Unplugged Activities in the Development of Computational Thinking with Poly-Universe

Abstract

This paper presents an educational experience of using Poly-Universe, a game created by Janos Saxon, with the aim of developing computational thinking (CT) skills through unplugged activities. It was implemented in the course “Algorithm Analysis,” with the participation of students in the sixth period of Computer Science at a University Center for Higher Education in Brazil. These students were facing various cognitive difficulties in using the four pillars of CT, namely abstraction, pattern recognition, algorithm, and decomposition. To address the students’ learning gaps, unplugged activities were implemented using Poly-Universe pieces—geometric shapes such as triangles, squares, and circles—exploring the connection through the pillars of CT. A mixed methodology integrating quantitative and qualitative approaches was applied to compare the progress of the students and their reactions when developing the activities. The results obtained evidenced that the level of learning involving the computational pillars on “Algorithm Analysis” had a significant evolution, from 30% to almost 80% in terms of achievement in academic tests. In addition, an increase in students’ engagement and collaboration was also registered. Therefore, the implementation of unplugged activities with Poly-Universe revealed a promotion of skills related to the pillars of CT, especially in the analysis of algorithms.

1. Introduction

The term CT emerged in the 1980s through the pioneering research of ref. [1], focusing on the human way of thinking independently from machines. For ref. [1], CT is a way of reasoning with the aim of solving complex problems. The concept was later popularized by ref. [2] in 2006. As in ref. [1] and for ref. [2], CT is based on the power and limits of computational processes, whether processed by a human or a machine. A complex problem is defined as one that lacks an immediate solution and therefore needs to be broken down into smaller, manageable parts to identify potential solutions.

When humans encounter a problem, they seek to mobilize a series of mental tools to support the resolution process by exploring possible paths to address the issue—sometimes easy, other times difficult and/or complex. Therefore, CT emerged as a contributor and reformulator of seemingly difficult problems into ones that can be solved, perhaps through fragmentation, incorporation, transformation, or simulation [2].

Ref. [2] identified four key pillars of CT: problem decomposition, pattern recognition, abstraction, and algorithms:

• The first pillar of CT is called problem decomposition, which consists of decomposing a problem into smaller, more manageable sub-problems.
• Next comes pattern recognition, which involves solving a problem using various forms, whether seen or unseen before, and also utilizing concepts from previously acquired knowledge, for example, in mathematics or any other area, to solve the problem. Individuals can also try to recall a similar situation they have experienced before or observe how the problem was solved in a different context. This pillar encourages the search for a similar situation and, thus, finding a possible solution through simulations or heuristics. Consequently, human thinking and rethinking lead to the pursuit of optimizing the processes related to the solution. When humans face a problem, they use a variety of mental strategies to explore different paths toward a solution, which can range from simple to highly complex [1,3,4].
• The abstraction pillar involves isolating the core idea from its context and determining how best to approach it. When dealing with a problem situation, once the ideas and their respective aspects are understood, the individual must organize, relate, associate, and systematize them in a way that allows them to comprehend the best form and composition.
• Then, they seek a way to represent and systematize this logically and incorporate it into the algorithm. Ref. [2] suggests that these steps support the actions of optimizing and finding the best designs and processes to address a problem, making it applicable in a broader context.

According to these theorists, CT serves as a methodology that individuals can use to solve problems on both macro and micro levels. To delve into a complex problem, one can apply the pillars of CT mentioned above. The art of problem-solving involves the distinct creative, critical, and strategic human ability to apply the fundamentals of CT in various fields of knowledge, either individually or collaboratively, through clear steps, so that a person or machine can effectively implement them [2]. Collaboration and creativity are also competencies associated with CT activities, e.g., [2,5,6].

CT has been widely explored in education and is now regarded as a vital tool for promoting the competencies needed in a complex and uncertain world, e.g., [7,8]. It is considered a fundamental skill not only for computer scientists but also for everyone and thus should be integrated into the curriculum alongside writing, reading, and arithmetic from the early years of basic education and across different disciplines throughout schooling [9,10]. A review by [11] highlights that the teaching and learning of CT-related competencies have been increasing, although differences persist between countries.

One of the challenges that arises, according to ref. [12,13], is how to effectively cultivate students’ CT literacy. For ref. [2], CT “represents a universally applicable attitude and a set of skills that everyone, not just computer scientists, should aim to learn and use”. Ref. [12] notes that CT may be cultivated by several activities as line-based programming languages, block-based programming languages, and robotics programming. However, the authors also highlight that, in addition to activities using digital means, unplugged activities are also mentioned in several studies—educational activities, games, or exercises that teach concepts—especially those related to computing, coding, or other technical skills without the use of electronic devices or screens [14,15]. In fact, research on unplugged activity has been a recurring strand within the broader trend in CT research, e.g., [15,16,17]. Unplugged activities focus on hands-on, experiential learning, making complex concepts accessible and engaging through physical interaction and collaboration, often using everyday objects to teach these concepts. Ref. [12] noted that the literature review showed that studies on CT skills were mainly conducted in computer science and STEM education, with board and card games being the most common unplugged activities for fostering CT skills in K–12 education.

Researchers also sought to investigate the development of CT skills through unplugged activities. Studies carried out by ref. [18] found that unplugged activities integrated into cross-curricular contexts improved both learning outcomes and CT skills among sixth-grade students. Other authors, such as, e.g., [3,12,19], report that students felt more motivated and had the feeling of being in control of the result because they understood the process involved in the unplugged activities proposed. Similar results were found by ref. [20] based on a meta-analysis of 49 studies on unplugged activities, which revealed that such interventions significantly enhance CT skills in primary school students, with a large overall effect. These findings highlight that the practical implementation of these strategies is effective for promoting computational literacy, while also emphasizing the need for careful design of such activities [15,21]. In this context, it is considered that many of the concepts and approaches within CT can be practiced through unplugged programming [16,17].

There is also an emerging interest in exploring how they can be integrated into hybrid pedagogical designs. Ref. [21], for example, combined unplugged activities such as cooking with block programming. Although their findings revealed some difficulties in students making explicit connections between the two types of activities, they also highlighted the potential of hybrid approaches to engage students in diverse ways and to foster CT through multiple representations and experiences. The potential of combining both approaches was evident in other studies. Ref. [22] found that unplugged activities appear to act as a catalyst when combined with programming. Ref. [23] mentioned that starting with unplugged activities before programming leads to a better understanding of algorithms and greater pedagogical effectiveness.

In this context, the game Poly-Universe offers an innovative tool for exploring CT through unplugged activities. Created by Hungarian artist János Szász Saxon, Poly-Universe, as presented in Figure 1, comprises three basic shapes: circle, square and triangle. Each shape is available in different sizes and four colours: red, yellow, blue and green. A Game set consists of 24 elements of the same shape, whereas the Family set includes the full collection of 72 elements, with one complete set for each shape [24,25].

The manipulation of Poly-Universe geometric shapes and their respective connections requires the pillars of CT. Figure 2 presents some activities entitled ‘Find the missing piece’ that involve CT:

Regarding empirical studies, recent research has begun to explore the pedagogical potential of Poly-Universe for fostering CT skills. Ref. [26], for example, conducted an interdisciplinary study that implemented Poly-Universe in Austrian secondary schools across biology, physics, and digital education. Their findings highlight how the visual and combinatorial nature of the game can be leveraged to support the development of key CT competencies such as abstraction, pattern recognition, and algorithmic thinking. However, while this represents a valuable contribution, the body of empirical evidence remains limited, and more targeted research is needed to systematically assess the integration of Poly-Universe into CT-oriented educational methodologies.

The general objective of this study is to identify and analyze the benefits of using Poly-Universe across the various pillars of CT in the context of an Algorithm Analysis course with sixth-period Computer Science students at a Brazilian University, while fostering engagement and collaboration.

To guide this investigation, the following research questions were formulated:

• Does the use of unplugged Poly-Universe activities enhance students’ engagement and collaboration in learning contexts?
• Does the use of unplugged Poly-Universe activities in algorithm analysis classes enhance students’ computational thinking skills across its various components?

2. Materials and Methods

2.1. Context

The foundations of CT and unplugged activities served as the basis for this experience report that took place in the Computer Science course at a Brazilian university. The investigation was carried out in the discipline “Algorithm Analysis”, which is part of the curriculum for the sixth period of the referred-to course. During the first two months and after the first assessment on “Asymptotic Analysis”, the teacher verified that the students presented a low performance in the content, which involves the pillars of CT (abstraction, pattern recognition, decomposition, and algorithm). In this context, the teacher aimed to promote learning through unplugged activities designed to develop CT skills, a basic requirement for analyzing an algorithm. The pedagogical tool was the use of Poly-Universe.

2.2. Participants

The study sample involved a class of 27 students, 20 male and 7 female, enrolled, as mentioned in the Context section, in the Bachelor’s Degree in Computer Science at a Higher Education Center and in the curricular component of “Algorithm Analysis” belonging to the sixth period of the second semester of 2023. A semester at the previously mentioned educational institution consists of two bimonths, where the student must obtain an average of 6.0 (six) to finally pass the curricular component.

2.3. Methodology

This study follows a mixed-methods approach [27] framed within an action research design [28]. The intervention emerged in response to persistent difficulties experienced by students in previous iterations of the “Algorithm Analysis” course. In this context, the integration of unplugged activities using Poly-Universe was explored as a strategy to enhance both learning outcomes and student engagement.

Mixed methods were considered appropriate given the focus of the study to assess the development of students’ CT skills—specifically abstraction, pattern recognition, decomposition, and algorithmic reasoning—as learning outcomes and to explore their engagement and collaboration as indicators of the learning process.

2.3.1. Action Research Approach

The study was designed as a small-scale educational intervention inspired by the principles of action research, which combines action and reflection in a real-world teaching environment [28]. In this approach, the researcher actively participates in planning, implementing, and observing the intervention [28]. The purpose was to broaden students’ understanding of programming by engaging them in unplugged activities that apply CT beyond digital tools—thus offering a complementary perspective to screen-based programming experiences.

Although action research is often implemented in multiple iterative cycles [29], this study followed a single-cycle format due to time limitations within the semester structure of the course. The intervention was implemented in two phases, as detailed in Section 2.6 Procedures.

2.3.2. Quantitative Component

The quantitative dimension of the study was structured to allow for a comparison of student performance after each instructional phase—first, after connected activities (computer use) and, later, after disconnected activities (Poly-Universe use). Both tasks targeted the four main pillars of CT, and student performance was assessed using a structured rubric. Results were analyzed using descriptive statistics to identify differences in performance before and after the intervention. Students completed two problem-solving tasks: one after using programming tools using the computer and another involving disconnected problem-solving with Poly-Universe. Both tasks targeted the four pillars of CT, and student performance was measured across levels such as abstraction, pattern recognition, decomposition, and algorithm. Results were analyzed using descriptive statistics to identify differences in performance before and after the intervention.

2.3.3. Qualitative Component

The qualitative component consisted of field notes taken by the instructor throughout the implementation of the unplugged activities. These observations captured students’ interactions, engagement levels, collaboration, and emotional responses. The qualitative data provided a contextual layer to the study and were analyzed through descriptive content analysis [28], identifying recurring patterns and meaningful episodes related to the learning process. While the quantitative data focused on assessing students’ performance in problem-solving activities based on the four pillars of CT, the qualitative data offered insight into the learning processes that led to those results—such as engagement, collaboration, and emotional responses. In this scope, the qualitative component also includes the teacher’s observations on the learning process and results. The combination of quantitative and qualitative data allowed understanding what students achieved and how they approached the learning activities, providing a more comprehensive picture of the intervention’s impact.

2.4. Instruments

To assess participants’ content knowledge and development of CT skills, two separate assessments were implemented: one after the plugged activities and another after the unplugged activities (Poly-Universe). Each assessment included a set of tasks aligned with the four main pillars of CT: abstraction, pattern recognition, decomposition, and algorithmic reasoning. The tasks were designed to elicit evidence of students’ reasoning and problem-solving strategies and were similar in format, structure, and level of difficulty to ensure comparability between the two assessments. To corroborate the assessments, Google Forms was implemented with closed and open questions seeking to verify the bases of CT. Student responses were evaluated using a rubric, with two levels of performance: (1) incomplete/incorrect and (2) correct/complete. This allowed for a descriptive statistical analysis of performance.

In parallel, qualitative data were collected through a teacher logbook, used systematically throughout the implementation of both activity types. This logbook functioned as a reflective and observational tool and included descriptive notes on students’ participation, collaboration, and problem-solving behaviors; motivational indicators, such as engagement, enthusiasm, hesitation, or frustration; spontaneous comments made by students during the sessions; and reflections from the teacher-researcher regarding classroom dynamics, pedagogical strategies, and student interactions. Entries were recorded after each session.

While this structure does not constitute a traditional pre- and post-test, it allowed for a comparative analysis of students’ performance in the two teaching contexts, as well as an analysis of their progress in unplugged activities.

2.5. Materials

To develop the unplugged activities, a complete set of Poly-Universe was used, that is, 24 triangles, 24 squares, and 24 circles, and a set of six activities to promote the priority development of skills related to the four pillars of CT. The activities were selected and taken from the Poly-Universe database. The selection of activities prioritized the development of CT skills. The six implemented activities are presented below in

Table 1. Unplugged activities with Poly-Universe. Source: [19].

Task 01
(1) First, choose a base shape: triangle, circle, or square. (T—triangle; C—circle; S—square) (2) Next, select a base color (the largest area of the element). (3) Fill the tree diagram with color classification. Start from the largest area, moving towards the smallest. Example: Let’s choose a base element circle (C) with a base color blue (B). One possible sequence for the other colors: G, R, Y. (4) After filling out the tree diagram, note down the code of each element.	Depending on the chosen geometric shape, answer only the item related to your choice. How many elements are there in the triangle set? How many elements are there in the circle set? How many elements are there in the square set? Solution(s) to the task: Codes:.......................
Task 02 There are 6 triangles of red base color in the set. Two of them are missing. Can you find them? Draw them and indicate their codes.
Task 03 There are 6 circles of blue base color in the set. Two of them are missing. Can you find them? Draw them and indicate their codes.
Task 04 There are 6 squares of yellow base color in the set. Two of them are missing. Can you find them? Draw them and indicate their codes.
Task 05
	Draw the element that could be chosen if the following descriptions were true: The LARGE field is red or the SMALL field is blue. The MEDIUM field is neither green nor blue. The BASIC field is yellow. Draw the element that could be chosen if the following descriptions were true: The LARGE field is blue or the SMALL field is not green. The MEDIUM field or the BASIC field is yellow. The SMALL field is green, but the MEDIUM field is not yellow. Draw the element that could be chosen from the set of triangles if the following descriptions were true: The BASIC field is green or not green. The MEDIUM field is blue, but the SMALL field is not red. The LARGE field is yellow or the MEDIUM field is blue, but only one of these is true.
Task 06 Let’s check how many and what kind of elements are in each set. Sets: T—Triangle, C—Circle, S—Square Colors: R-RED; G-GREEN; B-BLUE; Y-YELLOW Example of labeling elements: TGYRB            CBGRY            SRBYG

.

Tasks 01 to 06 are designed to engage students with the pillars of CT—problem decomposition, pattern recognition, abstraction, and algorithm design—through hands-on interaction with Poly-Universe elements. Each task requires students to decompose problems into smaller, manageable steps, such as choosing specific shapes and colors based on given conditions, which enhances their ability to break down complex instructions into simpler parts. For example, in Task 01, students must select and classify geometric shapes by their base color and size, fostering pattern recognition as they identify and differentiate between various attributes of the shapes. Task 02 builds on this by having students sequence these shapes in a logical order, which parallels the systematic thinking needed for algorithm design. Throughout these activities, abstraction is employed as students isolate critical features of the elements—such as shape, size, and color—ignoring irrelevant details to focus on the core problem. These activities require a sequential, logical approach, which simulates algorithmic thinking and guides students in developing step-by-step solutions.

2.6. Procedures

The study intervention took place in two stages, and in the first bimonth, the following were performed: (i) After the implementation and evaluation of plugged activities about Algorithm Analysis and following the first assessment, the teacher noticed that the students demonstrated difficulties in recognizing patterns, abstraction, decomposition, and algorithms in all the assessment items. (ii) Consequently, the teacher introduced Poly-Universe to the students. Then, the pieces were collected, and the teacher presented an activity for the students to identify the solution without the help of a computer. The deliberate focus of this initial activity was on the pillar of pattern recognition, which the students solved. Continuing with the unplugged approach, another activity was carried out, this time focusing on decomposition, which the students also managed to solve after some time. As noted in Section 2.5 of Materials, the activities were taken from the Poly-Universe database and were initially started individually, and then the students could exchange ideas in pairs. This process continued with six unplugged activities with the aid of Poly-Universe and concluded with the evaluation of the intervention carried out.

During these activities, the development of computational skills, i.e., the pillars of CT, was not explicitly presented.

Quantitative data were collected from two distinct assessment moments. Each assessment included four activities, one for each pillar of CT: abstraction, pattern recognition, decomposition, and algorithmic reasoning.

Student responses were classified as either correct or incorrect based on predefined objective criteria. The analysis focused on the percentages of correct responses, allowing for a comparative analysis of student performance.

Qualitative data were collected through a teacher logbook maintained by the instructor-researcher during the implementation of the activities. The analysis followed the principles of content analysis [30] and involved transcription of the field notes; coding: the transcribed texts were read multiple times, and meaningful units (e.g., words, phrases, or short segments) were highlighted and coded; and category formation: codes were grouped based on thematic similarity. A second researcher independently reviewed a subset of the data and the category structure. Any discrepancies were discussed and resolved through consensus.

3. Results

The presentation of results is organized to directly address the following research questions: Does the use of unplugged Poly-Universe activities enhance students’ engagement and collaboration in learning contexts? Does the use of unplugged Poly-Universe activities in algorithm analysis classes enhance students’ computational thinking skills across its various components?

3.1. Contributions of Unplugged Poly-Universe Activities to Students’ Engagement and Collaborative Practices During the Learning Process

Regarding the application of the activities, the researcher made several notes related to minor adjustments or corrections to the activities to facilitate their understanding. Content analysis of the teacher logbook identified some categories, namely Motivation and Engagement, Collaboration, and Success on CT Skills.

3.1.1. Motivation and Engagement

Evidence from the teacher logbook notes revealed a consistent pattern of high student motivation and engagement throughout the unplugged activities with Poly-Universe. These observations emerged from the content analysis of field notes, where Motivation and Engagement were identified as recurring and distinct thematic categories.

In fact, many students displayed strong curiosity and enthusiasm when interacting with the Poly-Universe geometric elements. The visual appeal, manipulability, and open-ended nature of the materials were frequently noted in the logbook as contributing factors to increased motivation. This was particularly visible during Task 01, where students explored a problem-solving challenge involving the construction of a geometric arrangement. Since the activity allowed for multiple correct solutions depending on the chosen tiles, students were observed comparing their results and engaging in spontaneous peer discussions. These behaviors were coded under both Engagement (active participation, problem-solving) and Motivation (expressions of interest and excitement).

One example, documented in the logbook and illustrated in Figure 3, was a student who recreated the Poly-Universe tiles at home using paper cards to continue solving a task. The student brought the solution to class and enthusiastically shared it with the teacher and peers. This behavior was interpreted as a strong indicator of self-motivated engagement extending beyond the classroom setting.

3.1.2. Collaboration

The field notes reported changes in student collaboration. Prior to the intervention, students typically worked independently and relied heavily on the computer. During the unplugged activities, however, there was a marked increase in pair and group work. Students began exchanging ideas, seeking feedback, and co-constructing solutions—signs of growing social engagement and a shift in classroom interaction dynamics.

Figure 3 serves to visually illustrate some of these observed behaviors, such as students manipulating the Poly-Universe pieces in groups, showing their work to others, or discussing multiple solution strategies. These images were selected to complement the logbook-based content analysis by offering non-verbal evidence of cooperation during the activities.

3.1.3. Success on CT Skills

The teacher’s field notes provided evidence of students’ successful accomplishment of tasks involving the pillars of CT during unplugged activities with Poly-Universe. Through oral reflections and informal questioning, students verbalized actions such as “breaking the problem down into steps” (decomposition) and “finding the rule in the pattern” (pattern recognition). In tasks like the “Find the missing piece” challenge, they also demonstrated the ability to isolate relevant elements (abstraction) and represent logical relationships between the pieces, thereby systematizing their reasoning in ways that resemble algorithmic design.

3.2. Contributions of Unplugged Poly-Universe Activities in Algorithm Analysis Classes to the Development of Students’ CT Skills Across Its Various Pillars

The analysis of student performance across the two assessment moments—one following the plugged (computer-based) activities and the other after the unplugged activities using Poly-Universe—revealed significant differences in how CT skills were demonstrated and articulated (Figure 4).

In the first assessment (after the plugged activities), students showed difficulty in solving the activities associated with the four CT pillars. Only 30% of students correctly answered the task related to algorithmic reasoning and 30% the task related to pattern recognition, 24% succeeded in decomposition, and 22% in abstraction (Figure 4). These results suggest limited understanding and application of CT concepts when using digital tools alone.

In contrast, after the unplugged activities, students’ performance improved substantially: 80% of students correctly solved the algorithmic reasoning task, 75% the pattern recognition task, 67% the decomposition task, and 72% the abstraction task (Figure 4). These improvements in all the activities of the CT pillars (approx. 50%) indicate that the unplugged activities supported not only content understanding but also the ability to approach problems using CT strategies.

In summary, students became more capable of explicitly identifying which CT pillars they were engaging with during the unplugged activities, a result that is consistent with what was reported in Section 3.1.3.

4. Discussion

The study presents an experience aimed at developing CT skills in Computer Science students at a Brazilian University. Through unplugged activities using Poly-Universe materials, there was a highly positive advancement in the development of these skills, such as abstraction, pattern recognition, algorithms, and decomposition, as well as an improvement in student autonomy. This study demonstrated that the unique characteristics of Poly-Universe, including its use of geometric shapes and the hands-on manipulation of these elements through activities, were instrumental in enhancing CT. Specifically, the game’s structure facilitates the development of critical skills such as pattern recognition, abstraction, decomposition, and algorithm design. By breaking down complex problems into more manageable parts, students could engage in meaningful problem-solving processes, which directly promoted CT [2]. This innovative approach, without the use of machines, also encouraged interaction and collaborative work among students while also strengthening motivation and engagement.

The Algorithm Analysis course, part of the Computer Science curriculum, aims to develop students’ ability to understand, design, and evaluate algorithmic solutions using both formal and practical tools. In this study, learning outcomes were assessed through two sets of tasks aligned with the four pillars of CT—abstraction, pattern recognition, decomposition, and algorithmic reasoning. These tasks were administered after two different instructional phases: the first using computer-based (plugged) activities and the second based on unplugged activities using Poly-Universe.

The academic progress observed was substantial. The percentage of students who correctly completed the tasks related to CT pillars increased from the plugged phase to the unplugged phase by approximately 50% across all pillars, which demonstrates a clear improvement in learning outcomes. This quantitative evidence aligns with the qualitative findings reported in the teacher’s field notes, which documented students’ successful accomplishment of tasks involving the pillars of CT during unplugged activities. In particular, students were observed verbalizing their reasoning through strategies such as breaking problems down into steps (decomposition), identifying rules in patterns (pattern recognition), and isolating relevant elements to represent logical relationships (abstraction and algorithmic reasoning). The Poly-Universe activities, by requiring the manipulation of abstract geometric pieces in open-ended problem-solving, provided opportunities for students to make their reasoning processes explicit. This convergence of quantitative and qualitative results illustrates the value of the mixed-methods approach adopted in this study since it allows us to interpret the observed gains not only as improved task performance but also as evidence of students’ enhanced ability to apply and articulate CT concepts.

These findings are in line with the existing literature that emphasizes the value of unplugged learning for developing CT, particularly in contexts where abstraction and problem-solving are central [12,20,23,26]. While our study focuses on higher education, the evidence similarly supports the idea that unplugged strategies—especially when well-designed—can enhance conceptual understanding and learning outcomes.

Furthermore, the model employed—transitioning from plugged to unplugged activities—aligns with recent research such as [21], which highlights the complementary nature of unplugged and digital programming. These findings are in line with the existing literature that emphasizes the value of unplugged learning for developing CT, particularly in contexts where abstraction and problem-solving are central [12,23,26]. Moreover, they support calls for integrating manipulatives and cognitive embodied learning strategies into algorithm instruction [31], especially when students struggle with abstract representations in traditional digital environments.

Therefore, incorporating the pillars of CT through activities in the school environment and/or the classroom will be a game-changer for teachers seeking a differentiated methodology with an impact on the teaching and learning process, as well as assisting them in decision-making processes in the classroom. In the future, it will be interesting to know in more depth the contribution of the Poly-Universe material to each of the pillars of CT using a quasi-experimental plan.

5. Limitations

This study adopted a mixed-methods approach within an action research framework, enabling a holistic understanding of both learning outcomes and processes. While this design allowed for insight into the use of Poly-Universe in teaching algorithm analysis, several limitations must be acknowledged.

One limitation is that the study was conducted in a single course with one cohort (27 students), limiting the generalizability of the findings. Although the context is representative of a typical Computer Science classroom in a Brazilian university, broader application would require replication in other settings and with larger, more diverse populations.

Another limitation is that the assessment instruments—although aligned with the pillars of CT—were designed by the researchers and reviewed internally, without external validation. Future studies should consider the development or adaptation of validated instruments to assess CT competencies in unplugged contexts.

A further limitation is that the observational data were collected through a teacher-maintained logbook rather than a standardized tool for engagement measurement. While the observations were systematic and subjected to content analysis, the absence of a validated instrument, such as the In-Class Engagement Measure (IEM) [32], may limit the replicability and objectivity of the qualitative findings.

Additionally, the study was not designed as a pre–post trial; rather, it traces the development of learning outcomes across two assessments. Consequently, the observed gains cannot be attributed causally to the unplugged activities. Part of the improvement may reflect practice effects (greater familiarity with task types on a second exposure), and a formal equating of item difficulty across rounds was not conducted, which may have influenced scores. Additionally, the absence of random assignment and a concurrent control group limits internal validity and leaves alternative explanations (e.g., short-term maturation, classroom dynamics) plausible. Future work should employ a controlled design (plugged-only control vs. unplugged intervention), use equated or counterbalanced test forms, and include adequate intervals to mitigate practice effects.

Lastly, the study implemented only a single intervention cycle due to time constraints within the semester. Future research should explore the long-term impact of integrating Poly-Universe activities across different content areas and over extended periods.