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.

Teaching computational thinking for children has reached ample dimensions along the last decade, and has culminated in the creation of various products, programs and projects that aim to democratize the access to this body of knowledge. However, cognitive, motor and affective factors have appeared as particular challenges when we are teaching computational thinking to children in the literacy phase, where the stories are a particularly relevant part of the learning process and children's development. Thus this study has aimed to articulate the inclusion of storytelling in experiences of teaching computational thinking to young children, analyzing this possibility considering the proposition of an artifact based on transmedia storytelling for teaching computational thinking and seeks to understand what are the implications of child-computer interaction in this artifact.

In recent years, the exponential growth of positioning-enabled devices have allowed us to study the mobility behavior of individuals analyzing their collected tracks. In this context, a small, but steadily increasing part of the literature is looking at the semantic aspects of mobility. This paper presents a contribution to this trend, and is concerned with the definition of semantic regularity profiles. We based our methodology on the entropy of both spatial and temporal frequency of visits of individuals to places to perform an activity. This allows us to define the concept of semantic regular or irregular user behavior identifying users who are more or less loyal to the same places in contrast to the flexibility in visiting different places to perform an activity. We experiment on a crowdsensed trajectory dataset annotated by the visited Points of Interest which represent the activity performed. Analysis evidence that the regularity depends on the particular activity to be performed.

A context-sensitive groupware uses information about a group and its participants' context in order to provide useful information and services to improve group work. A relevant issue when designing Context-Sensitive Systems (CSS) is how to represent contextual information and the system's behavior. This paper presents a Context Metamodel and shows preliminary experiments in two context-sensitive groupware performed to illustrate its usage and potential in designing CSS. The metamodel offers integrated support for modeling structural and behavioral aspects involved in context management and usage.

A great number of techniques were already applied to the non-adversarial variation of the multiagent patrolling problem. Experiments suggest that, for general graphs, all those approaches are inferior to a strategy based on the travelling salesman problem (TSP), in which agents are distributed equidistantly along the TSP-cycle. This approach, however, is neither optimal nor scalable. In this article, we present a novel patrolling strategy which tries to overcome these limitations. Inspired on Newton's law of gravitation, our approach consists in assigning to each node of the graph an abstract mass which grows while the node remains unvisited, creating a force that attracts agents to it. We defined some variations of our approach and experimentally compared them to the TSP-based strategy, concluding that our approach performs better in general.

Context-Sensitive Systems (CSS) demand that designers consider new aspects and challenges in comparison to traditional applications. In a preliminary experiment, we observed that developers have difficulties when including the concept of context in their applications. This paper presents a CSS Design Process, which details the main activities for context specification and the design of CSS and provides a systematic way to execute these tasks. Two studies were conducted to evaluate the process usage: a detailed execution of each defined activity on the design of a context-sensitive expert recommender system; and an evaluation of the process usage by distinct designers on their CSS projects. These studies showed a good acceptance of our proposal and indicated the feasibility of using the process on real projects.