Students’ Perception Towards Do It Yourself (DIY) Workshop in Learning Internet of Things (IoT): A Case Study of CS110 Students in UiTM Sarawak Branch First Author\[1\]\*, Second Author2 (Double Blind Review: Please do not type or edit anything here until final-camera ready submissions) *1First affiliation, City and Country (Please do not type or edit anything here, until final camera-ready paper submission)* *2Second affiliation, City and Country (Please do not type or edit anything here, until final camera-ready paper submission)*
ARTICLE INFO ABSTRACT

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Published 1 March 2024

 The Internet of Things (IoT) has revolutionized the digital era, putting everyday things on the Internet. The increasing adoption of the IoT in various sectors has led to the exploration of innovative teaching methods and tools in education. Recently, a new topic called “Basic IoT” has been added to the Digital Electronics course offered in the Diploma in Computer Science (CS110) at UiTM. Before the topic was introduced, the students rarely worked directly with hardware components and knew little about IoT. Conventional teaching methods may fall short of providing students with hands-on experience. Therefore, five (5) DIY workshop sessions were conducted to expose the students to IoT. This research aims to determine the level of students' perceptions towards the DIY IoT workshop, evaluate the difficulty level for all modules throughout the DIY IoT workshop, and assess the effectiveness of DIY workshops in learning IoT. During the workshop, students were introduced to the fundamentals of the Internet of Things, the ESP32 microcontroller and the installation of Arduino IDE software, the method of lighting LEDs using ESP32, the method of connecting ESP32 to a WiFi network, as well as the method of reading data from sensors and sending data to Google Sheets. Online questionnaires were disseminated at the end of the workshop, and a short interview was conducted to gain the students’ perceptions of the DIY IoT workshop. Data analysis was conducted in three primary phases; descriptive statistics, mean scores, and t-tests using the Statistical Package for the Social Sciences (SPSS). The result of this study shows that students have positive perceptions towards the DIY workshop in learning IoT. It also offers invaluable insights into the role of experiential learning in IoT education and provides actionable recommendations for optimizing the workshop.

Keywords:

IoT

DIY Workshop

Computer Science

Digital Electronic

Arduino

Experiential Learing

DOI:

10.24191/jcrinn.v9i1
# 1.0 introduction The Internet of Things (IoT) has emerged as a transformative technology in today's digital age. It has the potential to revolutionize the way we live and work, connecting everyday objects to the Internet and enabling these objects to communicate and exchange data. IoT devices, which can vary from home items to industrial equipment, are mini-computer processors that employ machine learning to act on sensor data. Mouha (2021) described those sensors, connectivity, data processing, and the user interface are the four components that make up a full Internet of Things system. IoT is progressively becoming an important aspect of our life that can be sensed everywhere around us. Everything from healthcare and transportation to agriculture and manufacturing stands to benefit significantly from the IoT. As such, students need to acquire a solid understanding of IoT concepts and technologies. The Digital Electronics course offered in CS110 has recently added Basic IoT as a new topic. However, before this topic was introduced, CS110 students generally dealt with software tools. They rarely worked directly with hardware components and were unfamiliar with the IoT. In addition, the traditional instructional approaches may not fully equip students with practical, experiential learning opportunities. To comprehend the concept of IoT, it is crucial for the students to recognize the hardware components that will be used in IoT systems, including the function of each component. Traditional methods of teaching IoT often involve theoretical lectures, laboratory exercises, and simulations. Although these methods are essential for teaching theoretical knowledge, they may be insufficient in equipping students with practical experiences that closely resemble real-world IoT implementation. Thus, this is where the Do-It-Yourself (DIY) IoT workshop comes into play, whereby it offers students the opportunity to actively engage in building IoT devices, experimenting with sensors and actuators, and developing their own IoT solutions. The study by Kuznetsov and Paulos (2010) mentioned that DIY projects can enhance students' creativity, problem-solving, and critical thinking skills while promoting sustainable development. The primary goal of this workshop was to teach students how to build a basic IoT system by introducing them to the necessary hardware components. Through these hands-on activities, students were able to gain a deeper understanding of IoT hardware, explore its capabilities, and witness firsthand how these components contribute to the broader IoT landscape. ![A hand holding a piece of paper with wires Description automatically generated](65a4c973659df_media/media/image1.jpg) ![A person sitting at a desk with a computer Description automatically generated](65a4c973659df_media/media/image2.jpeg) Fig. 1. Demonstration of an IoT project during the DIY IoT workshop using a soil moisture sensor, an ESP32, and an Adafruit IO Dashboard. Incorporating IoT into the curriculum has resulted in a significant pedagogical shift, necessitating a bridge between software-centric learning and hands-on hardware experiences. In this research paper, we aim to explore the level of students' perceptions on DIY workshops in learning IoT, focusing on students' experiences in higher learning institutions. To address the challenges of this transformation, this study aims to evaluate the difficulty level for all modules throughout the DIY IoT workshop. The difficulty of these modules will reveal students' learning curves in learning IoT through hands-on activities. Another central objective of this research is to assess the effectiveness of the DIY workshop in facilitating IoT education, as it is vital in tailoring instructional methods to their needs and expectations. ## 1.1 Objectives 1) To evaluate the level of difficulty for all modules throughout the DIY IoT workshop. 2) To assess the effectiveness of the DIY workshop in learning IoT. 3) To determine the level of students’ perceptions towards the DIY IoT workshop. # 2.0 Literature Review ## 2.1 Internet of Things (IoT) The Internet of Things, or IoT, refers to a network of physical objects that are embedded with sensors, software, and other technologies, allowing them to communicate with other devices and systems over the Internet or other communication networks (Gillis, 2021). Mouha (2021) said IoT involves the connection of sensors and actuators embedded in physical objects through wired and wireless networks, often using the same Internet Protocol (IP) that connects the Internet. IoT is a revolutionary approach that has transformed various aspects of our daily lives, including smart cities, smart homes, pollution control, energy saving, smart transportation, and smart industries (Alam et al., 2020). In recent years, IoT has garnered considerable attention due to its capacity to profoundly transform multiple industries. A systematic literature review by Granell et al. (2019) identified the advantages of integrating IoT technologies across various sectors and industries. The research shows that the Internet of Things (IoT) can improve efficiency, sustainability, and production. Kumar et al. (2019) viewed IoT as an emerging paradigm that allows smart devices to communicate with the internet, thereby offering inventive resolutions to a multitude of challenges and concerns. As the IoT continues to evolve, experts and developers are working together to make the technology bigger and better, fixing many problems with the systems that are already in place. ## 2.2 IoT in Education Alzahrani and Alshahrani (2020) highlighted the benefits and challenges of integrating IoT into education. They found that IoT can enhance students' critical thinking, problem-solving, and collaboration skills. In a literature review by Chen and Wu (2019), the authors emphasized the importance of user-centered design in developing IoT applications for education. The study highlighted the need for a user-centered approach to ensure that IoT applications are accessible, usable, and effective for learners. The incorporation of IoT into the curriculum of computer science studies is not new. Bajracharya et al. (2021) explained that there are two (2) categories where IoT is incorporated into educational activities, either by adopting IoT to help with teaching and learning, or by including IoT courses into the existing curriculum. Ahmed et al. (2022) reported the inclusion of the IoT module into the computer science curriculum, and the students reported increased interest in exploring IoT in the future. IoT was also introduced into the curriculum of computer science in high school (Abichandani et al., 2022) and the first year of college (Izumi et al., 2022), where the students were reported to be able to grasp the concept of IoT and sensors. ## 2.3 IoT DIY Workshop Kuznetsov and Paulos (2010) discussed the potential roles of user-centered technology, specifically DIY projects, in empowering learners while promoting sustainable development. The study found that DIY projects can enhance students' creativity, problem-solving, and critical thinking skills while promoting sustainable development. Many approaches have been employed to teach IoT concepts to students. Ronoh et al. (2021) presented a survey of IoT learning methods, including problem-based learning, flipped laboratories, flipped classrooms, cooperative learning, and collaborative learning. The same authors found that Arduino and Raspberry Pi are the most common hardware platforms used for teaching IoT. Budihartono et al. (2022) conducted a series of workshops consisting of presentations, demonstrations, and training to improve the students’ learning of IoT technologies. Results from a questionnaire conducted after the workshop showed almost all students were able to understand the IoT topics discussed during the workshop. A study by Wang and Wang (2019) discussed the use of widely available educational IoT kits for beginners or non-major students. The study found that IoT kits can enhance students' understanding of IoT concepts and provide hands-on experiences. The authors emphasized the need for innovative teaching methods to integrate IoT into the curriculum effectively. A study by Bajracharya et al. (2021) also discussed the benefits and challenges of choosing an education kit for learning IoT, then concluded that there are many opportunities for using educational kits for teaching and learning IoT. The use of educational kits will speed up the process of learning IoT concepts. # 3.0 Methodology This section outlines the methodology employed to conduct a quantitative survey aimed at assessing students' perceptions towards DIY workshops in learning IoT. The target respondent consists of all Computer Science diploma students of UiTM Sarawak branch, Samarahan Campus 2 who registered for the ITT270 Digital Electronics course during the semester of March–August 2023. A structured questionnaire was utilized as the primary data collection instrument. This questionnaire was developed based on a review of the existing literature. It comprises closed-ended questions featuring a 5-Likert scale response format, enabling students to provide feedback regarding their perceptions of the conducted DIY workshop. Additionally, in-depth information about the workshop is gathered through interviews with the students. At the final session of the DIY workshop, students were provided with a link to complete an online questionnaire. 19 out of 21 students enrolled in ITT270 courses responded to the survey, resulting in a response rate of 90.47%. In this study, Statistical Package for the Social Sciences (SPSS) was employed for comprehensive data analysis. The analysis was conducted in three primary phases. First, descriptive statistics was performed to offer a comprehensive summary of the demographic profile. Secondly, mean scores were calculated to determine the level of difficulty and students’ perception towards the DIY IoT workshop. Lastly, t-tests were employed to assess the effectiveness of the DIY workshop in learning IoT. # 4.0 Result and Discussion ## 4.1 Demographic Profile Table 1 lists the demographic profile of respondents according to gender, semester of study, age, and race. Most of the respondents (68.4%) were male and the other 31.6% of the respondents were female students. About 94.7% of the respondents were from semester 4 and only a respondent from semester 3 of the study. This study was participated with the most respondents by Malay students (31.6%), followed by Iban (26.3%), and the least by Bajau, Banjar, and Melanau with 5.3% for each race. Table 1: Demographics of Respondents | | | | | | --------------------- | --------------- | ------------- | ------------------ | | **Demographic** | **Label** | **Frequency** | **Percentage (%)** | | **Gender** | Male | 13 | 68.4 | | ** ** | Female | 6 | 31.6 | | **Semester of study** | 3 | 1 | 5.3 | | ** ** | 4 | 18 | 94.7 | | **Age** | 19 | 1 | 5.3 | | ** ** | 20 | 18 | 94.7 | | **Race** | Bajau | 1 | 5.3 | | ** ** | Banjar | 1 | 5.3 | | ** ** | Bidayuh | 3 | 15.8 | | ** ** | Iban | 5 | 26.3 | | ** ** | Kadazan - Dusun | 2 | 10.5 | | ** ** | Malay | 6 | 31.6 | | ** ** | Melanau | 1 | 5.3 | ** ** ## 4.2 Levels of difficulty for all modules throughout the DIY IoT workshop All the respondents are required to answer questions about the difficulty level of the modules that had been used throughout the workshop so that the level of difficulty of the modules can be determined. The questions are provided in 5 Likert-scale for each item. Scale 1 refers to very difficult while scale 5 is for very easy. To determine the levels of difficulty, we averaged the difficulty level score and categorized it into five levels of difficulty, which are very high, high, moderate, low, and very low (Moidunny, 2009) as shown in Table 2. Table 2: Level of Difficulties | | | | -------------- | ----------------------------- | | **Mean Score** | **Mean interpretation table** | | 1.00 – 1.80 | Very High | | 1.81 – 2.60 | High | | 2.61 – 3.20 | Moderate | | 3.21 – 4.20 | Low | | 4.21 – 5.00 | Very Low | Source: Moidunny (2009) The mean score in Table 3 concludes that the level for all modules used throughout the DIY IoT except Module 5 is easy for students to understand and follow since the mean score is between 3.21 to 4.20. Module 5 was categorized as moderately difficult since the mean score is 3.1930, which is neither easy nor difficult to understand by the students. Table 3: Mean Score for Level of Difficulties

Variables

Mean Score

Level of Difficulty

Module 1(a): Installation and Setup of Arduino IDE for ESP32

4.0000

Low

Module 1(b): Programming ESP32 to print to serial monitor and blink an LED

3.8070

Low

Module 2: Reading data from sensors

3.5965

Low

Module 3: Connecting ESP32 to Wi-Fi network

3.7400

Low

Module 4: Sending data from sensor to cloud

3.5614

Low

Module 5: Developing a DIY-based IoT system

3.1930

Medium
## 4.3 Assessing the Effectiveness of the DIY Workshop in Learning IoT Table 4 tabulates the mean and analysis of the Paired t-test for students’ knowledge level before and after they attended the DIY IoT workshop. The result reveals that there is a difference in the mean of students’ knowledge level towards the DIY IoT workshop. Therefore, a further analysis has been done to assess whether there was a significant improvement in students’ knowledge level before and after they attended the DIY IoT workshop. Analysis of the Paired t-test in Table 4 concludes that there is a significant improvement in knowledge level among students toward DIY IoT workshop (p-value \< 0.05). This means that students have an improvement in knowledge level after attending the DIY IoT workshop. Thus, we can conclude that the DIY workshop is effective in learning IoT. Table 4. Paired T–test Analysis
  N Mean Mean

t-test

(p-value)

 Conclusion
    (Before) (After)
Knowledge 19 1.74 3.95

-9.340

(0.000)

 Significant Effective
## 4.4 Reliability Analysis The accuracy or reliability of data that has been used in this study was analyzed by conducting a Reliability Test. The data is concluded as reliable if the value of Cronbach’s Alpha is more than 0.7 (Pallant, 2011). The Cronbach’s Alpha value for all variables is displayed in Table 5. The analysis shows that all the variables used in this study were reliable as Cronbach’s Alpha values were more than 0.7 for all variables. Table 5: Reliability Analysis | | | | | -------------- | ------------------- | -------------------- | | **Variable  ** | **Number of Items** | **Cronbach’s Alpha** | | Challenge | 7 | 0.917 | | Interest | 7 | 0.913 | | Readiness | 8 | 0.818 | ## 4.5 Students’ Perception Towards DIY IoT Workshop The students’ perception towards the DIY IoT workshop was measured using three variables which are challenge, interest, and readiness. In order to measure the perception, the respondents needed to answer the questions for these three variables. Same as questions for the level of difficulty of the modules, the questions need to be answered using 5 Likert-scale for each item. Scale 1 refers to strongly disagree while scale 5 is for strongly agree. To determine the level, we calculated the mean score for all variables and categorized it into five levels, which are very low, low, moderate, high, and very high (Moidunny, 2009) as shown in Table 6. Table 6: Level of Perceptions | | | | -------------- | ----------------------------- | | **Mean Score** | **Mean interpretation table** | | 1.00 – 1.80 | Very Low | | 1.81 – 2.60 | Low | | 2.61 – 3.20 | Moderate | | 3.21 – 4.20 | High | | 4.21 – 5.00 | Very High | Source: Moidunny (2009) Table 7 displays the mean score for all the variables used to measure the perception of students towards the DIY IoT workshop. The variable challenge gives a mean score of 3.2331. It means that the students moderately agreed that the condition caused some challenges for them while attending the DIY IoT workshop. In terms of interest and readiness, they highly agreed with their preferences and preparations when attending the DIY IoT workshop. The findings reveal that they are interested in joining the workshop and prepared very well before attending the workshop. Table 7: Mean Score for Perception Level
Variable  

Mean Score

Level of Perception
Challenge 3.2331 Medium
Interest 3.7820 High
Readiness 3.9605 High
# 5.0 Conclusion In conclusion, the DIY IoT workshop was conducted to address the limited knowledge of IoT among students who were taking the Digital Electronics course. The workshop aimed to expose students to the components of IoT systems and develop a simple IoT system. The integration of the DIY workshop in learning IoT has shown promising results. This hands-on learning experience provided students with valuable insights into the fundamentals of IoT, allowing them to work directly with hardware components and gain practical skills. The effectiveness of the DIY workshops in learning IoT was further supported by the students' positive perceptions. Besides, by incorporating the DIY IoT workshops into the curriculum, educators can better prepare students for the IoT-driven future and equip them with the skills and knowledge necessary to excel in the rapidly evolving digital landscape. Overall, the DIY IoT workshop has been a valuable initiative in bridging the gap between theoretical knowledge and practical application, enabling students to gain hands-on experience and a better understanding of IoT concepts and components. Therefore, in keeping with the present technical advancements in the Fourth Industrial Revolution (IR4.0), we hope that this kind of program can be continued in the future to highlight the talent and creativity of students in the development of products based on the IoT concept. # Acknowledgements/Funding The authors would like to acknowledge the support of Universiti Teknologi MARA (UiTM) Cawangan Sarawak, Kampus Samarahan 2 for providing financial support through Tabung Amanah Pembangunan Akademik (TAPA) for the DIY IoT workshop. # Conflict of Interest The authors declared that they have no conflicts of interest to disclose. # References Abichandani, P., Lobo, D., & Shekhar, P. (2022). 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