GIMME: Group Interactions Manager for Multiplayer sErious games

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Heroine’s learning journey: motivating women in stem online courses through the power of a narrative

Publication . Costa, Luis Felipe Coimbra; Gomes, Samuel; Santos, Ana Moura; Xexéo, Geraldo Bonorino; De Lima, Yuri Oliveira; Prada, Rui; Martinho, Carlos; Dias, João

Although Science, Technology, Engineering, and Mathematics (STEM) are essential for the development of society, men hugely outnumber women in the majority of STEM fields in higher education, a factor that hinders inclusion and restricts the possibility of having different points-of-view. Previous studies indicate multiple causes of low female motivation in STEM degrees and careers, which inspired several initiatives to increase female interest in STEM. A proven way to captivate an audience to change its attitude is the heroic narrative model, a style of narrative in which a character goes through a sequence of difficulty-increasing and attitude-shaping quests. This paper proposes a heroic narrative model named Heroine's Learning Journey (HLJ) targeted at counteracting low female participation in STEM courses. In particular, the HLJ model is developed especially for enhancing STEM online courses, by using a narrative that can encourage female students to engage and prevail in them. The HLJ model is divided into three acts, each composed of several stages symbolizing steps tailored to a female student's development. The model was applied to set up the structure of a preexisting Machine Learning online course with hundreds of enrolled students. Although a first version of the course already presented a higher-than-expected female enrollment per se (approximate to 37.3%), with HLJ, we verified an even higher female enrollment (approximate to 59.2% ), slightly surpassing male enrollments. The feedback provided in learners' responses to a final, voluntary and anonymous questionnaire, allowed to obtain the degree of satisfaction of participants at the end of the course with the HLJ. The responses indicated that, at the end of the second edition of the online course, students were able to acknowledge the existence of a STEM gender imbalance, and appreciated the motivating nature of the HLJ model. From several student's feedback and comments submitted in the questionnaire, one can conclude that the attitude-shaping character of the HLJ was greatly appreciated, in addition to the technical content of the course. All these preliminary results are indicative of the usability of HLJ to foster gender balance in STEM online courses. Thus, the present study contributes to STEM Education by leveraging the motivation of young women to enter and prevail in these areas of study.

2024Journal article

Open access

The influence of reward on the social valence of interactions

Publication . Alves, Tomas; Gomes, Samuel; Dias, João; Martinho, Carlos

Throughout the years, social norms have been promoted as an informal enforcement mechanism for achieving beneficial collective outcomes. Among the most used methods to foster interactions, framing the context of a situation or setting in-game rules have shown strong results as mediators on how an individual interacts with their peers. Nevertheless, we found that there is a lack of research regarding the use of incentives such as scores to promote social interactions differing in valence. Weighing how incentives influence in-game behavior, we propose the use of rewards to promote interactions varying in valence, i.e. positive or negative, in a two-player scenario. To do so, we defined social valence as a continuous scale with two poles represented by Complicate and Help. Then, we performed user tests where participants where asked to play a game with two reward-based systems to test on whether the scoring system influenced the social interaction valence. The results indicate that the developed reward-based systems were able to foster interactions diverging in social valence scores, providing insights on how factors such as incentives overlap individual's established social norms. These findings empower game developers and designers with a low-cost and effective policy tool that is able to promote in-game behavior changes.

2020Conference object

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Reward-mediated individual and altruistic behavior

Publication . Gomes, Samuel; Alves, Tomás; Dias, João; Martinho, Carlos

Recent research has taken a particular interest in observing the dynamics between individual and altruistic behavior. This is a commonly approached problem when reasoning about social dilemmas, which have a plethora of real-world counterparts in the fields of education, health, and economics. Weighing how incentives influence in-game behavior, our study examines individual and altruistic interactions in the context of a game task, by analyzing the players’ strategies and interaction motives when facing different reward attribution functions. Consequently, a model for interaction motives is proposed, with the premise that the motives for interactions can be defined as a continuous space, ranging from self-oriented (associated with individual behaviors) to others-oriented (associated with altruistic behaviors). To evaluate the promotion of individual and altruistic behavior, we leverage Message Across, an in-loco two-player videogame with adaptable score attribution systems. We conducted a user testing phase (N = 66) to verify to what extent individual and altruistic score functions led players to vary their strategies and interaction motives orientations. Our results indicate that both of these metrics varied significantly and according to our expectations, leading us to believe in the suitability of applying an incentive-based strategy to moderate the emergence of in-game behavior perceivable as individual or altruistic.

2022Conference object

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Modeling students’ behavioral engagement through different in-class behavior styles

Publication . Gomes, Samuel; Costa, Luis; Martinho, Carlos; Dias, João; Xexéo, Geraldo; Moura Santos, Ana

Background The growing necessity of providing better education, notably through the development of Adaptive Learning Systems (ALSs), leveraged the study of several psychological constructs to accurately characterize learners. A concept extensively studied in education is engagement, a multidimensional construct encompassing behavioral expression and motivational backgrounds. This metric can be used to not only guide certain pedagogic methodologies, but also to endow systems with the right tutoring techniques. As such, this article aims to inspire improved teaching styles and automatic learning systems, by experimentally verifying the influence of in-class behaviors in students’ engagement. Results Over 16 math lessons, the occurrence of students’ and instructors’ behaviors, alongside students’ engagement estimates, were recorded using the COPUS observation protocol. After behavior-profiling the classes deploying such lessons, significant linear models were computed to relate the frequency of the students’ or instructors’ behaviors with the students’ engagement at different in-class periods. The models revealed a positive relation of students’ initial individual thinking and later group activity participation with their collective engagement, as well as a positive engagement relation with the later application of instructor’s strategies such as giving feedback and moving through class, guiding on-going work. Conclusions The results suggest the benefit of applying a workshop-like learning process, providing more individual explanations and feedback at the beginning of an interaction, leaving collective feedback and students’ guidance of on-going work for later on. Based on the relations suggested by our models, several guidelines for developing ALSs are proposed, and a practical illustrative example is formulated.

2023-03-11Journal article

Open access

Designing a mood-mediated multi-level reasoner

Publication . Gomes, Samuel; Rocha, José Bernardo; Dias, João; Martinho, Carlos

Psychology-oriented research extensively studied how affect influences human decision making. Particularly, the cognitive tuning assumption suggests that mood can serve to regulate between shallow and deliberative decisions - a more negative mood means higher deliberation. This work proposes a model that mimics the cognitive tuning assumption. To that end, the Collective Risk Dilemma (CRD) game For the Planet was designed and created, a process allowing for distinct levels of reasoning was defined and tested in that game, and mood was integrated to control the level of reasoning of such a process. Several distinct Artificial Intelligence (AI) profiles were created to verify that the developed model was able to dynamically change the level of reasoning and adjust the resulting AI behavior in our CRD game. Results revealed that the distinct AI profiles were influenced by their affective states and experienced circumstances, and that the emergent behaviors were consistent with the cognitive tuning assumption, thus demonstrating that we managed to construct an innovative and flexible model that can use mood to dynamically adjust the level of reasoning of an AI agent.

2024-07Journal article

Restricted access