To learn mathematics in the 21st century not only means obtaining mathematical proficiency, but also critical thinking, creativity and technology literacy [
1]. Collaborative problem solving is one of the recommended pedagogies to promote the active learning of mathematics. Furthermore, collaborative problem solving led to better performance in standardized tests in mathematics than a traditional transmissive approach [
2,
3,
4,
5], particularly when the problems were related to the real life of the students [
6] and used technology [
7]. It was also reported that students educated using collaborative problem solving appreciate their knowledge of mathematics and science even in their future workplace. This influenced their academic performance and career choice [
8].
Furthermore, it is predicted that, in the next few years, half of STEAM-related jobs will be in computing [
9,
10]. Children and young people use smartphones and tablets on a daily basis, but their use is mainly for entertainment, not for learning purposes. On the contrary, mathematicians consider the effective use of technological tools as a “valuable component of the practice of doing mathematics” [
11] (p. 9). A similar perception can be given of the scientists [
10,
12]. This has led to the large-scale development and piloting of materials aimed at fostering computational thinking [
13], but not all of them are suitable for problem-based learning. Cápay and Magdin [
14] used black boxes as the main concept for tasks developing computational thinking and they provoked very intensive reaction. Burbaite et al. [
15] designed an activity where the students could learn about the physical principles of functioning an ultrasonic sensor, connecting knowledge of physics with knowledge from computer science. Students were able to gain conceptual knowledge in physics and design the algorithm at the same time. Another example of an interdisciplinary approach can be found in the work of Lytle et al. [
16], aimed at an agent-based simulation with a special focus on student-perceived ownership of developed programs. Students using the use–modify–create approach felt more confident and perceived the code developed in the guided part, with their slight changes, as more familiar compared to the transmissive approach in the control group. Several studies [
17,
18] have shown that the design-based approach can improve the computational thinking of participating students and enhance the students’ awareness of the different tasks that can be performed using the computer and their self-efficacy in using computers.
1.1. Interdisciplinary Teaching
A lot of current scientific problems can be addressed only if experts from several scientific fields collaborate together. New scientific fields (e.g., physical chemistry, biostatistics, and theoretical physics) have even been established. However, the school curriculum is divided into separate subjects. In Slovakia, even science subjects are separated to physics, chemistry and biology in secondary education [
19]. Both mathematics and science education aim to enable students to understand the wonder of the world around us. They share strategies for solving problems and for scientific inquiry. These approaches include logical thinking, hypothesizing, observations, analysis and experimentation. Even university students are not used to solving practical problems and, therefore, they are not able to interpret the obtained results [
20].
St. Clair and Hough [
21] grouped arguments supporting an interdisciplinary approach to education into six groups. An interdisciplinary approach (i) is in agreement with the current body of knowledge about the needs of the secondary students; (ii) offers a substantial learning environment and, therefore, has a positive impact on the learning process as well as on achievement; (iii) provides students with a more holistic approach to problems; (iv) is global in content and better prepares participating students for critical citizenship; (v) improves the skills necessary for problem-solving by demonstrating different views and perspectives; (vi) encourages collaboration among teachers.
1.2. Computational Thinking
Weintrop et al. [
9] stress the ability of mathematics and science to develop key skills in computation. Departing from the poor-technological view, we can focus on four main categories: “data practices, modelling and simulation practices, modelling and simulation practices, computational problem-solving practices and systems thinking practices” (p. 127). It is important to realize that computational thinking is more than using technology: it is a way of thinking while solving complex problems [
22]. Computational thinking was defined by Aho as “the thought processes involved in formulating problems so their solutions can be represented as computational steps and algorithms” [
23] (p. 832).
Similar to proficiency in mathematics [
24], computational thinking may be also developed via problem-solving activities [
25]. Gretter and Yadav [
26] see “collecting, analysing and representing data, decomposing problems, using algorithms and procedures, making simulation” as the key component of computational thinking (p. 511). Bocconi et al. [
27], based on studies [
28,
29], define six components constituting computational thinking: (1) abstraction, (2) algorithmic thinking, (3) automation, (4) decomposition, (5) debugging, and (6) generalization. Abstraction is understood as reducing the details in order to make the artefact more understandable. The essence of abstraction is competence in choosing the proper feature to hide and proper representation, so that the hiding results in easier problems with a suitable solution. Algorithmic thinking is a systematic way of thinking applied to splitting a complex problem into a series of (not necessarily ordered) steps utilising the different tools available in the moment. Automation can be defined as a procedure aimed at saving the labour which the computer is uses to perform a (ordered) set of repetitive tasks instead of the slow and inefficient work of humans. Decomposition is a way of breaking down the artefacts into smaller parts that can be “understood, solved, developed and evaluated separately” [
28] (p. 8), which makes complex systems simpler to design. Debugging is a looking-back ability when the outcomes are analysed and evaluated. Generalization, as a part of computer thinking, is connected to “identifying patterns, similarities and connections, and exploiting those features” [
28] (p. 8), relating to previous experience with similar problems and adopting developed algorithms to solve the comprehensive class of similar problems. Although the positive effect of the use of technology on students’ performance in mathematics and science was confirmed, very few studies investigated the use of these applications for mathematics. The results of Kosko et al. [
30] suggest that integration of the applications over a three-week period significantly increased the mathematics achievements of participating students.
The ability to use the technology is not self-developing. The fact that students are able to use tablets or smartphones or any other technology for communication or browsing the internet does not imply an ability to use it for more sophisticated purposes, such as measuring the distance, temperature or size of an angle, calculating repetitive tasks or processing the measured data. It is necessary to provide students with the opportunity to experience this kind of use of the technology. The invention of mobile technologies allowed students to unplug the computers, leave the classroom and move outdoors [
31].
The instrumental approach [
32] seems to be a reliable framework to understand what is going on during the activities, supporting both mathematical learning and computational thinking. The technology introduced in the classroom can be considered as an artefact. Only when students learn to use it, when they develop the utilization scheme, does the artefact become a tool, an instrument [
33]. The development of the utilization scheme can be described as having three levels: (1) usage schemes, (2) instrumental action schemes including gestures and operative invariants, and (3) instrumented collective activity schemes [
34]. Usage schemes are directly related to the artefacts themselves. They are developed through manipulations and examinations by the artefact. Instrumented action schemes or instrument-mediated action schemes are higher-order, coherent and meaningful mental schemes, acquired from existing elementary usage schemes when a student manipulates an instrument with the aim of solving the problem. The developed schemes are specific to each activity. When an application is introduced in the classroom, students first have to become familiar with its basic features, developing the usage scheme. Only then they can use it for solving the problem and fostering the instrumental action schemes. Instrumented collective activity schemes or collective instrument-mediated activity schemes are the schemes developed in the context of collective, particularly collaborative, activity. The students are both influenced by artefacts’ potentialities and constraints (instrumentation) and influencing the artefact via their preconceptions, knowledge, beliefs and usual ways of work (instrumentalisation) [
34]. The two described dual processes are united in the instrumental genesis when the instrument arises as the result of the interactions between the student (subject of the activity) and the artefact [
35,
36,
37,
38].
The main aim of this study is to demonstrate the potential of interdisciplinary problem-solving activities, including several STEAM disciplines, to develop both the mathematical proficiency and computational thinking of involved students. Various activities were designed to develop computational thinking [
13,
14], but only a few of them were focused on the students’ tendency to use technology to solve problems. In this article, we looked for the answer to the research question formulated as follows: What components of computational thinking may be developed by involving students in interdisciplinary STEAM activities using technology? How is this development manifested?