FADE-CTP: A Framework for the Analysis and Design of Educational Computational Thinking Problems

Abstract

In recent years, the emphasis on computational thinking (CT) has intensified as an effect of accelerated digitalisation. While most researchers are concentrating on defining CT and developing tools for its instruction and assessment, we focus on the characteristics of computational thinking problems (CTPs)- activities requiring CT to be solved-and how they influence the skills students can develop. In this paper, we present a comprehensive framework for systematically profiling CTPs by identifying specific components and characteristics, while establishing a link between these attributes and a structured catalogue of CT competencies. The purposes of this framework are (i) facilitating the analysis of existing CTPs to identify which abilities can be developed or measured based on their inherent characteristics, and (ii) guiding the design of new CTPs targeted at specific skills by outlining the necessary characteristics required for CT activation. To illustrate the framework functionalities, we begin by analysing prototypical activities in the literature, a process that leads to the definition of a taxonomy of CTPs across various domains, and we conclude with a case study on the design of a different version of one of these activities, the Cross Array Task (CAT), set in different cognitive environments. This approach allows an understanding of how CTPs in different contexts display unique and recurring characteristics that promote the development of distinct skills. In conclusion, this framework can inform the development of assessment tools, improve teacher training, and facilitate the analysis and comparison of existing CT activities, contributing to a deeper understanding of competency activation and guiding curriculum design in CT education.

Supplementary information: The online version contains supplementary material available at 10.1007/s10758-025-09833-x.

Keywords: Analytical framework; Cognitive environment; Computational thinking; Digital education; Learning activity design; Skill development.

Fig. 1

Visualisation of the CT-cube, from Piatti et al. (2022). This model considers the type of activity (problem setting, algorithm, assessment), the artefactual environment (embodied, symbolic, formal), and the autonomy (inactive role, non-autonomous active role, or autonomous active role)

Fig. 2

Visualisation of the components of a CTP. CTPs include (1) the problem solver (in green) characterised by the artefactual environment, i.e., the set of reasoning and interaction tools, (2) the system, which consists of an environment with its descriptors (in blue) and an agent with its actions (in violet), and (3) the task (in yellow) characterised by the set of initial states, algorithms and final states

Fig. 3

Graphical template for the analysis of CTPs components and characteristics. The same colour scheme as in Fig. 2 is applied

Fig. 4

Visualisation of our taxonomy of CT competencies. The overall structure is based on the CT-cube (Piatti et al., 2022). The sub-skills are derived from validated CT models (Brennan and Resnick, ; Shute et al., ; Weintrop et al., 2016). The same colour scheme as in Fig. 1 is applied

Fig. 5

Comparison of the CAT setting between the unplugged and virtual CAT. Adapted from Piatti et al. (2022)

Fig. 6

Virtual CAT components and characteristics

Fig. 7

Virtual CAT profile

Fig. 8

The CAT activity, adapted fromPiatti et al. (2022). The task requires the problem solver to instruct the agent to reproduce a reference schema solely through verbal communication, with the option of supplementing instructions via gesturing on a support schema if deemed necessary. A removable screen separates the participant and researcher to regulate potential visual cues

Fig. 9

CAT components and characteristics

Fig. 10

CAT profile

Fig. 11

The first part of the Graph Paper Programming activity, adapted from Codeorg (2015b). The task requires the problem solver to instruct the agent to reproduce the reference schema with instructions written on a steps array using a predefined set of arrow symbols

Fig. 12

Graph Paper Programming (part 1) components and characteristics

Fig. 13

Graph Paper Programming (part 1) profile

Fig. 14

The second part of the Graph Paper Programming activity, adapted from Codeorg (2015b). The task requires the problem solver to fill the empty colouring schema following the program provided. The figure illustrates the expected final state

Fig. 15

Graph Paper Programming (part 2) components and characteristics

Fig. 16

Graph Paper Programming (part 2) profile

Fig. 17

The Triangular Peg Solitaire. The game is played on a board containing 15 spots, with 14 pegs placed on it at the start of the game (top). The task requires the problem solver to strategically move one peg at a time to eliminate all other pegs on the board until only one remains (bottom), jumping a peg over a neighbouring peg on the diagonal or horizontal lines, with the constraint that there must be a free landing spot for the jumping peg (adapted from Berlekamp et al. (2004))

Fig. 18

A Triangular Peg Solitaire solution adapted from Bell (2007, 2008) and Barbero and Gómez-Chacón (2018). The task requires the problem solver to solve the game using paper and pencil by meticulously documenting their entire thought process. The solution can be presented in multiple ways, such as graphically using a Cartesian notation (top) or by numbering the boxes progressively and expressing the movements used (bottom)

Fig. 19

Triangular Peg Solitaire (board variant) components and characteristics

Fig. 20

Triangular Peg Solitaire (board variant) profile

Fig. 21

Triangular Peg Solitaire (paper & pencil variant) components and characteristics

Fig. 22

Triangular Peg Solitaire (paper & pencil variant) profile

Fig. 23

Item 14 of the CTt adapted fromRomán-González et al. (2017b). The task requires the problem solver to select the correct set of instructions to make the agent cross a predefined path to reach a desired position

Fig. 24

CTt (item 14) components and characteristics

Fig. 25

CTt profile

Fig. 26

The Thymio Lawnmower Mission adapted fromChevalier et al. (2020). A group of pupils must program the Thymio II robot to pass over all eight green lawn squares and avoid the fence (left). A special visual programming language platform, with graphical icons that are straightly interpretable, is used for this task (right)

Fig. 27

Thymio Lawnmower Mission (control group) components and characteristics

Fig. 28

Thymio Lawnmower Mission (control group) profile

Fig. 29

Thymio Lawnmower Mission (test group) components and characteristics

Fig. 30

Thymio Lawnmower Mission (test group) profile

Fig. 31

The Remote Rescue with Thymio II (R2T2) mission on Mars adapted from Mondada et al. (2016). Sixteen worldwide teams of pupils collaborate with 16 Thymio to restart the main generator of a simulated damaged power Mars station (left) in five phases using a visual programming language or textual programming language programming platforms (right)

Fig. 32

R2T2 mission components and characteristics

Fig. 33

R2T2 mission profile

Fig. 34

The Ozobot maze adapted fromBryndová and Mališů (2020). The task requires the pupil to instruct the Ozobot to cross a maze avoiding obstacles and reaching the room where the red person is. Commands such as increasing the speed, changing direction and making some cool movements (spinning like a tornado) are given in Color Codes

Fig. 35

Ozobot maze components and characteristics

Fig. 36

Ozobot maze profile

Fig. 37

The BBC micro:bit (left) and its block programming interface (right)

Fig. 38

The Mini-golf challenge challenge adapted from Assaf et al. (2021). The task requires a group of pupils to define the behaviour of the mini-golf lane movable obstacles, sounds, and lights by programming the BBC micro:bit.

Fig. 39

Mini-golf challenge components and characteristics

Fig. 40

Mini-golf challenge profile

Fig. 41

The Angry Bird hitting the Green Pig maze adapted from Studiocodeorg (2020a). The problem solver must write a program to get the Angry Bird through the maze to hit the Green Pig (left) by selecting the instruction blocks (middle) and assembling them in the workspace (right)

Fig. 42

Angry Bird maze components and characteristics

Fig. 43

The Plants vs Zombies maze adapted fromStudiocodeorg (2020c). The problem solver must write a program to get the Zombie through a maze to eat the plant (left) by selecting the instruction blocks (middle) to be assembled in the workspace (right)

Fig. 44

Plants vs Zombies maze components and characteristics

Fig. 45

Classic maze profile

Fig. 46

The Store the Marbles activity adapted from ALGOREA (2020). The task requires the problem solver to program the robot to produce an algorithm valid for different situations using a visual programming language

Fig. 47

Store the Marbles components and characteristics

Fig. 48

Store the Marbles profile

Fig. 49

The Zoombinis Allergic Cliffs puzzle from Zoombinis (2021). The player must determine which characteristics allow the Zoombinis to cross the bridge without being sent back. The bottom cliff does not accept creatures with flat hair, while the top cliff rejects all others

Fig. 50

Zoombinis Allergic Cliffs puzzle components and characteristics

Fig. 51

Zoombinis Allergic Cliffs puzzle profile