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Teaching computer science (CS) at all school levels is an increasingly important issue that has recently attracted the attention of institutions and policymakers. However, there is a shortage of qualified teachers which highlights an urgent need for training initiatives. To design such initiatives, it is crucial to assess teachers' baseline CS knowledge, identifying possible gaps. This study aims to articulate a proposal for a classification of tools that can be employed to assess teachers' CS knowledge. A literature review was conducted to collect relevant studies that present and/or utilize tools for assessing teachers' CS knowledge and competencies. We have identified key dimensions under which the tools can be examined: the data collection method, the nature of tasks assigned, how their reflection and action are triggered, whether the tool focuses on teachers' products or cognitive processes, and whether it also assesses their pedagogical content knowledge. For each dimension, we have identified a range of possible descriptors, leading to the development of a synoptic table categorizing the tools. This framework also enables us to compare the tools based on their features, highlighting their strengths and applications. This work makes two contributions: first, it introduces a framework for describing and comparing tools to unveil teachers' CS knowledge. Second, it offers a collection of tools for assessing it. This approach not only aids in selecting suitable tools for specific contexts but also provides a foundation for designing CS-knowledge assessment tools, enriching the resources available for analyzing educational needs and developing high-quality teacher training programs.

Information representation is a key concept in informatics, that involves representing real-world problems through symbolic encoding of data that can be automatically processed. Despite that, the presence and role of this concept in curricula, learning materials, and activities, is much less significant w.r.t. other informatics areas, as programming (coding) or algorithms. In this experience report we present a learning unit we designed, consisting of five two-hour lessons, that aims at introducing pupils to the fundamental concepts around information representation, and that we piloted in five fourth-grade classes. We also present and reflect upon the feedback asked of teachers, after the learning unit was concluded in their classes.

Abstraction is a fundamental skill and concept in computer science and it is also a difficult skill to teach. The purpose of the working group is to analyse different perspectives of abstraction's conceptualisation and ways of teaching the skill. Therefore as a result of the working group we will be first identifying how abstraction is discussed and defined in key literature. As a team we will agree on the perspectives and models we will like to explore in teaching context. Finally we will work with computing educators and computing education researchers to design a pedagogical framework that will enable the development of the abstraction skills.

We study how students and teachers conceptualize the notion of 'algorithm', a fundamental concept in computer science and computer science curricula. We analyze the work produced by CS students and teachers during a workshop conducted repeatedly over several years in some outreach activities for schools, computing education courses, and professional development opportunities for teachers. Participants were divided into groups, given some procedures written in natural language, and asked to decide together which of the procedures might be taken as algorithms. The procedures were purposely designed to present flaws or features that could activate discussion. After that, groups were asked to agree upon a definition of 'algorithm' and make its fundamental properties explicit. The activity triggered reflections around the idea of algorithm and its interpreter, going beyond stereotyped definitions, and leading participants to deepen their comprehension of the notion. We report on the aspects that were more debated by the groups, and those that were recurrent in the resulting definitions. We argue that this kind of activities should be offered more often both to students during their study career, and to teachers in professional development opportunities, to prompt them to reflect on computing foundations also in a non-technical, more holistic way.

In this paper we study the problem-solving ability of the Large Language Model known as GPT-3 (codename DaVinci), by considering its performance in solving tasks proposed in the “Bebras International Challenge on Informatics and Computational Thinking”. In our experiment, GPT-3 was able to answer with a majority of correct answers about one third of the Bebras tasks we submitted to it. The linguistic fluency of GPT-3 is impressive and, at a first reading, its explanations sound coherent, on-topic and authoritative; however the answers it produced are in fact erratic and the explanations often questionable or plainly wrong. The tasks in which the system performs better are those that describe a procedure, asking to execute it on a specific instance of the problem. Tasks solvable with simple, one-step deductive reasoning are more likely to obtain better answers and explanations. Synthesis tasks, or tasks that require a more complex logical consistency get the most incorrect answers.

This experience report presents a participatory process that involved primary school teachers and computer science education researchers. The objective of the process was to co-design a learning module to teach iteration to second graders using a visual programming environment and based on the Use-Modify-Create methodology. The co-designed learning module was piloted with three second-grade classes. We experienced that sharing and reconciling the different perspectives of researchers and teachers was doubly effective. On the one hand, it improved the quality of the resulting learning module; on the other hand, it constituted a very significant professional development opportunity for both teachers and researchers. We describe the co-designed learning module, discuss the most significant hinges in the process that led to such a product, and reflect on the lessons learned.

In a project partially supported by research grant PANN20_00690 to Italy's CINI National Lab "Informatica e Scuola", we compared the effectiveness of two alternative instructional methods applied to scaffold the learning of iterations for children at grades 2-3. Eight university groups across the Country collaboratively run the project in two successive rounds throughout the year 2022. Teachers' feedback collected across the two rounds helped fine-tune the deployment of the interventions. The experiment results show that the two alternative interventions have measurable outcome differences in the short term.

Programming plays a paramount role in many educational policies and initiatives. However, the current focus on coding skills poses a risk of giving pupils an over simplistic and impoverished idea of what programming means and involves. Their experiences would be much more significant if learning were aimed at understanding the richness of the nature of programs. In fact, programs are strange creatures that escape simple definitions. They are real, in that they affect our real lives; they are abstract, in that they process abstract entities; and they are concrete, in that they take up space in digital devices memory, and can be copied, transferred, corrupted. Thus, understanding the multifaceted nature of programs is crucial knowledge for all citizens of the digital era, and a fundamental component of such an understanding is getting a sense of how programs are created and work (i.e., the programming process). To the best of our knowledge, there is no Nature of Programs framework (e.g., a set of statements that describe what the nature of programs is), that teachers and policy makers can use to shape their practice and targets. The goal of the WG is developing such a framework, by collecting and organizing contributions from CER, CS experts, and educators.

This Research Full Paper presents a study of programming strategies to manipulate data series presented in a range of CS1 courses. We collected and analyzed programs from multiple institutions in different countries, covering a range of programming languages (Python, Java, Go, and C). We started from a list of core strategies (that is, strategies that process the series as a whole, e.g., goals/plans for counting, linear search, etc.) drawn from the literature. We then expanded and refined the list, based on the analysis of the collected programs. We used a mixed method: we first analyzed the programs qualitatively to identify the recurring goals; then we conducted a quantitative analysis of the frequencies of goals. The result of the qualitative analysis is a data-driven list of basic goals that are required in order to cover the input, storage, and processing of data series, independently of the chosen programming language. The list has 13 well-separated goals (no goal in the list is a sub-goal of another one) that share a similar structure and the same level of abstraction. The quantitative analysis shows that the category of core goals is, not surprisingly, the most frequent one; around half of the programs contain a core goal, with total, count, and linear search being the most recurrent ones. Besides them, goals related to input and storage play a significant role as well: taken together, they occur with a frequency similar to that of core goals. The list of goals, if used to select or design practical exercises and teaching materials, can be a valid reference for CS1 instructors who want to foster the acquisition of strategic knowledge by their students. Implication for teaching and learning are discussed.

In order to support students struggling with learning to program, we designed new learning material, around two basic principles from recent research on introductory programming education: propose program comprehension tasks[4] beside writing tasks, and teaching strategic programming knowledge explicitly[5].

When learning to program, different types of knowledge and skills have to be built: syntactic knowledge, i.e., knowledge of the language features; conceptual knowledge, i.e., knowledge of the semantics of the various constructs; strategic knowledge, i.e. the ability to use syntactic and conceptual knowledge in the most appropriate and effective way to solve programming problems. Strategic knowledge also includes the ability to exploit and combine solutions to known problems to solve new ones and is one of the hardest skills to acquire when learning to programm [5, 6, 11]. Terms such as goals[10] patterns [5, 6, 11]. schema,[9] strategies[4], and variables' roles[3] have been used to refer to this kind of knowledge. Several authors suggest that strategic knowledge should be taught explicitly, however there are three main issues to address:

Students often struggle with advanced computing courses, and comparatively few studies have looked into the reasons for this. It seems that learners do not master the most basic concepts, or forget them between courses. If so, remedial practice could improve learning, but instructors rightly will not use scarce time for this without strong evidence. Based on personal observation, program tracing seems to be an important pre-requisite skill, but there is yet little research that provides evidence for this observation. To investigate this, our group will create theory-based assessments on how tracing knowledge affects learning of advanced topics, such as data structures, algorithms, and concurrency. This working group will identify relevant concepts in advanced courses, then conceptually analyze their pre-requisites and where an imagined student with some tracing difficulties would encounter barriers. The group will use this theory to create instructor-usable assessments for advanced topics that also identify issues caused by poor pre-requisite knowledge. These assessments may then be used at the start and end of advanced courses to evaluate to what extent students' difficulties with the advanced course originate from poor pre-requisite knowledge.

Program-writing skills are often assessed with computer-based practical exams where students are required to design, write and test small programs according to specifications written in natural language. The evaluation of such tasks is usually based on the correctness of outputs (possibly with the support of automatic tools) and the quality of code. In this paper, we propose to evaluate program-writing assignments by using Soloway's notion of plans [34]. We illustrate this idea by defining a marking scheme for some typical program-writing tasks and apply this approach to evaluate the programs written during the final examination of an introductory programming course. We then compare the resulting marks with the marks originally assigned by the instructors of the course. Findings show that the use of plans in the marking process is feasible and requires an effort similar to the one required by a traditional evaluation process, and the focus on plans when examining the source code of students gives useful insights about their difficulties.

This working group asserts that Program Comprehension (PC) plays a critical part in the writing process. For example, this abstract is written from a basic draft that we have edited and revised until it clearly presents our idea. Similarly, a program is written in an incremental manner, with each step being tested, debugged and extended until the program achieves its goal. Novice programmers should develop their program comprehension as they learn to code, so that they are able to read and reason about code while they are writing it. To foster such competencies our group has identified two main goals: (1) to collect and define learning activities that explicitly cover key components of program comprehension and (2) to define possible learning trajectories that will guide teachers using those learning activities in their CS0/CS1 or K-12 courses.

The Bebras International Challenge on Informatics and Computational Thinking is a contest open to pupils of all school levels (from primary up to upper secondary) based on tasks rooted on core informatics concepts, yet independent of specific previous knowledge such as for instance that acquired during curricular activities.This paper describes the design choices, the architecture, and the main features of the web-based platform used to carry out the Italian Bebras contest.This platform includes functionalities needed by students, teachers, and Bebras staff during the execution of the challenge, tools to support the preparation of tasks and the training of students, instruments to evaluate the results and analyse data collected during the challenge.The platform is online since 2015 and it has managed the participation of around 25,000 teams and a significant amount of training sessions.

We present a new classification method for Bebras tasks based on the ISTE/CSTA operational definition of computational thinking. The classification can be appreciated by teachers without a formal education in informatics and it helps in detecting the cognitive skills involved by tasks, and makes their educational potential more explicit.

This paper analyses the results of the 2014 edition of the Italian Bebras/Kangourou contest, exploiting the Item Response Theory statistical methodology in order to infer the difficulty of each of the proposed tasks starting from the scores attained by the participants. Such kind of analysis, enabling the organizers of the contest to check whether or not the difficulty perceived by pupils was substantially different from that estimated by those who proposed the tasks, is important as a feedback in order to gain knowledge to be used both in ranking participants and in organizing future editions of the contest. We show how the proposed analysis essentially highlights that the 63% of tasks was perceived at the same level of difficulty estimated by those who proposed them, but a 37% of tasks were either easier or more difficult than expected.

Operator precedence languages were introduced half a century ago by Robert Floyd to support deterministic and efficient parsing of context-free languages. Recently, we renewed our interest in this class of languages thanks to a few distinguishing properties that make them attractive for exploiting various modern technologies. Precisely, their local parsability enables parallel and incremental parsing, whereas their closure properties make them amenable to automatic verification techniques, including model checking. In this paper we provide a fairly complete theory of this class of languages: we introduce a class of automata with the same recognizing power as the generative power of their grammars; we provide a characterization of their sentences in terms of monadic second-order logic as has been done in previous literature for more restricted language classes such as regular, parenthesis, and input-driven ones; we investigate preserved and lost properties when extending the language sentences from finite length to infinite length ($\omega$-languages). As a result, we obtain a class of languages that enjoys many of the nice properties of regular languages (closure and decidability properties, logic characterization) but is considerably larger than other families---typically parenthesis and input-driven ones---with the same properties, covering “almost” all deterministic languages.

This article describes the state of informatics education in the Italian secondary schools, highlighting how the learning objectives set up by the Ministry of Education are difficult to meet, due to the fact that the subject is often taught by teachers not holding an informatics degree, the lack of suitable teaching material and the expectations of pupils and families, who tend to identify informatics with the use of computer applications.

In this paper we consider the classes REC1 and UREC1 of unary picture languages that are tiling recognizable and unambiguously tiling recognizable, respectively. By representing unary pictures by quasi-unary strings we characterize REC1 (resp. UREC1) as the class of quasi-unary languages recognized by nondeterministic (resp. unambiguous) linearly space-bounded one-tape Turing machines with constraint on the number of head reversals. We apply such a characterization in two directions. First we prove that the binary string languages encoding tiling recognizable unary square languages lies between NTIME(2n) and NTIME(4n); by separation results, this implies there exists a non-tiling recognizable unary square language whose binary representation is a language in NTIME(4n log n). In the other direction, by means of results on picture languages, we are able to compare the power of deterministic, unambiguous and nondeterministic one-tape Turing machines that are linearly space-bounded and have constraint on the number of head reversals.