A Markovian Model for Adaptive E-assessment G.S.NANDAKUMAR 1 , S.THANGASAMY 1 , V.GEETHA 2 , V.HARIDOSS 3 1-Department of Computer Science & Engineering, 2- Department of Computer Applications, 3-Department of Science & Humanities Kumaraguru College of Technology, Coimbatore. INDIA sunandakumarr@gmail.com , nandakumar.gs.cse@kct.ac.in Abstract:- The development of Information Communication Technologies (ICT) has increased the popularity of web based learning and E-assessment. The success of any online assessment is largely dependent on the quality of the question bank from which the questions are drawn. Various techniques are available for dynamically generating questions during E-assessment with different difficulty levels. Calibrating the question bank to know the measurement characteristics of the questions is a necessary part of large E-assessment. Classification of a question involves assigning a difficulty level to each question. An adaptive E-assessment strategy has been formulated to test the proficiency in ‘Programming using C’ language. This paper deals with the application of Markov chain to assess the reliability of question classification and to classify the performance of the students based on the attainment of handling difficulty levels over a period of time. Key-words: Adaptive E-assessment, Question Classification, Multiple Choice Questions (MCQ), Degree of Toughness (DT), Markov Chain, Steady State Probability. 1 Introduction Student assessment is a vital part in the learning process to categorize them based on the knowledge gained by the students. The advancement of ICT in last few decades has increased use of computers in assessment through online examinations enabling quick and uniform assessment of a very large number of learners. Adaptive assessment is a popular form of computer based assessment. When it comes to assessing the depth of knowledge gained by individual learners, adaptiveness is the key functionality. Adaptive testing has great potential to make learning environment more personalized to the learners. Multiple-choice questions (MCQ) are a widely used method for an adaptive test. Different sets of questions have to be generated for different students, keeping their enthusiasm to face the test steadily [18]. This requires a large set of MCQ stored in a question bank to cater to individual student needs. The bank should be as large as possible and the difficulty level of the questions should be wide enough to cover the entire range of test takers’ ability. A good question bank should have sufficient questions to attain high measurement accuracy throughout the measurement range. Classification of questions is primarily concerned with assigning a difficulty level to each question in the bank. Thus, a high-quality question bank will contain sufficient numbers of useful questions that permit efficient, informative testing. This criterion primarily demands that at all difficulty levels there should be sufficient number of calibrated questions. Hence there is a need to calibrate the questions in the question bank with different difficulty levels. A number of adaptive assessment tools have been extensively used by academic institutions, and well known organizations for specific examinations [9], [15], [21]. An adaptive testing strategy has been formulated to test the proficiency of students in programming using ‘C’ language in an engineering college. This test has been designed to classify the students according to their ability and Intelligence Quotient (IQ). A large number of multiple questions were collected from several course experts and the questions have been classified with different difficulty levels. The purpose of classification is to ensure that students are evaluated consistently. This increases the reliability of the assessment. In most of the literature, classification has been done using Item Response Theory (IRT) models [8], [18]. A Markov chain is a mathematical system that permits transitions from one state to another in a state space. It is a random process usually characterized as memoryless; the next state depends only on the current state and not on the sequence of events that preceded it. This specific WSEAS TRANSACTIONS on COMPUTERS G. S. Nandakumar, S. Thangasamy, V. Geetha, V. Haridoss E-ISSN: 2224-2872 179 Volume 15, 2016