Efficiency of LSA and K-means in Predicting Students’ Academic Performance Based on Their Comments Data

Shaymaa E. Sorour, Tsunenori Mine, Kazumasa Goda, Sachio Hirokawa

2014

Abstract

Predicting students’ academic performance has long been an important research topic in many academic disciplines. The prediction will help the tutors identify the weak students and help them score better marks; these steps were taken to improve the performance of the students. The present study uses free style comments written by students after each lesson. These comments reflect their learning attitudes to the lesson, understanding of subjects, difficulties to learn, and learning activities in the classroom. (Goda and Mine, 2011) proposed PCN method to estimate students’ learning situations from their comments freely written by themselves. This paper uses C (Current) method from the PCN method. The C method only uses comments with C item that focuses on students’ understanding and achievements during the class period. The aims of this study are, by applying the method to the students’ comments, to clarify relationships between student’s behaviour and their success, and to develop a model of students’ performance predictors. To this end, we use Latent Semantic Analyses (LSA) and K-means clustering techniques. The results of this study reported a model of students’ academic performance predictors by analysing their comment data as variables of predictors.

References

  1. Adhatrao, K., Gaykar, A., Dhawan, A., Jha, R., & Honrao,V., 2013. Predicting Students' using ID3 and C4.5 classification algorithms. International Journal of Data Mining & Knowledge Management Process (IJDKP), 3 (5), 39-52.
  2. Antai, R., Fox, C., & Kruschwitz, U., 2011.The Use of Latent Semantic Indexing to Cluster Documents into Their Subject Areas. In: Proceedings of the Fifth Language Technology Conference. Springer. ASME Design Engineering Technical conferences, DETC2001/DTM-21713.
  3. Bachtiar, A. F., Kamei, K., & Cooper, W. E., 2012. A Neural Network Model of Students' English Abilities Based on Their Affective Factors in Learning. Journal of Advanced Computational Intelligence, and Intelligent Informatics, 16 (3), 375-380.
  4. Berry, W. M., Dumais, S., & O'Brien, G., 1995. Using linear algebra for intelligent information retrieval. SIAM Review, 37 (4), 573-595.
  5. Bharadwaj, B. K., Pal, S., 2011a. Data Mining: A prediction for performance improvement using classification. International journal of Computer Scien ce and Information security (IJCSIS), 9 (4), 136-140.
  6. Bharadwaj, B. K., Pal. S., 2011b. Mining Educational Data to Analyze Students' Performance. International Journal of Advance Computer Science and Applications (IJACSA), 2 (6), 63-69.
  7. Botana, J., Leo, A., Olmos,R.,& Escudero,I., 2010. Latent Semantic Analysis parameters for Essay Evaluation using Small-Scale Corpora. Journal of Quantitative Linguistics, 17 (1), 1-29.
  8. Csorba, K., Vajk, I., 2006. Double Clustering in Latent Semantic Indexing. In proceedings of SIAM, 4th Slovakan-Hungarian Joint Symposium on Applied Machine Intelligence, Herlany, Slovakia.
  9. Dhillon, I. S., Modha, D. S., 2001. Concept Decompositions for Large Sparse Text Data Using Clustering. Kluwer Academic Publishers, 4(1-2), 143- 175.
  10. Dumais, S., 1991. Improving the retrieval of information from external sourse. Behavior Research Methods, Instruments, and Computers, 23, 229-236.
  11. Goda, K., Hirokawa, S., & Mine, T., 2013. Correlation of Grade Prediction Performance and Validity of-SelfEvaluation Comments. SIGITE'13, Florida USA, 35- 42.
  12. Goda, K., Mine, T., 2011. Analysis of Students' Learning Activities through Quantifying Time-Series Comments. Proc. KES 2011, Part II (LNAI 6882), 154-164.
  13. Hill, A., Dong, A., & Agogino, A. M., 2002. Towards Computational Tools for Supporting the Reflective Team. Artificial intelligence in Design 7802, Dordrecht, Netherlands: Kluwer Academic Publishers, 305-325.
  14. Kabakchieva, D., (2013). Predicting Student Performance by Using Data Mining Methods for Classification. Cybernetics and Information Technologies, 13 (1), 61- 72.
  15. Kovacic, J. Z., Green, S. J., 2010. Predictive working tool for early identification of 'at risk' students, Open Polytechnic, New Zealand.
  16. Landauer, T. K., Dumais, S. T., 1997. A solution to Plato's problem: The Latent Semantic Analysis theory of the acquisition, induction, and representation of knowledge. Psychological Review, 104, 211-240.
  17. Landauer, T. K., McNamara, D., S., Dennis, S., & Kintsch, W., 2013. Handbook of Latent Semantic Analysis, Lawrence Erlbaum Associates, Psychology Press. New York, 2nd edition.
  18. Mansur, M. O., Sap, M. & Noor, M., 2005. 'Outlier Detection Technique in Data Mining. A Research Perspective', In Postgraduate Annual Research Seminar.
  19. Minami, T., & Ohura, Y. 2013. Lecture Data Analysis towards to Know How the Students' Attitudes Affect to their Evaluations. 8 International Conference on Information Technology and Applications (ICITA), 164-169.
  20. Osmanbegovic, E., Suljic, M., 2012. Data Mining Approach for Predicting Student Performance. Journal of Economics and Business, X (1) 3-12.
  21. Rosario, B., 2000. Latent Semantic Indexing: An overview, INFOSYS 240, Spring (final paper).
  22. Salton, G., McGill, M. J., 1983. Introduction to Modern Information Retrieval, McGraw-Hill, New York.
  23. Sembiring, M., Dedy, Z., Ramliana, S., & Wani, E., 2011. Prediction of student academic performance by an application of data mining techniques International Proceedings of Economics Development and Research IPEDR, 6.
  24. Witter, D., Berry, M. W., 1998. Downloading the latent semantic indexing model for conceptual information retrieval. The computer journal, 41, 589-601.
  25. Yadav, K., Bharadwaj, B. K., & Pal, S., 2011. Data Mining Applications: A comparative study for predicting students' performance. International journal of Innovative Technology and Creative Engineering (IJITCE), 1(12).
  26. Zaiane, O., 1999. Principles of Knowledge Discovery in databases, chapter 8. Data Clustering lecturing slides for CmPUT 690, University of Alberta.
Download


Paper Citation


in Harvard Style

E. Sorour S., Mine T., Goda K. and Hirokawa S. (2014). Efficiency of LSA and K-means in Predicting Students’ Academic Performance Based on Their Comments Data . In Proceedings of the 6th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-020-8, pages 63-74. DOI: 10.5220/0004841000630074


in Bibtex Style

@conference{csedu14,
author={Shaymaa E. Sorour and Tsunenori Mine and Kazumasa Goda and Sachio Hirokawa},
title={Efficiency of LSA and K-means in Predicting Students’ Academic Performance Based on Their Comments Data},
booktitle={Proceedings of the 6th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2014},
pages={63-74},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004841000630074},
isbn={978-989-758-020-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - Efficiency of LSA and K-means in Predicting Students’ Academic Performance Based on Their Comments Data
SN - 978-989-758-020-8
AU - E. Sorour S.
AU - Mine T.
AU - Goda K.
AU - Hirokawa S.
PY - 2014
SP - 63
EP - 74
DO - 10.5220/0004841000630074