Students modeling is an integral part of any form of education and this becomes more challenging with the advent of new tools of ICT especially Intelligent Tutoring Systems (ITS). Educators must have to take help of such tools to identify the curriculum gaps towards Outcome Based Education (OBE). One of the ways to reduce such gaps is to identify the personalized requirements of tutorials for learners after going through some topics divided into subtopics. This paper proposes a technique to identify the personalized tutorial gaps by analyzing the responses provided by students against MCQ type questions. The proposed method has been implemented within a web based environment. Prototype of the tool having integrated with the proposed method shows that the students can identify their tutorial requirement without the help of human tutor and hence discovering the student's understanding.
ICT, OBE, Tutorial, Gap, Students Modeling.
 Handbook of training evaluation and measurement methods. (2016). Routledge, Oxon, UK.
 Spady W. (1994). Choosing outcomes of significance, Educational Leadership, Vol. 5, No. 1, pp. 18–23.
 Spady W., Marshall K. (1994). Beyond traditional outcome-based education, Educational Leadership, Vol. 49, No. 2, pp. 67–72.
 Zaman W., Das A., Basu P.N. (2007). A survey on computer based training----an Indian perspective, Proc. of Int. Conf. on Emerging Trends in Electrical Engineering, Dept. of EE, Jadavpur University at Science City, Kolkata, India, pp. 492-497.
 Bag R., Das A. (2010). Developing an intelligent tutoring system following Bayesian approach, Int. J. of Advanced Engineering & Applications, Vol. 2, pp. 114-119.
 Mitra M., Das A. (2015). A fuzzy logic approach to assess web learner’s joint skills, Int. J. of Modern Education and Computer Science, Vol. 9, pp. 14-21. DOI:10.5815/ijmecs.09.02
 Bose D., Das A. (2015). Using fuzzy trapezoidal rule for web learner’s competence assessment, Int. J. of Electronics and Communication Technology, Vol. 6, No.1, pp. 169-173.
 Banerjee S., Bag R., Das A. (2016). Design and development of a web based teaching performance assessment tool with student feedback and fuzzy logic, 3rd Int. Conf. on Foundations and Frontiers in Computer, Communication and Electrical Engineering (C2E2-2016), 15th-16th January, 2016 at SKFGI, Mankundu, Hooghly, WB, India, pp. 475-479, DOI: 10.1201/b20012-93
 Mitra M., Das A. (2013). Applying a fuzzy technique for web-based learner’s performance evaluation, Proc. of National Conf. on Control, Communication & Device Electronics (N3CD-2013), JIS Engineering College, Kalyani, WB, India, pp. 125-131.
 Sison R., Shimura M. (1998). Student modelling and machine learning, Int. J. of Artificial Intelligence in Education, Vol. 9, pp. 128-158.
 Koedinger K.R., Nathan M.J. (2004). The real story behind story problems: effects of representations on quantitative reasoning, The Journal of Learning Sciences, Vol. 13, No. 2, pp. 129-164.
 Koedinger K.R., McLaughlin E.A. (2010). Seeing language learning inside the math: cognitive analysis
yields transfer, In Proc. of the 32nd Annual Conf. of the Cognitive Science Society, Austin, TX, pp. 471-476.
 Cen H., Koedinger K., Junker B. (2006). Learning factors analysis-a general method for cognitive model evaluation and improvement, In Proc. of the 8th Int. Conf. on Intelligent Tutoring Systems, pp. 164-175.
 Li N., Cohen W., Koedinger K.R., Matsuda N. (2011). A machine learning approach for automatic student model discovery, In Educational Data Mining.