Search

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.

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.

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.

Presents the introductory editorial for this issue of the publication. [ABSTRACT FROM PUBLISHER]