Towards Implementing Computational Thinking in Mathematics Education in Austria

Abstract

When Austria introduced the new mandatory subject “Basic Digital Education” in September 2018, computational thinking finally made it into the curriculum. Schools can choose to offer specific subjects or dispense the content into already existing subjects where seen fit. But research shows that it is better to implement computational thinking in other subjects than to teach it as a stand-alone subject because it tends to be separated from real-world problems. The new content was installed rashly and without any suitable preparation for teachers or schools. As a result, schools need support in implementing teaching concepts for cross-curricular introductions to computational thinking. This paper highlights possible applications of computational thinking in the current mathematics curriculum presented as a quantitative study.

Full text

TOWARDS IMPLEMENTING COMPUTATIONAL THINKING IN
MATHEMATICS EDUCATION IN AUSTRIA
Corinna Kröhn1, Jakob Skogø², Sara Hinterplattner³ and Barbara Sabitzer4
1Johannes Kepler University Linz, Department of STEM Education, Austria,
corinna.kroehn@jku.at
2Johannes Kepler University Linz, Department of STEM Education, Austria,
jakob.skogo@jku.at
3Johannes Kepler University Linz, Department of STEM Education, Austria,
sara.hinterplattner@jku.at
4Johannes Kepler University Linz, Department of STEM Education, Austria,
barbara.sabitzer@jku.at
INTRODUCTION
When Austria introduced the new mandatory subject “Digital Education” in September
2018, computational thinking finally made it into the curriculum. Schools can choose
to offer specific subjects or dispense the content into already existing subjects where
seen fit. Computational thinking (CT) tools and methods found their way into multiple
scientific fields (Orton, et al., 2016) but overall, research shows that it is better to
implement CT in other subjects than to teach it as a stand-alone subject because it tends
to be separated from real-world problems (Weintrop, et al., 2015). Therefore, Weintrop
et al. (2015) published a set of ten core CT skills, which we will refer to in this poster
as computational thinking skill (CTS-#) 1 to 10 (see table 1).
Set of computational thinking skills (CTS)
- CTS-1: Ability to deal with open-ended problems
- CTS-2: Persistence in working through challenging
problems
- CTS-3: Confidence in dealing with complexity
- CTS-4: Representing ideas in computationally
meaningful ways
- CTS-5: Breaking down large problems into smaller
problems
- CTS-6: Creating abstraction for aspects of problem at
hand
- CTS-7: Reframing problems into a recognizable
problem
- CTS-8: Assessing strengths/weaknesses of a
representation of data/representational system
- CTS-9: Generating algorithmic solutions
- CTS-10: Recognizing and addressing ambiguity in
algorithms
Table 1: Set of computational thinking skills (Weintrop, et al., 2015)
ANALYSIS
This poster presents our interpretation as to where Weintrop’s 10 CTS can be
implemented and trained in the subtopics of the current curriculum of Austria’s lower
secondary mathematics education. Topics in the Austrian mathematics curriculum in
lower secondary education are divided into four areas: (1) working with numbers and
units, (2) working with variables, (3) working with geometric shapes and bodies, and

(4) working with models and statistics (RIS, 2020). There are 33 subtopics in grade 5,
25 in grade 6, 26 in grade 7, and 19 in grade 8, giving us a total of 103 subtopics.
CTS-1 could be found in solving and interpreting equations or formulas, comparing
different models, working with linear functions, calculating approximations and
bounds and in justifying the Pythagorean Theorem. In total we found 19 applications
for this skill. As CTS-2 is a very opened skill, it is possible to match it to any topic of
the curriculum. 28 topics could be matched to CTS-4, whereas most of them were
found in grade five. Each topic in the mathematics curriculum of years five to eight
deals with breaking down large problems into smaller ones. Therefore, CTS-5 is also
one of the leading skills. On the contrary, there were only 7 topics that could be
matched to the skill “creating abstraction for aspects of problem at hand”. CTS-7 could
be found in each lesson, as it represents one of the core aspects of mathematics, while
CTS-8 is present only in the topic “working with statistics and models”. Moreover,
CTS-9 “generating algorithmic solutions” is part of an everyday mathematics lesson.
Recognizing ambiguity in algorithms (CTS-10) is a skill that is not found very often in
grades five to eight.
An interesting fact is that subtopics with CTS decrease from grade 5 (60%) to grade 8
(53%) but the overall implementation is very stable. It needs further investigation if
this is related just to the topics or to the matching of CTS. Of course, the matching of
CTS witch the single subtopics is subjective and needs further review of more than just
three teachers.
CONCLUSION AND OUTLOOK
In this poster we have examined possible applications of CT in the current mathematics
curriculum. We found out that already lots of CTS are implemented without adding
extra content, whereas some of them are highly represented and some are very special
and rare. In the upcoming months, we will concentrate on the further investigation of
matching the CTS to the subtopics.
REFERENCES
Orton, K., Weintrop, D., Beheshti, E., Horn, M., Jona, K., & Wilensky, U. (2016).
Bringing Computational Thinking Into High School Mathematics. Proceedings
of International Conference of the Learning Sciences.
RIS. (2020). Rechtsinformationssystem des Bundes. Retrieved March 12, 2020, from
Bundesrecht konsolidiert: Lehrplan neue Mittelschule:
https://www.ris.bka.gv.at/Dokumente/Bundesnormen/NOR40207228/NOR402
07228.pdf
Weintrop, D., Behesti, E., Horn, M., Orton, K., Kemi, J., Trouille, L., & Wilensky, U.
(2015). Defining Computational Thinking for Mathematcis and Science
Classrooms. New York: Springer Science+Business Media.