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Programming online judges (POJs) have been increasingly used in CS1 classes, as they allow students to practice and get quick feedback. For instructors, it is a useful tool for creating assignments and exams. However, selecting problems in POJs is time consuming. First, problems are generally not organized based on topics covered in the CS1 syllabus. Second, assessing whether problems require similar effort to be completed and map onto the same topic is a subjective and expert-dependent task. The difficulty increases if the instructor must create variations of these assessments, e.g., to avoid plagiarism. Thus, here, we research how to support CS1 instructors in the task of selecting problems, to compose one-size-fits-all or personalized assignments/exams. Our solution is to propose a novel intelligent recommender system, based on a fine-grained data-driven analysis of the students' effort on solving problems in the integrated development environment of a POJ system, and automatic detection of topics for CS1 problems, based on problem descriptions. Data collected from 2714 students are processed to support, via our artificial intelligence (AI) method recommendations, the instructors' decision-making process. We evaluated our method against the state of the art in a simple blind experiment with CS1 instructors (N = 35). Results show that our recommendations are 88% accurate, surpassing our baseline (p = 0.05). Finally, our work paves the way for novel POJ smart learning environments, wherein instructors define learning tasks (assignments/exams) supported by AI.

Emotions can influence cognitive development and are key elements to the teaching-learning process. Positive emotions (e.g., engagement) can improve the ability to solve problems, store information, and make decisions. On the other hand, negative emotions (e.g., boredom) reduce the capacity to process information at a deeper level, preventing learning to become effective. Therefore, students' emotions must be regulated to hinder negative and to promote positive emotions during learning. To support the choice of the best intervention to regulate individual emotions, this article proposes an algorithm based on simulated data considering different individual performances in solving Algebra exercises. The results suggest that the proposed model has high success rates (over 90%) in the choice of interventions and may be applied in real scenarios.

Studies in the literature reported several positive benefits provided by the use of technology in online education, especially in the gamified tutoring system. However, despite the benefits of intelligent tutoring systems, recent studies indicate the presence of a gender gap not considered in the construction of the attributes present in the gamified tutoring system. To investigate this impact by observing users' behavioral changes in gamified online educational environments, the present study aims to investigate the effects of the stereotype threats using a quantitative experiment with a Factorial Design in three gamified environments (stereotypical male version, stereotypical female version and control environment). Was conducted an experiment with 150 individuals (high school and undergraduate students) without considering age, ethnicity, or social class. The results show that the participants allocated to the male learning environment present an increase in aggressiveness level. Furthermore, the results also show the stereotypical male and female learning environments increased the participants' performance level. Another finding was that the threatening condition provided a significant increase in the participants' flow level among males subjected to a threatening condition, which did not manifest in the case of females. In addition, this study also observed the effect of the stereotype threat on men and women in the threatening condition by division in the 34-year age group, resulting in a significant increase in the level of flow among men. This study showed previous results show that the gamified environment influences psychological variables as aggressiveness, intellectual performance, and flow level, they raise questions about the direction of these changes and the impact they may have on users' usability and performance in these systems.

Gamification has been widely used to design better educational systems aiming to increase students' concentration, motivation, engagement, flow experience, and others positive experiences. With advances in research on gamification in education, over the past few years, many studies have highlighted the need to tailor the gamification design properties to match individual students' needs, characteristics and preferences. Thus, different studies have been conducted to personalize the gamification in education. However, the results are still contradictory and need to be better understood to advance this field. To provide a complete understanding of this research domain, we conducted a systematic literature review to summarize the results and discussions on studies that cover the field of tailored gamified education. Following a systematic process, we analysed 2108 studies and identified 19 studies to answer our research questions. The results indicate that most of the studies only consider students' gamer types to tailor the systems, and most of the experiments do not provide sufficient statistical evidence, especially regarding learning performance using tailored gamified systems. Based on the results, we also provided an agenda with different challenges, opportunities, and research directions to improve the literature on tailored gamification in education. Our study contributes to the field of gamification design in education.

Students face several difficulties in introductory programming courses (CS1), often leading to high dropout rates, student demotivation, and lack of interest. The literature has indicated that the adequate use of gamification might improve learning in several domains, including CS1. However, the understanding of which (and how) factors influence gamification's success, especially for CS1 education, is lacking. Thus, there is a clear need to shed light on pre-determinants of gamification's impact. To tackle this gap, we investigate how user and contextual factors influence gamification's effect on CS1 students through a quasi-experimental retrospective study (N=399), based on a between-subject design (conditions: gamified or non-gamified) in terms of final grade (academic achievement) and the number of programming assignments completed in an educational system (i.e., how much they practiced). Then, we evaluate whether and how user and contextual characteristics (e.g., age, gender, major, programming experience, working situation, internet access, and computer access/sharing) moderate that effect. Our findings indicate that gamification amplified to some extent the impact of practicing. Overall, students practicing in the gamified version presented higher academic achievement than those practicing the same amount in the non-gamified version. Intriguingly, those in the gamified version that practiced much more extensively than the average showed lower academic achievements than those who practiced comparable amounts in the non-gamified version. Furthermore, our results reveal gender as the only statistically significant moderator of gamification's effect: in our data, it was positive for females but non-significant for males. These findings suggest which (and how) personal and contextual factors moderate gamification's effects, indicate the need to further understand and examine context's role, and show that gamification must be cautiously designed to prevent students from playing instead of learning.

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.

Autism is a neurodevelopmental disorder with deficits in communication, social interaction, and restricted and repetitive patterns of behavior. Due to these restrictions, one can cite the literacy process as a learning problem for people with autism. Although psychological interventions have been used to mitigate this problem, technology plays an essential role in this public. Recent studies show that children with autism demonstrate interest and best performance results when in contact with technology. Nevertheless, it is still a challenge to find studies that gather technological approaches to help the improvement of literacy skills. Based on this premise, this work focus on identifying technological approaches used to aid in the literacy process of children with autism. To achieve our goal, we conducted a systematic mapping where six studies were analyzed to answer our research questions. We identified several computational contributions, like software, desktop, and web applications that benefit the enhancement of the learning process of children with autism.

There are many claims that gamification (i.e., using game elements outside games) impact decreases over time (i.e., the novelty effect). Most studies analyzing this effect focused on extrinsic game elements, while fictional and collaborative competition have been recently recommended. Additionally, to the best of our knowledge, no long-term research has been carried out with STEM learners from introductory programming courses (CS1), a context that demands encouraging practice and mitigating motivation throughout the semester. Therefore, the main goal of this work is to better understand how the impact of a gamification design, featuring fictional and competitive-collaborative elements, changes over a 14-week period of time, when applied to CS1 courses taken by STEM students (N = 756). In an ecological setting, we followed a 2x7 quasi-experimental design, where Brazilian STEM students completed assignments in either a gamified or non-gamified version of the same system, which provided the measures (number of attempts, usage time, and system access) to assess user behavior at seven points in time. Results indicate changes in gamification's impact that appear to follow a U-shaped pattern. Supporting the novelty effect, the gamification's effect started to decrease after four weeks, decrease that lasted between two to six weeks. Interestingly, the gamification's impact shifted to an uptrend between six and 10 weeks after the start of the intervention, partially recovering its contribution naturally. Thus, we found empirical evidence supporting that gamification likely suffers from the novelty effect, but also benefits from the familiarization effect, which contributes to an overall positive impact on students. These findings may provide some guidelines to inform practitioners about how long the initial contributions of gamification last, and how long they take to recover after some reduction in benefits. It can also help researchers to realize when to apply/evaluate interventions that use gamification by taking into consideration the novelty effect and, thereby, better understand the real impact of gamification on students' behavior in the long run.

Gamification refers to the attempt to transform different kinds of systems to be able to better invoke positive experiences such as the flow state. However, the ability of such intervention to invoke flow state is commonly believed to depend on several moderating factors including the user's traits. Currently, there is a dearth of research on the effect of user traits on the results of gamification. Gamer types (personality traits related to gaming styles and preferences) are considered some of the most relevant factors affecting the individual's susceptibility to gamification. Therefore, in this study we investigate how gamer types from the BrainHex taxonomy (achiever, conqueror, daredevil, mastermind, seeker, socializer and survivor) moderate the effects of personalized/non-personalized gamification on users' flow experience (challenge-skill balance, merging of action and awareness, clear goals, feedback, concentration, control, loss of self-consciousness and "autotelic" experience), enjoyment, perception of gamification and motivation. We conducted a mixed factorial within-subject experiment involving 121 elementary school students comparing a personalized version against a non-personalized version of a gamified education system. There were no main effects between personalization and students' flow experience, perception of gamification and motivation, and enjoyment. Our results also indicate patterns of characteristics that can lead students to the high flow experience (e.g., those who prefer to play multiplayer have a high flow experience in both personalized and non-personalized versions). Based on our results, we provided recommendations to advance the design of gamifed educational systems.

The application of the collective intelligence (CI) concept has yielded significant results in many knowledge areas and it has substantial potential to yield results in the educational context. In view of distance education and online educational technologies, the use of CI has enormous potential to improve collaboration, social learning, and problem solving. However, there is still not a clear understanding of how CI has been used in combination with online educational technologies, due to a lack of secondary studies in this context. Thus, in this article, we conducted a systematic literature review with the objectives of determining how CI has been used in online learning environments and identifying the benefits and/or the difficulties that are encountered in the process of its use. We have analyzed 354 studies and only 30 have met our inclusion criteria. The main contributions to online learning of these 30 studies were categorized and discussed. Our results have shown that CI enables the exploration. of the potential of collaboration and collaborative learning in social environments to create more diverse educational contents and to improve educational aspects using group intelligence. Moreover, approximately 74% of the studies presented positive evidence of learning benefits. However, approximately 40% of the studies also presented positive evidence of implementation difficulties. These and other results presented in this article demonstrate that more research is necessary to explore the potential of CI in online learning technologies.

The flow experience (i.e., challenge-skill balance, action-awareness merging, clear goals, unambiguous feedback, concentration, sense of control, loss of self-consciousness, transformation of time, and "autotelic" experience) is an experience highly related to the learning experience. One of the current challenges is to identify whether students are managing to achieve this experience in educational systems. The methods currently used to identify students' flow experience are based on self-reports or equipment (e.g., eye trackers or electroencephalograms). The main problem with these methods is the high cost of the equipment and the impossibility of applying them massively. To address this challenge, we used behavior data logs produced by students during the use of a gamified educational system to predict the students' flow experience. Through a data-driven study (N = 23) using structural equation modeling, we identified possibilities to predict the students' flow experience through the speed of students' actions. With this initial study, we advance the literature, especially contributing to the field of student experience analysis, by bringing insights showing how to step towards automatic students' flow experience identification in gamified educational systems.

Tools for automatic grading programming assignments, also known as Online Judges, have been widely used to support computer science (CS) courses. Nevertheless, few studies have used these tools to acquire and analyse interaction data to better understand the students' performance and behaviours, often due to data availability or inadequate granularity. To address this problem, we propose an Online Judge called CodeBench, which allows for fine-grained data collection of student interactions, at the level of, eg, keystrokes, number of submissions, and grades. We deployed CodeBench for 3 years (2016-18) and collected data from 2058 students from 16 introductory computer science (CS1) courses, on which we have carried out fine-grained learning analytics, towards early detection of effective/ineffective behaviours regarding learning CS concepts. Results extract clear behavioural classes of CS1 students, significantly differentiated both semantically and statistically, enabling us to better explain how student behaviours during programming have influenced learning outcomes. Finally, we also identify behaviours that can guide novice students to improve their learning performance, which can be used for interventions. We believe this work is a step forward towards enhancing Online Judges and helping teachers and students improve their CS1 teaching/learning practices.

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.

Interactive or Dynamic Geometry System (DGS) is a tool that help to teach and learn geometry using a computer-based interactive environment. Traditionally, the interaction with DGS is based on keyboard and mouse events where the functionalities are accessed using a menu of icons. Nevertheless, recent findings suggest that such a traditional model of interaction has a steep learning curve and is inadequate to develop DGS for devices with multi-touch screens. Thus, we propose a new interaction model for DGS based on a gesture dictionary which enables the construction and manipulation of geometric objects without the need of accessing a menu of icons. The dictionary is divided into three types: (i) kernel gestures: which are the basis for defining gestures; (ii) navigation gestures: related to the manipulation and editing of geometric objects; and (iii) basic gestures of construction: to construct geometric objects. To validate our interaction model, a DGS for mobile device, GeoTouch, has been developed using the proposed dictionary. Usability tests were performed comparing GeoTouch with three other DGS for mobile devices. The results indicate that the GeoTouch interface has fewer usability problems than the other three DGS software and it is easier to learn and interact with.

"Flow experience" is a term used to describe the state of being fully immersed in what you are doing. The Flow State Scale (FSS-2) was developed to assess how people feel when they are in the flow state while participating in certain sports activities. The goal of this study was to obtain a short adapted version of the FSS-2 for the Brazilian-Portuguese language and for general activities (FSS-BR-S). To do this, we translated it both ways (forwards and backwards) and verified that the translation was accurate. Methods: After getting answers from 396 Brazilian participants, we performed (1) the construct validity of the FSS-BR-S and (2) the psychometric item quality analysis. The confirmatory factorial analysis shows that a FSS-BR-S factorial model is the best fit for the data (χ2 = 44.36, p =.023, df = 27, χ2/df = 1.64, CFI = 0.99, TLI = 0.98, and RMSEA = 0.04). Reliability tests done in this structure show that the FSS-BR-S (which only has nine items) has good internal consistency. The item quality analysis reveals that its difficulty and differentiating parameters are good for estimating the overall flow state. The test information curve for the short version demonstrates that it is very useful for estimating the flow states of each disposition. Discussion and Conclusions: Based on these findings, we can conclude that the FSS-BR-S has demonstrated sufficient validity to be used with Brazilians. [ABSTRACT FROM AUTHOR]

Flow is the affective state in which a learner is so engaged and involved in an activity that nothing else seems to matter. In this sense, to help students in the skill development and knowledge acquisition (referred to as learners' growth process) under optimal conditions, the instructional designers should create learning scenarios that favor the learner's flow. One-way to obtain these scenarios is to incorporate the conditions postulated by the Flow Theory in the context of the instructional design process. However, little attention has been drawn to this integration, and how to apply the Flow Theory based on learning theories that provide theoretical justifications during the instructional design process. Thus, in this paper, we propose a framework to integrate the learner's growth process and the Flow Theory. It provides adequate support for the instructional design of learning scenarios that lead and maintain learners in flow state. We demonstrate the usefulness of this framework by presenting an application that helps designers to search and select learning objects that have the potential to maintain the learner's flow in a learning scenario.

When the goal of group activities is to support long-term learning, the task of designing well-thought-out collaborative learning (CL) scenarios is an important key to success. To help students adequately acquire and develop their knowledge and skills, a teacher can plan a scenario that increases the probability for learning to occur. Such a scenario defines pedagogically sound structures that prevent off-task behavior and engage students in more meaningful interactions. The main difficulty in designing effective CL scenarios is transforming the teacher's intentions into elements that constitute the learning scenario. This problem is frequently observed when novice teachers attempt to improve the quality of learning and instruction by blending collaborative activities with individual activities without careful planning. With the goal of helping teachers in planning collaborative scenarios, we have developed an intelligent authoring tool referred to as CHOCOLATO using Semantic Web technologies (e.g. ontologies) in order to represent knowledge about different pedagogies and practices related to collaboration. Through the use of this knowledge, CHOCOLATO can provide intelligent guidance that helps teachers to create theory-based CL scenarios which has proven to be effective in a variety of situations. We evaluated it by conducting two experiments. We were interested in verifying whether the recommendations given by CHOCOLATO help novice teachers to design pedagogically sound CL activities, and if these activities help students to learn collaboratively in real classroom settings. The first experiment had the participation of 58 pre-service teachers that created CL scenarios with and without our authoring tool and the second experiment was carried out in a Brazilian public school together with 218 students. The results suggest that the guidance provided by CHOCOLATO do help novice teachers plan, understand and share CL scenarios more easily. They also suggest that the continuous utilization of well-designed theory-based CL activities create favorable conditions for students (particularly less knowledgeable ones) to improve their overall performance throughout the school year. (Contains 11 figures.)

Introduction: There is a global effort to address the school dropout phenomenon. The urgency to act on it comes from the harmful evidence that school dropout has on societal and individual levels. Early Warning Systems (EWS) for school dropout at-risk student identification have been developed to anticipate and help schools have a better chance of acting on it. However, several studies point to a doubt that Correct EWS may come too late because they use only publicly available and general student and school information. We hypothesize that having a tool to assess more subjective and inter-relational factors would help anticipate where and when to act to prevent school dropout. This study aimed to develop a multidimensional measure for assessing relational factors for predicting school dropout (SD) risk in the Brazilian context. Methods: We performed several procedures, including (a) the specialized literature review, (b) the item development of the Relational Factors for the Risk of School Dropout Scale (IAFREE in Portuguese), (c) the content validity analysis, (d) a pilot study, and (e) the administration of the IAFREE to a large Brazilian sample of high school and middle school students (N = 15,924). Results: After the theoretical steps, we found content validity for five relational dimensions for SD (Student-School, Student-School Professionals, Student-Family, Student-Community, and Student-Student) that include 12 facets of risk factors. At the empirical stage, confirmatory analysis corroborated the proposed theoretical model with 12 first-order risk factors and 5 s-order dimensions (36 items). Further, through the Item Response Theory analysis, we assessed the individual item parameters of the items, providing a brief measure without losing psychometric quality (IAFREE-12). Discussion: We discuss how this model may fill gaps in Correct EWS models and how to advance it. The IAFREE is a good measure for scholars investigating the risk of SD. These results are important for implementing an early warning system for SD that looks into the complexity of the school dropout phenomenon. [ABSTRACT FROM AUTHOR]