Deriving Association between Student’s Comprehension and Facial expressions using Class Association Rule Mining M. Mohamed Sathik 1 ,G.Sofia 2 1 Sadakathullah Appa College, Tirunelveli, India mmdsadiq@gmail.com 2 Research and Development Centre, Bharathiar University, Coimbatore, India. joesofi@gmail.com Abstract – The scope of this study was to discover the association between facial expressions of students in an academic lecture and the level of comprehension shown by their expressions. This study focussed on finding the relationship between the specific elements of learner’s behaviour for the different emotional states and the relevant expression that could be observed from individual students. The experimentation was done through surveying quantitative observations of the lecturers in the classroom in which the behaviour of students are recorded and were statistically analyzed. The main aim of this paper is to derive association rules that represent relationships between input conditions and results of domain experiments. Hence the relationship between the physical behaviors that are linked to emotional state with the student’s comprehension is being formulated in the form of rules. We present Predictive Apriori algorithm that is able to find all valid class association rules with high accuracy. The rules derived by Predictive Apriori are pruned by objective and subjective measures. Keywords: Class Association Rules, Predictive Apriori algorithm, Pruning, Objective measure, Subjective measure. I. INTRODUCTION Today’s learning community focus on the vision of faculty and students working collaboratively towards deep, meaningful, high quality learning. The achievements of digital communication lead learning communities into a new dimension. There is an increase in virtual schools worldwide as education mediated by computer is considered very important for the future [12]. Nowadays, Learning Management Systems (LMS) are being installed more and more by universities, community colleges, schools, businesses, and even individual instructors in order to add web technology to their courses and to supplement traditional face-to-face courses [10]. LMS systems accumulate a vast amount of information which is valuable for analyzing the students’ behaviour and could create a gold mine of educational data [7]. Teacher student Interaction plays a vital role in the classroom environment. [5] In the classroom, lecturers and students--both consciously and unconsciously--send and receive nonverbal cue several hundred times a day. Lecturers should be aware of nonverbal communication in the classroom for two basic reasons: to become better receivers of student’s messages and to gain the ability to send positive signals that reinforces students' learning. Lecturers should be skilled at avoiding negative signals that stifle their learning. Studies have evaluated that student emotional states are expressed with specific behaviours that can be automatically detected [17]. A preliminary study, carried out as the first part of this research, proved that the communicative impact of the face is so powerful in interaction. The most expressive way students display emotions is through facial expressions. Facial expressions are the primary source of information, next to words, in determining the student’s emotional feelings to express their comprehension. It also strongly recommends that there is a direct connection between the facial expressiveness of the students and their level of comprehension. Momentary expressions that signal emotions include muscle movements such as raising the eyebrows, wrinkling the forehead, shrinking or enlarging the eyes or curling the lip [9]. This research specifically focused on studying the relationship between facial expressions of the students in an academic lecture and the level of comprehension shown by their expressions. The aim was to identify physical behaviours that are linked to emotional states, and to identify how these emotional states are linked to student’s comprehension. The significance of the study was statistically interpreted. Hence it derives the Association rules which show the relationship between facial expressions of students in an academic lecture and the level of comprehension shown by their expressions. M. Mohamed Sathik et.al / International Journal of Engineering and Technology (IJET) ISSN : 0975-4024 Vol 5 No 3 Jun-Jul 2013 2442