Attracting youngsters to choose to study computer science or computing (widely known as informatics in Europe) at high school has always been a challenge for computer science educators. The idea of developing a contest in informatics and computer fluency for school students originated in the Institute of Mathematics and Informatics, Lithuania (Dagiene, 2005, 2006). The contest has now been extended to become a “challenge” and is being held in more than 50 countries. The challenge name “Beaver” – in Lithuanian “Bebras” – was chosen in connection with the hard-working, intelligent, goal seeking and lively wild animal.

The Bebras challenge is an informatics education community-building model and is designed to promote informatics learning at schools by solving short concept-based tasks (Dagiene and Stupuriene, 2016). Alongside the initial goal of the Bebras project to motivate students to be more interested in topics of informatics, there is a strong intention to deepen algorithmic and operational thinking; more recently this is also extended to computational thinking.

Tasks are the most important component for developing computational thinking. In accordance with requirements, each Bebras task should include at least one informatics concept, attract children’s attention by a story, picture or interactivity, be short (fit on a computer screen), and not require specific technical knowledge. Part of the task development is the categorization of tasks with the intention of having a broad range of tasks across different content areas.

In this paper we examine the relationship between computational thinking and Bebras challenges with the intention of developing a new categorization system for informatics educational tasks that includes both content areas of computer science (knowledge) and computational thinking (skills). The developed categorization system was presented and tested at an international Bebras workshop in May 2016.

The term computational thinking was popularized in 2006 with Jeanette Wing’s article (2006) but actually originated with Seymour Papert’s constructionist learning ideas (1996). There are differences between these two definitions in that Wing’s definition is more focused on problem solving and Papert’s definition is more focused on ideas and analysis (Mannila et al., 2014). Subsequent research has expanded and interpreted the term further (Grover and Pea, 2013; Kalelioglu et al., 2016; Lu and Fletcher, 2009; Selby and Woollard, 2013; Wolz et al., 2011).

Computational thinking is not entirely embraced by all; critics suggest that the term is narrowing (Denning, 2009) or that computational thinking processes are widespread in other sciences (Hemmendinger, 2010). Indeed, definitions of computational thinking tend to be by example (Lee et al., 2011). However, there is a huge interest in computational thinking as a means of explaining the thinking processes in computer science in school education (K-12); in USA computational thinking underlies the new curricular developments of the Computer Science Teacher Association (CSTA) and Code.org; in England, computational thinking is at the core of a mandatory new Computing curriculum from age 5–16; and Google has launched a teacher development MOOC (Massive Open Online Course) purely around computational thinking. Attention has turned to the identification of a set of skills that can be seen to comprise a broad definition of computational thinking, and that encompass the logical and problem-solving skills and thought processes that are applied by computer scientists in their work.

A broad approach to computational thinking sees it as a problem-solving process that includes (but is not limited to) the following characteristics (ISTE&CSTA, 2011):

The work by Computing At School defines the five key computational thinking skills used in K-12 as 1) abstraction, 2) decomposition, 3) algorithmic thinking, 4) evaluation and 5) generalization (Csizmadia et al., 2015). A pertinent question relates to how much computational thinking development is around computer programming. Lu and Fletcher (2009) take the view that computational thinking can be separated from programming, and should be taught before programming teaching starts. Definition of computational thinking includes understanding the consequences of scale, not only for reasons of efficiency but also for economic and social reasons (Wing, 2006). The Computer Science Teacher Association in USA adds broader attitudes like the ability to deal with complexity and open-ended problems, tolerance for ambiguity, and ability to work with others to achieve a common goal.

The Bebras challenge exists to promote students’ interest in the fundamentals of informatics from the very beginning of their school lives and to motivate students to learn and master technology (Dagiene and Futschek, 2008). One of the drivers of the Bebras community is a shared understanding that learning such fundamental concepts and principles at an early age is very important for a deeper understanding of various computer science topics. Bebras focuses on informatics concepts by supporting an understanding of computer science phenomena and the development of computational thinking.

In practical terms, the Bebras contest is held within school time, and is available to students online using contest management environments or systems set up specifically for this purpose. It is held annually in the second week of November (Bebras International Challenge on Informatics and Computational Thinking, 2016). Each contest consists of 18 to 24 questions (tasks) to be solved by the students within 45–55 minutes. The contest is designed for a range of age groups at primary and secondary school students. Different task sets are chosen for different age groups, from 6–7 years old to the oldest students in school. The participants are supervised by teachers who may choose to integrate the contest into their teaching activities. Some countries use the Bebras challenge to strengthen collaborative learning. For example, in Germany pupils solve Bebras tasks by pairs during a contest and discussions are allowed between the pairs.

In the past few years, the number of the Bebras challenge participants has been growing substantially and exceeded 1.3 million in 2015. Many countries (Lithuania and UK among them) then organize a second round, usually face-to-face, by inviting participants who have scored highly to colleges or universities to undertake a further task solving contest.

In education, tasks (problems, questions) pay a significant role in engaging learners and keeping their motivation (Dagiene, 2010). Tasks should be chosen from a range of topics and contexts, using real data and an engaging scenario. The development of tasks for an educational contest is very important: they must cover fundamentals and as many sub-areas of discipline as possible. Moreover, the tasks have to be selected carefully, with regard to the different aspects of each task (i.e. how the topic is pitched) and interpretation of its attractiveness to pupils (whether it stimulates learning and discovery).

In general, it is important to develop a categorization system or taxonomy of educational tasks. Categorization is the basic cognitive process of arranging objects (in this case Bebras tasks) into categories, is a fundamental process in human and machine intelligence and is central to investigations and research in cognitive science (Cohen and Lefebvre, 2005). According to Jacob (2004), categorization is the process of dividing the world into groups of entities whose members are in some way similar to each other.

Since the Bebras project began there has been an interest in classifying and categorizing tasks. Early on in the project the following seven categories were used (Opmanis et al., 2006):

These categories were also used for developing new tasks as the main criteria to know which informatics topics and concepts need to be covered. Few years later the Bebras tasks’ categories were revised and a modified system was proposed (Dagiene and Futschek, 2008): 1)

Information comprehension;

Using a categorization system (2008 version) a recent set of tasks are shown in Table 2 (only task names). This gives an indication of kinds of problems and corresponding informatics topics included in the Bebras challenge.

As can be seen from Table 2, the most popular category is Algorithmic thinking with 31 tasks; the category Structures, patterns and arrangements has less than half of this number – 16 tasks, then Information comprehension has got 8 tasks, Puzzles (logical) – 6 tasks, Social, ethical, cultural, and legal issues and Using computer systems – 3 and 2 tasks, respectively. Some tasks belong to more than one category.

This categorization system has been used to the present day; however, there have been several criticisms (for example, in the paper of Kalaš and Tomcsányiová, 2009). In addition, limitations have become apparent over time. The limitations of this categorization are as follows:

Slovakian researchers had proposed an alternative categorization of the Bebras tasks (Kalaš and Tomcsányiová, 2009) using only four categories: 1)

Digital literacy;

A further reason to renew the categorization system is that the Bebras project objectives have changed, reflecting the global shift of focus from computer fluency to computational thinking. Bebras is now officially named as the international challenge on informatics and computational thinking; thus the categorization of tasks must include the ways in which computational thinking skills can be developed through completing the challenge.

In order to develop a fine-grained classification, we need task categories which combine the following aspects:

An example of 2) might be classifying tasks as: information and data; algorithms; languages and automata; informatics systems; and informatics, man, and society (Pohl and Westmeyer, 2015). Bebras tasks can include a wide range of concepts within informatics including algorithms and programs, both sequential and concurrent; data structures like heaps, stacks and queues; modelling of states, control flow and data flow; human-computer interaction; graphics; etc. Using a clear problem statement nearly all aspects of computer science as well as information technologies can be topics of Bebras tasks.

In Section 4 we saw that the categories have changed for Bebras tasks over the years the contest has been in existence. Here we introduce a revised set of categories which take into consideration the fact that completing a Bebras task demonstrates skills in computational thinking.

The area of computational thinking covers a range of different skills relating to problem-solving. The issue becomes the need to select a categorization system which is true to the definition of computational thinking whilst encompassing the range of skills that students utilize when solving Bebras tasks. There are two advantages to incorporating this into the revised category system: 1)

Task development can focus more closely on how computational thinking skills are being developed or utilized;

Before arriving at the system described above, other suggestions have been considered along the way. In seeking to both address the limitations of previous categorization and to have a system that was easily understood the authors needed to consult with informatics educators, particularly with the Bebras community.

An expert evaluation was planned during the annual Bebras community meeting in May 2016, where there would be representatives from almost every country in the community. There were around 80 members of the Bebras community present and an early version of the proposed system was explained and exemplified.

Feedback was taken from the members of the Bebras community and members were given tasks to categorize according to a version of this system. Feedback was taken both verbally and in writing and the comments of the community were used to refine the system.

In particular, the experience of sharing the categorization illuminated some of the points raised above:

The informal evaluation of the categorization informed the proposed schema, but it will be formally evaluated in the next round of the Bebras challenge. In this paper we are seeking to propose this as a new categorization and exemplify it with a number of recent tasks.

We present here some typical Bebras tasks and discuss the concepts that may be learned by these tasks. These tasks illustrate that we want to let the students discover the informatics concepts by themselves. To solve the tasks, the participants have to explore the stated problem domain and have to work with data, structures, activities and problems which are typical for informatics. Usually there are many different ways for finding a solution and thus the contest also supports students that prefer different types of solution strategies.

Six examples follow which illustrate the way that individual tasks can be categorized (Table 7). The tasks have been written by representatives from various countries within the Bebras community. Each task has been further developed within the community at task workshops.

As the Bebras challenge has become so widely known and used in schools in so many countries, any change of categorization system should be evaluated carefully. In our initial development of the new categories we have consulted with members of the Bebras community and presented an earlier version of the proposed system. The resultant system incorporates feedback from Bebras task developers and organizers from several countries. There are additional considerations that will have an impact on the implementation.

Firstly, this approach is more complex than the previous one. This will give us a more finely-grained classification that will produce much more useful output as the number of available tasks for teaching increases. However, a more finely-grained system requires more knowledge and understanding to implement correctly. It may be that task developers in different countries are not able (or willing) to assign this level of detailed categorization to each Bebras task. The question will arise as to who will carry out the categorization after the tasks have been chosen by the Bebras international workshop participants.

Secondly, computational thinking may not be familiar to all participating countries and understanding of the component skills presented here may not be shared. The community will need clear examples of computational thinking skills in Bebras tasks and explanations should be available to ensure some consistency of allocation of computational thinking skills to task.

Thirdly, related to this, we will need to develop some precision in allocating computational thinking skills to tasks. The description by Wing (2008) may lead us to think that computational thinking is everywhere and the composite skills all crop up in all tasks. A liberal interpretation such as this may render the computational thinking skill allocation to be meaningless. Computational thinking skills should only be allocated to a task where there is some element of computer science in the task that develops this skill.

With due attention to the points raised above, the purpose of this development is to build up a bank of Bebras tasks from recent years which are categorized using this framework. This will enable teachers and others to find useful tasks that they can use in the curriculum. It will also help task developers to focus on writing tasks around topics that are under-represented in the bank of tasks. An online search facility could be implemented to assist teachers looking for Bebras tasks on certain topics via keywords, concepts or computational thinking skills.

In this paper a categorization for Bebras tasks has been presented. This will be useful to task developers who hopefully will be able to spread new tasks over a range of categories. It will be most useful to teachers in school once tasks are classified according to this system: tasks can then be selected from previous contests that fit particular criteria to support either curriculum teaching or practice for the Bebras contest.

Tasks are very important both for competitors (students) and task developers (teachers): students should be encouraged to think about computer science, educators should think about harmonization of syllabus of informatics. Creative, interesting tasks are the main driver for the Bebras contests and motivation students to learn informatics.