D8.6 Dissemination, training and exploitation results

Mauerhofer, C., Rajagopal, K., & Greller, W. (2011). D8.6 Dissemination, training and exploitation results. LTfLL-project.Report on sustainability, dissemination and exploitation of the LtfLL projectThe work on this publication has been sponsored by the LTfLL STREP that is funded by the European Commission's 7th Framework Programme. Contract 212578 [http://www.ltfll-project.org

Encoding Theory of Mind in Character Design for Pedagogical Interactive Narrative

Computer aided interactive narrative allows people to participate actively in a dynamically unfolding story, by playing a character or by exerting directorial control. Because of its potential for providing interesting stories as well as allowing user interaction, interactive narrative has been recognized as a promising tool for providing both education and entertainment. This paper discusses the challenges in creating interactive narratives for pedagogical applications and how the challenges can be addressed by using agent-based technologies. We argue that a rich model of characters and in particular a Theory of Mind capacity are needed. The character architect in the Thespian framework for interactive narrative is presented as an example of how decision-theoretic agents can be used for encoding Theory of Mind and for creating pedagogical interactive narratives

Using Student Mood And Task Performance To Train Classifier Algorithms To Select Effective Coaching Strategies Within Intelligent Tutoring Systems (its)

The ultimate goal of this research was to improve student performance by adjusting an Intelligent Tutoring System\u27s (ITS) coaching strategy based on the student\u27s mood. As a step toward this goal, this study evaluated the relationships between each student\u27s mood variables (pleasure, arousal, dominance and mood intensity), the coaching strategy selected by the ITS and the student\u27s performance. Outcomes included methods to increase the perception of the intelligent tutor to allow it to adapt coaching strategies (methods of instruction) to the student\u27s affective needs to mitigate barriers to performance (e.g. negative affect) during the one-to-one tutoring process. The study evaluated whether the affective state (specifically mood) of the student moderated the student\u27s interaction with the tutor and influenced performance. This research examined the relationships, interactions and influences of student mood in the selection of ITS coaching strategies to determine which strategies were more effective in terms of student performance given the student\u27s mood, state (recent sleep time, previous knowledge and training, and interest level) and actions (e.g. mouse movement rate). Two coaching strategies were used in this study: Student-Requested Feedback (SRF) and Tutor-Initiated Feedback (TIF). The SRF coaching strategy provided feedback in the form of hints, questions, direction and support only when the student requested help. The TIF coaching strategy provided feedback (hints, questions, direction or support) at key junctures in the learning process when the student either made progress or failed to make progress in a timely fashion. The relationships between the coaching strategies, mood, performance and other variables of interest were considered in light of five hypotheses. At alpha = .05 and beta at least as great as .80, significant effects were limited in predicting performance. Highlighted findings include no significant differences in the mean performance due to coaching strategies, and only small effect sizes in predicting performance making the regression models developed not of practical significance. However, several variables including performance, energy level and mouse movement rates were significant, unobtrusive predictors of mood. Regression algorithms were developed using Arbuckle\u27s (2008) Analysis of MOment Structures (AMOS) tool to compare the predicted performance for each strategy and then to choose the optimal strategy. A set of production rules were also developed to train a machine learning classifier using Witten & Frank\u27s (2005) Waikato Environment for Knowledge Analysis (WEKA) toolset. The classifier was tested to determine its ability to recognize critical relationships and adjust coaching strategies to improve performance. This study found that the ability of the intelligent tutor to recognize key affective relationships contributes to improved performance. Study assumptions include a normal distribution of student mood variables, student state variables and student action variables and the equal mean performance of the two coaching strategy groups (student-requested feedback and tutor-initiated feedback ). These assumptions were substantiated in the study. Potential applications of this research are broad since its approach is application independent and could be used within ill-defined or very complex domains where judgment might be influenced by affect (e.g. study of the law, decisions involving risk of injury or death, negotiations or investment decisions). Recommendations for future research include evaluation of the temporal, as well as numerical, relationships of student mood, performance, actions and state variables

Towards an Intelligent Tutor for Mathematical Proofs

Computer-supported learning is an increasingly important form of study since it allows for independent learning and individualized instruction. In this paper, we discuss a novel approach to developing an intelligent tutoring system for teaching textbook-style mathematical proofs. We characterize the particularities of the domain and discuss common ITS design models. Our approach is motivated by phenomena found in a corpus of tutorial dialogs that were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor for textbook-style mathematical proofs can be built on top of an adapted assertion-level proof assistant by reusing representations and proof search strategies originally developed for automated and interactive theorem proving. The resulting prototype was successfully evaluated on a corpus of tutorial dialogs and yields good results.Comment: In Proceedings THedu'11, arXiv:1202.453

A Bayesian belief network computational model of social capital in virtual communities

The notion of social capital (SC) is increasingly used as a framework for describing social issues in terrestrial communities. For more than a decade, researchers use the term to mean the set of trust, institutions, social norms, social networks, and organizations that shape the interactions of actors within a society and that are considered to be useful and assets for communities to prosper both economically and socially. Despite growing popularity of social capital especially, among researchers in the social sciences and the humanities, the concept remains ill-defined and its operation and benefits limited to terrestrial communities. In addition, proponents of social capital often use different approaches to analyze it and each approach has its own limitations. This thesis examines social capital within the context of technology-mediated communities (also known as virtual communities) communities. It presents a computational model of social capital, which serves as a first step in the direction of understanding, formalizing, computing and discussing social capital in virtual communities. The thesis employs an eclectic set of approaches and procedures to explore, analyze, understand and model social capital in two types of virtual communities: virtual learning communities (VLCs) and distributed communities of practice (DCoP). There is an intentional flow to the analysis and the combination of methods described in the thesis. The analysis includes understanding what constitutes social capital in the literature, identifying and isolating variables that are relevant to the context of virtual communities, conducting a series of studies to further empirically examine various components of social capital identified in three kinds of virtual communities and building a computational model. A sensitivity analysis aimed at examining the statistical variability of the individual variables in the model and their effects on the overall level of social capital are conducted and a series of evidence-based scenarios are developed to test and update the model. The result of the model predictions are then used as input to construct a final empirical study aimed at verifying the model.Key findings from the various studies in the thesis indicated that SC is a multi-layered, multivariate, multidimensional, imprecise and ill-defined construct that has emerged from a rather murky swamp of terminology but it is still useful for exploring and understanding social networking issues that can possibly influence our understanding of collaboration and learning in virtual communities. Further, the model predictions and sensitivity analysis suggested that variables such as trust, different forms of awareness, social protocols and the type of the virtual community are all important in discussion of SC in virtual communities but each variable has different level of sensitivity to social capital. The major contributions of the thesis are the detailed exploration of social capital in virtual communities and the use of an integrated set of approaches in studying and modelling it. Further, the Bayesian Belief Network approach applied in the thesis can be extended to model other similar complex online social systems

Learner models in online personalized educational experiences: an infrastructure and some experim

Technologies are changing the world around us, and education is not immune from its influence: the field of teaching and learning supported by the use of Information and Communication Technologies (ICTs), also known as Technology Enhanced Learning (TEL), has witnessed a huge expansion in recent years. This wide adoption happened thanks to the massive diffusion of broadband connections and to the pervasive needs for education, highly connected to the evolution in sciences and technologies. Therefore, it has pushed up the usage of online education (distance and blended methodologies for educational experiences) to, even in lately years, unexpected rates. Alongside with the well known potentialities, digital-based educational tools come with a number of downsides, such as possible disengagement on the part of the learner, absence of the social pressures that normally exist in a classroom environment, difficulty or even inability from the learners to self-regulate and, last but not least, depletion of the stimulus to actively participate and cooperate with lectures and peers. These difficulties impact the teaching process and the outcomes of the educational experience (i.e. learning process), being a serious limit and questioning the broader applicability of TEL solutions. To overcome these issues, there is a need of tools to support the learning process. In the literature, one of the known approach to improve the situation is to rely on a user profile, that collects data during the use of the eLearning platforms or tool. The created profile can be used to adapt the behaviour and the contents proposed to the learner. On top of this model, some researches stressed the positive effects stimulated by the disclosure of the model itself for inspection purposes by the learner. This disclosed model is known as Open Learner Model (OLM). The idea of opening learners' profile and eventually integrate them with external on-line resources is not new and it has the ultimate goal of creating global and long-run indicators of the learner's profile. Also the representation aspect of the learner model plays a role, moving from the more traditional approach based on the textual and analytic/extensive representation to the graphical indicators that are able to summarise and to present one or more of the model characteristics in a way that is considered more effective and natural for the user consumption. Relying on the same learner models, and stressing the different aggregation and representation capabilities, it is possible to either support self-reflection of the learner or to foster the tutoring process to allow proper supervision by the tutor/teacher. Both the objectives can be reached through the graphical representation of the relevant information, presented in different ways. Furthermore, with such an open approach for the learner model, the concepts of personalisation and adaptation acquire a central role in the TEL experience, overcoming the previous limits related to the impossibility to observe and explain to the learner the reasons for such an intervention from the tool itself. As a consequence, the introduction of different tools, platforms, widgets and devices in the learning process, together with the adaptation process based on the learner profiles, can create a personal space for a potential fruitful usage of the rich and widespread amount of resources available to the learner. This work aimed at analysing the way a learner model could be represented in visual presentation to the system users, exploring the effects and performances for learners and teachers. Subsequently, it concentrated in investigating how the adoption of adaptive and social visualisations of OLM could affect the student experience within a TEL context. The motivation was twofold. On one side was to show that the approach of mixing data from heterogeneous and not already related data sources could have a meaningful didactic interpretations, whether on the other one was to measure the perceived impact of the introduction on online experiences of the adaptivity (and of social aspects) in the graphical visualisations produced by such a tool. In order to achieve these objectives, the present work analysed and addressed them through an approach that merged user data in learning platforms, implementing a learner profile. This was accomplished by means of the creation of a tool, named GVIS, to elaborate on the collected user actions in platforms enabling remote teaching. A number of test cases were performed and analysed, adopting the developed tool as the provider to extract, to aggregate and to represent the data for the learners' model. The GVIS tool impact was then estimated with self- evaluation questionnaires, with the analysis of log files and with knowledge quiz results. Dimensions such as the perceived usefulness, the impact on motivation and commitment, the cognitive overload generated, and the impact of social data disclosure were taken into account. The main result found by the application of the developed tool in TEL experiences was to have an impact on the behaviour of online learners when used to provide them with indicators around their activities, especially when enhanced with social capabilities. The effects appear to be amplifies in those cases where the widget usage is as simplified as possible. From the learner side, the results suggested that the learners seem to appreciate the tool and recognise its value. For them the introduction as part of the online learning experience could act as a positive pressure factor, enhanced by the peer comparison functionality. This functionality could also be used to reinforce the student engagement and positive commitment to the educational experience, by transmitting a sense of community and stimulating healthy competition between learners. From the teacher/tutor side, they seemed to be better supported by the presentation of compact, intuitive and just-in-time information (i.e. actions that have an educational interpretation or impact) about the monitored user or group. This gave them a clearer picture of how the class is currently performing and enabled them to address performance issues by adapting the resources and the teaching (and learning) approach accordingly. Although a drawback was identified regarding the cognitive overload, the data collected showed that users generally considered this kind of support useful. There is also indications that further analyses can be interesting to explore the effects introduced in the teaching practices by the availability and usage of such a tool

Evaluation of topic-based adaptation and student modeling in QuizGuide

This paper presents an in-depth analysis of a nonconventional topic-based personalization approach for adaptive educational systems (AES) that we have explored for a number of years in the context of university programming courses. With this approach both student modeling and adaptation are based on coarse-grained knowledge units that we called topics. Our motivation for the topic-based personalization was to enhance AES transparency for both teachers and students by utilizing typical topic-based course structures as the foundation for designing all aspects of an AES from the domain model to the end-user interface. We illustrate the details of the topic-based personalization technology, with the help of the Web-based educational service QuizGuide—the first system to implement it. QuizGuide applies the topic-based personalization to guide students to the right learning material in the context of an undergraduate C programming course. While having a number of architectural and practical advantages, the suggested coarse-grained personalization approach deviates from the common practices toward knowledge modeling in AES. Therefore, we believe that several aspects of QuizGuide required a detailed evaluation—from modeling accuracy to the effectiveness of adaptation. The paper discusses how this new student modeling approach can be evaluated, and presents our attempts to evaluate it from multiple different prospects. The evaluation of QuizGuide across several consecutive semesters demonstrates that, although topics do not always support precise user modeling, they can provide a basis for successful personalization in AESs

Apresentando uma Arquitetura Pedagógica e Técnica Usada em Sinergia com Recursos Multimídia na Construção Cooperativa de Saberes

Neste trabalho é apresentada uma arquitetura pedagógica e técnica utilizada poreducadores e educandos nos âmbitos da educação básica e superior para construçãocolaborativa de conhecimento. A infra-estrutura envolve a convergência de teorias emetodologias de ensino-aprendizagem, softwares de baixo custo, linguagens padrão daInternet, tanto quanto os conceitos de empreendedorismo e cooperatividade sistêmica.As conseqüências do uso desta convergência são: a melhoria da comunicação entre osdiversos agentes envolvidos nas ações de ensino-aprendizagem; o crescimento de suashabilidades e competências de leitura e escrita tanto quanto apropriação dos conceitostecnológicos utilizados. Posteriormente, quando os agentes reutilizam e compartilham oconhecimento desenvolvido de modo efetivo, eles contribuem para que outrosindivíduos também se apropriem do conhecimento, não só no ambiente escola, mastambém, em diversos espaços e tempos