In Germany, the integration of CS into all school subjects and levels is crucial. While various approaches are dedicated to this topic ( Chesterman, 2023 ; Kyriazopoulos et al., 2022 ; Mannila, 2023 ; Schmid et al., 2018 ), there is still no clear assignment to a specific subject in elementary school in Germany. As important skills for everyday life are addressed in science and social science education in elementary schools, called Sachunterricht, it seems appropriate to anchor CS education also in that subject. Science and social science education in German elementary schools include all natural and social sciences. CS content that should be addressed in elementary school can be found in the educational standards of the German Informatics Society ( Gesellschaft für Informatik (GI) e. V., 2019 ). They distinguish between CS contents such as algorithms or computing systems, and processes such as modeling and implementing or communicating and cooperating. There, modeling and implementation is also described as a creative process. Accordingly, these skills can be found in the natural scientific and technical perspective in elementary education ( Gesellschaft für Didaktik des Sachunterrichts, 2013 ). For example, Humbert et al. (2020) explain the creation of processes for controlling robots as a concrete activity that targets the competencies of the areas of modeling and implementation.

As described above and also confirmed in a survey of prospective teachers, it appears that technical and natural scientific education in particular seem to offer potential for promoting creativity on the one hand and making use of creative potential on the other ( Holzapfel et al., 2022 ).

The existence of a large variety of learning material, especially for use in elementary school, opens up new spaces for the development of creative possibilities. Following Froebel’s original ideas and his gifts for creative-playful learning, Zuckerman et al. (2005) refer to physical computing toolkits as digital Froebel gifts, as these are non-predefined materials for designing and modeling various real worlds ( Brosterman & Togashi, 1997 ). These enable constructive and creative access to CS and interdisciplinary content, which is at the focus of the approach described here.

One approach to foster the acquisition of basic CS skills was chosen by Tengler et al. (2022) , who implement storytelling with robots at elementary school level with the intention of bringing CS education into elementary school. They conclude that the combination of stories and robotics offers a suitable approach to CS in elementary school. Tzagkaraki et al. (2021) also emphasized in their literature review that floor robots have a positive effect on the learning process and also have an influence on the acquisition of basic CS skills and creativity. The use of floor robots that children can program on their own has therefore already proven to be positive in elementary school and may offer creative approaches to other learning topics that have not previously been the focus of research.

Based on this, a concept that was previously developed for a project in European kindergartens and which is intended to provide support for the development of creative processes ( Dittert et al., 2021 ) was implemented in the project presented. While the original focus was on accompanying children on their way to free exploration, we now want to focus specifically on the creative processes of designing worlds, telling stories and algorithmizing. Following this idea, an approach for promoting and exploring the creativity of elementary school children was developed, in which the children learn the basics of programming and are given the opportunity to build on their creativity.

In order to make conclusions about children’s creativity, a suitable test instrument is required. In line with current research discussions, this project began by considering how creativity can be operationalized ( Holzapfel et al., 2022 ). The current discourse is quite clear: creativity appears to be a construct that is made up of divergent and convergent thinking and requires both equally ( Brophy, 2006 ; Cropley, 2006 ; Garaigordobil, 2006 ; Runco, 2006 ). In other words, those who think creatively think innovatively and differently, but also in a solution-oriented and goal-oriented way ( Sternberg & Lubart, 1998 ).

For the field of creativity research in science education, this is summed up by the definition of Bliersbach & Reiners (2017: p. 324) :

“Creativity describes the potential inherent in every human being to create something new and relevant for his or her environment with the help of various metacognitive strategies, breaking out of known structures and the recombination of knowledge, to create something new and relevant for their respective environment.”

This definition is also easily transferable to technical education and thus to the presented study. All four sub-areas are addressed here: the creative person, the creative process, the creative product and the creative environment ( Rhodes, 1961 ). These sub-areas all are relevant for teaching and thus also for learning basic algorithmic thinking skills. In this way, it is possible to focus on the creative person, teacher or student, the classroom (environment), the learning process or even the product that the learners develop. The study includes all of them, with a specific focus on the measurement of the creativity of the person in this article.

A total of N = 16 fourth graders from the state of Lower Saxony in Germany were surveyed (Ø age ten; eight girls and eight boys, no binary/other). Participation was voluntary, and school authorities as well as all parents indicated their consent. The elementary school from which the children came is located in an urban area.

The algorithmic thinking test is based on the Beginners Computational Thinking test ( Zapata et al., 2021 ) and adapted to the present scenario. It consists of four single-choice items in which the children are asked to solve tasks relating to a robot on a coordinate field. They select the correct answer from four options. In addition, there is a task in which the children have to draw the robot’s path to a given step sequence. This test is used slightly modified form pre and post, e.g. moving one step to the left instead of one step to the right.

The convergent thinking task was developed based on the Compound Remote Associates Test (CRA) according to Landmann et al. (2014) . The participants are asked to complete word sequences. They are each given three words with a picture and asked to select and circle the matching picture from a box with eight. As a solution, three new compound words result from the three given words and the searched word. This test instrument was originally developed for young adults. In order to adapt it to the target group, simple series were selected from the long list of possible word series. In addition, the instrument was shortened and simplified in terms of language. Additionally, attention was paid to ensure that the word searched for is either always added at the front or always at the end. Also, each word is always presented with a picture, so that the reading skills of the children, which still vary in the fourth grade, should have less influence on the test result. The children are asked to complete a total of eight rows. Based on the CRA, they have one minute to complete each row.

In accordance with the theory that creativity contains both a divergent and a convergent component, a test instrument was developed that takes both aspects into account. Three scales on divergent thinking were adapted from the Torrance Test of Creative Thinking ( Torrance, 1966 ) and translated into German. There were two figural parts and one verbal part. In part one of the figural scale, participants are asked to complete four pictures according to their own imagination and, if possible, to label them (see Figure 3 ). In order to reduce the cognitive requirements for the children as much as possible, the instructions were also shortened and adapted to the target group in terms of language. In addition, the children are offered fewer circles (figural two) and lines (verbal) to complete so that there is not too much pressure to fill in more. In figural part two, the children are given 18 circles to draw as they wish (see Figure 4 ). In the verbal part, the children are requested to write down as many ideas as possible for using a tin can to a maximum of 50 lines (e.g., as a pencil box). In all parts they are asked to try to think of things that no one else will think of. The children have four minutes for the figural part one, six minutes for the figural part two and six minutes for the verbal part.

Coding of algorithmic thinking. With respect to the assessment of algorithmic thinking in pre- and post-test, correct solution steps were coded with one point, incorrect solution steps with zero points (no partial credit). The total number of correctly solved solution steps across all items was calculated, and the sum scores thus indicate the individual level of algorithmic thinking before and after the intervention, respectively (maximum score: 6, Cronbach’s ɑ = 0.612).

Coding of convergent thinking. With regards to the assessment of convergent thinking, each correctly solved item was coded with one point (maximum score: 8, Cronbach’s ɑ = 0.856). The sum score of correctly answered items across all 8 items was determined and z-standardized, indicating the individual level of convergent thinking. In order to be able to compare children with “high convergent thinking skills” and “low convergent thinking skills”, a median split was performed, leading to a “high convergent thinking skills” group and a “low convergent thinking skills” group of (roughly) equal sample size.

Coding of divergent thinking. In relation to the three subscales addressing divergent thinking as described above, the following coding procedure was realized:

Transferring pictures into verbal descriptions. When coding the pictorial ideas of the participants (figural part one and two), the coders were advised to enter the most specific verbal phrase into the data set. Thus, for example, if a participant drew a “football”, the coders entered “football” into the data set, and not just “ball”. While, compared to a procedure where generic terms are coded, like for example “ball” for any type of ball, this procedure necessarily leads to a lower consensus between coders. Still, it ensures the most specific coding of ideas which is necessary for being able to code flexibility and originality in a straightforward way. In cases where the two raters used different verbal expressions but meant exactly the same thing (e.g., “letter D” vs. “D”, or “high heels” vs. “stiletto heels”), the coders were advised to change one of their original codings in order to reach an agreement. In cases of disagreement between coders with respect to the meaning of the drawings, the issue was solved via discussion, and a final agreement was coded. In addition, the coders were advised to try to stick to the same verbal description throughout the whole coding process (e.g., coding “stiletto heels” for all drawings depicting high heels, stiletto heels, or pumps). With respect to the verbal ideas of the participants, the issue of the meaning of drawings did not appear, as the participants wrote down their answers verbally. Thus, in case of the third creative task, no measures to ensure inter-rater agreement needed to be taken.

According to Torrance (1966) , fluency refers to the number of ideas generated by a person. As the three divergent thinking tasks used in the present study pose very different ways and opportunities to come to different solutions, fluency scores were determined for each part separately, by determining the number of ideas generated by a person (e.g., “3” for figural task one). For each part, these scores were z-standardized, and a combined fluency score (across all divergent thinking parts) was obtained via adding up and again z-standardizing the z-scores of the three creative tasks.

Following Torrance (1966) , flexibility refers to the number of different ideas generated by a person. Thus, for each divergent thinking task, it was counted how many different ideas were mentioned (again, separately for the three tasks). In this respect, as always, the most specific interpretation was coded, a person mentioning a “football” and a “volleyball” was coded having two different ideas, while a person drawing a “ball” two times was coded as mentioning one idea. For this purpose, duplicates were removed and the remaining number of entries was counted. Again, the scores for the three tasks were z-standardized and added up to obtain a combined flexibility score, which, ultimately, was also z-standardized.

According to Torrance (1966) , originality is a measure reflecting the rarity of an idea in the total sample. Thus, for each code in the data set it was determined how many other persons had obtained the same code at least once (for figural part one: separately for the four figures; for figural part two: across all circles; for verbal part: across all lines). In the data set, it was first entered how often an idea occurred in total (i.e., one means that only one person mentioned this idea, while a large number means that many others had the same idea); missing answers were not taken into account. At this stage, a high value indicates low originality and vice versa. In consequence, for each idea, the reciprocal value was calculated by dividing one by the total number of mentions of that idea. Following this procedure ensures that high values represent high originality. Again, separately, these three scores were z-standardized and added up and again z-standardized to obtain a combined originality score.

After computing the three separate divergent thinking scores, namely fluency, flexibility, and originality, a combined total divergent thinking score was computed by adding up the three z-standardized single scores; the result was again z-standardized. In order to be able to compare children with low or with high divergent thinking, a median split was performed, leading to a “high divergent thinking skills” sub-sample and a “low divergent thinking skills” sub-sample of (roughly) equal sample size.

Using the results of the total convergent and divergent thinking scores, four different groups can be distinguished, namely 1) Low level of convergent and low level of divergent thinking skills (n = 2); 2) Low level of convergent and high level of divergent thinking skills (n = 4); 3) High level of convergent and low level of divergent thinking skills (n = 6); 4) High level of convergent and high level of divergent thinking skills (n = 4), respectively. Following the theory outlined above, the fourth group is called “creative”.