1. Introduction
Today, the teaching and learning of science are closely connected to laboratory exercises [1,2,3,4,5]. Laboratories provide controlled environments that ensure the safety of students as they put theory into practice [6,7,8,9]. In the field of learning microcontroller electronic circuits, hands-on experiences encompass three stages: (a) understanding the special traits of the microcontroller, such as its architecture and special function registers, (b) writing code to program the microcontroller, and (c) construction of the electronic circuit, either by using a breadboard or modular boards. These three stages apply across all educational levels, from early childhood to university students (e.g., [10,11,12,13,14]).
The Arduino board’s user-friendly programming along with the strong community support has contributed to its widespread popularity [15,16,17,18]. Additionally, the development of Arduino shields has probably provided an additional advantage to users [19,20,21]. Shields are modular boards that can be mounted on an Arduino, offering pre-designed circuits and connections, including prototype shields, network shields, and LCD shields [22,23].
Researchers suggest that active learning, facilitated by hands-on exercises such as circuits with Arduino modular boards, can positively impact student performance [24,25]. Additionally, this experiential approach, engaging students with the real world through multiple senses, may positively influence their attitudes [26,27,28]. Therefore, the interactive nature of modular boards contributes to their pedagogical value.
Teachers, with their wealth of experience, appreciate the benefits of integrating real equipment and modular boards into the educational process. Nevertheless, the literature lacks a comprehensive examination of the effectiveness of modular boards in teaching microcontroller circuits. Additionally, students’ attitudes toward these boards and their perceived usefulness have remained quite unexplored. Hence, this pilot study aims to investigate the performance of 58 university students in a series of 4 exercises, with the aim of uncovering the impact of modular boards. In each exercise, students completed three pre/post-tests focusing on the three learning units, namely the first decided on microcontroller functions, the second on coding, and the third on circuits. Furthermore, students’ attitudes were recorded. Therefore, this article aims to enrich our knowledge and understanding of the impact of Arduino modular boards on the educational process, shedding light on aspects of the field that have, so far, remained uninvestigated.
1.1. Background
Active student participation in the learning process can enhance their performance, particularly when it involves inquiry-based learning within a laboratory setting [29,30,31,32,33,34,35]. In recent years, the utilization of Arduino boards in this context has grown, with researchers and educators incorporating them as a useful tool across a broad spectrum of activities and fields [36,37]. For instance, Arduino boards have found applications as pH meters for conducting chemistry experiments among middle school students [38]. Furthermore, Arduino has been employed in educational robotics applications [39,40].
In the field of electronic circuits, according to Ntourou et al. [41], the use of Arduino did not significantly affect motivation but could influence the development of computational thinking in primary school students. Similarly, research by Fidai et al. [42] demonstrated that Arduino applications may have a positive effect on students’ computational thinking. Lastly, research by García-Tudela et al. [31] demonstrated that primary education studies involving Arduino mainly aimed at programming development.
In terms of students’ attitudes, Kırıkkaya and Başaran’s research [43], which involved 50 university students, demonstrated that the use of Arduino positively influenced their attitudes toward technology. On the contrary, other researchers like Hadiati et al. [44] reported the opposite. In detail, Hadiati et al. included 77 university students and divided the sample into 3 groups. The first group received teacher support, the second group focused on exercises with a problem-solving approach, while the third group emphasized technical skills. The findings indicated that the three groups exhibited no statistically significant differences in their attitudes toward science.
As mentioned above, the use of Arduino boards as a learning tool for various subjects is quite common. However, a gap exists in the literature concerning the learning of electronic circuits and the influence of modular boards on students’ performance across three learning units: microcontroller, coding, and circuits. In conclusion, despite the widespread utilization of Arduino in education and research, the focus has not been directed towards the effect of modular boards or their usability. Consequently, there is a need for studies that delve into the learning of microcontroller electronic circuits using Arduino modular boards and explore their potential impact on students’ performance and attitudes. Thus, this article contributes the following: (a) knowledge about the impact of boards in three learning modules, (b) information about the perceived usability of boards by students, and (c) insights into students’ attitudes towards modular boards.
1.2. Research Questions
This article aims to explore the performance of 58 university students in 4 exercises, investigate their attitudes towards microcontroller circuits, and assess the usability of modular boards. To accomplish this and shed light on aspects of the field, we have addressed the following research questions (RQ):
RQ1. Is students’ performance on the three learning units (microcontroller, coding, circuit) comparable?
RQ2. From the students’ perspective, are modular boards considered usable for learning microcontrollers?
RQ3. Do the students exhibit positive attitudes toward the modular boards?
2. Materials and Methods
2.1. Participants
This pilot study involved 58 students (49 male, 9 female) with an average age of 22.48 years and a standard deviation (SD) of 2.682. The study was conducted at the Department of Information and Electronic Engineering, International Hellenic University, Sindos campus, Thessaloniki, and lasted four weeks. All procedures performed in the current study involving human participants were in accordance with the ethical standards of the Research Ethics Committee of the International Hellenic University. The students had basic knowledge of electronic circuits and programming, and they were randomly selected from those enrolled in the “Computer Systems Organization and Architecture” course.
2.2. Modular Boards
Two different modular boards had been designed and conducted for the research following the Arduino Uno pinout, as illustrated in Figure 1. The first modular board incorporated an LCD2x16, pins for connecting a 4 × 4 keypad, and a designated area for users to attach a mini breadboard for circuit extensions. The second modular board included a push-button, potentiometer (for analog-to-digital converter), dip-switches, buzzer, LED, and RGB LED.
2.3. Procedure
The 58 students participated in 4 exercises; each exercise involved the completion of 3 tests before the intervention (pre-test) and 3 tests after the intervention (post-test). Each one of the three tests was designed for each learning unit: microcontrollers, coding, and circuits, respectively.
In the first exercise, students handled the digital pins of the Arduino Uno as outputs, controlling components such as the LED, buzzer, and RGB LED. The second exercise focused on handling digital pins as inputs and controlling components such as dip-switches and push-buttons. In the third exercise, students worked on the analog-to-digital converter (ADC) and established bidirectional communication through a serial port. The fourth exercise was related to the use of an LCD2x16 and the implementation of a simple voltmeter. For more information about the exercises, you can visit the following website:
Each exercise lasted two hours, and the entire duration of the intervention was four weeks. Upon completing the exercises, students were asked to respond to an attitude questionnaire regarding their experience and the System Usability Scale (SUS) questionnaire [45] to assess the usability of the modular boards.
2.4. Instruments
In this pilot study, to assess students’ performance in three learning units (microcontroller, coding, and circuit), exercises and tests were developed within the framework of the European Union’s Erasmus+ project ‘Engine’ (accessible at
Five questions (Q) were used to capture students’ attitudes regarding their experience with the modular boards, as shown in Table 1.
Students employed a 7-point Likert scale that extended from “Strongly Disagree” to “Strongly Agree”. This selection of the 7-point scale was based on the belief that it effectively captured the perspectives and emotions of the participants [46,47]. For statistical analysis, students’ responses were scored on a scale from 1 (strongly disagree) to 7 (strongly agree).
Finally, the SUS questionnaire was utilized, consisting of 10 statements/items. Students rated their responses on a 5-point Likert scale, which ranged from “Strongly Disagree” to “Strongly Agree”. The selection of the SUS questionnaire was based on its ease and has already been translated and adapted for the Greek language [48].
2.5. Data Analysis
Cronbach’s alpha was calculated to evaluate the reliability of questionnaires (microcontroller, coding, and circuit). This measure assesses the internal consistency of these questionnaires, indicating how closely the items within each questionnaire are related to one another [49,50]. Furthermore, paired samples t-tests, ANOVA, and ANCOVA were carried out for data analysis using IBM SPSS Statistics 26 [51]. To enhance the statistical analysis, bootstrapping techniques were applied. This involved estimating 95% confidence intervals (CI) based on 1000 samples. Bootstrapping is advantageous as it makes no assumptions about the specific underlying data distribution and treats non-normally distributed data as if they were normally distributed [52,53,54].
3. Results
3.1. Students’ Performance on the Four Exercises
Table 2 shows the pre-test and post-test results for the four exercises. According to the table, there is a consistent increase in scores from pre-test to post-test across all exercises and for all learning units (microcontroller, coding, circuit).
The reliability of the microcontroller learning unit was calculated and found to be acceptable, with a Cronbach’s alpha of 0.687 and inter-item correlations of 0.350. Similarly, the reliability of the coding learning unit was calculated and found to be acceptable, with a Cronbach’s alpha of 0.731 and inter-item correlations of 0.396. Lastly, the reliability of the circuits learning unit was calculated and found to be acceptable, with a Cronbach’s alpha of 0.706 and inter-item correlations of 0.375. These values are considered acceptable, especially given the relatively small number of items [55,56].
To assess differences between the pre-tests and post-tests, paired-samples t-tests were conducted, as indicated in Table 3. According to Table 3, we can see that in all exercises and for all comparable pairs, post-test performance was higher than the corresponding pre-tests. In fact, these findings are statistically significant (p = 0.001), indicating that the intervention was successful across all three learning units: microcontroller, coding, and circuit.
3.2. Students’ Performance on the Three Learning Units
We calculated the mean score of the four exercises across the three learning units, as illustrated in Figure 2 and presented in Table 4.
Conducting an ANOVA test on the pre-tests for the three learning units (microcontroller, coding, circuit) revealed F(2, 171) = 0.511, p = 0.601. Consequently, no statistically significant differences in student performance were found at the beginning for the three units. However, the ANCOVA test on the post-tests for the three learning units, with pre-tests as covariates, revealed F(2, 171) = 9.477, p = 0.000, η² = 0.122. As a result, statistically significant differences in student performance were found at the end of the intervention. Furthermore, a Tukey’s-b post hoc test showed that student scores in the coding learning unit were higher than microcontroller and circuit units.
3.3. Usability Results
The students rated the modular boards with a SUS score of 79.22. Table 5 shows the scores for the ten items of the SUS questionnaire.
According to the Pearson correlation, as shown in Table 6, a strong association is evident between the SUS score and students’ performance in the coding unit. In contrast, there appears to be no significant correlation with the microcontroller or the circuit unit. In other words, high-scoring students in the coding unit perceived the modular board as quite useful.
3.4. Students’ Attitudes towards the Modular Boards
From the students’ responses to the five questions, the mean score was calculated, as illustrated in Table 7.
In addition, Figure 3 shows the mean scores on the five questions with the 95% CI.
The ANOVA test conducted on the five questions revealed significant differences in student responses, with F(4, 285) = 11.329, p = 0.000. Among the five questions, the lowest score was assigned to Q1, which assessed the ease of creating an electronic circuit using the modular board. This suggests that, with the adoption of pre-assembled circuits, students were not exposed to each component and its interconnection, thereby influencing their attitudes. Conversely, the highest score was observed in Q2, related to the satisfaction students experienced with the time required to complete the activities. In other words, while the use of modular boards may have restricted the utilization of discrete electronic components, it heightened the satisfaction with the efficiency of activity completion.
In analyzing the responses across the three learning units, the most favorable attitudes were articulated in response to Q4, particularly regarding the knowledge acquired in coding. To elaborate, students expressed a perspective in alignment with their test performance, indicating that the incorporation of modular boards positively influences their coding proficiency. Conversely, the least favorable responses were noted in the context of the circuit unit (Q3). Consequently, employing such boards could prove advantageous when the primary intervention goal is the development of programming skills. Finally, students’ attitudes regarding their understanding of microcontrollers, as indicated by Q5, are notably positive. It is worth noting that students did not engage with a low-level programming language (e.g., assembly), which could have enabled them to delve deeper into comprehending the internal architecture and processes of the microcontroller.
4. Discussion
The existing literature lacks studies addressing the impact of modular boards on students’ performance and attitudes in the context of microcontroller circuits. To bridge this gap, we conducted a pilot study involving 58 university students and captured their performance and attitudes. Additionally, students completed the SUS questionnaire to assess the usability of modular boards in microcontroller circuits, a factor that could potentially impact their learning experience.
Concerning student performance before and after the intervention, statistically significant differences were observed in favor of post-tests over pre-tests for all learning units and exercises. In other words, the intervention involving microcontroller modular boards can be considered successful, aligning with the findings of previous studies that have highlighted the advantages of using Arduino boards (e.g., [41,57]).
Regarding the student performance across the learning units of microcontroller, coding, and circuit (RQ1), the ANOVA results for the pre-tests initially indicated similar performance on the three units, with F(2, 171) = 0.511, p = 0.601. However, when performing ANCOVA for the post-tests, statistically significant differences between the learning units were revealed, with F(2, 171) = 9.477, p = 0.000. Furthermore, a Tukey’s-b post hoc test demonstrated that student performance in the coding unit was higher than microcontroller and circuit. Hence, the utilization of modular boards, which offer pre-designed circuits and connections and easily snap onto the Arduino, enabled students to concentrate on coding during the intervention. Consequently, this approach yielded statistically significant outcomes. Furthermore, across the three learning units, it seems that modular boards have a greater impact on coding. These findings are consistent with studies emphasizing the advantages of Arduino applications in programming (e.g., [31]). Additionally, the utilization of pre-assembled circuits from modular boards and programming Arduino Uno using the user-friendly Arduino IDE software (v1.8.19) does not allow the user to delve into the architecture of the microcontroller or the interconnection of electronic components.
As for the usability of the modular boards (RQ2), students completed the SUS questionnaire, and the results yielded a score of 79.22. In this questionnaire, a score ranging from 50 to 70 is considered marginally acceptable and indicates room for improvement, while a score exceeding 70 is considered acceptable [57,58]. As shown in Table 5, students’ responses indicate that modular boards appear to be a usable approach for teaching microcontrollers, contributing to an enhanced and positive learning experience [59,60,61,62]. Furthermore, a strong correlation was observed between the SUS score and student performance in the coding learning unit (r = 0.384, p = 0.006). This suggests a compelling association, indicating that students who achieved higher scores in coding proficiency also demonstrated enhanced usability perceptions regarding the modular boards. However, such a correlation was not observed in the microcontroller (r = 0.164, p = 0.254) and circuit (r = 0.206, p = 0.152) learning units, underscoring the unique connection between coding and perceived usability in the context of Arduino boards.
As for students’ attitudes towards the modular boards (RQ3), their responses to the five questions can generally be characterized as positive, with an average score of 4.76 or higher (max = 7). Particularly, students believe that they acquired the most knowledge in the coding learning unit, in contrast to the circuit unit. In descending order, students expressed satisfaction with the time required to complete the exercises, noted that their programming knowledge had been enhanced, recognized a better understanding of the microcontrollers, and felt that their knowledge of interconnecting components in microcontroller circuits had been improved. These results are consistent with students’ performance on the post-tests of the exercises and reinforce the results from RQ1. Moreover, the students’ conviction that their understanding of the coding was further enriched aligns with the insights gained from RQ2 and the usability results. It is possible that having ready-made circuit boards in their hands prevented students from actively engaging with the circuit, leaving them with the perception that they did not fully address this aspect of the intervention.
In conclusion, the intervention in learning microcontroller circuits with modular boards for Arduino can be regarded as a successful and beneficial approach for students. Notably, the emphasis on coding is favored, as the circuit implementation is readily available on the modular board. On the one hand, this offers students the advantage of perceiving the time spent on exercises as satisfactory. However, it also may reduce their active involvement in the creation of the circuit. In a curriculum where comprehensive learning of electronics is the primary objective, the use of modular boards should be exercised wisely. Conversely, in a curriculum where electronic circuits serve as a tool rather than a core focus, the utilization of modular boards may be useful to be encouraged. Regardless, based on the educational intervention with the modular boards, it seems that the learning unit most enhanced is coding, while the unit least enhanced is the circuit. However, this observation may be influenced by the use of real components/boards. Perhaps the incorporation of breadboards or simulators [63,64] in microcontroller circuits teaching will provide additional insights into aspects of the field. Thus, future studies should investigate and compare the impact of modular boards on student performance and attitudes with both traditional circuit implementations and circuit simulations. Finally, future studies should examine the impact of different programming languages. For instance, the use of assembly language might constrain students’ coding performance but potentially might have a positive impact on their comprehension of microcontroller architecture.
5. Conclusions
In this pilot study involving 58 university students, our objective was to investigate the impact of modular boards on students’ performance and attitudes toward microcontroller circuits. Additionally, students assessed the usability of the modular boards using the SUS questionnaire. Through a series of four exercises with Arduino boards, the pre-tests and post-tests revealed statistically significant improvements in knowledge scores following the intervention. Among the three learning units—microcontroller, coding, and circuit—it became evident that the use of modular boards principally benefitted the coding unit, as the circuit was already pre-constructed on the boards (RQ1). Moreover, students assigned a high SUS score, indicating that they perceive modular boards as a usable system for learning microcontrollers (RQ2). Notably, a strong association emerges between student performance in the coding unit and the scores obtained from the usability questionnaire. Regarding students’ attitudes, their responses were generally positive towards modular boards (RQ3). In particular, the highest score among the three learning units was for the coding. However, while students expressed satisfaction for the time required to complete the exercises, they encountered challenges in the circuit learning unit. Therefore, Arduino and modular boards with microcontroller circuits may serve as valuable tools and promising strategies for teachers aiming to effectively teach coding. However, in an educational approach focusing on other domains rather than coding (e.g., circuits or microcontroller architecture) with an emphasis on component interconnection, caution should be exercised when using modular boards. In conclusion, our study recommends that teachers incorporate modular boards into their pedagogical tools, and advocate for their systematic use, highlighting their significant contributions to increased knowledge acquisition, particularly for coding. Additionally, students demonstrate a high level of perceived usability for these boards. Simultaneously, learning through modular boards reinforces students’ positive attitudes. This collective impact has the potential to increase students’ engagement in activities and enrich their overall educational experience.
6. Limitations and Future Work
This pilot intervention, involving 58 students, aimed to assess the impact of modular boards on student performance and attitudes in learning microcontroller electronic circuits. While the sample size may pose some limitations on the statistical analysis, this concern was addressed through the use of bootstrapping in this particular research. Additionally, although the current study utilized four different exercises covering the basic functions of the microcontroller, it did not address other types of exercises, such as interrupt functions, which could potentially impact the pre/post-test scores.
To strengthen and extend the study’s findings in this field, future research endeavors should encompass various educational levels. Additionally, researchers should include and compare different programming approaches for the microcontroller, such as through a lower-level language (e.g., assembly) that may enhance the microcontroller’s learning unit. Furthermore, forthcoming studies could evaluate students’ circuit implementations using modular boards, breadboards, and simulators. The incorporation of a breadboard in comparing circuit implementation methods might provide better insights into students’ performance in the circuit learning unit.
S.T. implemented the modular boards and, together with T.S. conceived, designed, and wrote this paper. All authors have read and agreed to the published version of the manuscript.
All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the International Hellenic University (number 05/05.05.2022).
Informed consent was obtained from all subjects involved in the study.
The data presented in this study are available upon request from the corresponding author.
The authors declare no conflicts of interest.
Footnotes
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Enlarge this image.Figure 1. Modular boards. 1: Arduino Uno, 2: LCD2x16, 3: mini breadboard, 4: buzzer, 5: LED, 6: RGB LED, 7: Potentiometer, 8: push-button, and 9: dip-switches.
Attitude questionnaire.
| Question | Strongly Disagree | Strongly Agree | ||||||
|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | ||
| Q1. The process of creating an electronic circuit with the modular board was easy | ||||||||
| Q2. I am satisfied with the time it took me to complete the activities | ||||||||
| Q3. My knowledge of interconnecting components in microcontroller circuits has been enhanced | ||||||||
| Q4. My knowledge of microcontroller coding and programming has been enhanced | ||||||||
| Q5. My knowledge of microcontrollers has been enhanced | ||||||||
Pre-test/post-test statistics.
| Exercise | Test | Learning Unit | Mean | SD | Skewness | Kurtosis |
|---|---|---|---|---|---|---|
| 1 | Pretest | Microcontroller | 3.870 | 2.792 | 0.247 | −1.041 |
| Coding | 3.282 | 2.823 | 0.174 | −1.279 | ||
| Circuit | 3.851 | 2.911 | 0.186 | −1.010 | ||
| Posttest | Microcontroller | 7.370 | 2.308 | −0.920 | 0.273 | |
| Coding | 8.211 | 1.638 | −0.560 | −0.798 | ||
| Circuit | 7.520 | 2.136 | −1.748 | 3.790 | ||
| 2 | Pretest | Microcontroller | 5.085 | 2.761 | −0.010 | −1.131 |
| Coding | 5.150 | 2.929 | 0.197 | −0.686 | ||
| Circuit | 4.510 | 3.148 | −0.044 | −1.120 | ||
| Posttest | Microcontroller | 8.346 | 1.767 | −1.658 | 3.892 | |
| Coding | 7.923 | 2.221 | −0.726 | −0.647 | ||
| Circuit | 7.279 | 2.360 | −1.124 | 1.287 | ||
| 3 | Pretest | Microcontroller | 2.720 | 2.746 | 0.673 | −0.925 |
| Coding | 2.655 | 2.631 | 0.870 | 0.041 | ||
| Circuit | 2.998 | 2.362 | 0.335 | −0.784 | ||
| Posttest | Microcontroller | 7.177 | 1.626 | −0.210 | −1.024 | |
| Coding | 6.686 | 1.860 | −0.844 | 1.086 | ||
| Circuit | 6.276 | 2.199 | −0.430 | 0.150 | ||
| 4 | Pretest | Microcontroller | 2.373 | 2.301 | 0.546 | −0.621 |
| Coding | 4.525 | 3.279 | 0.168 | −1.235 | ||
| Circuit | 2.830 | 2.614 | 0.628 | −0.564 | ||
| Posttest | Microcontroller | 4.074 | 2.315 | 0.501 | −0.650 | |
| Coding | 8.919 | 1.707 | −2.350 | 6.736 | ||
| Circuit | 5.922 | 2.055 | −0.230 | −0.615 |
Paired-sample t-test statistics based on 1000 bootstrap samples.
| Mean | Bias | Std. | t | Sig. | 95% CI | |||
|---|---|---|---|---|---|---|---|---|
| Lower | Upper | |||||||
| Exercise 1 | PretestMicrocontroller—PosttestMicrocontroller | −3.463 | 0.021 | 0.361 | −9.356 | 0.001 | −4.130 | −2.763 |
| PretestCoding—PosttestCoding | −4.984 | 0.017 | 0.450 | −10.775 | 0.001 | −5.868 | −4.070 | |
| PretestCircuit—PosttestCircuit | −3.647 | 0.026 | 0.479 | −7.700 | 0.001 | −4.557 | −2.648 | |
| Exercise 2 | PretestMicrocontroller—PosttestMicrocontroller | −3.020 | 0.033 | 0.424 | −6.836 | 0.001 | −3.807 | −2.137 |
| PretestCoding—PosttestCoding | −2.585 | −0.005 | 0.360 | −7.172 | 0.001 | −3.282 | −1.913 | |
| PretestCircuit—PosttestCircuit | −2.579 | 0.000 | 0.474 | −5.434 | 0.001 | −3.461 | −1.644 | |
| Exercise 3 | PretestMicrocontroller—PosttestMicrocontroller | −4.564 | 0.015 | 0.401 | −10.974 | 0.001 | −5.323 | −3.765 |
| PretestCoding—PosttestCoding | −4.040 | 0.015 | 0.466 | −8.593 | 0.001 | −4.892 | −3.102 | |
| PretestCircuit—PosttestCircuit | −3.252 | 0.031 | 0.416 | −7.797 | 0.001 | −4.044 | −2.426 | |
| Exercise 4 | PretestMicrocontroller—PosttestMicrocontroller | −1.679 | 0.002 | 0.407 | −4.181 | 0.001 | −2.411 | −0.829 |
| PretestCoding—PosttestCoding | −4.438 | 0.017 | 0.526 | −8.315 | 0.001 | −5.444 | −3.379 | |
| PretestCircuit—PosttestCircuit | −3.278 | 0.002 | 0.433 | −7.441 | 0.001 | −4.128 | −2.399 | |
Mean and standard deviation (SD) for the three learning units.
| Test | Learning Unit | Statistic | Bias | Std. Error | 95% CI | ||
|---|---|---|---|---|---|---|---|
| Lower | Upper | ||||||
| Pre-test | Microcontroller | Mean | 3.544 | 0.009 | 0.276 | 3.010 | 4.117 |
| SD | 1.952 | −0.027 | 0.155 | 1.611 | 2.232 | ||
| Coding | Mean | 3.935 | −0.000 | 0.313 | 3.312 | 4.566 | |
| SD | 2.230 | −0.032 | 0.164 | 1.890 | 2.534 | ||
| Circuit | Mean | 3.552 | 0.001 | 0.317 | 2.949 | 4.168 | |
| SD | 2.237 | −0.028 | 0.142 | 1.916 | 2.483 | ||
| Post-test | Microcontroller | Mean | 6.759 | 0.006 | 0.210 | 6.368 | 7.180 |
| SD | 1.440 | −0.016 | 0.119 | 1.179 | 1.648 | ||
| Coding | Mean | 7.942 | −0.006 | 0.189 | 7.555 | 8.345 | |
| SD | 1.360 | −0.015 | 0.156 | 1.017 | 8.296 | ||
| Circuit | Mean | 6.787 | 0.010 | 0.222 | 6.323 | 7.191 | |
| SD | 1.603 | −0.003 | 0.220 | 1.143 | 2.014 | ||
SUS score per question based.
| Item | Statement (In English Language) | Score (Max = 5) |
|---|---|---|
| 1 | I think that I would like to use this system frequently | 4.33 |
| 2 | I found the system unnecessarily complex | 1.91 |
| 3 | I thought that the system was easy to use | 4.40 |
| 4 | I think that I would need the support of a technical person to be able to use this system | 2.38 |
| 5 | I found that the various functions in this system were well integrated | 4.36 |
| 6 | I thought that there was too much inconsistency in this system | 1.90 |
| 7 | I would imagine that most people would learn to use this system very quickly | 4.24 |
| 8 | I found the system very cumbersome to use | 1.84 |
| 9 | I felt very confident using the system | 4.09 |
| 10 | I needed to learn a lot of things before I could get going with this system | 2.34 |
Pearson correlation between SUS score and learning units.
| Microcontroller | Coding | Circuit | |
|---|---|---|---|
| SUS score | r = 0.164 | r = 0.384 | r = 0.206 |
Scores of attitudes questionnaire (max = 7).
| Q | Mean | SD | Skewness | Kurtosis |
|---|---|---|---|---|
| 1 | 4.76 | 0.961 | 0.388 | 1.128 |
| 2 | 5.84 | 0.914 | −1.396 | 4.373 |
| 3 | 5.45 | 1.259 | −1.190 | 2.213 |
| 4 | 5.74 | 0.965 | −0.299 | −0.289 |
| 5 | 5.71 | 0.817 | 0.196 | −0.804 |
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; Sapounidis, Theodosios 2
1 Department of Information and Electronic Engineering, International Hellenic University (IHU), 57400 Thessaloniki, Greece;
2 Department of Education, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece