Personalized Gamification for Learning: A Reactive Chatbot Architecture Proposal.

A key factor for successfully implementing gamified learning platforms is making students interact with the system from multiple digital platforms. Learning platforms that try to accomplish all their objectives by concentrating all the interactions from users with them are less effective than initially believed. Conversational bots are ideal solutions for cross-platform user interaction. In this paper, an open student-player model is presented. The model includes the use of machine learning techniques for online adaptation. Then, an architecture for the solution is described, including the open model. Finally, the chatbot design is addressed. The chatbot architecture ensures that its reactive nature fits into our defined architecture. The approach's implementation and validation aim to create a tool to encourage kids to practice multiplication tables playfully.

Gender and Age Differences in Preferences on Game Elements and Platforms.

This paper analyzes different player type models and game elements in the literature, particularly focusing on the case of online games. Research based on an exploratory study is presented; it aims to explore the different types of interaction with gameful digital applications. The study is based on a survey and provides findings from the literature review and empirical insights about users' differences and preferences regarding game elements. The results reveal demographics regarding player profiles and the relationships between gender, age, culture, and the influence of different game design elements and platforms. The main contribution of this study fulfills the need for knowledge about the relationship between game element design, platforms/devices, and players (types and preferences).

Health Promotion for Childhood Obesity: An Approach Based on Self-Tracking of Data.

At present, obesity and overweight are a global health epidemic. Traditional interventions for promoting healthy habits do not appear to be effective. However, emerging technological solutions based on wearables and mobile devices can be useful in promoting healthy habits. These applications generate a considerable amount of tracked activity data. Consequently, our approach is based on the quantified-self model for recommending healthy activities. Gamification can also be used as a mechanism to enhance personalization, increasing user motivation. This paper describes the quantified-self model and its data sources, the activity recommender system, and the PROVITAO App user experience model. Furthermore, it presents the results of a gamified program applied for three years in children with obesity and the process of evaluating the quantified-self model with experts. Positive outcomes were obtained in children's medical parameters and health habits.

Serious games for rehabilitation: Gestural interaction in personalized gamified exercises through a recommender system.

One of the principal problems of rehabilitation is that therapy sessions can be boring due the repetition of exercises. Serious games, and in particular exergames in rehabilitation, can motivate, engage and increase patients' adherence to their treatment. Also, the automatic personalization of exercises to each patient can help therapists. Thus, the main objective of this work is to build an intelligent exergame-based rehabilitation system consisting of a platform with an exergame player and a designer tool. The intelligent platform includes a recommender system which analyzes user interactions, along with the user's history, to select new gamified exercises for the user. The main contributions of this paper focus, first, on defining a recommender system based on different difficulty levels and user skills. The recommender system offers the ability to provide the user with a personalized game mode based on his own history and preferences. The results of a triple validation with experts, users and rehabilitation center professionals reveal a positive impact on gestural interaction and rehabilitation uses. Also, different methods are presented for testing the rehabilitation recommender system.

Improving Cognitive Visual-Motor Abilities in Individuals with Down Syndrome.

Down syndrome causes a reduction in cognitive abilities, with visual-motor skills being particularly affected. In this work, we have focused on this skill in order to stimulate better learning. The proposal relies on stimulating the cognitive visual-motor skills of individuals with Down Syndrome (DS) using exercises with a gestural interaction platform based on the KINECT sensor named TANGO:H, the goal being to improve them. To validate the proposal, an experimental single-case study method was designed using two groups: a control group and an experimental one, with similar cognitive ages. Didactic exercises were provided to the experimental group using visual cognitive stimulation. These exercises were created on the TANGO:H Designer, a platform that was designed for gestural interaction using the KINECT sensor. As a result, TANGO:H allows for visual-motor cognitive stimulation through the movement of hands, arms, feet and head. The "Illinois Test of Psycholinguistic Abilities (ITPA)" was applied to both groups as a pre-test and post-test in its four reference sections: visual comprehension, visual-motor sequential memory, visual association, and visual integration. Two checks were made, one using the longitudinal comparison of the pre-test/post-test of the experimental group, and another that relied on comparing the difference of the means of the pre-test/post-test. We also used an observational methodology for the working sessions from the experimental group. Although the statistical results do not show significant differences between the two groups, the results of the observations exhibited an improvement in visual-motor cognitive skills.