Karla Muñoz

Adding features of educational games for Teaching Physics

Virtual laboratories (VLs) have to overcome important challenges to improve student knowledge, understanding and motivation. This research aims to test the hypothesis that, through adding features of serious games to VLs and integrating artificial intelligence (AI) techniques, an enhancement of student motivation, knowledge and understanding can be attained. This work introduces the Olympia architecture, which is based on a previous architecture that combines VLs and intelligent tutoring systems (ITSs). In addition, Olympia enables the combination of serious games with ITSs, resulting in an educational game virtual laboratory (GVL). The GVL provides affective feedback through sound, a more engaging look-and-feel and defines student actions through the game mechanics module. Olympia was tested in a case study on teaching linear momentum in an undergraduate Physics course. For the first evaluation, a VL and a GVL were implemented. The results showed that students were motivated and learned in a similar way with both the GVL and VL environments. Later, several additions were integrated in both environments: the probabilistic student model was improved, tutorial videos were added, and the feedback was refined. For the second evaluation the results suggest that using the GVL resulted in higher learning gains than using VL.

An emotional student model for game-play adaptation

Game-based learning offers key advantages for learning through experience in conjunction with offering multi-sensorial and engaging communication. However, ensuring that learning has taken place is the ultimate challenge. Intelligent Tutoring Systems (ITSs) have been incorporated into game-based learning environments to guide learners’ exploration. Emotions have proven to be deeply intertwined with cognitive and motivational factors. ITSs attempt to recognise and convey emotion in order to enhance students’ learning and engagement. The ITS student model is responsible for attainment of adaptability and understanding of learners’ needs. It is not clear which emotions are relevant to the teaching-learning experience, or what antecedents and interpersonal differences are involved in determining an emotion. Therefore, student modelling involves uncertainty. Creating an emotional student model that can reason about students’ observable behaviour during online game-play is the main goal of our research. The analysis, design and implementation for this model are our central focus here. The model uses as a basis the Control-Value theory of achievement emotions and employs motivational and cognitive variables to determine an emotion. A Probabilistic Relational Model (PRM) approach was applied to facilitate the derivation of three Dynamic Bayesian Networks (DBNs) corresponding to three types of achievement emotions. Results from a prototyping exercise conducted along with the outcome-prospective emotions DBN are presented and discussed. In future work a larger population of students will be employed to develop an accurate DBN model to incorporate into PlayPhysics, an emotional game-based learning environment for teaching Physics.

Designing and Evaluating Emotional Student Models for Game-Based Learning

Research in game-based learning environments aims to recognise and show emotion. This chapter describes the main approaches and challenges involved in achieving these goals. In addition, we propose an emotional student model that can reason about students’ emotions using observable behaviour and responses to questions. Our model uses Control-Value Theory (Pekrun et al., The control value theory of achievement emotions. An integrative approach to emotions in education. In: Schutz, P.A., Pekrun, R. (eds.) Emotion in Education, pp. 13–36. Elsevier, London, 2007) as a basis for representing behaviour and was designed and evaluated using Probabilistic Relational Models (PRMs), Dynamic Bayesian Networks (DBNs) and Multinomial Logistic Regression. Olympia, a game-based learning architecture, was enhanced to incorporate affect and was used to develop PlayPhysics, an emotional game-based learning environment for teaching Physics. PlayPhysics’ design and emotional student model was evaluated with 79 students of Engineering at Tecnologico de Monterrey, Mexico City campus (ITESM-CCM). Results are presented and discussed. Future work will focus on conducting tests with a larger population of students, implementing additional game challenges and incorporating physiological signals to increase the accuracy of classification.

Inferring Knowledge from Active Learning Simulators for Physics

Active Learning Simulators (ALS) allow students to practice and carry out experiments in a safe environment - at any time, and in any place. Furthermore, well-designed simulations may enhance learning, and provide the bridge from conceptual to practical understanding. By adding an Intelligent Tutoring System (ITS), it is possible to provide personal guidance to students. The main objective of this work is to present an ALS suited for a Physics scenario in which we incorporate elements from ITS, and where a Probabilistic Relational Model (PRM) based on a Bayesian Network is used to infer student knowledge, taking advantage of relational models. A discussion of the methodology is addressed and preliminary results are presented. Ours first results go in the right direction as proved by a relative learning gain.

SceneMaker: Creative Technology for Digital StoryTelling

The School of Creative Arts & Technologies at Ulster University (Magee) has brought together the subject of computing with creative technologies, cinematic arts (film), drama, dance, music and design in terms of research and education. We propose here the development of a flagship computer software platform, SceneMaker, acting as a digital laboratory workbench for integrating and experimenting with the computer processing of new theories and methods in these multidisciplinary fields. We discuss the architecture of SceneMaker and relevant technologies for processing within its component modules. SceneMaker will enable the automated production of multimodal animated scenes from film and drama scripts or screenplays. SceneMaker will highlight affective or emotional content in digital storytelling with particular focus on character body posture, facial expressions, speech, non-speech audio, scene composition, timing, lighting, music and cinematography. Applications of SceneMaker include automated simulation of productions and education and training of actors, screenwriters and directors.