ANALYZING VIDEOGAMES TO LEARN HOW TO THINK CRITICALLY

The reflections contained in this research work deal with the educational challenge launched by the cultural and social phenomenon of videogames, which have become more than pure forms of entertainment and fun, more and more metaphors of the big game of the reality of life. Many of the earliest scholarly studies emanated from the research laboratories of pedagogical departments were typically concerned with the possible effect of games and young players. For a long time videogames have been forgotten as educative medium because they have been considered as mere trifles \u2013 low art \u2013 carrying none of the weight, gravitas or credibility of more traditional media. The seemingly bewildering variety of game types makes it almost inevitable that game theorists, journalists and marketers have attempted to find ways of classifying and making more manageable the object of their attentions. By far the most frequently used tool has been genre. The generic classification of videogames is so widely employed that it is often easy to overlook it altogether or merely consider it as natural. One of the possible forms of videogame education is that of promoting its understanding and educative usage. The present work, starting from the construction of an evaluation grid, aims at analyzing videogame products in order to learn how to think critically. From an educational point of view, the data presented are meant to be functional tools to stimulate thinking activity and to activate appropriate mental processes in children. The research dealt with the analysis of 50 video games designed for children aged 3 to 10 years of age

Learning how to learn: an adaptive dialogue agent for incrementally learning visually grounded word meanings

We present an optimised multi-modal dialogue agent for interactive learning of visually grounded word meanings from a human tutor, trained on real human-human tutoring data. Within a life-long interactive learning period, the agent, trained using Reinforcement Learning (RL), must be able to handle natural conversations with human users and achieve good learning performance (accuracy) while minimising human effort in the learning process. We train and evaluate this system in interaction with a simulated human tutor, which is built on the BURCHAK corpus -- a Human-Human Dialogue dataset for the visual learning task. The results show that: 1) The learned policy can coherently interact with the simulated user to achieve the goal of the task (i.e. learning visual attributes of objects, e.g. colour and shape); and 2) it finds a better trade-off between classifier accuracy and tutoring costs than hand-crafted rule-based policies, including ones with dynamic policies.Comment: 10 pages, RoboNLP Workshop from ACL Conferenc

How Decision Makers Learn to Choose Organizational Performance Measures

This study, framed by decision making, program theory, and performance measurement theory, explored the knowledge and experience that enable decision makers to identify organizational performance measures. It used a mixed method, exploratory sequential research design to discover the experience, knowledge, and skills (EKS) senior decision makers felt were important in learning to choose organizational performance measures. From the analyzed interviews, a survey was designed to measure the importance of the EKS characteristics. Qualitative analysis identified 55 life, work, or educational experience; knowledge; or skill characteristics and 23 effective measure characteristics. Regression analysis and PCA were used to extract 6 components. One-way ANOVA found no significant differences in these factors between gender groups, age groups, and process complexity levels, but found differences for decision-making tenure. MANOVA found no significant differences by the same dimensions. The limited sample size and high number of variables confounded component extraction. Further research with a suitable sample size is required before findings can be generalized

How to Feel About and Learn Mathematics: Therapeutic Intervention and Attentiveness

In mathematics teacher education, tasks that centre on doing mathematics are used for a variety of purposes, including learning new mathematics. In our research, we focus on doing mathematics as a therapeutic intervention. Many pre-service teachers in our program narrate impoverished mathematics experiences. We engage pre- service elementary school teachers in non-routine problem solving and examine how this affects their experiences with mathematics. Specifically, we focus on change in affective responses as a precursor to development in mathematical thinking and as an indicator of potential changes in practice. Our study shows that doing mathematics evokes changes in how teachers think and feel about doing, learning, and teaching mathematics

How Do Management Students Prefer to Learn? Why Should We Care?

This paper reports the findings of a study of the learning styles of students in the operations management class at a regional comprehensive university in southeastern United States. Extant learning styles are found to be highly diverse and differ by student gender. However, in contrast to at least one prior study, the learning styles of our respondents did not differ by student major. Five areas of opportunity for future research arising from the results of this study are identified in the paper’s conclusion

ACE Models of Endogenous Interactions

Various approaches used in Agent-based Computational Economics (ACE) to model endogenously determined interactions between agents are discussed. This concerns models in which agents not only (learn how to) play some (market or other) game, but also (learn to) decide with whom to do that (or not).Endogenous interaction, Agent-based Computational Economics (ACE)

Diversity Training Workshop Series: How to Learn and Interact within a Diverse Community

This capstone focuses on a diversity initiative designed for higher education institutions in the United States and it targets students of all levels and degrees. The theme selected for this capstone is: “Diversity Training Workshop Series: How to Learn and Interact within a Diverse Community” and it will be a co-curricular program that follows the interests promoted by Affirmative Action. Due to the wide range of intersectionalities and the abstract definition of Diversity, in this paper, diversity is defined solely as the “composition of the student body”. The trainings outline the needs for students to recognize and acknowledge non-visible identity characteristics and the contextual factors that shapes it that includes but it is not limited to: ethnicity and race, age, learning ability, social class, cultural heritage, military status, athlete status, student with children, sexual orientation, inmate and others. The goal of this capstone is to foster understanding, social interaction, and integration while promoting inclusiveness and active collaboration among the student community. Online research, inquiries, and literature review concentrate on identity groups and their social interactions, the pros and cons of transmitting specific values and information for a single identity group, and the social identity development of the student throughout emerging adulthood. This three part workshop series is designed for an interactive self-identity exploration including social class, class culture, and advocacy along with inter-group assignments for integration and collaboration. The program planning and the curriculum is based on Social Identity Theories and interactive training methodology by David Kolb (1984) and Paulo Freire (1998)