IAFOR Journal of Education: Technology in Education

Volume 10 – Issue 2 – 2022

Using Computational Thinking to Facilitate Language Learning: A Surveyof Students’ Strategy Use in Austrian Secondary Schools
Marina Rottenhofer
Johannes Kepler University Linz, Austria
Lisa Kuka
Johannes Kepler University Linz, Austria
Sandra LeitnerJohannes Kepler University Linz, Austria
Barbara Sabitzer
Johannes Kepler University Linz, Austria51

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Abstract
After Jeanette Wing in 2006 described computational thinking (CT) as a fundamental skill foreveryone just like reading or arithmetic, it has become a widely discussed topic all over theworld. Computational thinking is a problem-solving skill set that is used to tackle problems incomputer science. However, these skills, such as pattern recognition, decomposition,
abstraction, generalization, and algorithmic thinking, are useful in other domains, as well. This
study focuses on the use of CT skills to approach complex linguistic learning tasks in theforeign language classroom. To foster these problem-solving skills, an innovative method isused. The authors take advantage of computer science (CS) models (e.g. Unified Modeling
Language – UML) and transform them into a teaching and learning tool. This paper describes
the design and implementation of a survey used to detect students’ use of learning strategies
that are linked to computational thinking. This survey is an instrument used in a multiple-casestudy and was administered at the beginning of the interventions. The participants of the study
were learners of English and Spanish (n=66) from two secondary schools. Results indicatedthat the students were medium to low users of learning strategies that demand problem-solving
skills related to computational thinking. Differences by gender were also found, with females
reporting higher use of learning strategies than males. To conclude, the study showed a low
use of strategies among students and highlighted the importance of introducing students tolearning strategies and fostering skills needed for future professional life.
Keywords: computational thinking, digital literacy, foreign language learning, learning
strategy, modeling, visualization

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Fast technological development shapes our future and has an impact on our personal, social aswell as professional lives. For this reason, schools are confronted with high demands to equip
students with knowledge and skills that help them to cope with the challenges of the future.
According to the Future of Jobs Report 2020 (World Economic Forum, 2020), the top skills
required in 2025 are divided into four groups: problem-solving, self-management, working
with people, and technology use and development. Analytical thinking, active learning, andlearning strategies as well as complex problem-solving are at the very top of this ranking. One
problem-solving skill set, which has the potential to prepare students for future demands iscomputational thinking (CT).
Since 2006, CT has gained considerable attention as one of the core skills next to reading,writing, as well as arithmetic (Wing, 2006) and has already become part of compulsory
education in many countries, including Austria (BMBWF, 2018). With this transformation, CT
and CS models have found their way into the foreign language classroom as well. In ourmultiple case study that is based on Yin’s model (2009), diagrams from the field of computerscience (CS) are implemented as a teaching and learning strategy to foster computational
thinking in foreign language education. In computer science, on the other hand, diagrams basedon the UML (Unified Modeling Language) [Seidl et al., 2015] or Chen notation (Chen, 1976)
are used to visually depict software systems or database structures. With the use of these
diagrams in a different context as a teaching and learning strategy, the authors reach several
goals at once. Firstly, many years of implementation and research have shown that modelingwith CS diagrams is a useful visualization strategy for learners of all ages, is easy to acquire
for teachers and students, and is applicable in all subjects (Demarle-Meusel et al., 2020;
Rottenhofer et al., 2021; Sabitzer & Pasterk, 2015). Secondly, learners get in contact with arepertoire of static and dynamic CS diagrams outside computer science lessons which may help
them to familiarize themselves with this field, spark their interest, and introduce basic computer
programming concepts. Thirdly, depicting learning content with a model requires cognitive
flexibility and fosters computational thinking skills such as abstraction, generalization, patternrecognition, and algorithmic thinking. To summarize, learners do not only get in touch withcomputer science concepts but also receive a useful learning tool that they can apply in different
learning settings to solve complex tasks and memorize information long term. In the current
research, CS models are implemented as graphic organizers in several foreign language
learning settings. This paper presents the results of a survey that learners received at the
beginning of the intervention. This survey aimed to examine to what extent the participants use
learning strategies that are connected to computational thinking. For this, a survey on learning
strategies had been modified from the two German questionnaires LSN – Learning Strategy
Use (Martin & Nicolaisen, 2015) and LIST – Learning Strategies at University (Wild &Schiefele, 1994) by linking it to the areas of computational thinking.
Literature Review
In the 1980s, computational thinking (CT) was first mentioned by Papert (1980) in his workon teaching computer literacy at an early age where he saw CT as the result of his
constructionist learning theory. Twenty-six years later, the term was boosted by Jeanette Wing
as “a universally applicable attitude and skill set everyone, not just computer scientists, would
be eager to learn and use” (2006, p. 1). Since then, much research has been done and numerous
definitions emerged, many of which focus on programming, leading to the assumption that
programming is a necessary tool to teach CT (Voogt et al., 2015). However, everyone should
acquire CT, not only programmers (National Research Council, 2010) and students should get
exposed to CT long before programming (Lu & Fletcher, 2009). To date, several researchers

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have investigated the integration of CT in foreign language learning (FLL) [Barr & Stephenson,
2011; Hsu & Liang, 2021; Lu & Fletcher, 2009; Parsazadeh et al., 2021]. However, to the bestof the authors’ knowledge, none of them have investigated hands-on approaches to foster CT
in FLL in depth.
In this study, computer science (CS) models are used as a form of graphic organizer (GO) tofoster CT skills and get students engaged with computer science concepts outside the CS
lessons. GOs originally derive from Ausubel’s cognitive learning theory (1962), where heapplied them as advance organizers at the beginning of the learning process. A graphicorganizer is defined as a “visual and graphic display that depicts the relationships between
facts, terms, and or ideas within a learning task” (Hall & Strangman, 2002, p. 2). According toWillis (2007, p. 315), this creative approach “coincides with the brain’s style of patterning”
and allows students to connect the information to previously stored memories, cluster
information, discover patterns, and sort and store new data. This description is well-alignedwith CT and demonstrates the usefulness of using models to foster these problem-solving skills.
Furthermore, according to research, the use of GOs is particularly useful for students withlearning difficulties (Dexter & Hughes, 2011; Kim et al., 2004; Sousa, 2017). These results
confirm the authors’ experiences of the benefit of modeling, especially for pupils with learning
deficits. A major cause of learning difficulties in FLL such as dyslexia lies in struggles withrecognizing and using language patterns in the new language. Even if pupils suffer fromdyslexia, they may still have good intellectual abilities. However, they may not be able to noticesimilarities and differences between vocabulary and word formation patterns (i.e. semantic
processing) in the foreign language compared to their native language (Schneider & Crombie,
2012).
The difficulties in recognizing language patterns make learning difficult. However, modelingwith CS diagrams can support these pupils in their learning process. By teaching withappropriate diagrams in common FLL environments, all pupils, but especially pupils withlearning difficulties, benefit as they acquire learning content easier and thereby learn to speak
the foreign language more effectively. The following sub-section presents learning theories
connected to graphic organizers and computational thinking.
Modeling, Computational Thinking, and Theories of Learning
The use of CS models as GOs is a teaching method that combines cognitivist and constructivist
learning theories and computer science concepts to foster computational thinking skills.
Cognitivism emerged in the late 1950s and, in comparison to behaviorism that is based on thestimulus-response theory, relied on cognitive sciences by focusing on cognitive processes(Ertmer & Newby, 2013). Several cognitive learning theories support the use of GOs such as
the subsumption theory, schema theory, dual coding theory, and cognitive load theory.
According to Ausubel’s (1962) subsumption theory on meaningful learning, learning andretention are facilitated when new information is related to already existing cognitive
structures. To achieve this, he suggested the use of advance organizers. Anderson and Pearson(1988) claimed that the subsumption theory is consistent with his schema theory, where aperson has understood a text when they have found a mental “home” for the information in thetext, or else “that he or she has modified an existing mental home in order to accommodate thatnew information” (Anderson & Pearson, 1988, p. 2). The dual coding theory postulates that
there are two systems, verbal and imagery, for processing information (Clark & Paivio, 1991).
In other words, when information is presented in both forms, e.g. verbally and visually with a54

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model, chances of retrieval are increased. Lastly, the cognitive load theory by Sweller et al.
(1998) assumes that the working memory has a limited capacity and can therefore only deal
with a limited amount of information at a certain time. Used appropriately, GOs can reduce
cognitive load and lead to better learning outcomes (Rahmat, 2020).
Constructivism is often considered a branch of cognitivism. However, the main difference isthat constructivist psychologists believe “that the mind filters input from the world to produce
its own unique reality” (Ertmer & Newby, 2013, p. 55). In other words, what we know of thereal world is constructed personally with our own interpretations—“humans create meaning as
opposed to acquiring it” (Ertmer & Newby, 2013, p. 55). Out of Piaget’s constructivism, Papert
developed the learning theory constructionism, where the focus shifts “from universals to
individual learners’ conversation with their own favorite representations, artifacts, or objectsto-think with” (Ackermann, 2001, p. 4). According to Ali and Yahaya’s systematic review,
constructivist learning theory is primarily used in computational thinking focusing on primaryand secondary school levels, followed by constructionism (2020). However, they also claimthat there are many studies on CT that do not focus on learning theories at all. Bellettini et al.
postulate a social-constructivism approach to informatics and CT where the teacher’s role is to“support the construction of knowledge through setting up contexts and scaffolding materialfavoring the activation of the learning process, in which the ultimate actor is the learner itself”(2018, p. 4). This means that teachers should motivate students to use active techniques in theirlearning process.
Computational Thinking and Language LearningThis section describes the core elements of computational thinking that are the focus of thecurrent study. In the literature, CT is represented with different manifestations, core concepts,
and skills. The Joint Research Center (JRC) from the European Commission (Bocconi et al.,2016) conducted a literature review and analyzed the skills emerging from the most prominent
papers on CT. As a result, they developed a list of core elements, which are abstraction,
algorithmic thinking, automation, decomposition, debugging, and generalization. In this study,
the authors refer to the elements proposed by the JRC, extend them with pattern recognition(Curzon et al., 2019), and link them to foreign language teaching. Additionally, this section
gives best practice examples on how to use modeling and CT as techniques that support
students in creating new knowledge and engaging them actively in the learning process.
Decomposition
Decomposition is the process of dividing a bigger problem into smaller sub-problems (Barr &Stephenson, 2011). This divide-and-conquer strategy helps to facilitate the understanding of aproblem and, thus, can be solved systematically as well as individually. In language education,
this is a skill widely used. For example, when writing a paper only a few people would write itstraight from the beginning to the end. Usually, the structure of it is well thought-through andheadlines like “introduction”, “methodology”, “conclusion”, and so forth. are created first.
Then, additional arguments or topics are found for the main body. The introduction andconclusion are also known to be written last. This process illustrates decomposition at its best.
Abstraction
Abstraction describes the process of reducing complexity by omitting unnecessary details.
Thus, the main characteristics of a problem or item are defined. Everyone handles abstract55

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objects daily, for example, when using a map. Every map is a simplified presentation of reality.
When learning about giving directions in the language classroom, subway maps are a common
tool taken from real life. Another example is writing a summary. A summary is characterized
by leaving out unnecessary details and concentrating on the most important information.
Hence, training on writing summaries and encouraging students to take notes or highlightimportant information in a text, also helps to strengthen computational thinking skills. In
computer science, class and object diagrams are used to visualize various components of asystem and their relations (Seidl et al., 2015). Whereas class diagrams describe the abstract
model of a system (e.g. animal), object diagrams illustrate concrete objects (e.g. cats and dogs).
In the language classroom, these models can be used to develop new vocabulary about specifictopics, illustrate relations and hierarchies, and categorize these items. Figure 1 shows a simple
example of one class. As can be seen, the name of a class is always a noun, attributes are seen
as adjectives, and methods as verbs. Thus, students can also practice the difference between
these word classes and word formation.
Figure 1
Class Diagram

Another model, which is used in computer science frequently, is the entity-relationship model(ER model) [Chen, 1976]. It consists of three elements – rectangles as “entity-types” that areused as nouns, diamond shapes as “relationship types”, and the ellipses as “attributes” that
describe the characteristics of the nouns. The ER model can be used as an intermediate stepwhen writing summaries, supporting especially students with learning difficulties when writing
texts. Figure 2 shows a model where elements of a text on COVID-19 were transformed into
an ER diagram with concrete and generalized terms. Usually, in computer science, the ER
diagram only uses generic terms instead of specific terms since it represents a type of a system
and not an instance (Bagui & Earp, 2003). However, in the language classroom, this can beadapted by using concrete terms of a text and/or abstract terms.

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Figure 2
Entity-Relationship Diagram

Pattern Recognition and Generalization
Finding patterns is something inherently human, and the brain can remember patterns moreeasily (Grabmeier, 2018). As soon as patterns, similarities, and connections are found, ageneralization of these can be done, and already known problem-solving strategies which
worked for a similar scenario can be re-used. Also, in many cases, it is possible to draw
conclusions from a part or general to the whole. Every language educator who used an
inductive method is already familiar with pattern recognition and generalization. For example,
the teacher provides various grammatical items such as sentences in the past tense using regularverbs. Subsequently, the students have to find grammatical rules based on the examples given.
Figure 3 illustrates how the use of an activity diagram can visualize the grammatical rules, suchas the use of “for” and “since” in English. Also, it can function as a step-by-step guide.
Figure 3
Activity Diagram showing the Use of For and Since in English

Another example in which generalization in the language classroom is used is by giving
examples and prompts in which generalized terms like genre, title, author, and so on, are used.

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The students then have to find the actual genre, title, and author of the presented text, that is,gothic novel, Frankenstein, and Mary Shelley.
Algorithmic Thinking
An algorithm is often described as a step-by-step guide comparable to a recipe. Teaching
students to write good recipes can be compared to writing an algorithm. Not only is it important
to be precise in its formulation, but also to think systematically about which step comes after
the other. How long do you have to beat the eggs to make your cake heavenly fluffy? Usually,teachers give the exercise to simply write a recipe, but for students with learning difficulties, it
may be a good idea to sketch the information at first via an activity diagram. With thisintermediate step, they not only have the structure first but also the key vocabulary needed forthe exercise. Figure 4 shows an example of an activity diagram created for a recipe.
Figure 4
Algorithm for Making Tea

Testing and Debugging
It is not enough to find solutions for problems; it is also necessary to systematically analyze
these solutions using skills such as testing, tracing, as well as reasoning. Based on this accurate
analysis, errors can be fixed and results predicted and verified. In the language classroom,
students can be trained to achieve this by correcting (one’s own) errors, for example, in a filling
the gap exercise or when learning how to give feedback.
Automation
Automation is a work-saving process in which a machine or computer is instructed to perform
a series of repetitive tasks quickly and efficiently compared to the processing power of ahuman. This is the only skill that usually is not very common in the language classroom,58

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although there would be possibilities to include programming as well, for example, with theuse of the programming language Scratch or exercises from machinelearningforkids.com.
Methods
BackgroundIn the school year 2020/21, a multiple case study (Yin, 2009) on modeling as a teaching andlearning strategy to foster computational thinking was conducted. The subjects of the casestudies were partner schools of the COOL (computer sciences-supported, cross-curricular, andcooperative open learning) Lab at the Johannes Kepler University in Linz. The JKU COOL
Lab is an innovative teaching and learning lab for teachers, children of all ages, and university
students. It focuses on computer science, computational thinking, and digital literacy. The lab
has many offerings including workshops, weekly clubs for gifted students, theater shows on
digital education, teacher training, and so forth. In addition to offerings for all interested parties,
the lab works intensively with several partner schools where projects are implemented andresearched over a longer period. In the multiple case study, modeling was implemented in four
foreign language classes of two partner schools to find out more about (1) teachers’ andstudents’ perceptions of modeling as a teaching and learning strategy, (2) the chances andchallenges of the implementation of modeling and (3) computational thinking as a problemsolving strategy. This paper focuses on computational thinking as a problem-solving strategy
and presents the results of a survey administered to all the participants of the multiple casestudy at the beginning of each of the interventions. This survey aimed to find out more about
students’ use of learning strategies that are related to computational thinking. In particular, thefollowing research questions were explored:
1. Is there a connection between learning strategies and the areas of computational
thinking as a problem-solving strategy?
a. If yes, what strategies are associated with computational thinking?
2. Do students use strategies associated with computational thinking to better understandand process learning content?
3. Does the use of learning strategies differ by gender?
Participants
The questionnaire was administered to a total of 66 students (nf = 31, nm = 35) from two partner
schools (PSn) of the JKU COOL Lab. In those partner schools, several teachers collaborated
intensively with the researchers and two of them were willing to participate in this study. Thus,
random sampling was not possible. Before conducting the study, written permission was
obtained from the school principals as well as the parents of the participants. Both groups ofPS1 (English class) and PS2 (Spanish class) were involved in the multiple case study forseveral months working with models as a teaching and learning strategy to foster computational
thinking skills. To get an insight into students’ computational thinking strategy use, the survey
was administered at the beginning of the intervention. In the English group composed of 51
students, there were 29 males and 22 females with a mean age of 14.25 and a standard
derivation of 1.369. The Spanish group consisted of 15 students, 6 males, and 9 females witha mean age of 13.27 and a standard deviation of 1.981. At the beginning of the study, none of
the students were familiar with modeling and the concept of computational thinking. The
demographic information of the participants is presented in Table 1.

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Table 1
Participants in the Study
School Subject

N

Male Female

PS1
PS2

51
15

29
6

EnglishSpanish

22
9

Mean
Age
14.25
13.27

SD
1.369
1.981

Instrument
In this study, a paper-based questionnaire on learning strategies was administered, consistingof 37 Likert-formatted items. For this survey, the authors adopted items from the LSN(Learning Strategy Use) questionnaire from Martin and Nicolaisen (2015) and combined itwith four items from the LIST (Learning Strategies at University) questionnaire (Wild &Schiefele, 1994) bringing it up to 37 items.
The four LIST items were the following:
1.
2.
3.
4.

I try to organize the material so that I can easily remember it.
I visualize the material to be learned.
I learn key terms by heart to help me remember important areas of content.
I memorize a self-made overview with the most important terms.

The frequency was measured with a five-point Likert scale ranging from 1 (very rarely) to 5
(very often). The questionnaire was issued in German and was translated for this paper.
Procedure
The questionnaire was administered to the students at the beginning of the multiple case study
in their regular language lessons. The participants of the study had no previous knowledge ofmodeling and computational thinking. The survey had no time limit to make sure the students
were not under any pressure and could think deeply about their answers. The students needed
approximately 10-15 minutes to respond to all the items of both Part 1 and Part 2 of thequestionnaire.
To identify which learning strategies are used that relate to CT skills and visualization, threeexperts independently analyzed the first part of the questionnaire and filtered out the items (137) that can be assigned to the CT skills mentioned in section 2 on the one hand and tovisualization strategies on the other. After this analysis, the experts discussed the respective
selection and decided on the items used and their assignment to the respective categories. The
statistical analysis was then conducted using the statistical software IBM SPSS Statistics 23.
Findings
Learning Strategies Related to Computational Thinking
The first research question sought to answer whether there is a connection between learning
strategies and the core elements of computational thinking and if yes, which ones. The expert
analysis has shown that a total of 22 Likert items can be related to computational thinking andvisualization strategies. Specifically, 18 items from the Martin and Nicolaisen questionnaire

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(2015) relate to computational thinking, and two items each of the LIST relate to visualization
and CT (Wild & Schiefele, 1994). Table 2 shows an overview of the remaining items and theallocation to the individual areas. Since all the CT skills are intertwined, some items havemultiple assignments. From the core CT skills proposed in section 2, all the skills except“automation” could be associated with items in the questionnaire.
Use of Strategies Related to Computational Thinking
The second research question investigated whether students use strategies associated withcomputational thinking to better understand and process learning content. To find out which of
the CT skills according to the learning strategies are used the most, descriptive statistics,
including means and standard deviation of the six CT categories as well as the category related
to visualization was used. As illustrated in Table 3, three categories are above the middle of
the Likert scale and four are below it. Testing and debugging strategies are used mostfrequently (M= 3.28; SD= .76), closely followed by decomposition strategies (M= 3.10; SD=.78) and algorithmic thinking (M= 3.03; SD= .79). On average, the categories below the mid
point are: generalization (M= 2.88; SD= .90), abstraction (M= 2.83; SD=.89), patternrecognition (M=2.78; SD= .87) and lastly, visualization (M= 2.39, SD=1.09). According toTable 3, all categories had a mean score at the medium or low level. None of the categories
had a mean value at a high level above 4.0.
Besides the descriptive analysis of the seven categories mentioned above, individual items
were also ranked and highlighted as the five most and least commonly used learning strategies.
As seen in Table 4, the most common strategy is to use the internet or dictionary when words
are unclear (M=3.91; SD=1.32), whereas the least common strategy (see Table 5) is to create
drawings or sketches to better see how things belong together (M=1.95; SD=1.07). Looking at
all 22 items, none of the items has a mean value at a high level above 4.0. Half of the items(N=11) have a mean score at the medium level above 3.0, whereas 10 items are above 2.0 andonly one item below.

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Table 2
Survey Part 1: Likert-Items
Nr.

Item

Category

1

When I have to study for an exam, I make a short summary.

AB

2

I often do drawings or sketches to better see how things belong together.

V

3

I underline the important passages in the textbook.

AB

4

I try to organize the material so that I can easily remember it.

AL5

I visualize the material to be learned.

V

6

When I learn something new, I try to figure out what to do with thatGE

knowledge (what is the practical use?).
7

I wonder how what I am learning relates to what I have known so far.

AB

8

I wonder if what I am learning or hearing is logical.

TD

9

I wonder if there could be other explanations for what I read or hear.

GE, TD, PR

10

Instead of studying for a long time, I spread the work over several days.

DC11

I repeat things (such as foreign language vocabulary) in small portions, but

DC, AL, PR

regularly (e.g. every day for 10-15 min).
12

I learn key terms by heart to help me remember important areas of content.

AB, GE

13

I memorize a self-made overview with the most important terms.

AB, GE

14

When my learning is not going well, I try to change something and see if it

TDgoes better.
15

Before I start to work, I set myself clear goals.

DC

16

While studying, I check whether I am still on the right track.

TD

17

When I stop working, I check whether I have achieved my goals.

TD

18

When I study, I make a realistic schedule.

AL, DC19

I make sure that I have enough time the day before an exam to review all ofDCthe material again.
20

Before an exam or a lecture, I think about what to do if things do not go well.

AL

21

I look for more information in books or on the Internet if something is notTDquite clear to me.
22

If I do not understand words, I look them up on the Internet or in a dictionary.

TD

Abbreviations: DC Decomposition, PR Pattern Recognition, AB Abstraction, AL Algorithmic Thinking, GE
Generalization, TD Testing & Debugging, V Visualization

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Table 3
Descriptive Statistics – Computational Thinking Skills. N= 66
Minimum

Maximum

Mean

Std. Deviation

Decomposition

1.40

5.00

3.1010.77854

Pattern Recognition

1.00

4.67

2.7778.86791

Abstraction

1.00

4.75

2.8258.89224

Algorithmic Thinking

1.25

5.00

3.0253.79124Generalization

1.00

4.50

2.8750.90219Testing & Debugging

1.29

4.86

3.2835.75696Visualization

1.00

4.50

2.39391.09374

Table 4
Top 5 of the Most Commonly Used Learning Strategies. N= 66
Nr.
1. If I do not understand words, I look them up
on the Internet or in a dictionary.
2. I wonder if what I am learning or hearing is
logical.
3. I make sure that I have enough time the day
before an exam to review all of the material
again.
4. I try to organize the material so that I caneasily remember it.
5. Before I start to work, I set myself clear goals.

Category Min.

Max.

Mean

Std.
Dev.

22

TD

1

5

3.91

1.321

8

TD

1

5

3.73

1.089

19

AL

1

5

3.46

1.251

4

AL

1

5

3.41

1.265

15

DC

1

5

3.38

1.034

Abbreviations: DC Decomposition, AL Algorithmic Thinking, TD Testing & Debugging

Table 5Top 5 of the Least Commonly Used Learning Strategies. N= 66
Nr.

Category

Min.

Max.

Mean

Std.
Dev.

2

V

1

5

1.95

1.073

1

AB

1

5

2.52

1.099

3. When I study, I make a realistic schedule.

18

AL, DC

1

5

2.61

1.341

4. I wonder how what I am learning relates towhat I have known so far.

7

AB

1

5

2.65

1.295

5. While studying, I check whether I am stillon the right track.

16

TD

1

5

2.73

1.103

1. I often do drawings or sketches to bettersee how things belong together.
2. When I have to study for an exam, I make ashort summary.

Abbreviations: DC Decomposition, AB Abstraction, AL Algorithmic Thinking, TD Testing & Debugging, V
Visualization

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Learning Strategies, Computational Thinking, and Gender
The last research question sought to answer whether strategy use related to CT differs bygender. The independent Sample T-Test revealed that female students reported statisticallymore frequent use of learning strategies related to CT than male students did. Female students
have a higher mean score in relation to all learning strategies (Mf = 3.31; SDf = .58, Mm= 2.80;
SDm= .63, p<.05) as well as in the different CT categories (see Figure 5). However, whenlooking at the single CT categories, only decomposition, abstraction, generalization, andtesting and debugging were found to be statistically different (P<.05).
Figure 5
Mean Score of CT Strategy Use Related to Gender. N=66

A Pearson correlation coefficient was computed to assess the linear relationship between
gender and CT categories, as well as overall strategy use. As reported in Table 6, there is astatistically positive correlation between gender (1=male, 2=female) and decomposition,
abstraction, generalization, and testing and debugging as well as the overall strategy use.
Table 6
Pearson Correlation Coefficient between Gender and Strategy Use. N= 66

genderPearsonCorrelationSig.
(2-tailed)N

gender
1

66

DC
.254*

PR
AB
.219 .357**

AT
.211

GE
.259*

TD
.384**

V
SUM.176 .393**

.039

.077

.003

.088

.036

.001

.158

.001

66

66

66

66

66

66

66

66

*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).

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Discussion
This research sought to investigate the connection between learning strategies and the areas ofcomputational thinking as well as students’ use of the respective strategies. The results of thesurvey indicate that the participants were medium to low users of learning strategies thatdemand problem-solving skills related to computational thinking. These findings are consistent
with previous studies on learning strategy use. For example, Aslan (2009) investigatedlanguage learning strategies and also found a medium level of strategy use regardless of gender.
However, he found that higher-achieving students use more learning strategies. The low use oflearning strategies, in general, may have several reasons. One explanation may be that students
do not know which strategies are effective (Morehead et al., 2016). Korenell and Bjork (2007),on the other hand, found that many students’ goal is to pass exams and not to store information
long-term. Another possible explanation for the rare use of learning strategies could be the time
factor. Previous studies have shown that many students do not split learning content over alonger period, but rather wait until just before an exam, often until the last day (Blasiman etal., 2017; Susser & McCabe, 2013; Taraban et al., 1999). There are similarities between theattitudes expressed by the participants of this study and those mentioned above. It is apparent
from Tables 4 and 5 that many students make sure to have enough time the day before the exam
(Item 19) and pay less attention to making a realistic schedule (Item 18) or checking whether
they are still on the right track (Item 16). Hence, it could be hypothesized that the lack of timeis the reason why students prefer quick searches for information (Item 22) rather than timeconsuming strategies (Items 1, 2). Time constraints could also be the reason why students areless concerned about linking new information to prior knowledge (Item 7). However, it seems
that students still organize their work, try to set goals (Items 4, 15), and question the new
information (Item 8).
The results also demonstrate a statistically significant difference in learning strategy use bygender and correlate well with previous studies in the context of language learning wherefemales surpassed males. In his work on language learning, Oxford reports on females “using
more varied strategy types and employing strategies more frequently than males” (1993, p. 85).
Furthermore, he claims that when students are not explicitly asked by the teacher to use acertain L2 learning strategy, they tend to use those favoring their learning style. For example,
analytic learners (often males) prefer strategies involving logic, whereas the global learner(often females) prefer to use social strategies including searching for the main idea andintuitively guessing. In a study on gender and language learning strategies in learning English,
Aslan (2009) also found a significant difference in strategy use, indicating that females, on
average, employed more strategies than males in all domains and subscales investigated.
Although a great amount of literature reports a significant gender difference proposing thatfemales generally use more learning strategies than males, few studies came to the opposite
conclusion. For instance, Tercanlioglu (2004) conducted a study on foreign language learning
strategies with 184 pre-service teachers from Turkey, showing a gender difference favoringmales. According to her, the cultural background could be one of the reasons that the results
are not consistent with many previous studies.
Limitations
This survey helped to illuminate strategy use of students and served as the basis for the
implementation of CS modeling in foreign language learning to foster computational thinking
skills. Nevertheless, the study also has its limitations. One of them includes the self-selection

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bias resulting from the collaboration with the partner schools of the JKU COOL Lab. Anotherlimitation of this study is the sample size. Further research and wider trials are needed to beable to generalize the results and to determine which other factors besides gender influencestrategy use. Moreover, to be able to fully understand this phenomenon, the use of further datagathering instruments such as interviews is also advisable, so that the case can be viewed fromdifferent angles leading to richer results and conclusions. A major reason why only thequestionnaire was used at the time of the study was due to the difficult circumstances causedby the COVID-19 pandemic. Therefore, further investigations with interviews are planned toget a more holistic picture.
Recommendations and Conclusion
This survey aimed to investigate the use of learning strategies that can be linked to the coreelements of computational thinking (CT). For this, an expert group analyzed and identified
items of the two German questionnaires LSN (Learning Strategy Use) [Martin & Nicolaisen,
2015] and LIST (Learning Strategies at University) [Wild & Schiefele, 1994], and developed
a list of learning strategies related to computational thinking. By analyzing the degree ofstrategy use among students, this study established that all participants in the study were only
medium to low degree strategy users. Furthermore, results show that females reported
statistically higher use of learning strategies related to CT than male students. When looking
at the six CT skills as well as visualization strategies, testing and debugging strategies marked
the highest usage, closely followed by decomposition strategies and algorithmic thinking. The
category of visualization skills occupied the last place in the ranking. Concerning individual
strategies, item 22 (If I do not understand words, I look them up on the Internet or in adictionary) was the most frequently used strategy, and item 2 (I often do drawings or sketches
to better see how things belong together) was the least frequently used strategy.
These results indicate that although students are generally medium to low users of strategies,
they prefer fast strategies like researching information online to techniques that are more timeconsuming, such as visualization strategies. It is also possible that students are not aware of theeffectiveness of various strategies, especially for retaining information long-term. CT skills
such as decomposition, abstraction, pattern recognition, and algorithmic thinking are essential
for future professional life. Thus, an important implication is that teachers should raise strategy
awareness and offer students opportunities to gain these skills by providing suitable activities
such as modeling. With this approach, students’ interest in more time-consuming visualization
strategies can be increased as they might see long-term benefits that outweigh expenditure oftime.
The results of this survey work as the basis for the implementation of computer science models
as a teaching and learning strategy to foster CT skills. The experience and research on modelingand CT in language teaching and other subjects have shown promising results in recent years.
Nevertheless, future work is planned to investigate the reasons behind the low use of strategies
generally and visualization techniques in particular. Moreover, further studies could shed more
light on the contribution of higher CT strategy use on learning achievement. To conclude, withmodeling as an innovative teaching and learning strategy and other appropriate activities, theauthors hope to foster students’ CT skills, reduce cognitive load, and promote strategy use andsustainable learning.

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Corresponding author: Mag. Marina Rottenhofer
Email: marina.rottenhofer@jku.at70