A Computational Model of Learners Achievement Emotions Using Control Value Theory
Venue: J. Educ. Technol. Soc.

Formal instruction is transforming into a more flexible and interactive process, focusing on student preferences for learning and engagement (Moore, Dickson Deane, Galyen, 2011) As a result, Virtual Learning (VL) and Game Based Learning (GBL) environments have gained popularity and acceptance. GBL environments typically comprise features, such as storytelling, sound effects and feedback, which facilitate an emotional connection with the learner (Sykes, 2013) The key to attaining positive and successful experiences in VL and GBL environments is to achieve personalization (Janssen, van den Broek, Westerink, 2011) Emotion has shown to be important in many contexts including Evolution and Neuroscience. Here we focus on Educational Psychology and Computing contexts, where student modeling has recently focused on affect, because it has been shown to influence student understanding, performance and motivation. However, to date, the methods employed to reason about emotion in ITSs have shown highly promising in laboratories, but not in the classroom (Arroyo et al. 2009) For reasoning about emotion, the majority of the models use theories that have not originated from an educational setting (Conati Maclaren, 2009; Jaques, Vicari, Pesty, Martin, 2011; Landowska, 2013) Therefore, it may be possible that the targeted emotions do not actually occur during the teaching/learning experience. Also, the classification accuracy of these models is presented mainly using only percentages, so it is unclear that the effects are not random. On the other hand, the Control value theory of achievement emotions by Pekrun, Frenzel, Goetz and Perry (2007) assumes that control and value appraisals are the most essential to determine emotion in an educational context. Achievement emotions are derived from performing activities and the pursuit of goals. Performance and achievement are judged against previously defined standards of quality. It was observed that this theory has not previously been utilized to create a computational model of student emotion. Therefore, here we focus on this objective. For reasoning about student emotion, we employ a Cognitive based Affective User Modeling approach, which allows applying what is known, in this case in the psychological educational field, to predict emotion (Martinho, Machado, Paiva, 2000) Our model predominantly uses answers during in game dialogues and contextual variables, e.g. mouse location and the number of times help is asked, because it is mainly targeted at on line GBL environments. Our model is implemented using a dynamic sequence of Bayesian Networks (BNs) since they can effectively manage the uncertainty of the domain and appropriately represent the temporal interdependencies. A preliminary version of our model was discussed in Munoz, Mc Kevitt, Lunney, Noguez and Neri (2013) the model presented here is the final result of performing further tests and employing more formal tools to conduct our analysis. Related work GBL environments, i.e. Edutainment, enhance learning by providing immediate feedback to student actions in simulated contexts. They have proven to attain student attention and engagement more easily than VL environments (Munoz et al. 2009) Their success depends on the composition of diverse elements that gives them an emotional character (Sykes, 2013) such as penalizing errors and rewarding learning, e.g. through sounds, colors, narrative, and scoring. These elements combine to create a unique game experience known as gameplay. Lazzaro (2004) argues that this emotional experience is the source of the appeal of playing games. GBL environments can also be combined with ITSs to achieve adaptable and personalized instruction (Conati Maclaren, 2009) The new generation of ITSs aims to recognize and respond appropriately to student affect (Alexander, Sarrafzadeh Hill, 2008; Conati Maclaren, 2009; D'Mello, Craig, Witherspoon, McDaniel, Graesser, 2008; D'Mello, Olneyc, Williams, Hays, 2012; Jaques et al.