Bebras as a Teaching Resource: Classifying the Tasks Corpus Using Computational Thinking Skills Violetta Lonati, Dario Malchiodi, Mattia Monga, Anna Morpurgo Dept. of Computer Science, Università degli Studi di Milano, Milan, Italy {lonati, malchiodi, monga, morpurgo}@di.unimi.it ABSTRACT We present a new classification method for Bebras tasks based on the ISTE/CSTA operational definition of compu- tational 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. 1. THINKING COMPUTATIONALLY WITH BEBRAS TASKS The Bebras “International Challenge on Informatics and Computational Thinking” (http://bebras.org/) [4] had about one and a half million participants from more than 50 coun- tries in the last edition. Bebras tasks can be the starting point for further educational activities (a recent proposal is [2]), provided they are categorized to make them easier to retrieve and to use during curricular activities. Indeed, tasks categorization is an issue in the Bebras community since the beginning [3]. A survey we conducted in Italy after the Be- bras’ last edition (2016) confirms the need by teachers for such a classification: we propose to base it on the opera- tional definition of computational thinking [1] developed by ISTE (International Society for Technology in Education) and CSTA (Computer Science Teachers Association). To decide whether a task belongs to a class ot not, one should answer the question“Would you choose this task to promote or teach this Computational Thinking skill?”. Logically organizing data. Typical tasks in this class deal with: organization of data according to given criteria (i.e., database), use of data structures to make data easier to process, organization of data so that they enjoy relevant properties as in cryptography or compression. Logically analyzing data. Beside “logical problems” that require logical inference, deductive reasoning, and draw- ing conclusions about the data presented in the task, in this class we find tasks that require accurate observations (e.g., recognizing patterns), or a systematic approach to establish whether the data of the problem satisfy certain properties. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). ITiCSE ’17, July 3–5, 2017, Bologna, Italy. c  2017 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-4704-4/17/07.. DOI: http://dx.doi.org/10.1145/3059009.3072987 Representing information. Typical tasks in this class deal with the digital representation of data, or their vi- sual representation with diagrams like histograms or charts. Other tasks refer to data structures to represent relevant properties (e.g., graphs for binary relations). Algorithmic thinking. Tasks in this class require to go beyond generic intuitive approaches, towards settings that enable automatic processing, for instance by: decompos- ing a problem into components; combining primitive opera- tions; understanding some formal procedure (e.g., execute it or compute/recognize its output); applying some transition rules to a system in a given configuration; and so on. Identifying strategies. Problem solving and in partic- ular finding a non-trivial algorithmic strategy to tackle a problem is the theme of this class of tasks. Analyzing algorithmic solutions. This class contains tasks concerning global characteristics of the considered al- gorithm, like correctness or complexity. Other typical tasks in this class are those inspired by optimization problems. Implementing algorithmic solutions. Tasks in this class may be referred to as programming or coding tasks since the focus is on the implementation of algorithms ac- cording to a formal syntax defined in the task. 2. REFERENCES [1] Computational thinking for K-12 education. https://csta.acm.org/Curriculum/sub/CurrFiles/ CompThinkingFlyer.pdf, 2011. [2] V. Dagien˙ e and S. Sentance. It’s computational thinking! Bebras tasks in the curriculum. In ISSEP 2016, vol. 9973 of LNCS, p. 28–39, 2016. [3] V. Dagien˙ e and G. Futschek. Bebras international contest on informatics and computer literacy: Criteria for good tasks. In LNCS, vol. 5090, p. 19–30, 2008. [4] V. Dagien˙ e and G. Stupuriene. Informatics education based on solving attractive tasks through a contest. IFIP–KEYCIT 2014, p. 51–62, 2014.