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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.

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.)

The unprecedented global movement of school education to find technological and intelligent solutions to keep the learning ecosystem working was not enough to recover the impacts of Covid-19, not only due to learning-related challenges but also due to the rise of negative emotions, such as frustration, anxiety, boredom, risk of burnout and the so-called "Covid fatigue". Although this is not a new problem, it was deepened during the pandemic, and we need to face old and new challenges in different ways. Despite focusing only on the learning system's inefficiencies and on the hegemony of solutions to tackle the learning gap, we also need to shed light on the strengths and the positive aspects of the learning process to promote wellbeing. As highlighted by John Self, an intelligent tutoring system would behave as if it genuinely cared about the student's success. This note from John Self sheds light on the importance and reflection of what success means and for whom. This study presents a roadmap for positive psychology and artificial intelligence in education. It is intended to identify and understand how the intersection of Positive Psychology and Artificial Intelligence in Education can support the promotion of learning and wellbeing of students, teachers, and other educational stakeholders. As such, a bibliometric analysis of positive psychology and artificial intelligence in education was made as the so-called Positive Artificial Intelligence in Education (P-AIED). The search string was performed in 2021, and the total number of studies gathered was 10,777. After all the PRISMA steps, 256 studies were approved according to the inclusion criteria. The main conclusions were the high number of institutions and researchers with related publications indicate a new trend for the community of AIED; the high number of collaboration from different countries indicates a possible global movement toward P-AIED; Positive Emotion and Engagement were the main Positive Psychology constructs identified in the studies; the lack of well-grounded theories of Positive Psychology indicates an excellent research opportunity; Positive Learning Analytics (P-LA), Positive Educational Data Mining (P-EDM) and Positive Intelligent Tutoring Systems (P-ITS) are three hot topics for P-AIED. [ABSTRACT FROM AUTHOR]

The field of Artificial Intelligence (AI) has the potential to improve teaching and learning, for example, by analyzing data produced in educational environments. Furthermore, it can also worsen inequality, as it requires students and instructors to have access to the infrastructure (smartphones or computers) required by the majority of those tools to generate and analyse data. However, access to such infrastructure is not a reality for many students around the world. To shine a light on this problem, this paper investigates, through a Systematic Mapping Study (SMS), initiatives that enable a more inclusive data analysis using AI in education, especially in scenarios with few connectivity resources. We identified that these initiatives are scarce and they are focused in the first phase of the data analysis task: the data collection. Based on the SMS results, we propose a set of recommendations for researchers to offer directions toward a more inclusive analysis of educational data using AI. [ABSTRACT FROM AUTHOR]

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. [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]