Video games and Intellectual Disabilities: a literature review.

Los videojuegos son omnipresentes en la sociedad y esta tecnología ha trascendido su lado lúdico inicial para convertirse también en una herramienta educativa y de entrenamiento cognitivo. En este sentido, diferentes estudios han demostrado que los jugadores expertos obtener ventajas en diversos procesos cognitivos respecto a no-jugadores y jugar con juegos de video puede resultar en especial los beneficios que en algunos casos podría generalizarse a otras tareas. En consecuencia, los juegos de video podría ser utilizado como una herramienta de formación para mejorar las capacidades cognitivas en poblaciones atípicas, como las relativas a las personas con discapacidad intelectual (DI). Sin embargo, la literatura sobre los videojuegos en personas con ID es escasa. En este trabajo se ejecutó una revisión narrativa de los estudios sobre el uso de los videojuegos en relación a las personas con ID.Video games are ubiquitous in the society and this technology has transcended its initial playful side to become also an educational and cognitive training tool. In this sense, different studies have shown that expert game players gain advantages in various cognitive processes respect to non-players and that playing with video games can result in particular profits that in some cases could be generalized to other tasks. Accordingly, video games could be used as a training tool in order to improve cognitive abilities in atypical populations, such as relating to individuals with intellectual disabilities (ID). However, literature concerning video games in people with ID is sparse. In this paper we executed a narrative review of the studies about the use of video games in relation to people with ID.• Fundación Valhondo Calaff (Cáceres), para Marta Rodríguez Jiménez • Università di Padova. Beca CPDA 127939, para Silvia LanfranchipeerReviewe

Multi-Armed Bandits for Intelligent Tutoring Systems

We present an approach to Intelligent Tutoring Systems which adaptively personalizes sequences of learning activities to maximize skills acquired by students, taking into account the limited time and motivational resources. At a given point in time, the system proposes to the students the activity which makes them progress faster. We introduce two algorithms that rely on the empirical estimation of the learning progress, RiARiT that uses information about the difficulty of each exercise and ZPDES that uses much less knowledge about the problem. The system is based on the combination of three approaches. First, it leverages recent models of intrinsically motivated learning by transposing them to active teaching, relying on empirical estimation of learning progress provided by specific activities to particular students. Second, it uses state-of-the-art Multi-Arm Bandit (MAB) techniques to efficiently manage the exploration/exploitation challenge of this optimization process. Third, it leverages expert knowledge to constrain and bootstrap initial exploration of the MAB, while requiring only coarse guidance information of the expert and allowing the system to deal with didactic gaps in its knowledge. The system is evaluated in a scenario where 7-8 year old schoolchildren learn how to decompose numbers while manipulating money. Systematic experiments are presented with simulated students, followed by results of a user study across a population of 400 school children

Evaluating Learner Engagement with Gamification in Online Courses

Several reasons underlie the low retention rates in MOOCs. These reasons can be analysed from different perspectives, either in terms of the course design or the enrolled students. On the student side, we find little social interaction, boredom, tiredness, and a lack of motivation and time. These challenges can be addressed by adaptive gamification that proposes the design of personalised, hedonic learning experiences. Studies to date have adopted either the one-fits-all approach or the adaptive approach. Nevertheless, the adaptive solutions have considered a static player profile throughout the entire experience. This paper presents the design and evaluation of a dynamic adaptive gamification approach which—based on students’ interactions with game elements and also their opinions about these elements—dynamically updates the students’ player profile to better figure out which game elements suit them. We evaluated the engagement of students with gamification elements by means of a course composed of a knowledge "pill" related to the topic of “recycling plastics from the sea”, offered through the nanoMOOCs learning platform. We propose metrics such as the mean number of interactions with the gamification dashboard, the time spent by participants with game elements, and the opinions of students about these elements to compare the Dynamic Adaptive Gamification (DynamicAG) and the Static Adaptive (StaticAG) approaches. An experimental study with 66 high school students showed significant differences between both approaches. Specifically, the DynamicAG group spent twice as much time with the Dashboard than the StaticAG group. Moreover, students in the DynamicAG group were more engaged with game elements (mean number of interactions = 12.13) than those in the StaticAG group (mean number of interactions = 3.21)

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

Adaptive Gamification for Learning Environments

(Scimago Q1, ATIEF A+)International audienceIn spite of their effectiveness, learning environments often fail to engage users and end up under-used. Many studies show that gamification of learning environments can enhance learners' motivation to use learning environments. However, learners react differently to specific game mechanics and little is known about how to adapt gaming features to learners' profiles. In this paper, we propose a process for adapting gaming features based on a player model. This model is inspired from existing player typologies and types of gamification elements. Our approach is implemented in a learning environment with five different gaming features, and evaluated with 266 participants. The main results of this study show that, amongst the most engaged learners (i.e. learners who use the environment the longest), those with adapted gaming features spend significantly more time in the learning environment. Furthermore, learners with features that are not adapted have a higher level of amotivation. These results support the relevance of adapting gaming features to enhance learners' engagement, and provide cues on means to implement adaptation mechanisms

Understanding the Role of Interactivity and Explanation in Adaptive Experiences

Adaptive experiences have been an active area of research in the past few decades, accompanied by advances in technology such as machine learning and artificial intelligence. Whether the currently ongoing research on adaptive experiences has focused on personalization algorithms, explainability, user engagement, or privacy and security, there is growing interest and resources in developing and improving these research focuses. Even though the research on adaptive experiences has been dynamic and rapidly evolving, achieving a high level of user engagement in adaptive experiences remains a challenge. %????? This dissertation aims to uncover ways to engage users in adaptive experiences by incorporating interactivity and explanation through four studies. Study I takes the first step to link the explanation and interactivity in machine learning systems to facilitate users\u27 engagement with the underlying machine learning model with the Tic-Tac-Toe game as a use case. The results show that explainable machine learning (XML) systems (and arguably XAI systems in general) indeed benefit from mechanisms that allow users to interact with the system\u27s internal decision rules. Study II, III, and IV further focus on adaptive experiences in recommender systems in specific, exploring the role of interactivity and explanation to keep the user “in-the-loop” in recommender systems, trying to mitigate the ``filter bubble\u27\u27 problem and help users in self-actualizing by supporting them in exploring and understanding their unique tastes. Study II investigates the effect of recommendation source (a human expert vs. an AI algorithm) and justification method (needs-based vs. interest-based justification) on professional development recommendations in a scenario-based study setting. The results show an interaction effect between these two system aspects: users who are told that the recommendations are based on their interests have a better experience when the recommendations are presented as originating from an AI algorithm, while users who are told that the recommendations are based on their needs have a better experience when the recommendations are presented as originating from a human expert. This work implies that while building the proposed novel movie recommender system covered in study IV, it would provide a better user experience if the movie recommendations are presented as originating from algorithms rather than from a human expert considering that movie preferences (which will be visualized by the movies\u27 emotion feature) are usually based on users\u27 interest. Study III explores the effects of four novel alternative recommendation lists on participants’ perceptions of recommendations and their satisfaction with the system. The four novel alternative recommendation lists (RSSA features) which have the potential to go beyond the traditional top N recommendations provide transparency from a different level --- how much else does the system learn about users beyond the traditional top N recommendations, which in turn enable users to interact with these alternative lists by rating the initial recommendations so as to correct or confirm the system\u27s estimates of the alternative recommendations. The subjective evaluation and behavioral analysis demonstrate that the proposed RSSA features had a significant effect on the user experience, surprisingly, two of the four RSSA features (the controversial and hate features) perform worse than the traditional top-N recommendations on the measured subjective dependent variables while the other two RSSA features (the hipster and no clue items) perform equally well and even slightly better than the traditional top-N (but this effect is not statistically significant). Moreover, the results indicate that individual differences, such as the need for novelty and domain knowledge, play a significant role in users’ perception of and interaction with the system. Study IV further combines diversification, visualization, and interactivity, aiming to encourage users to be more engaged with the system. The results show that introducing emotion as an item feature into recommender systems does help in personalization and individual taste exploration; these benefits are greatly optimized through the mechanisms that diversify recommendations by emotional signature, visualize recommendations on the emotional signature, and allow users to directly interact with the system by tweaking their tastes, which further contributes to both user experience and self-actualization. This work has practical implications for designing adaptive experiences. Explanation solutions in adaptive experiences might not always lead to a positive user experience, it highly depends on the application domain and the context (as studied in all four studies); it is essential to carefully investigate a specific explanation solution in combination with other design elements in different fields. Introducing control by allowing for direct interactivity (vs. indirect interactivity) in adaptive systems and providing feedback to users\u27 input by integrating their input into the algorithms would create a more engaging and interactive user experience (as studied in Study I and IV). And cumulatively, appropriate direct interaction with the system along with deliberate and thoughtful designs of explanation (including visualization design with the application environment fully considered), which are able to arouse user reflection or resonance, would potentially promote both user experience and user self-actualization