This research full paper presents the perception of programming teachers and students in the context of neurodiverse students. Among the wide range of disabilities, N eurode-velopmental Disorders encompass types of disabilities that are not easily visible, such as Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD). A section of the literature does not consider these disorders to be disabilities. Therefore, neurodiverse describes a person whose brain functions differently than typical. Given this, we can look at these students as people who learn differently and who need to have their specific needs met. With the inclusion of students with disabili-ties in technical and undergraduate courses, they face several challenges, both for these students and teachers. Specifically, several peculiarities can impact this process regarding teaching programming, such as the creation of algorithms based on problems described in extensive texts. Despite efforts to meet the needs of students with disabilities in teaching programming, we still do not have enough studies that address this issue. The main objective of this research is to investigate the current reality of teaching programming to neurodiverse people, what the difficulties are, and how teachers and students are trying to solve them. Thus, given the phenomenon's complexity, we created a case study with two units of analysis: (1) neurodiverse students and (2) their respective programming teachers. The phenomenon studied was programming teaching, and the context was the first programming discipline of two undergraduate courses and a high school technical course. We carried out semi-structured interviews with six students and three teachers. We identified through research that teachers need adequate training to serve atypical students and that educational institutions still need an efficient inclusion process for these students. When we observe the students' vision, we realize that for them to learn to program, there are essential demands, such as the difficulty in understanding the application of programming concepts in practice and the creation of programs without assistance. This study is necessary to show the demand for new approaches to meet the specific needs of neurodiverse students in programming disciplines. To achieve this, inclusive pedagogical strategies and resources must be created and adapted to the characteristics and abilities of these students. Initiatives like this can promote equal opportunities in the classroom, including accessible learning environments, use of explicit language, and individualized support, among others.

This Full Research Paper aims to identify and compare the skills involved in learning programming and the difficulties that students with cognitive disabilities may face, discussing the impacts generated by these skills for these students. Several studies talk about what skills a person needs to learn to program. However, we do not have enough to consider the needs of students with disabilities, especially cognitive impairments. Did we conduct a study to answer the following research questions: RQ1 - What skills are involved in programming teaching? RQ2 -Can cognitive impairments affect the skills needed to program? We lift the part the skills involved in learning programming and the cognitive skills impediments in the literature. Finally, we made a critical analysis impact on that audience. By cross-referencing this information, we can highlight that the skills needed to program impact students with cognitive impairments such as math, logic, and problem-solving. This impact does not necessarily mean that these students cannot learn to program. We believe adapting materials, exercises and assessments can foster learning for this audience.

This Research-to-Practice Full Paper examines the Bebras Challenge tasks, a Computational Thinking (CT) contest that does not require programming knowledge. Numerous studies in the last decade shed light on disseminating CT practices. However, there is still a gap in understanding CT as a construct, that is, CT as a complex cognitive dimension. Furthermore, few studies analyze how many constructs can be recognized inside CT by empirical data. Moreover, algorithmic thinking is one of the essential skills in CT, even when we do not apply programming activities. In this exploratory paper, we investigate the Bebras Challenge tasks. Our primary aim is to figure out how many and which are the constructs associated with CT skills derived from data, especially related to algorithmic thinking. The results point out three primary constructs supported by data. We claim that these constructs are a mix of CT skills, mainly algorithmic thinking, abstraction, generalization, evaluation, and pattern recognition. In addition, our study provides additional evidence supporting the idea that algorithmic thinking can be explored by different perspectives beyond “a set of order instructions”, even without programming practices. Thus, this result reinforces the notion that CT can be understood as a mix of several skills to solve problems. Moreover, algorithmic thinking is a complex cognitive process of thought which may be explored through different strategies. We believe that these findings may help recognize which constructs and abilities underline CT, primarily algorithmic thinking without programming activities.

While several approaches have been proposed to assess computational thinking (CT) abilities, it is still unclear how many and which these abilities are. Despite the undisputed importance of assessment, the fact is that there is not enough evidence on which abilities are merely theoretical and which can be empirically observed in the context of CT. This paper is part of a larger investigation in which we try to answer a simple question: can CT abilities be quantitatively defined and measured? In this particular study, we try to answer a simple question: How many CT abilities can be empirically observed using factor analysis? We approach this question, using a dataset containing answers of 1564 students from Lithuania in the Bebras Challenge from 2015. Firstly, we used confirmatory factor analysis to verify a theory that claims that five CT abilities are assessed by the contest. Our analysis shows the theory is not statistically supported. Secondly, we used principal component analysis as an exploratory analysis to try to derive an appropriate number of factors from the data. Surprisingly, the analysis suggests there are only two main recognizable factors. Finally, we briefly discuss these factors and hypothesize that the first is called evaluation ability, which would include abstraction, generalization, and decomposition, while factor 2 is algorithmic thinking and logical reasoning.

Educational Robotics (ER) has revealed several benefits in the educational context, not only helping the teaching of disciplines, but also making possible the development of several abilities, such as teamwork, problem-solving, and creativity. Among various robotics kits, LEGO®Robotics has been shown one of the best results considering some evaluated criteria (modularity level, hardware, curriculum, price, etc.). Some studies analyze the teaching practices, some compare technologies, and others evaluate the kits in a pedagogical way. However, it is essential to investigate all these contexts together in order to improve the impact produced by the ER in education and to know the best teaching practices associated with the most powerful technologies. The objective of this Research Full Paper is to identify: a) environments and programming languages adopted in the LEGO®Robotics context, b) educational practices applied during classes based on LEGO®Robotics, and c) the educational levels in which robotics has been applied with positive results. To achieve these goals, we planned and carried out a systematic review of the literature. Our main findings are: a) the most widely used environment and programming language are LabVIEW along with the LEGO®'s block-based programming language, b) we identified LEGO®Robotics is used for teaching programming, interdisciplinary contents, participation in tournaments, robotics, and computational thinking, c) LEGO®Robotics is used with success by students of different levels, such as K12, undergraduate, and graduated. Finally, we discuss some problems and limitations related to ER and point out that there is no standardization of teaching practices or methodologies for evaluating results, indicating that more research is needed to find the best scenario regarding technologies, methods, and target audience.

Several instruments have been used for assessing Computational Thinking (CT) abilities. In this exploratory and preliminary study, we investigate how appropriate the Bebras challenge is as an instrument to assess and measure CT abilities. Bebras is an international challenge whose goal is to promote Computer Science and CT. The test can be answered without any prior knowledge on computer science. Our broad research question is whether we can evolve Bebras into a full fledged instrument to assess and measure CT abilities. In this paper, we instantiate a few more specific research questions: Is Bebras performance a good predictor of success for students within programming courses? Is there any correlation between Bebras performance and students' grades? Do students improve their performance in Bebras tests when exposed to the contents of a programming course? Our dataset consists of the grades of 138 students who attended introductory programming courses at two Brazilian universities and their performance in two simulated Bebras tests. The first test was applied at the beginning of the term and therefore before any exposition to programming classes. The second one was applied at the end of the term. The results suggest that the performance on Bebras is only moderately correlated to the student grades. We conclude that it is not very likely that CT measures can be derived from the Bebras test as it is currently designed. While further research is needed on how we can leverage the Bebras effort to extend it into a CT assessment instrument, we performed a preliminary study on the use of Item Response Theory (IRT) as a means to improve the selection of questions and the design of the test. We expect the results of this research can contribute both to the development and discussion on CT assessment as well as to the Bebras effort to educate CT.

Several initiatives have been created to promote Computational Thinking (CT) abilities in students. There are multiple approaches of assessing CT and wide abilities and skills involved. However, the evidence on how to assess CT has not yet been systematically grouped or reviewed. The goal of our study is to identify and classify approaches to promote CT and the different ways of assessing CT abilities. To achieve this goal, a systematic mapping study was planned and executed. The results reveal that: (i) programming courses are the most common pedagogical approaches to promote CT for K-12 students; (ii) multiple skills are involved in CT, but solving problems, algorithms, and abstraction are most common abilities assessed; and (iii) codes and multi-choice questionnaires are the most common artifacts for assessing CT abilities. This study points out to the fact that there are open questions for exploring and developing new researches for promoting and assessing CT abilities.