october 2012 - TOJET the Turkish online journal of educational ...

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TOJET: The Turkish Online Journal of Educational Technology – October 2012, volume 11 Issue 4 LITERATURE REVIEW Kort et al.(2001) proposes a fundamental learning framework, whose abscissa means “Emotion Axis,” while the ordinate suggests “Learning Axis.” The farther an axis value on the right side of Emotion Axis is, the higher the positive energy will be and the further it on the left side is, the stronger the negative energy will be. On the other hand, the higher the “Constructive Learning” is, the stronger learning interests the use will have. Otherwise, the higher the “Un-Learning” is, the lower learning interests the user will have. It is made up of four models: interface pattern, expert pattern, student pattern, and tutor pattern (Koedinger and Corbett,2006). In recent years more and more tutoring systems focus on creating learners’ emotions in a tutoring environment, including emotional expression, empathy (Lester et al., 1999), and learners’ affection recognition(Conati et al., 2002). Besides, there are other studies concluding that the introduction of emotions to study is very likely to arouse users’ motivation. Emotions play a vital role in knowledge acquisition for humans (Vesterinen, 2001). They have been regarded as a considerably important factor to intrigue learners’ motivation, whereas motivation plays a vital role in knowledge acquisition (Mao & Li, 2010). In this case, “Affective Tutoring Systems (ATS)” is developed to detect students’ situations of learning and affection so as to give them timely emotional feedback and to correct their emotions of learning (Mao and Li, 2010). ATS is, in fact, designed based on the concept of ITS, aimed at mimicking real human reaction so as to adjust to students’ affection and situation in an effective manner(Sarrafzadeh et al., 2003; Sarrafzadeh et al., 2004; Vicente, 2003). According to Gerald (2004), negative emotions will weaken the learning status, while positive ones do benefit learning achievement. Moreover, Ammar et al., (2010) argues that the establishment of facial expression may strengthen the affective tutoring system in terms of the relationship of ATS and users. This study has proved that affective computing is good for monitoring users’ behavior in the learning process (Ammar et al., 2010; Lin et al., 2011a; Lin et al., 2011b; Tsai et al., 2010). In early days, emotion recognition used to focus on mainly recognizing face and speech. Speech recognition detects mostly the frequency and energy of voices so as to generate a set of rules to identify emotions by means of statistics or machine learning. As for facial expression recognition, a great number of experts and researchers have conducted many relevant studies in these years. Among of them, “Facial Action Coding System (FACS)” developed by Ekman et al. (1978) may be the most well-known. This system identifies dozens of “action units (AUs)” based on facial muscles. It adopts facial contour beforehand to detect the positions of face, eyebrows, mouth, and nose. These positions are then served as points spreading into a plane so as to form an expression of features. Based on the movement of these feature points, each point’s displacement vector is carefully caculated. With respect to the recognition part, the collected data is added to various detectors such as SVM, Neural Network, and HMM for facial expression identification or hypothesis developed in accordance with psychologists’ definition. “Active Shape Model (ASM)” was firstly proposed by T.F. Cootes in 1992. Its former idea was “Active Contour Model (ACM).” The main concept of ASM is to establish an active shape model by training images of similiar objects. By applying the iterative method to decreasing the difference between the shape of model and that of the targeting object, the shape model can finally match the shape of targeting ojbect by means of adjusting the parameters. In this way, the extraction of feature points will be more likely to be accurate and precise. Digital art is an emerging genre of arts. It comes from Technical art, integrating computers, network, and multi-media altogether to present various aspects (Liao, 2003). Lin et al. (2004) believes that artistic works shall be able to reach viewers’ inner feelings. In addition to the affection demonstrated by the work itself, viewers are supposed to have deeper thoughts (Lin, et al., 2012; Wang et al., 2011). In this case, this art course may include art history, art appreciation, art creation, and art criticism (Liao, 2003). Moreover, lecturers may try to integrate information technology into art courses and to overcome issues of network connection in order to increase their teaching performance on art and culture, improve their instruction, and exert the positive effects of education (Lu, 2009). AUTHOR ARTWORK Research Method This system is divided into two parts: (1) Course and Interactive mechanism; (2) Emotion and Learning Status Recognition mechanism. Moreover, the two main parts are divided into smaller modules seen as Figure 1. Copyright © The Turkish Online Journal of Educational Technology 419
TOJET: The Turkish Online Journal of Educational Technology – October 2012, volume 11 Issue 4 Figure 1 Flowchart of Affective Tutoring System Ontology-based Semantic Learning Status Recognition System The OMCSNet is served as the database for semantic hypothesis. After generating a hypothesis concerning the user’s learning status value, this study applies a semi-automatic voting mechanism to obtain the final learning status. This data will then become an indication for system agent to choose courses for users. To develop an ontology-based semantic learning status recognition system, this study adopts the OMCSNet (Open Mind Common Sense corpus, a semantic Network, 2003), a creation developed by Massachusetts Institute of Technology (MIT) Media Lab, to work with the voting mechanism so that this study is able to create the OMCSNetKai to complete the task (Lin et al., 2011a; Lin et al., 2011b; Tsai et al., 2010). Algorithm of Facial Expression Recognition This mechanism of facial expression recognition and learning status recognition is created in accordance with the Detected Emotion based on Active Shape Model (DEASM) developed by this study. DEASM is the creation generated by Active Shape Model Library (ASMLibrary 5.0) SDK and the algorithm proposed by Ammar (2010). DEASM is applied to extract information of instant images via the webcam so that this study may match each frame with the active shape models to obtain the coordinates of feature points needed for the algorithm. Afterwards, this study conducts the algorithm of facial expression recognition to recognize users’ emotions. After extracting the necessary targeting feature points, this study conducts the definition of six spans of facial feature points (Di) proposed by Ammar (2010) to go with the six fundamental human emotions and develops the algorithm of facial expression recognition. Intelligent Tutoring System This study is aimed at developing an ATS on the basis of ITS, capable of detecting users’ emotions and giving them proper feedback. ATS suggests that the system is able to recognize a user’s learning and emotional status so that it may give the user a timely emotional feedback to help him/her back on track (Mao and Li, 2010). Despite the fact that many researchers have attempted to apply ITS to teach all sorts of fields such as algebra, geometry, mathematics, physics, and computer programming, rarely does it be applied to teach art courses, or even digital art. The following part is concerning the introduction of how this study makes use of the ATS it develops to teach digital art course (Lin et al., 2011a; Lin et al., 2011b; Tsai et al., 2010). The interactive user agent module is divided into two subordinate modules: sound feedback and animation feedback. Based on six fundamental emotions, agents may react to users’ emotions in various sounds and animation. For instance, if the user has an expression of sadness, the agent will comfort him/her by wearing a caring look and asking, “Are you alright?” In accordance to the six fundamental emotions defined by Ekman & Friesen (1978) plus the “Neutral” emotion, The snapshot of the system is shown in Figure 2. Copyright © The Turkish Online Journal of Educational Technology 420
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