DATA MINING TECHNIQUE AS A TOOL FOR INSTRUCTORS’ PERFORMANCE EVALUATION IN HIGHER EDUCATIONAL INSTITUTION

ASANBE M.O, OLAGUNJU M.P

Abstract


Educational Data Mining (EDM) is an evolving field exploring pedagogical data by applying different machine learning techniques/tools. It can be considered as interdisciplinary research field which provides intrinsic knowledge of teaching and learning process for effective education. The main objective of any educational institution is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge that predicts teachers’ performance. This study presents an efficient system model for evaluation and prediction of teachers’ performance in higher institutions of learning using data mining technologies. To achieve the objectives of this work, a two-layered classifier system was designed; it consists of an Artificial Neural Network (ANN) and Decision Tree. The classifier system was tested successfully using case study data from a Nigerian University in the South West of Nigeria. The data consists of academic qualifications for teachers as well as their experiences and grades of students in courses they taught among others. The attribute selected were evaluated using two feature selection methods in order to get a subset of the attributes that would make for a compact and accurate predictive model. The WEKA machine learning tool was used for the mining. The results show that, among the six attributes used, Working Experience, and Rank are rated the best two attributes that contributed mostly to the performance of teachers in this study. Also, considering the time taken to build the models and performance accuracy level, C4.5 decision tree outperformed the other two algorithms (ID3 and MLP) with good performance of 83.5% accuracy level and acceptable kappa statistics of 0.743.  It does mean that C4.5 decision tree is best algorithm suitable for predicting teachers’ performance in relation to the other two algorithms in this work.

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References


Ogunde A.O and Ajibade D.A (2014): A Data Mining System for Predicting University Students’ Graduation Grades Using ID3 Decision Tree Algorithm. Journal of Computer Science and Information Technology. March 2014, Vol. 2, No 1, pp 21 – 46. [2] Albashiri K. A. (2013). Data Partitioning and Association Rule Mining Using a Multi-Agent System. International Journal of Engineering Science and Innovative Technology (IJESIT), Volume 2, Issue 5, pp 161-169. [3] Xingquan Zhu, Ian Davidson, “Knowledge Discovery and Data Mining: Challenges and Realitiesâ€, ISBN 9781-59904-252, Hershey, New York, 2007. [4] Romero C., Ventura S., Garcia E. (2008) Data mining in course management systems: Moodle case study and tutorial, Computers & Education, Vol. 51, No. 1, pp. 368-384, 2008 [5] Abu-Doleh J, Weir D. Dimensions of Performance Appraisal Systems in Jordanian Private and Public Organizations. International Journal of Human Resource Management, 2007; 18(1): 75-84. [6] C. Romero, S. Ventura (2007): "Educational data mining: A Survey from 1995 to 2005", Expert Systems with Applications (33), pp. 135-146, [7] Baker, R. S. J. D., and K. Yacef. (2009): The State of Educational Data Mining in 2009: AReview and Future Visions.Journal of Educational Data Mining 1 (1): 3– 17. [8] Rajni J. and Malaya D.B (2013): A Survey on Educational Data Mining and Research Trends. International Journal of Database Management Systems (IJDMS) Vol.5, No.3 [9] Varun Kumar and AnupamaChadha, “An Empirical Study of the Applications of Data Mining Techniques in Higher Educationâ€, International Journal of Advanced Computer Science and Applications, Vol. 2, No.3, March 2011 [10] Chin Chia Hsu and Tao Huang (2006): The use of Data Mining Technology to Evaluate Student’s Academic Achievement via multiple Channels of Enrolment. An empirical analysis of St. John’s University of Technology. [11] Osofisan A.O. and Olamiti A.O. (2009): Academic Background of Students and Performance in Computer Science Programme in a Nigerian University. European Journal of Social Science. Vol. 33 Issues 4. 2009.

MardikyanS., and Badur B. (2011). Analyzing teaching Performance of Instructors Using Data Mining techniques. Informatics in Education, 2011, Vol. 10, No. 2, pp 245 – 257. [13] Hemaid and El-Halees (2015): Improving Teacher Performance using DataMiningInternational Journal of Advanced Research in Computer and Communication EngineeringVol. 4, Issue 2, February 2015. [14] Surjeet K.Y and Saurabh P (2012): Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification World of Computer Science and Information Technology Journal (WCSIT) ISSN: 22210741 Vol. 2, No. 2, 51-56, 2012 [15] Pal K. and Pal S. (2013): Evaluation of Teacher’s Performance: A Data Mining Approach. International Journal of Computer Science and Mobile Computing. IJCSMC, Vol 2, Issue. 12, Dec., 2013, pg. 359 – 369. [16] Surjeet K.Y et al, (2012): Mining Educational Data to Predict Student’s Retention: A comparative study, volume 10, No 2. [17] Aranuwa F.O., and Sellapan P.,(2013): A data mining model for evaluation ofinstructors’performance in higher institutions of learning usingmachine learning algorithms, International Journal of Conceptions on Computing and Information Technology Vol. 1, sue 2, Dec’ 2013; ISSN: 2345 – 9808 [18] Bradburn N, Sudman S, WansinkB.Asking Questions: The Definitive Guide to Questionnaire Design. Jossey-Bass, 2004 [19] Zurada JM. (2006): “Introduction to Artificial Neural Systemâ€, Jaico Publishing House,. [20] Romero C, Olmo JL, Ventura S. (2013): A meta-learning approach for recommending a subset of white-box classification algorithms for Moodle datasets. Department of Computer Science, University of Cordoba, Spain,


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