Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/108881
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Type: Conference paper
Title: Concepts in K-9 Computer Science Education
Author: Barendsen, E.
Mannila, L.
Demo, B.
Grgurina, N.
Izu, C.
Mirolo, C.
Sentance, S.
Settle, A.
Stupurienė, G.
Citation: Proceedings of the 2015 ITiCSE on Working Group Reports, 2015 / Ragonis, N., Kinnunen, P. (ed./s), pp.85-116
Publisher: ACM
Issue Date: 2015
ISBN: 978-1-4503-4146-2
Conference Name: 2015 ITiCSE on Working Group Reports (ITICSE-WGR '15) (4 Jul 2015 - 8 Jul 2015 : Vilnius, Lithuania)
Editor: Ragonis, N.
Kinnunen, P.
Statement of
Responsibility: 
Erik Barendsen, Linda Mannila, Barbara Demo, Nataša Grgurina, Cruz Izu, Claudio Mirolo, Sue Sentance, Amber Settle, Gabrielė Stupurienė
Abstract: This exploratory study focuses on concepts and their assessment in K{9 computer science (CS) education. We analyzed concepts in local curriculum documents and guidelines, as well as interviewed K{9 teachers in two countries about their teaching and assessment practices. Moreover, we investigated the `task based assessment' approach of the international Bebras contest by classifying the conceptual content and question structure of Bebras tasks spanning five years. Our results show a variety in breadth and focus in curriculum documents, with the notion of algorithm as a significant common concept. Teachers' practice appears to vary, depending on their respective backgrounds. Informal assessment practices are predominant, especially in the case of younger students. In the Bebras tasks, algorithms and data representation were found to be the main concept categories. The question structure follows specific patterns, but the relative frequencies of the patterns employed in the tasks vary over the years. Our analysis methods appear to be interesting in themselves, and the results of our study give rise to suggestions for follow-up research.
Keywords: CS concepts, K-9 education, curricula, teachers, assessment, Bebras
Rights: © 2015 ACM. Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.
DOI: 10.1145/2858796.2858800
Published version: http://dl.acm.org/
Appears in Collections:Aurora harvest 8
Computer Science publications

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