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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.

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.

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.

One of the most useful ways to enhance collaboration is to create scenarios where learners are able to interact more effectively. Nevertheless, the design of pedagogically sound and well-thought-out collaborative learning scenarios is a complex issue. This is due to the context of group learning where the synergy among learners' interactions affects learning processes and, hence, the learning outcome. Although many advances have been made to support the designing of collaborative learning scenarios through technology, a more systematic approach is lacking. With the limitations of the current designing methods and tools, it is difficult to develop intelligent authoring systems that can guide users in order to produce more effective collaboration. One of the main difficulties with creating a more consistent (computer-understandable) approach to designing collaboration is the necessity of proposing better ways to formalize the group learning processes. In this paper, we present an innovative approach that uses ontologies and concepts from learning theories to create a framework that represents collaborative learning and its processes. Ontologies provide the necessary formalization to represent collaboration, while learning theories provide the concepts to justify and support the development of effective learning scenarios. Such an approach contributes to establish the foundations for the development of the next generation of intelligent authoring systems referred to as theory-aware systems. To verify the viability and usefulness of our proposed ontological framework in the context of systematic design, the development and use of an intelligent authoring tool for CSCL design is presented. This system is able to reason on ontologies to give suggestions that help users to create theory-compliant collaborative learning scenarios. We carried out several experiments with teachers in a geometry drawing course and the results indicate that the system helps teachers to create and interchange their scenarios more easily and facilitates the selection of important pedagogical strategies that influence positively the designing and effectiveness of group activities. (Contains 3 tables and 20 figures.)

According to the literature, educational technologies present several learning benefits to promote online education. However, there are several associated challenges, and some studies illustrate the limitations in elaborating educational technologies, called Design limitations. This aspect is responsible for unleashing various issues in the learning process, such as gender inequality, creating adverse effects on cognitive, motivational, and behavioral mediators, which opposes the fifth UN's Sustainable Development Goal. Therefore, many studies notice the harmful effects of stereotypes in educational technologies. These effects can be included in the design, like colors or other stereotyped elements, or how the activity is conducted. Based on this, the present study aimed to verify the predominance of color bias in educational technologies available on the WEB. This study developed a computational solution to calculate male and female color bias in the available educational technology web pages. The results suggest the prevalence of the development of educational technologies with a male color bias, with an imbalance among genders, without adequate customization for age groups. Furthermore, some environments, such as Computer Science, present a higher color bias for men when compared to women. Despite both scales being independent, results indicated interesting evidence of a substantial prevalence of colors associated with the male scale. According to the literature, this may be associated with dropout and lack of interest in female students, especially in sciences, technology, engineering, and mathematics domains. [ABSTRACT FROM AUTHOR]

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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. [ABSTRACT FROM AUTHOR]

CSCL investigates ways of promoting students' collaboration through technology. Although affective states and socio-emotional factors have an important effect on learning, few studies have classified and analyzed the results obtained by the scientific community to show their real impacts on in CSCL settings. The lack of a comprehensive overview and understanding of previous findings hampers the survey on research gaps, challenges and trends. This work addresses a systematic mapping of the literature for the summarization of results and discussion on research directions for the use of affective states and socio-emotional factors to support the development of intelligent CSCL environments. Six digital libraries were queried and 58 studies were deeply analyzed and categorized. Over 90% of the studies considered emotion and personality traits in the CSCL environment; most studies used personality recognition by questionnaires rather than automatic sources of inference; most technological approaches employed instruments to collect, analyze and/or represent emotions; and most papers that reported empirical experiments focused on “emotional awareness” and “interaction among students in a CSCL environment”. We have identified three main challenges to be addressed by the community in the next decade for an adequate incorporation of affective states in CSCL environments: emotional awareness, orchestration of students' interaction and group formation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [Copyright &y& Elsevier]