Research on E-learning system based on Affective Computing

More documents

Recommendations

Info

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.
REFERENCES[1] LiuJianming, WangJincai. “The distance education system based onaffective computing”. China medical education technology, 2008,22(1): 67-69[2] LuoQi, WanLiyong, WuYanwen. “Application of affective computingin e-learning system”. Open education research, 2006, 12(3): 80-83[3] MaXi-rong, Wang,Zhi-liang. “Study on modeling of emotioninteraction in long-distance education”.Computer science, 2005, 32(9):182-184[4] Picard, R.W., Affective computing. The MIT Press,Cambridge MA,1997[5] P. A. Jaques and R.M. Vicari. “A BDI approach to inferstudent’semotions in an intelligent learning environment”. Computers &Education, 2007, 49(2): 360-384[6] S. R. Marquardt. (2003). MBA website. [Online].Available:http://www.beautyanalysis.com
Page 1: Research on E-lear

Research on E-learning system based on Affective Computing

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?