scheduling for Content Delivery in VANET. International Journal of Innovative Research in Computer and Communication Engineering IJIRCCE, 2(1), 1000-1007.
[32] Viswanathan, A., Kannan, A. R., & Kumar, K. G. (2010). A Dynamic Approach to defend against anonymous DDoS flooding Attacks. International Journal of Computer Science & Information Security.
[33] Kalaivani, R., & Viswanathan, A. HYBRID CLOUD SERVICE COMPOSITION MECHANISM WITH SECURITY AND PRIVACY FOR BIG DATA PROCESS., International Journal of Advanced Research in Biology Engineering Science and Technology, Vol. 2,Special Issue 10, ISSN 2395-695X.
[34] Ardra, S., & Viswanathan, A. (2012). A Survey On Detection And Mitigation Of Misbehavior In Disruption Tolerant Networks. IRACST-International Journal of Computer Networks and Wireless Communications (IJCNWC), 2(6).