The literature indicates that, in general, TAs lack teaching experience, and many of them need appropriate pedagogical training, particularly because they need more knowhow on feedback resources, as well as in providing students with adaptive feedback. Given the mentioned perspective on feedback in introductory programming, one important question is: how can TAs assist in providing adaptive feedback that meets students' needs? To address this issue, guidelines can be provided to clarify what effective feedback is for novice student programmers. However, there is still a lack of guiding elements that constitute adaptive feedback. Thus, to mitigate these problems, we propose a taxonomy for providing adaptive feedback, covering different approaches and feedback resources in problem-solving by novice programmers. The body of knowledge represented in the proposed taxonomy is helpful to be presented to TAs, who should use it to guide their pedagogical decision-making process in relation to adaptive feedback in terms of synthesis and analysis. To our knowledge, a taxonomy for this purpose has yet to be published in the computing education literature. In summary, we intended to investigate the following research questions: (1) What can be adapted in the feedback provided to the beginning programming student during problem-solving? (2) What can be adapted from the feedback provided to the novice programmer during problem-solving? As a method for designing the proposed taxonomy, we followed the Design Science Research (DSR) methodology using its main stages: problem identification, elaboration, testing, and evaluation. As a result, we develop a novel comprehensive taxonomy of adaptive feedback that captures the many elements that one teacher might consider when helping a given student. The taxonomy includes various approaches and features to adaptive feedback, expressed in categories, subcategories, and contextual elements. The main contribution of this paper is to provide a taxonomy that can help TAs use a relevant pedagogical body of knowledge on how to provide adaptive feedback for programming by novices.

In this article, we investigate the influence of variables related to gamification strategies promoted by an online learning environment on the “gaming the system” behavior presented by elementary school students in mathematics. To this end, we performed our research in four main stages: exploratory database analysis, database annotation, detector development, and analysis of the results. We developed a data-driven automatic detector involving the training of single and joint machine learning algorithms to classify “gaming the system” behavior and obtain models with the best accuracy, including exploring, evaluating, and comparing different algorithms. The detector's development involved training machine learning algorithms as a single category, including decision tree, K-NN, neural network, and Support Vector Machine and ensemble categories, such as Random Forest, Gradient Boosting Machine, Adaptive Boosting, and XGBoost. In addition, three human tutors annotated the database with log data of problem-solving from the math domain for elementary school students, who evaluated each student's problem-solving attempt in game and non-game classes. In total, 12,514071 rows of data were labeled in two rounds of labeling, with Cohen's Kappa of 0.82 for inter-rater reliability for differentiating between gaming and non-gaming behavior. Our findings suggest that gamification strategies used by online learning environments can provoke “gaming the system” behavior in elementary school students. At the same time, we provide evidence from the literature and exploratory data analysis that the incentive to seek performance over learning has motivated students to manifest non-learning behaviors. We hope these insights will contribute to improving the planning and development of a gamification strategy in an online learning environment.

In this paper, we propose a Web-based interactive learning environment with a knowledge-based assistant for helping undergraduate students in solving problems on designing digital circuits, focusing the relationship between Boolean algebra and Digital Logic. Particularly, we have addressed problem solving situations that essentially consist of mapping a logic expression into another equivalent, for example, the task of simplifying digital circuits. To do that, we have considered to operate on two symbolic linked representations: Boolean Algebra and Logic, connecting these two representations to a visual one with logic gates. This integration comes from the importance to involve students in three equivalent representations, permitting them to make connections between them. The learning environment contains a knowledge-based assistant that works on a collection of scenarios with several associated problems, where the students are placed in a problem-solving situation. To each problem, there are pedagogical resources needed to provide the students with feedback and knowledge support based on their performance and identified needs. Furthermore, there is an adaptive mechanism that is responsible for how problems should be sequenced or ordered, and even we have designed a switcher mechanism, which permits the students to change from one to another representation. We evaluate our approach by using scenarios and conclude that the system is feasible.

In this paper, we propose an agent-based ITS for learning propositional logic using two linked viewpoints and representations to address the problem on checking the validation of a given argument, articulating syntactic and semantic inference approaches. One motivation for this integration comes from the importance to involve students in two complementary viewpoints, permitting students to make connections between the two viewpoints involving different strategies, used in problem solving situations. Basically, the system works by placing students in a problem-solving situation and providing needed knowledge support based on his performance. We discuss the system and show its architecture and functionality, as well as its implementation details. Through scenarios with adequate coverage, we evaluate the value of the proposed approach and conclude that it is feasible and useful.