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Personalized gamification explores user models to tailor gamification designs to mitigate cases wherein the one-size-fits-all approach ineffectively improves learning outcomes. The tailoring process should simultaneously consider user and contextual characteristics (e.g., activity to be done and geographic location), which leads to several combinations to tailor. Consequently, tools for automating gamification personalization are needed. However, which of those characteristics are relevant and how to do such tailoring are open questions. Furthermore, the required automating tools are lacking. We tackled these problems in two steps. First, we conducted an exploratory study, collecting participants' opinions on the game elements they consider the most useful for different learning activity types (LAT) via survey. Then, we modeled opinions through Conditional Decision Trees to address the aforementioned tailoring process. Second, as a product of the first step, we implemented a recommender system that suggests personalized gamification designs (which game elements to use), addressing the problem of automating gamification personalization. Our findings present empirical evidence that LAT, geographic locations, and other user characteristics affect users' preferences, enable defining gamification designs tailored to user and contextual features simultaneously, and provide technological aid for those interested in designing personalized gamification. The main implications are that demographics, game-related characteristics, geographic location, and LAT to be done, as well as the interaction between different kinds of information (user and contextual characteristics), should be considered in defining gamification designs and that personalizing gamification designs can be improved with aid from our recommender system. [ABSTRACT FROM AUTHOR]

One of the main goals of gamification in educational settings is to increase student motivation and engagement. To facilitate the design of gamified educational systems, in recent years, studies have proposed various approaches (e.g., methodologies, frameworks and models). One of the main problems, however, is that most of these approaches are theoretical, and do not provide a proof-of-concept. This paper advances the state of the art by providing a practical way to help implement this kind of system. In this study, we present, for the first time, how one can apply gamification elements in a learning system using the Design Sprint method, to guide designers and developers on replicating this process. Additionally, as starting point, we use a taxonomy composed of 21 game elements, proposed to be used within learning environments, organised into five game element categories, according to their goal/usage. Our main contribution is to present how to systematically implement the gamification elements focused on educational ends, which is of special value to practitioners, designers and developers. [ABSTRACT FROM AUTHOR]

Due to the growing interest in e-learning as an important process of teaching and learning, new mechanisms to evaluate its pedagogical effectiveness are necessaries. This review describes the scenario of 20 years corresponding to data mining research where the context is the e-learning and the main subject is the evaluation aspect, which is considered a latent problem within this environment. Our goal is to provide an unexplored review of EDM research of the teaching and learning process considering the educational perspective. In order to obtain a more wide and complete review, the search of the bibliographic material was realized with the terms “data mining” and “education” which resulted in 525 articles. As exclusion criterion, articles that did not emphasize the improvement of the teaching and learning process were discarded, resulting in 72 articles. As result of our review, the analyzed papers show that the researches in EDM expanded into several areas and themes, for example, oriented studies on interactions between the educational actors, monitoring, and evaluation of teaching-learning process, administrators’ evaluation about the adopted pedagogical actions, learning risks, and recommendation and recovery of educational media. The review allowed to present perspectives, identify trends and observe potential research directions, such as behavioral research, collaboration, interaction and performance in the development of teaching-learning activities. [ABSTRACT FROM AUTHOR]

Flow Theory has been increasingly applied to Computers and Education to address several topics within this field (e.g., motivation, engagement, learning performance and so on). At the same time, in comparison with other recent theories related to Computers and Education, (e.g., Self-Determination Theory, S-Curve Theory, Intrinsic motivation, etc.), is a young topic, with different open research questions. Additionally, the Computers and Education community still lacks a comprehensive understanding of how Flow Theory is used in the area. Thus, this paper presents a Systematic Literature Review aiming to identify how students' flow state are measured during learning activities, how such activities are designed, which are the flow models used in Computers and Education and which are the main benefits of being in the flow state for the students. The main findings of this work are: (1) there is positive evidence about the benefits of applying Flow Theory in Computers and Education, especially, for increasing students' learning, to generate students' satisfaction, and to enable exploratory behavior; (2) the majority of studies use questionnaires to manual identify students' flow state; (3) a great diversity of flow state scales have been used; (4) the majority of studies are not designing activities for leading students to the flow state, and (5) the Csikszentmihalyi' flow model is more used. Finally, we conclude this work by showing some promising and interesting research opportunities that are underexplored in current research and practice. [ABSTRACT FROM AUTHOR]

Applying artificial intelligence in education is relevant to addressing the current educational crises. Many available solutions apply Convolutional Neural Networks (CNNs) to help improve educational outcomes. Therefore, a series of works have been developed integrating techniques in different educational contexts, for instance, in online teaching practices. Given the various studies and the relevance of CNNs for educational applications, this paper presents a systematic literature review to discuss the state-of-the-art. We reviewed 133 papers from the IEEE Xplore, ACM Digital Library, and Scopus databases. Based on our revision, we discuss characteristics of studies such as publication venues, educational context, datasets, types of CNNs models, and performance of models. We evidence that the literature regarding CNNs still misses more studies discussing educational problems faced by Global South students, considering both teaching and learning perspectives. Such a population cannot be neglected during experiments due to specific educational weaknesses (for example, basic skills) demanding personalized solutions. • A systematic literature review of 133 published papers. • China covered most publications, followed by India. • There is a research gap regarding using CNNs in some regions of the Global South. • The main educational context with CNN applications is students' performance. [ABSTRACT FROM AUTHOR]