Research on E-learning system based on Affective Computing

<strong>Research</strong> on E-learning system based on Affective ComputingSun Duothe department of computer of Yangzhou universityYangzhou, Jiangsu, Chinawufeng94041@sohu.comAbstract—E-Learning is a kind of study system. In this systemwe can teach and learn through the Internet and other digitizedmedia. Affective Computing is related to, arises from ordeliberately influences emotions. It tries to construct a kind ofcomputer system which can recognize, synthesize andunderstand the human emotions to make intelligent, smart,friendly reaction. In this paper we combine the e-Learningsystem with Affective computing to propose the e-Learningsystem model based on affective computing. This system canresolve the problem of communication deficiency between thecomputer and users effectively.Keywords-e-Learning; Facial Expression; Recognition;Affective Computing Recognition; Affective ComputingI. INTRODUCTIONDifferent from traditional education, e-learning is definedas the use of new multimedia technologies and the internet toimprove the quality of learning by facilitating access toresources and services, as well as remote exchange andcollaboration. With the rapid development of informationtechnologies, e-Learning will be more popular and becomeone of the most important education means in the future.However, emotion deficiency caused by the lack of face toface communication with the instructor and the learner,makes the e-lesson a simple “textbook mover”[1].The network institute of Yangzhou University has carriedout an investigation on learning conditions for 500 distanceeducation learners in 2008. The author took out two resultsthat are related to emotion deficiency form the investigationresults. Two results are shown in table1 and table 2.Fromtables 1 and table 2, the influence that is caused from theseparation among teachers and students or students andstudents is obvious (obvious influence and great influenceoccupy 57.2%. The influence that is caused from theinteraction deficiency of teaching contents is obvious too.(Obvious influence and great influence occupy 57.2%).TABLE I. THE INFLUENCE IS CAUSED FROM THESEPARATION AMONG TEACHERS AND STUDENTS OR STUDENTSAND STUDENTSInfluencedegreeGreatinfluenceObviousinfluenceA littleinfluenceNoinfluenceStudentnumber75 198 177 50percent 15 39.6 35.4 10Lu Xue Songthe department of computer of Yangzhou universityYangzhou, Jiangsu, Chinaxslu@yzu.edu.cnTABLE II. THE INFLUENCE IS CAUSED FROM THEINTERACTION EFICIENCY OF TEACHING CONTENTSInfluencedegreeGreatinfluenceObviousinfluenceA littleinfluenceNoinfluenceStudent 85 201 160 54numberpercent 17 40.2 32 10.8So, how to evaluate a learner’s real time cognitive andemotional states in long distance learning environment torealize affective interaction, has attracted more and moreattention of education technologists, and become one of themost important worthy projects in the long distanceeducation.Affective computing is an innovative research inArtificial intelligence. It can be defined as “computing thatrelates to, arises from, or deliberately influences emotion”[2],has attracted education technologists’ eyes. In order toestablish good web learning environment like the realworld, Qi Luo [3], Xi-rong Ma [4], P.A. Jaques[5] haveexplored the potential of affective computing in e-learningmodels one by one. These e-Learning system modelsemphasize particularly on the evaluation of the learner’semotional states and adjustment of teaching strategiesandignore how to evaluate learning performance and how toadjust teaching strategies according to the contents ofevaluation module. To solve the problems, we proposed ane-Learning system model based on affective computing inthis paper, which can detect and aware the learner’semotional states by using facial expression and posturerecognition technologies, modify teaching strategiesautomatically according to the learning performance andemotion transition, and provide emotion stimulus accordingto special cognitive states, thus solving the problem ofemotion deficiency radically and providing better learningconditions.II. THEMODEL OF E-LEARNING SYSTEM BASED ONAFFECTIVE COMPUTINGIn the learning process, the expression and language oflearners are abundant. When they understand and accept thelearning content, their emotion is high and displays as smileexpression _____________________________________and cheerful spoken language. Otherwise, theiremotion is low and displays as brows closing expression,discouragement 978-1-4244-5265-1/10/$26.00 and so on. These emotional ©2010 behaviors IEEEarequite important feedback signals of learners. We may usethese feedback signals effectively and adjust teaching

strategies to serve personalized learning. The model ofE-learning system based on affective computing is Fig.1..TABLE III.The e-Learning System Model Based on AffectiveComputingAs interface between user and computer, this part ismainly responsible for the identity of the user to verify theinformation, collection of user feedback, showing coursesdealing with information and analysis of user feedbackinformation. User information input includes interface agents,affective computing server and user accounts database.Affective computing terminals in agent interface andaffective computing server are characteristics in thise-Learning system model. In the learning process, users aremonitored by affective computing terminals real-time, andthe information gathered about facial expression, speech, andother messages be passed to affective computing server.After a period of learning, the types and proportion of facialexpressions, speech appeared are automatic calculated. Userscan fill out a questionnaire survey to take the initiative toform a study evaluation to adjust the pace of learning; Thesystem forms a final evaluation with the support of cognitivepsychology automatically to feedback.In systems, guidance learning modules get the feedbackinformation mainly based "input user information" forstudents to find suitable courses. These are composed ofthree parts, which are personalized guidance from someagents, user database and curriculum database. Personalizedguidance agent is constituted of feedback agent andrecommendable learning agents, and feedback is the mainagent responsible for the results which passed frominformation from the user, such as learning evaluation, andthen convert into the corresponding evaluation parameters,moreover carry out the previous record of users learningfrom the user information database, and transfer agents ofrecommending courses; study recommended agents acceptparameters and record of learning, and,accordingly adjusted, from the courses informationdatabase search suitable for the level of user learningmaterials to the transfer agent interface, so that students canbe adjusted progress in time.III.REALIZATION OF KEY TECHNOLOGIESFacial expression recognitionHuman expression scientists Ekman and Friesen havedefined six kinds of basic expression, such as happy, sad,surprise, fear, anger, and disgust as well as 33 kinds ofdifferent expressions[6].Classification and recognition ofbasic expressions are proposed in the paper. First, facialcharacteristics are extracted.Supposed the distance and change among parts in the faceare measured by Euclidean distance. Six parameters ofone-dimensional vector have defined as a face expressionmodel X=(d1, d2, d3, d4, d5, d6), where d1 is the distancebetween two eyebrows, d2 is the distance between upper eyeside and nether eye side, d3 is the distance between eyebrowand nether eye side, d4 is the mouth high, d5 is the mouthwidth, d6 is the distance between mouth corner and upper lip.Therefore, an expression is a spot in six-dimensional space,and the expression is recognized though judging Euclideandistance in six-dimensional space.Adjusting teaching strategiesThe change of learners’ emotion is more complex in thelearning process. The learner contacts with a new knowledgepoint, two kinds of attitudes such as interested state andindifferent state may appear. With the learning processadvancing, learner's emotion is changed. For instance, thelearner has an idea to solve this problem. If the learnerdefeated repeatedly, he will suspect himself and changes intothe depression state,this kind of state is disadvantageous tolearning. The system should apperceive this kind ofemotional change and carry on emotion intervening, whichlet him turn to self-confident state. When he contacts with anext new knowledge point, the emotion turn to the state ofsurprise and puzzle too.Teaching strategies are adjusted according to student'semotion dynamically though production rules. We neglectother influencing factors and take the learning style as theexample.IV. CONCLUSIONIn this paper, through the analysis of each module showsthat the e-learning system based on affective computingcan manage and track real-time learning process, evaluatelearning effect, set down different learning schemes fordifferent learners, provide feedbacks and evaluations in time,thus setting individual learning conditions, inspiring learninginterest, enhancing learning consciousness and learningeffect, which will result in improvement of teaching quality.With the development of network technology, e-learningsystems based on affective computing would have morewider developing potential and application value, and wouldbe developed quickly in the near future, thus bringing goodsocial benefits and tremendous economic benefits forlong-distance education.ACKNOWLEDGEMENTThe author thanks her colleagues for their helpfuldiscussion.

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