This study explores the integration of Augmented Intelligence (AuI) in Intelligent Tutoring Systems (ITS) to address challenges in Artificial Intelligence in Education (AIED), including teacher involvement, AI reliability, and resource accessibility. We present MathAIde, an ITS that uses computer vision and AI to correct mathematics exercises from student work photos and provide feedback. The system was designed through a collaborative process involving brainstorming with teachers, high-fidelity prototyping, A/B testing, and a real-world case study. Findings emphasize the importance of a teacher-centered, user-driven approach, where AI suggests remediation alternatives while teachers retain decision-making. Results highlight efficiency, usability, and adoption potential in classroom contexts, particularly in resource-limited environments. The study contributes practical insights into designing ITSs that balance user needs and technological feasibility, while advancing AIED research by demonstrating the effectiveness of a mixed-methods, user-centered approach to implementing AuI in educational technologies.

This study evaluates the usability of Alfaba, a multi-sensory educational tool specifically designed to support literacy development for children and young people with Down Syndrome (DS). Alfaba integrates tactile, visual, and auditory feedback, aiming to facilitate letter recognition, phoneme association, and word construction in a manner aligned with the cognitive and motor needs of this population. A usability test was conducted with seven participants aged 7 to 17, focusing on their interactions with Alfaba to assess accessibility, engagement, and areas for improvement. The results highlight that Alfaba's tangible design and real-time feedback encourage independent learning, support error recognition, and foster motor skill development, making it a valuable literacy tool for individuals with DS. Additionally, the study identifies opportunities for enhancement, such as incorporating touchscreen capabilities, gamified elements, and customizable word groups to further engage users. Alfaba's adaptability to the Brazilian Portuguese phonetic structure also positions it as a culturally inclusive tool that addresses the specific phonological needs of Portuguese-speaking children with DS. This study contributes to the field of inclusive educational technology by offering design recommendations that can enhance Alfaba's impact on literacy and proposes directions for future research to further refine and assess its efficacy in controlled educational settings.

Understanding users and consequent personalization opportunities have become a major area of interest in gamification and UX research. Currently, personalization is mainly based on player typologies, which might give a partial picture of the plethora of user attributes. Addressing this challenge, in this study, we investigate the connections of the Hexad gamification user types, demographic factors, and gaming habits to understand how different user factors are related. Our results indicated significant but weak associations between user types and demographic factors and no significant association with gaming frequency-related factors. These results suggest that researchers and designers might need to consider more than the dominant factors to create personalized environments. We also provide exploratory suggestions on possible strategies to personalize gamification based on Hexad and other user factors. Our study contributes to the fields of user modeling and gamification, providing new insights into how different user characteristics are related while opening space for the conduction of new studies in the field.

The Internet of Things has become a comprehensive concept used in different contexts (e.g., smart cities, security, and education). In particular, in education, a vast literature has used the Internet of Things to promote smart education. However, there is still no overview of how the Internet of Things has been used to promote smart education. We addressed this challenge by conducting a systematic literature review on the use of the Internet of Things to promote smart education. Through a systematic literature review, we answered the following research questions: i) Which smart objects collect data from the physical world? ii) What types of information are collected? And, iii) what are the goals of collecting this data? Our analysis reveals that i) the most common smart objects in educational environments include sensors, cameras, ID tags, and wearable devices; ii) these devices collect data in twelve main categories, which include everything from microclimatic information to details of the emotional and behavioral state of the actors involved; iii) the purpose of this collection is to provide a more effective and personalized educational experience to individual needs. Our findings play a crucial role in educational technologies, establishing a foundation for future research endeavors and providing valuable insights into the use of the Internet of Things to promote smart education.

Flow experience is one of the most ambitious targets of any user interface designer. However, it has remained elusive to evaluate how well user interfaces give rise to flow experience outside conducting invasive self-reporting-based questionnaires, which remove the users from the flow experience and can't be massively applied. At the same time, otherwise, well-built systems do track the behaviour of users on the interface, and therefore, user behaviour data could act as a reliable proxy for assessing the experience of users. Currently, there is little empirical research or data about which indices of user behaviours might correspond with having a flow experience as well as the different psychological constituents of the flow experience. Therefore, facing the challenge of using users' behaviour data to model users' experience, we investigated the associations between users' behaviour data (e.g. mouse clicks, activity time in the system, and average response time) and their self-reported flow experience by using data mining (i.e. associations rules) analysing data from 204 subjects. Results demonstrate that the speed of users' actions negatively affects the flow experience antecedents while also positively affecting the loss of self-consciousness. Our study advances the literature, providing insights to identify users' flow experience through behaviour data.

Systematic reviews (SRs) are vital to gathering and structuring knowledge, yet descriptions of their procedures are often inadequate. In human–computer interaction (HCI), SRs are still uncommon but gaining momentum, which prompted us to explore how SRs are reported at CHI—the flagship HCI conference venue. To assess the reporting quality of CHI reviews that aim for a systematic approach, we conducted an umbrella review and applied reporting guidelines for SRs (PRISMA and ENTREQ) to our corpus. We contribute the first exploration of how well SRs at CHI meet guidelines for reporting quality, showcasing strategies for improvement in reporting and conducting SRs especially in the domains of appraisal, synthesis, and documentation (i.e., protocol development). Finally, we present guiding questions for HCI researchers and practitioners for reporting SRs, as well as suggestions for best practices.

Textual cohesion is an essential aspect of a formally written text, related to linguistic mechanisms that connect elements such as words, sentences, and paragraphs. Several studies have proposed approaches to estimate textual cohesion in essays automatically. There is limited research that aims to study the extent to which the use of machine learning approaches can predict the textual cohesion of essays written in different languages (not just English). This paper reports on the findings of a study that aimed to propose and evaluate approaches that automatically estimate the cohesion of essays in Portuguese and English. The study proposed regression-based models grounded in conventional feature-based machine learning methods and deep learning-based pre-trained language models. The study also examined the explainability of automated approaches to scrutinize their predictions. We analyzed two datasets composed of 4,570 (Portuguese) and 7,101 (English) essays. The results demonstrate that a deep learning-based model achieved the best performance on both datasets with a moderate Pearson correlation with human-rated cohesion scores. However, the explainability of the automatic cohesion estimations based on conventional machine learning models offered a stronger potential than that of the deep learning model.

Modeling users' experience in gameful systems is one of the main contemporary challenges in the field of human-computer interaction. One of the most desired and complex experiences to be identified is 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). Facing this challenge, we conducted a quantitative study (N = 313) based on structural equation modeling, aiming to model and predict the users' flow experience through their behavior (represented by performance-related, interaction with gamification, as well as the time they take in different actions) in the system. The main results indicate that i) gamification (i.e., doing well in points, badges, and leader-board) was positively related to users' experience of good challenge-skill balance, ii) whereas it was negatively related to users' concentration. Thirdly iii) user performance was positively related to users' concentration. However, overall, the results indicate that while associations between user behavior and flow experience could be established, there remains future work to be done to fully explain user flow experience while using a system. Our study contributes to the fields of human-computer interaction, gamification, and educational technologies, especially through insights related to modeling and predicting flow experiences in gameful systems through behavior data.

In recent decades, several studies have suggested and validated user models (e.g., Bartle, and Hexad) to represent different user profiles in games and gamified environments. However, when applying these user models in practice (e.g., to personalize gamification), several studies reported contradictory outcomes. Recently, some studies outlined that one of the possible explanations for these contradictory findings is that people can present changes in their user profiles over time. In this study (N = 118), we present an analysis of the consistency of gamification user orientations after six months of the initial identification, by analyzing the association between user orientations in the first and second data collection. Overall, our results corroborate prior research demonstrating that user orientations can not be considered stable over time and also that the strongest tendency of the users might not be sufficient to determine how users change. Furthermore, we were able to identify that some user orientations can be more stable than others and model some relationships between their profiles after six months. Based on the results, we indicate a research agenda that can further the knowledge about the topic, as well as indicate a set of suggestions on how to model user profiles based on our results.

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.

User modeling in gamified educational systems is a contemporary challenge. In particular, modeling the students' flow experience (i.e., challenge-skill balance, action-awareness merging, clear goals, unambiguous feedback, total concentration on the task at hand, sense of control, loss of self-consciousness, transformation of time, and autotelic experience) during a gamified system usage is highly challenging. It is because measurement' instruments usually are invasive, removing the users from the flow experience and/or cannot be applied massively (e.g., participant observation, questionnaires or electroencephalogram). We faced this challenge by conducting a data-driven study (N = 23), where we used a robust statistical method (i.e., partial least squares path modeling) to model the students' flow experience, based on their interaction data (e.g., number of mouse clicks) in a gamified educational system. The main results indicate a relationship between the interaction logs and four flow experience dimensions. Our finds contribute to the area of gamified educational systems, through the students' flow experience modeling. Finally, based on our results, we also provided a series of recommendations for future studies.

Programming is a complex, not trivial to learn and teach task, which gamification can facilitate. However, how gamification affects learning and the influence of context-related aspects on that effect demand research to better understand how and to whom gamification enhances programming learning. Therefore, we conducted an experimental study analyzing how gamification worked and the role of context-related aspects in terms of intervention duration and learners' familiarity with programming (i.e., the task's topic). It was a six-week study with 19 undergraduate students from an Algorithms class that measured their learning gains, intrinsic motivation, and number of completed quizzes. Mainly, we found gamification affected learning via intrinsic motivation, effect that depended on intervention duration and learners' familiarity with programming. That is, intrinsic motivation strongly predicted learning gains and gamification's effect on intrinsic motivation changed over time, decreasing from positive to negative as learners had less familiarity with programming. Thus, showing gamification can positively impact programming learning by improving students' intrinsic motivation, although that effect changes over time depending on one's previous familiarity with programming.

Personalization of gamification is an alternative to overcome the shortcomings of the one-size-fits-all approach, but the few empirical studies analyzing its effects do not provide conclusive results. While many user and contextual information affect gamified experiences, prior personalized gamification research focused on a single user characteristic/dimension. Therefore, we hypothesize if a multidimensional approach for personalized gamification, considering multiple (user and contextual) information, can improve user motivation when compared to the traditional implementation of gamification. In this paper, we test that hypothesis through a mixed-methods sequential explanatory study. First, 26 participants completed two assessments using one of the two gamification designs and self-reported their motivations through the Situational Motivation Scale. Then, we conducted semi-structured interviews to understand learners' subjective experiences during these assessments. As result, the students using the personalized design were more motivated than those using the one-size-fits-all approach regarding intrinsic motivation and identified regulation. Furthermore, we found the personalized design featured game elements suitable to users' preferences, being perceived as motivating and need-supporting. Thus, informing i) practitioners on the use of a strategy for personalizing gamified educational systems that is likely to improve students' motivations, compared to OSFA gamification, and ii) researchers on the potential of multidimensional personalization to improve single-dimension strategies. For transparency, dataset and analysis procedures are available at https://osf.io/grzhp.

Abstract Gamification has been discussed as a standout approach to improve user experience, with different studies showing that users can have different preferences over game elements according to their user types. However, relatively less is known how different kinds of users may react to different types of gamification. Therefore, in this study ( $$N=331$$ N = 331 ) we investigate how user orientation (Achiever, Disruptor, Free Spirit, Philanthropist, Player, and Socializer) is associated with the preference for and perceived sense of accomplishment from different gamification designs. Beyond singular associations between the user orientation and the gamification designs, the findings indicate no comprehensive and consistent patterns of associations. From the six user orientations, five presented significant associations: Socializer orientation was positively associated with Social, Fictional, and Personal designs, while negatively associated with Performance design; Player orientation was positively associated with Social (Accomplishment), Personal, and Ecological designs, while negatively associated with the Social design (Preference); Disruptor orientation was positively associated with Social design; Achiever orientation was positively associated with Performance and Social designs; and Free Spirit orientation was negatively associated with Social design. Based on the results, we provide recommendations on how to personalize gamified systems and set further research trajectories on personalized gamification.

Scripted Collaborative Learning (CL) promotes meaningful interactions among students. Unfortunately, in some situations, the use of scripts causes motivational problems which in turn may lead to negative learning outcomes. In the last years, gamification has emerged as a promising technique to motivate and engage students towards learning process. However, gamification requires personalization based on knowledge about game design, and their impact on the students' motivation and learning outcomes. To address these issues, we proposed the formalization of an ontology to support the gamification of CL scenarios in which the game elements are tailored for each situation. These CL scenarios are known as ont-gamified CL sessions, and this paper presents the results of one empirical study conducted to evaluate their effects on the participants' intrinsic motivation and learning outcomes. This study was carried out as quasi-experiment with 62 undergraduate Brazilian students who participated either in non-gamified or ont-gamified CL sessions. Findings indicate that the intrinsic motivation was better in ont-gamified CL sessions, and that there was no significant difference on learning outcomes. Based on these results, we concluded that our approach is likely to deal with motivational problems in scripted CL.

Gamification applied to education studies are focusing to encourage students to perform specific tasks, however many of these studies are still inconclusive about how much gamification can influence engagement. Also, the frameworks used to apply gamification in those systems are mainly structural (e.g. scoring and ranking systems) rather than content frameworks (where the game elements are applied to the content). Therefore, this paper aims at creating a narrative definition exclusively for gamification purposes. First, we developed an empirical research starting with a literature review of the narrative concept in other medias, such as games. Then, we mapped these definitions into features and crossed the similarities and differences across them, to find a common ground that could be applied in gamification contexts. Our results show that the narrative element definition to use in gamification contexts best drifts from the games ones, however its definition is unique to the point it could be isolated and used in future frameworks. We also found that the characteristics of this concept resembles some of User Experience.

Continuing education online has been steadily increasing worldwide and its students' profiles tend to be different from those in other formal modalities. Hence, it is relevant to understand the behavior of these students along their course trajectories. In this work, we investigate a specialization online course involving hearing healthcare professionals (n=96), to discover different navigation strategies and the factors which may influence it. To that end, we applied sequence analysis techniques to interpret different navigation patterns and selected demographic covariables which may influence these behavioral trajectories. Three clusters were found, with subtle differences among them and three variables with greater predictive power: age, professional experience and region.

Gamification has been widely employed in the educational domain over the past eight years when the term became a trend. However, the literature states that gamification still lacks formal definitions to support the design of gamified strategies. This paper aims to create a taxonomy for the game elements, based on gamification experts' opinions. After a brief review from existing work, we extract first the game elements from the current state of the art, and then evaluate them via a survey with 19 gamification and education experts. The resulting taxonomy taxonomy included the description of 21 game elements and their quantitative and qualitative evaluation by the experts. Overall, the proposed taxonomy was in general well accepted by most of the experts. They also suggested expanding it with the inclusion of Narrative and Storytelling game elements. Thus, the main contribution of this paper is proposing a new, confirmed taxonomy to standardise the terminology used to define the game elements as a mean to design and deploy gamification strategies in the educational domain.

The role of computing in society is fundamental for the political, economic and cultural development of countries, especially in a globalized context. Computational thinking is an extremely important skill in the education of young students, since the ability of intuitively abstracting information and knowledge promotes an expansion of the learning dimension. This study aims to gather approaches used for teaching computational thinking exclusively to high school students, highlighting their performance and motivation resulting from the applications of such approaches. In order to answer our research question, a systematic mapping of the analysis of 15 studies was conducted. The use of computer programming proved to be the most used way to disseminate computational thinking. The students who participated in approaches regarding the insertion of computational thinking in the classroom showed positive impact on the development of classroom activities, as well as motivation to learn.

Software Engineering (SE) is an important topic to be taught in Computer Science courses. However, teaching of theoretical concepts with no link to their practical applications or no examples in the student's context may discourage learning, justifying why teaching and learning are great challenges of education in universities. In attempt to bridge such gap, several approaches have been proposed and applied to improve teaching and learning SE such as project based learning (PBL), a well-known approach already applied to teach SE. Nevertheless, there's a lack of understanding about how to better apply PBL and how to take advantage of this approach, for future use. There is also a lack of experience report describing how to use, its challenges and difficulties, what could be hampering to widely adopt it. We present our experience applying a PBL approach combined with project management to create an environment considering aspects such as dealing with managers and real stakeholders. The goal is to bring students closer to the reality of developing a software project in the business context. Our experience indicates positive results on the adoption of a PBL approach. In general, students were enthusiastic and positive about the use of this approach, the presence of a manager and the importance of using real-world problems with real stakeholders.

Persuasion profiling has been shown to be a promising method to personalize persuasive messages that can influence users. In the context of learning, persuade students towards following pedagogical tasks is an important role of teachers and intelligent educational environments. To accomplish persuasion, it is necessary to measure students' susceptibility to influence principles. Susceptibility to Persuasion Scale (STPS) is an instrument that can measure users' susceptibility to influence principles. Currently, STPS is validated only for the English language. Thus, this study presents the translation, cultural adaptation, and initial validation of the Brazilian Portuguese version of the STPS questionnaire. We probed into the validity and reliability of the resulting questionnaire with 582 participants. Our results indicate that the final version of our questionnaire, which contains 19-items and 6-components, has a reasonable internal consistence (Cronbach's alpha = 0.78) and can adequately identify which influence principles have more impact on user' decisions.

Intelligent Tutoring Systems (ITSs) for Math still use traditional methods of input, such as computer keyboard and mouse. This strategy exposes students to a high extraneous cognitive load, due to the way the students are used to work, what could impair their learning. This paper presents a tool for Math ITSs that uses the student handwriting as data input. This tool was integrated into PAT2Math, an algebraic ITS, for an experimental evaluation in a public school with 48 students. It followed a design of control and experimental groups with pre and post-tests and a post questionnaire. Although our results have shown statistically significant improvement in student learning gains in both groups, no difference between groups was found. However, questionnaire responses suggest that handwriting provides a greater facility to the insertion of equations into ITSs for Math if compared with traditional methods (i.e., keyboard and mouse).

This project is inserted in the research and practice areas that support the development of educational resources, such as, serious games and training simulation, to understand how they should be planned, developed, evaluated, validated and used for the learning and assessment purposes. In this study, we have proposed and specified an agile, integrative and open method for educational resources development, named AIMED, in a holistic and multidisciplinary approach. This approach integrates the areas of simulation modeling, game design, instructional design, principles of agile development methods, project management practices and content domain. AIMED is based on integration of DevJSTA methodology, for creation of serious games, and AM-OER method, for development of open educational resources. A feasibility study was done with serious game development, which should be used and evaluated by domain experts, as well as, the methodology will be used and validated in new case studies.

The development and use of ontologies in creating more intelligent and effective applications have increased in recent years. This rise in popularity is due to the potential of ontologies in providing semantics for the data consumed by machines, allowing them to reason on these data. Providing sophisticated tools that improve the development of applications based on RDF and OWL can further accelerate the adoption of the Semantic Web. In this context, some types of ontology tools have been proposed, for instance, ontology editors, ontology reasoners, triple store tools, RDF frameworks and object triple mapping (OTM) systems. In particular, OTM systems (e.g., AliBaba, KOMMA, JOINT and so on) are of utmost importance for developing ontology-based applications through the object-oriented paradigm. They map RDF triples into data objects, enabling developers to handle RDF triples as objects in object-oriented applications. In a similar way, developers of relational database applications have been using object-relational mapping (ORM) systems, for example Hibernate, to map relational databases into objects as well as to manage the object states. The main object state that Hibernate provides is the detached one; however, except for Empire, current OTM systems do not provide detached objects. The great difference between these two types of objects is that the former one has its life cycle independent of the underlying database connection, while the latter one is bound to the connection. In this way, this paper presents an object triple mapping system that supports detached objects, named JOINT-Detached (JOINT-DE). By using JOINT-DE, developers of ontology-based applications can (i) use the objects coming from the triple store as objects of the business model, (ii) use such objects as data transfer objects (DTOs) between subsystems, and (iii) use of less resources (triple store connections) to load object properties. To evaluate the proposed tool, we first describe how we conducted an experiment to empirically compare our tool with another OTM widely used by the community. We measure performance and memory usage regarding create, retrieve and update operations. Our results indicate that: (1) JOINT-DE has better performance results in all three operations; (2) JOINT-DE has an advantage over AliBaba with respect to the memory usage in the retrieve operation; (3) AliBaba uses less memory than JOINT-DE in the update operation; and (4) we could not find statistically significant differences between the tools regarding memory usage in the create operation. Finally, to illustrate the benefits of the proposed system, a case study of a real application is presented, outlining the architectural limitations of the application using an existing OTM in the literature, as well as showing positive results to the use of JOINT-DE.

Infographic is a type of information visualization that uses graphic design to enhance human ability to identify patterns and trends. It is popularly used to support spread of information. Yet, there are few studies that investigate how infographics affect learning and how individual factors, such as learning styles and enjoyment of the information affect infographics perception. In this sense, this paper describes a case study performed in an online platform where 27 undergraduate students were randomly assigned to view infographics (n=14) and graphics+text (n=13) as learning materials about the same content. They also responded to questionnaires of enjoyment and learning styles. Our findings indicate that there is no correlation between learning styles and post-test scores. Furthermore, we did not find any difference regarding learning between students using graphics or infographics. Nevertheless, for learners using infographics, we found a significant and positive correlation between correct answers and the positive self-assessment of enjoyment/pleasure. We also identified that students who used infographics keep their acquired information longer than students who only used graphics+text, indicating that infographics can better support robust learning.

Some recent studies discussed the pros and cons of gender difference in the use of intelligent educational technologies. According to these studies, there is a difference in behavior, attitude, learning and emotion. However, most of the studies were conducted for Mathematics and in a well developed ecological setting. In this work, we investigated the Mars and Venus Effect, by measuring together different variables, such as learning performance (in Portuguese and Mathematics), age, location area, and learning technology. The study was a random pre/posttest control group experimental design, on which 191 students from public schools in Brazil participated. The experimental group used an adaptive learning technology (called MeuTutor) for nine months, while the control group did not use any educational technology during the study. The most relevant results are: i) the use of the adaptive learning technology improves student's performance in Mathematics and Portuguese for both male and female students, ii) the improvement of male student's performance is more significant, against female students, iii) when we focus on technology, male students had better performance in mathematics, but no significant difference in Portuguese.

This paper presents a dynamic Bayesian network model for the assessment of students' algebraic knowledge in step-based intelligent tutoring systems. The proposed work assesses knowledge about concept, skills, and misconceptions of learners. Furthermore, the proposed model is independent of the problems provided by the system (i.e., equations), because it considers the algebraic operation used by the student to solve a step as evidence instead of the final solution provided by the student. Results of evaluations comparing student's performance on a posttest with the inference of the proposed model showed statistically significant similarities between them, indicating that the inference performed by the model was accurate.

Nowadays, with the spread of massive open online courses (MOOC) there is a significant increase in the number of students learning through the Web. Consequently, there is a growing concern about the quality of the Web-based Educational Software (WES). Despite of various mechanisms to evaluate the quality of this software, most of the quality criteria are spread in different articles found in the literature. Therefore, this work aims to conduct a Systematic Literature Mapping to identify the criteria to assess the quality of WES, and classify them according to their contribution. A total of 78 studies were analyzed and classified into four categories of quality: pedagogical, technical, organizational and social. The results contribute to support the maintenance and improvement of the quality of the WES available to the community.

Interactive Geometry software (IGS) are tools that help students to explore geometry concepts using computers. Most of IGS interfaces developed to date are heavily based on the desktop model of user interaction where the user input comes from the selection of icons and drop-down menus with the help of a mouse and a keyboard. Nevertheless, with the widespread adoption of smartphones, tablets and other devices with multitouch screens the tradition interfaces of IGS became inadequate. Thus, this study presents the development of a dictionary of gestures for interacting with IGS. It completely removes the need of icons and drop-down menus to create and manipulate geometric objects. To evaluate these gestures we implement them in an IGS for mobile device called Geotouch. Then, we conducted a usability study with five experts to compare Geotouch with other three IGS currently available. The results indicate that Geotouch has less usability problems and enable to build geometric objects faster and with fewer errors.

Affective states such as personality trait, emotion and mood are critical elements that affect group learning. Nevertheless, few studies classify and analyze the research findings of the community to explicitly show the real impact and use of affective states in CSCL environments. Thus, this paper presents the development of a systematic mapping of the literature to answer three questions: (i) What are the affective states frequently used in CSCL environments? (ii) Were affective states in CSCL environment empirically evaluated? (iii) What are the types of environments that use affective states to support group learning? To answer these questions, seven digital libraries were queried and 31 studies were deeply analyzed. As main results, we verified that: (i) 54,84% of the studies deal with 'emotions' in CSCL environments, (ii) 51,61% of the studies were empirically evaluated, and (iii) LMS (38,71%) and E-Learning systems (22,58%) are the most common types of environments that use affective states to support collaborative learning. Finally, we also identified gaps and opportunities to conduct research on adaptive/intelligent CSCL systems to support better group formation and customization of group activities using affective states.

Interactive Geometry (IG) is a computational approach to teach geometry using interactive geometric objects. It is based on the learning-by-doing method where the representation and manipulation of geometric spaces can support students to construct their knowledge through experience. Several research findings indicate various benefits of using IG in classroom. Nevertheless, IG software developed to date has been based on the desktop model where the interaction between a user and the IG software occurs through the conventional inputs and outputs methods: keyboard, mouse, and a monitor. Currently, this model is being challenged by the widespread adoption and use of mobile devices. In this scenario before building an IG software that can be effectively used in mobile devices such as smartphones and tablets, where the input is based on touches and movements of fingers and the output is a small screen, there is a need to rethink the interface and what interaction methods should be employed. In response to this need, our paper examines the design and development of a GI application for mobile devices. In this study, we have proposed and implemented an interface and interaction model that is suitable for developing a GI software for mobile devices.

Intelligent Tutoring Systems (ITS) provide many features that improve learning and teaching experiences. ITS are usually interactivity-intense and content-specific. Interactivity-intense assignments are recommended for scaffolding learning, while content-specific systems can offer low flexibility regarding its possible pedagogical approaches and its uses by teachers. In order to overcome this limited flexibility, there are systems which let content-specific interactivity aside to provide authoring tools, with which teachers can author intelligent tutored assignments without programming. The generic model proposed herein intends to address this problem providing flexible authoring tools for interactivity-intense assignments with tutoring features, letting teachers benefit from the flexibility of content authoring tools as well as the interactivity usually restricted to content-specific ITS. We introduce an application framework which implements this model, which is available as free software.

The interface is the main mechanism of communication between user and system features. In educational software, successful user interface designs minimize the cognitive load on users, thereby users can direct their efforts to maximize their understanding of the educational concepts being presented. We investigated whether a reduced interface make few cognitive demands on users in comparison to a complete interface. In this context, this research aims at analyzing a reduced and a complete interface of an interactive geometry software, and verify the educational benefits they provide. To this end, we designed the interfaces and carried out an experiment involving 69 undergraduate students. The experimental results indicate that an interface that hides advanced and extraneous features helps novice users to perform slightly better than novice users using a complete interface. After receiving proper training, however, a complete interface makes users more productive than a reduced interface.

The development of good quality educational software is expensive, time-consuming and faces some underlying issues. In order to deal with such issues, many attempts were made, mainly on code reuse. Following a similar approach, the development of an application framework for implementing a family of interactivity-intense educational software called Interactive Learning Modules - iLM is presented. The framework main goal is to reduce development efforts while being part of a Software Product Line - SPL, an innovative technique regarding interactivity-intense educational software. The framework specification is presented by analyzing the common features of existing iLM. Therefore, the resulting component architecture is outlined. Currently, the design phase is finished and the implementation ongoing. Also, its instantiation is planned for an existing iLM, as a proof of concept.

There are different approaches that drive the development and use of educational software, such as Interactive Geometry Systems - IGS and Intelligent Tutoring Systems - ITS. Considering their benefits to teachers and students, these systems may be used to complement each other. The ongoing development of ITS features in an existing IGS called iGeom is presented. First, the limitations of both approaches are listed, describing possible benefits of using them together. Then, the resulting component architecture of the conducted analysis and software design is outlined. The ITS paradigm chosen was Example-tracing Tutors. The current state of research is the tutoring features implementation and planning for testing in classrooms and in distance learning courses.

This paper describes the development and evaluation of a Bayesian network model of student misconceptions in the domain of decimals. The Bayesian model supports a remote adaptation service for an intelligent t utoring system within a project focused on adaptively presenting erroneous examples to students. We have evaluated the accuracy of the student model by comparing its predictions to the outcomes of the interactions of 255 students with the software. Student s’ logs were used for retrospective training of the Bayesian network parameters. The accuracy of the student model was evaluated from three different perspectives: its ability to predict the outcome of an individual student’s answer, the correctness of the answer, and the presence of a particular misconception. The results show that the model is capable of producing predictions of high accuracy (up to 87%).

Design of collaborative learning (CL) scenarios is a complex task, but necessary if the goal of the collaboration is learning. Creating well-thought-out CL scenarios requires experience and knowledge about different learning/instructional theories and practices related to collaboration. Inexperienced designers, who may not have all the needed knowledge to formulate pedagogically sound collaborative learning plans, may have difficulties to design CL activities. Thus, although CL has shown to be effective, it is not often used in classroom adequately. To tackle this problem, this paper presents an ontology for CL, that aims at modeling not only the learning activities, but also the rationale for their configuration in pedagogical terms. This ontology allows modeling the expected interactions among students as well as the mechanisms that promote or enhance collaboration structured across several sessions and involve individual and collaborative activities. Finally, the paper discusses an application of the presented ontology to support the design of pedagogically sound CL scenarios.