Next Article in Journal
Humanizing Immigration and Refugee Experiences: A Student-Centered Approach to Teaching with Children’s Picturebooks in the Elementary Classroom
Previous Article in Journal
Educational Experiences of Black Children and Youth in Canada: A Scoping Review
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Education Sciences
Volume 16
Issue 5
10.3390/educsci16050729
education-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

An Exploratory Study of Pre-Service Teachers’ Perceptions of Using a Block-Based Coding Tool: Acceptance and Experiences of Coding Using Scratch

by
Yu-Chun Kuo
1,* and
Yu-Tung Kuo
2
1
Department of Critical Literacy, Technology & Multilingual Education, Rowan University, Glassboro, NJ 08028, USA
2
Department of Applied Engineering Technology, North Carolina A&T State University, Greensboro, NC 27401, USA
*
Author to whom correspondence should be addressed.
Educ. Sci. 2026, 16(5), 729; https://doi.org/10.3390/educsci16050729
Submission received: 29 January 2026 / Revised: 9 April 2026 / Accepted: 9 April 2026 / Published: 5 May 2026
(This article belongs to the Special Issue Immersive and Interactive Technologies in Education: Innovations, Applications, and Experiences)
Versions Notes

Abstract

This study aimed to investigate pre-service teachers’ perceptions of coding, including their acceptance of coding and their experiences of using a block-based tool to code. With a focus on acceptance and affective factors, we examined the influence of proposed factors on pre-service teachers’ intention to adopt coding, as well as the relationships of acceptance factors. Participants were pre-service teachers from a university in the northeastern United States. Data were collected using an online survey. Both quantitative and qualitative approaches were performed to analyze the data. The results indicated that pre-service teachers’ affective experiences significantly influenced their intention to code. Pre-service teachers’ perceived ease of use and usefulness significantly predicted their intention to code. Gender and coding skills played an important role in pre-service teachers’ intentions for coding adoption. Overall, pre-service teachers’ experiences with the coding activity were positive, leading to more positive changes in their perceptions of coding than negative changes.

1. Introduction

With the trend in emphasis on incorporating computer science (CS) education into K-12 settings in recent years (Childs et al., 2024; Gokoglu & Kilic, 2023; Timur et al., 2021), learning to code has become a topic that has gained increased attention and interests from teachers, teacher educators, school leaders, and researchers (Ari et al., 2022; Schmidt-Crawford et al., 2019). Coding or programming has become a basic and essential skill to learn, and it is as important as the skills of reading, writing, and arithmetic (Anderson, 2016; Timur et al., 2021). With the importance of coding or programming being recognized, many countries (e.g., the United States, Australia, the United Kingdom, etc.) have sought approaches or strategies to integrate coding into the K-12 curriculum (Basarmak, 2021; Timur et al., 2021).
Learning coding or programming skills can benefit students in many ways, including the development of higher-order thinking skills (e.g., problem solving, creativity, critical thinking, computational thinking, etc.), and improvements in students’ engagement, self-efficacy, and motivation to learn (Blackley & Howell, 2019; Brooke, 2024; Y. T. Kuo & Kuo, 2025; Mason & Rich, 2019). In addition, possessing coding skills can prepare students for future careers in STEM (e.g., computer and information technology, engineering, etc.) or other fields (e.g., business, education, healthcare, etc.) that require the employment of 21st century skills (Mason & Rich, 2019; Timur et al., 2021). Therefore, it is important to begin learning coding early by engaging younger students in coding activities as a stepping stone to success in their future professional or academic careers (Ari et al., 2022; Basarmak, 2021; Timur et al., 2021).
Teaching coding is considered important to fulfill the increasing demand of CS education in K-12 settings (Ari et al., 2022). Emergent arguments from educators and K-12 stakeholders indicated that teaching coding not only provides students with initial experiences in computational thinking, but also helps them to become an innovative creator (Schmidt-Crawford et al., 2019). To provide a seamless pathway of integrating coding into K-12 lessons, teachers serve as a pivotal role in such a process, and there is a necessity to offer training sessions and professional development opportunities in coding for both in-service teachers and pre-service teachers (Hunsaker & West, 2019; Sekyere-Asiedu et al., 2020; Zha et al., 2019). Researchers have indicated the importance of coding education, and the lack of such opportunities in teacher education (Ari et al., 2022; Schmidt-Crawford et al., 2019; Sekyere-Asiedu et al., 2020). For example, Poth (2019) indicated coding as an excellent opportunity to provide pre-service teachers with the skills to teach in the future classroom, including creativity, critical thinking, communication, collaboration, and problem solving. Zha et al. (2019) called for the need for teacher educators to offer coding preparation to pre-service teachers, and pointed out an issue with the lack of understanding about how pre-service teachers would engage in CS or programming learning and teaching.
There are various barriers that teachers may encounter when teaching coding or CS, including physical (e.g., lack of computer or Internet access), institutional (e.g., unsupportive administrators), knowledge (e.g., coding or programming concepts, pedagogical approaches, etc.) and emotional barriers (e.g., dispositions, beliefs, etc.) (Mason & Rich, 2019). Teachers’ knowledge has a significant impact on their practice (Ertmer & Ottenbreit-Leftwich, 2010), and their lack of knowledge in content, technology, or pedagogy could prevent teachers from successfully teaching coding or programming (Mason & Rich, 2019). It can be challenging for teachers to teach coding or integrate coding into daily lessons, as many teachers have little to no coding or programming experience (Broley et al., 2023), and few teachers possess an adequate level of competencies and confidence to incorporate coding into teaching (Hunsaker & West, 2019). Having negative attitudes or perceptions towards coding topics or programming education would make teachers think that coding or programming is difficult (Ari et al., 2022). To enhance teachers’ competence and confidence in teaching coding and to eliminate their knowledge and emotional barriers for coding, it is necessary to provide them with effective training in coding (Mason & Rich, 2019).
Developing pre-service teachers’ understanding of CS education and their positive perceptions towards integrating CS into teaching is crucial for increasing pre-service teachers’ intention to integrate coding into future teaching in the K-12 classroom (Ari et al., 2022; Butler & Leahy, 2021; Vasconcelos & Kim, 2022). Yukselturk and Altiok (2017) reported that engaging pre-service teachers in a course using Scratch to learn programming significantly decreased their negative attitudes toward programming. Lloyd and Chandra (2020) found that pre-service teachers’ prior experience with programming is related to their understanding of the critical roles of coding skills for students. Gokoglu and Kilic (2023) pointed out the importance for teachers to have a sufficient level of knowledge about learning tools and teaching strategies to teach students coding. They further suggested the necessity to discover the factors that have an effect on pre-service teachers’ learning and teaching of programming. Currently, there is a growing amount of research on CS education with CS teachers (Ari et al., 2022). However, limited research has focused on non-CS pre-service teachers (Ari et al., 2022; Chang & Peterson, 2018). The technology acceptance model (TAM), as an important information systems theory, provides explanations about how users accept and use new technology (Davis, 1989). It is unclear about pre-service teachers’ intention to use coding tools (Klonou et al., 2025). Specifically, the research addressing the factors that affect pre-service teachers’ intention to teach coding is scarce (Gokoglu & Kilic, 2023). Furthermore, there is a lack of research exploring how affective variables relate to TAM in the context of pre-service teachers involved in the learning or teaching of coding. To enhance the understanding of pre-service teachers’ perceptions of coding, this study aims to investigate teachers’ intention to adopt coding in future teaching, with a focus on factors including ease of use, usefulness, and affective variables.

2. Literature Review

2.1. Coding and the Coding Platform

Coding is a cognitive activity or process that involves problem solving and mastering programming concepts and skills (Bers, 2018). Papert (1980) was among the first to identify the potential of computer programming in education. In 1980, he taught children to program a computer by using LOGO, a programming language (Papert, 1980). His example represents the value of teaching coding in K-12 settings. Coding practices play an important role in students’ experience of using and learning technology, especially that logic thinking and problem solving are inherent in such practices (Basarmak, 2021). Learning to code provides students with opportunities to experience and understand how computers operate and how coding controls computers (Schmidt-Crawford et al., 2019). The coding experience also helps to engage students in the problem-solving processes, such as identifying problems, breaking down the solution into parts, creating or organizing parts, and testing the program (Schmidt-Crawford et al., 2019).
Scratch is a block-based coding platform that provides an easy coding environment with visual block-like interfaces that are simple and colorful (Timur et al., 2021). Although it was originally designed for users of ages 8 to 16, people of all ages are welcome to use it in various settings (Resnick et al., 2009). The use of Scratch has the potential to enhance users’ computational thinking skills and their attitudes and self-efficacy towards computer programming (Marcelino et al., 2018; Yukselturk & Altiok, 2017). The interface of Scratch provides different categories of code blocks that users can use to create actions or purposes, such as motion, looks, sound, events, control, etc. (Timur et al., 2021). There are advantages of using block-based coding or programing languages, especially for children or users who are new to coding or programming (Hamiti et al., 2021; Karaahmetoglu & Korkmaz, 2019; Timur et al., 2021). For example, they allow users to work with an easy-to-understand language, rather than complex syntax rules (Karaahmetoglu & Korkmaz, 2019; Hamiti et al., 2021). Instead of writing the code, users can group code blocks to produce the desired outcomes through the drag-and-drop method (Timur et al., 2021). Users do not need to worry about the errors caused by misplacing syntax, and they are able to present abstract programming concepts in a concrete way (Hamiti et al., 2021). In teacher education, Scratch can be an excellent tool to develop pre-service teachers’ knowledge and skills related to coding or programming, as well as the further integration of Scratch as a tool to teach the subject content (Gleasman & Kim, 2020; Yukselturk & Altiok, 2017). With its simple visual interface that is suitable for novice users, Scratch was chosen for pre-service teachers to use in this study.

2.2. Technology Acceptance Model (TAM)

Davis (1989) developed the technology acceptance model (TAM) based on Ajzen and Fishbein’s (1980) theory of reasoned action (TRA). TRA is a well-researched intention model that was devised to predict and explain human behavior by taking into account the perspectives of social psychology (Fishbein & Ajzen, 1975). As such, this psychological concept provides explanations about how people’s attitudes, beliefs, and social influences shape their intentions that lead to actions (Fishbein & Ajzen, 1975), which serves as a theoretical base for the future development of TAM. Originating from the field of information systems, TAM was first developed to understand people’s resistance to computers or end-user systems in the workplace, as well as the factors that could facilitate the integration of computer systems into business (Davis, 1989; Davis et al., 1989). TAM has become one of the most popular and widely tested models in system use or technology acceptance studies (Teo et al., 2008; Wu et al., 2011). It has been empirically proven to be successful in predicting user acceptance and rejection of computer-based technology (Teo et al., 2008; Wu et al., 2011). Numerous studies have found that TAM explains approximately 40% of variance in usage intention and behavior (Venkatesh & Davis, 2000).
TAM posits that the actual usage of a system is directly influenced by behavioral intention (Davis et al., 1989). An individual’s behavioral intention to use and accept a technology or system is determined by two primary variables, including perceived usefulness and perceived ease of use (Davis, 1989). Perceived usefulness is defined as the degree to which an individual believes that using a specific technology or application system would enhance his or her job performance or productivity. Perceived ease of use refers to the degree to which an individual believes that using a particular technology or application system would be free of effort. In the early conception of TAM, external variables, such as system design characteristics, user involvement in design, and the nature of the implementation process, were hypothesized to have an indirect impact on behavioral intention, mediated through perceived usefulness and perceived ease of use (Davis, 1989). Since its first appearance, TAM has evolved into different versions of models with the inclusion of other variables or constructs that were added to refine or test the applicability of TAM in diverse contexts (Al-Adwan et al., 2023; Dumpit & Fernandez, 2017; Venkatesh et al., 2003).
According to Davis et al. (1989), perceived usefulness and perceived ease of use are core factors that predict behavioral intentions in TAM, with the former being the major determinant of one’s intention to use technology, and the latter being the significant secondary determinant. A considerable amount of studies in TAM have confirmed the significant roles of these two core factors on people’s intention or actual use towards accepting or rejecting technology (Dumpit & Fernandez, 2017; Kelly, 2014; Tao et al., 2020). TAM has been widely applied for the studies in the field of education. It has been used to examine teachers’ and students’ use or adoption of various types of technology tools in in-person or online settings (Han & Sa, 2022; Y. C. Kuo, 2026; Y. C. Kuo & Kuo, 2025; Klonou et al., 2025; Teo et al., 2008). When applying TAM in teaching and learning contexts, similar results were reported on the significant impact of teachers’ perceived usefulness and perceived ease of use on their intention to use technology (Y. C. Kuo et al., 2024; Y. C. Kuo, 2026; Scherer et al., 2019; Teo et al., 2011). For example, Scherer et al. (2019) conducted a meta-analysis on TAM for teachers’ adoption of digital technology in education, and found that TAM is a powerful model that fits well for both pre- and in-service teachers. Compared to perceived ease of use, perceived usefulness has a superior effect on behavioral intention in teacher education and professional development practices (Scherer et al., 2019). As for TAM in coding or programming education, the research on its application for teachers is limited. For example, Klonou et al. (2025) examined kindergarten teachers’ use of online computational thinking tools, and found that perceived ease of use and perceived usefulness are significantly correlated with intention. Davletova et al. (2026) explored 40 pre-service teachers’ use of gamified programming platforms, and the results showed the highest rating for the overall acceptance of the platform through the descriptive analysis.

2.3. Affective Experiences and Their Relationship to TAM

Affect is “a term that refers to the emotional side of an individual’s reactions or responses to an occurrence” (Y. C. Kuo, 2015, para. 1). The term of affect has been used interchangeably with other terms such as emotions, moods, and feelings (Lee et al., 2021). There is an intricate relationship between affect and cognition in learning processes (Lee et al., 2021; Pekrun, 2006). The affective aspects of learning that represent individuals’ emotions, feelings, temperament, values, beliefs, or interests have an impact on behaviors and the cognitive aspects of learning (e.g., cognitive mental processes, information processing, etc.) (Y. C. Kuo, 2015; Y. C. Kuo et al., 2026; Ni et al., 2018). It is believed that affective factors are critical for learners’ achievement, goal pursuit, action tendencies, and engagement (Linnenbrink-Garcia & Pekrun, 2011; Pekrun et al., 2017). In this study, the emotion that pre-service teachers perceived when using Scratch may potentially link to their intention for coding. Therefore, we assume that affective factors may have an influence on TAM in this study.
The affective experiences in this study include positive emotions, negative emotions, and situational interest. Emotions refer to affective states that result from one’s reaction to their progress toward a goal or lack of progress (Anderman & Wolters, 2006; Ni et al., 2018). There are several ways to classify emotions. The most common way of categorizing learners’ emotions in learning settings is to divide emotions into positive and negative ones (Lee et al., 2021). Positive emotions include joy, enjoyment, satisfaction, hope, and confidence, while negative emotions include anger, anxiety, boredom, confusion, frustration, or shame (Pekrun et al., 2002). Interest, referring to an immediate action to a new task, is an affective state that is critical to motivation for learning (Lee et al., 2021). Situational interest refers to the interest aroused by the immediate environment (Sun & Rueda, 2012).

2.4. Other Factors Related to TAM

Personal factors (e.g., demographic variables, individual characteristics, etc.) may play an important role in TAM (Islamoglu et al., 2021; Teo, 2013; Venkatesh et al., 2014). The relevant literature suggests that TAM, as a multifaceted concept, is reciprocally related to demographic variables, such as gender and age (Islamoglu et al., 2021; Venkatesh et al., 2014). However, previous studies that examined TAM based on gender or age differences show inconsistent results, with some indicating significant influences (Islamoglu et al., 2021; Teo, 2014) and some showing non-significant influences (Alasmari & Zhang, 2019; Moura et al., 2020; Teo et al., 2015). For example, Teo et al. (2015) examined gender differences in pre-service teachers’ perceived technology acceptance, and found that gender had a significant influence on perceived ease of use but not on perceived usefulness and intention to use technology. Male pre-service teachers had significantly higher scores in perceived ease of use than female pre-service teachers. Alasmari and Zhang (2019) examined students’ acceptance of using mobile learning technology in Saudi Arabia, and found that age and gender did not significantly moderate their effects on the relationships of the proposed variables and mobile technology acceptance.
The existing literature suggests that individuals’ prior experiences and perceptions of using technology tools (e.g., perceived difficulty levels, skills, etc.) may have an influence on their technology acceptance (Almaiah et al., 2016; Almaiah et al., 2021; Talukder, 2012; Teo, 2013; Venkatesh et al., 2014). Teo (2013) indicated that individual contexts such as evoking anxious or emotional reactions are related to the intention to use technology. Teachers with higher levels of skills or lower levels of perceived difficulty for a specific technology tool are more likely to show higher acceptance to use it, compared to their counterparts (Islamoglu et al., 2021; Y. C. Kuo et al., 2023). Therefore, we assume that pre-service teachers’ prior experiences with coding and their coding skills or perceived coding difficulty may be influential on their intention to use the coding tool.

3. Research Questions

  • Do pre-service teachers’ coding acceptance (i.e., perceived ease of use, perceived usefulness, and intention to use Scratch) differ in terms of gender, age, prior coding experience, prior experience with Scratch, coding skills, and perceived difficulty levels of coding?
  • Do pre-service teachers’ coding acceptance (i.e., perceived ease of use, perceived usefulness, and intention to use Scratch) differ in terms of their affective experiences (i.e., positive emotions, negative emotions, and situational interest) for coding?
  • What are the relationships between pre-service teachers’ perceived ease of use, perceived usefulness, and intention to use Scratch in the classroom?
  • Do pre-service teachers’ perceived ease of use and perceived usefulness predict their intention to use Scratch in the classroom?
  • What are pre-service teachers’ coding experiences of using Scratch in the project?

4. Method

4.1. Research Design

A mixed-methods design approach was undertaken for this study. Through the survey technique, both the quantitative and qualitative data were collected. The participants of this study were the pre-service teachers who volunteered to participate in the survey research. The following sections provide details about the participants, procedure, and data collection and analysis.

4.2. Participants

The participants were 47 students enrolled in the educational technology courses from a northeastern university in the United States (see Table 1). The undergraduate-level courses were face-to-face and taught by the same instructor. Most of the undergraduate students, as pre-service teachers, were in their sophomore (51.1%) or junior (10.6%) year. There were more female students (87.2%) than male students (12.8%). Most of them were aged between 18 and 21 years (74%). Most of these students (68.1%) did not have any experience with coding, with about 31.9% of the students reporting having some experience with coding. Only two students had used Scratch prior to the class. In terms of coding skills, more than half (57.4%) reported having no coding skills at all. About 36.2% of them reported possessing a basic level of coding skills. Very few of them reported possessing a medium (4.3%) or high (2.1%) level of coding skills.

4.3. Data Collection

The study was conducted using an online survey. The online survey was provided to students at the end of the coding project. The study was approved by the university’s Institutional Review Board (IRB), and informed consent forms were obtained from the students who participated in the survey. The survey questionnaire consisted of eight sections: student background information, perceived ease of use, perceived usefulness, intention to use Scratch, positive emotions, negative emotions, situational interest, and student coding experience (see Table 2). The students’ background information included gender, age, grade level, prior experience with coding and Scratch, and coding skills (see Table 1). An open-ended question was included in the survey for pre-service teachers to share their coding experience in the project.
The three scales, including perceived ease of use, perceived usefulness, and intention, which were used in Teo’s (2009) study, were adapted to measure pre-service teachers’ coding acceptance. The term of computers was changed to Scratch. Some item examples include “I find Scratch easy to use”, “I will use Scratch in future”, etc. The positive and negative emotions scales were adapted from the achievement emotions questionnaire developed by Pekrun et al. (2011). The situational interest scale, developed by Chen et al. (1999), was adapted to measure pre-service teachers’ perceived levels of situational interests in coding. The term of class was replaced with the Scratch activity. Item examples include “I enjoyed being in the Scratch activity”, “I got tense and nervous in the Scratch activity”, etc. The scales that measure perceived ease of use, perceived usefulness, and intention are 7-point Likert scales. The situational interest and positive and negative emotion scales are 5-point Likert scales. The Cronbach’s coefficient alpha values calculated based on the sample of this study were high, indicating the good reliability of the scales: perceived ease of use (0.96), perceived usefulness (0.95), intention (0.94), positive emotions (0.87), negative emotions (0.93), and situational interest (0.90) (see Table 2).

4.4. Procedure

Students in the Educational Technology classes participated in a coding project, in which they were engaged in the development of interactive media using Scratch. The project required students to explore the interface of Scratch, different categories of the block palette provided in Scratch, such as motion, looks, sound, events, controls, etc., and the actions associated with the blocks. Before the project started, the instructor had a presentation that introduced students to the concept of coding and the importance of coding in K-12 education. The instructor also provided students with an overview of the Scratch website and the development interface of Scratch that students can use to create interactive media for teaching or learning purposes, and supplemental resources (e.g., handouts, guidance, etc.) for using Scratch. Examples and short video tutorials were shown to the students about different types of code blocks, as well as the steps to create actions for selected blocks in Scratch. The project took four weeks to complete. In week one, the introduction of the project and the basics of Scratch were covered by the instructor. In weeks two to four, the students explored the features of Scratch and created a simple product. During the project, the instructor served as a facilitator who provided further guidance or support to assist students who encountered difficulties or problems when they explored or developed the media product using the features provided by Scratch.

4.5. Data Analysis

Data were analyzed using quantitative and qualitative approaches. Quantitative approaches included descriptive analyses, t-tests, ANOVAs, and correlation and regression analyses. SPSS 27 was used for data analyses. Descriptive analyses, t-tests, and ANOVAs were performed for research questions one and two. The correlational analysis was conducted for research question three. Regression analyses were performed for research question four. The normality of the data and homogeneity of variance were examined to ensure that the data were adequate for the proposed quantitative analyses. Content analysis, a method for analyzing the text data by transforming them into a concise summary of key results with established categories or themes, was performed to analyze the qualitative data for research question five.

5. Results

This section includes information about the results of the data analysis for the research questions proposed in this study.

5.1. RQ1: Do Pre-Service Teachers’ Coding Acceptance (i.e., Perceived Ease of Use, Perceived Usefulness, and Intention to Use Scratch) Differ in Terms of Gender, Age, Prior Coding Experience, Prior Experience with Scratch, Coding Skills, and Perceived Difficulty Levels of Coding?

T-test analyses in Table 3 show that male pre-service teachers had significantly higher average scores in perceived ease of use (t = 4.87, p < 0.001), but not perceived usefulness (t = 1.52, p > 0.05) and intention (t = 1.84, p > 0.05). Age did not have a significant influence on coding acceptance (see Table 4), according to the ANOVA analysis. Prior coding experience did not significantly influence pre-service teachers’ coding acceptance (see Table 5). In terms of prior experience with Scratch, as there were only two pre-service teachers indicating having used Scratch, a T-test was not performed (see Table 6). It appears that the pre-service teachers who used Scratch previously had higher average scores than their counterparts. In terms of coding skills, pre-service teachers who reported having no to basic coding skills had significantly lower scores in perceived ease of use (t = −5.69, p < 0.001), perceived usefulness (t = −6.63, p < 0.001), and interaction (t = −2.35, p < 0.05) than those reporting having a medium or high level of coding skills (see Table 7). Pre-service teachers who perceived coding at a low level of difficulty had a significantly higher score in intention for adoption, compared to those perceiving coding at a high level of difficulty (t = 3.12, p < 0.01) (see Table 8).

5.2. RQ2: Do Pre-Service Teachers’ Coding Acceptance (i.e., Perceived Ease of Use, Perceived Usefulness, and Intention to Use Scratch) Differ in Terms of Their Affective Experiences (i.e., Positive Emotions, Negative Emotions, and Situational Interest) for Coding?

According to T-test analyses, levels of positive emotions had a significant influence on pre-service teachers’ perceived ease of use (t = −6.49, p < 0.001), perceived usefulness (t = −2.60, p < 0.05), and intention (t = −4.20, p < 0.001) (see Table 9). Pre-service teachers who possess high levels of positive emotions had significantly higher average scores in perceived ease of use, perceived usefulness, and intention, compared to those with low levels of positive emotions. Levels of negative emotions significantly influenced perceived ease of use (t = 2.83, p < 0.01), but not perceived usefulness (t = 0.78, p > 0.05) and intention (t = 1.18, p > 0.05) (see Table 10). Levels of situational interest had a significant influence on pre-service teachers’ perceived ease of use (t = −4.31, p < 0.001), perceived usefulness (t = −5.50, p < 0.001), and intention (t = −6.21, p < 0.001) (see Table 11). Pre-service teachers who possess high levels of situational interest had significantly higher average scores in perceived ease of use, perceived usefulness, and intention than those with low levels of situational interest.

5.3. RQ3: What Are the Relationships Between Pre-Service Teachers’ Perceived Ease of Use, Perceived Usefulness, and Intention to Use Scratch in the Classroom?

Table 12 shows the correlations among perceived ease of use, perceived usefulness, and intention to use Scratch. All correlations were positive and significant at a p-value of 0.01. Perceived ease of use (r = 0.68, p < 0.01) and perceived usefulness (r = 0.79, p < 0.01) were positively related to intention to use Scratch. The strongest correlation was found between perceived usefulness and intention to use Scratch (r = 0.79, p < 0.01).

5.4. RQ4: Do Pre-Service Teachers’ Perceived Ease of Use and Perceived Usefulness Predict Their Intention to Use Scratch in the Classroom?

The multiple regression model (see Table 13) was significant, F (2, 44) = 51.27, p < 0.001. The model explained 70% of the variance in the intention to adopt digital games. Both variables, perceived ease of use (t = 3.048, p < 0.01) and perceived usefulness (t = 5.843, p < 0.001), significantly predicted the intention to use Scratch. Between the two significant predictors, perceived usefulness was the strongest predictor for intention to use Scratch.

5.5. RQ5: What Are Pre-Service Teachers’ Coding Experiences of Using Scratch in the Project?

Most of the pre-service teachers shared their positive perspectives towards learning coding using Scratch, with a few of them showing negative perspectives (see Table 14). Several pre-service teachers indicated their change in perspective on coding after participating in the coding activity, with the majority of them showing positive changes in perspective (e.g., “I thought coding was impossible for me to do before, but now I think I understand it better.”) and a few of them presenting negative (e.g., “I knew coding would be hard, but it was even harder than I had imagined.”) or no changes in perspectives towards learning coding.
In terms of positive perspectives, pre-service teachers thought that coding is an important skill and it enhances students’ thinking or problem-solving skills (e.g., “I do think coding or programming is an important skill to possess because it will enhance students’ thinking and problem-solving skills. It requires them to utilize critical and logistical thinking that they do not always have the opportunity to use.”). A few indicated the necessity to engage in coding at a younger age (e.g., “It would have been easier if I was exposed to things like this as a younger student.”). Overall, pre-service teachers thought that the activity helped them to learn coding, develop a better understanding of coding, and enhance their confidence or ability to code (e.g., “I did not truly understand how coding works. After this activity I have a better understanding.”). The use of Scratch benefited pre-service teachers in their learning experience with coding (e.g., “Scratch made it easier than I thought.”; “I have always wanted to learn how to code and this website can help me do that.”). Several pre-service teachers thought that Scratch is easy to use and a great tool to learn coding (e.g., “I thought I would never understand how to code, but the Scratch activity made me realize that it is easier than I thought it would be.”). On the other hand, several pre-service teachers indicated that they felt the coding process is difficult and that they struggled. Some just did not like or enjoy coding. In addition to positive or negative perspectives, a few pre-service teachers indicated the need to spend more time to practice coding, or to receive additional support in the future learning of coding (e.g., “Coding is a skill that I can do but would need more practice with.”).

5.6. Integration of Findings

The results from the content analysis provide additional information about their coding experiences beyond the quantitative analyses. The quantitative analyses focus on examining the relationships among the proposed variables. The content analysis results address the changes in pre-service teachers’ perspectives about coding, which were not assessed through the quantitative analyses. The content analysis also provides information about why pre-service teachers showed positive or negative perceptions of coding, which adds to the results of quantitative analyses that compared the groups of low and high emotions in relation to coding acceptance.

6. Discussion

6.1. Gender, Coding Skills, and Perceived Difficulty Levels of Coding Had a Significant Effect on Pre-Service Teachers’ Coding Acceptance

Male pre-service teachers were found to have a significantly higher level of perceived ease of use than female pre-service teachers. Compared to female pre-service teachers, male pre-service teachers are more likely to perceive Scratch as being easy to use, become skillful in using it, and operate it in the way that they want. It may imply that female pre-service teachers would need more support when using coding tools. This result is consistent with the findings of previous research where gender was found to significantly impact ease of use, and male teachers usually had a greater interest in using technology and were more confident in their ability to use new technology, compared to female teachers (Teo et al., 2015). Similarly, Islamoglu et al. (2021) found the critical role of gender differences in TAM by moderating the influence of proposed variables on pre-service teachers’ intention to adopt mobile technology. In CS education, gender stereotypes are often linked to programming tools, and males are usually considered to take a dominant role in coding or programming (Ari et al., 2022; Brooke, 2024).
Pre-service teachers’ coding skills were found to significantly influence their coding acceptance. Pre-service teachers with a medium or high level of coding skills present higher levels of perceived ease of use, usefulness, and intention to adopt coding in their future teaching, compared to those with no to basic coding skills. The positive influence of coding skills on pre-service teachers’ coding acceptance makes sense. It indicates the significant relationship of pre-service teachers’ coding skills to their perceived ease of use and perceived usefulness towards coding tools. Assessing pre-service teachers’ coding skills may help to identify their coding acceptance for using other types of coding tools. Limited research has explored the relationship between users’ technology skills and their technology acceptance (Park & Park, 2020), and none was examined for coding or programming education. Park and Park (2020) explored the external variables of TAM in their study involving people in the field of construction technology and identified the usage knowledge of technology as an important factor affecting acceptance variables. In addition, pre-service teachers’ perceived levels of difficulties for coding were found to have a significant impact on their intention to adopt coding in the future, corresponding to prior research indicating that teachers’ perceived difficulty with using new technology would affect their decision to adopt or use technology (Islamoglu et al., 2021; Y. C. Kuo et al., 2023).

6.2. Pre-Service Teachers’ Coding Acceptance Significantly Differed in Terms of Levels of Positive Emotions, Negative Emotions, and Situational Interest

Pre-service teachers’ positive and negative emotions and situational interest play an important role in explaining teachers’ coding acceptance. Compared to negative emotions, positive emotions and situational interest appeared to have a more prominent influence on pre-service teachers’ coding acceptance. Levels of positive emotions and situational interest towards coding significantly affected all TAM variables; however, negative emotions had a significant impact only on pre-service teachers’ perceived ease of use of the coding tool, but not their perceived usefulness and coding acceptance. In alignment with prior TAM studies where researchers explored external factors that had a potential effect on the acceptance model, several affective factors were found to be contributable to TAM, including the positive and negative feelings of an individual (Al-Adwan et al., 2023; Li et al., 2024; Rosli & Saleh, 2024). For example, Li et al. (2024) explored college students’ adoption of translation technologies and found that students’ perceived enjoyment played a crucial role in the technology adoption process, with a significant impact of perceived enjoyment on students’ perceived ease of use.

6.3. Perceived Ease of Use and Perceived Usefulness Significantly Predicted Intention to Use Scratch for Coding

Perceived ease of use and perceived usefulness were found to be positively correlated with intention in a significant way. Both perceived ease of use and perceived usefulness were significant predictors of the intention to adopt coding, with ease of use being the strongest predictor. This result implies that pre-service teachers who perceived greater ease of use and usefulness towards coding showed higher levels of intention to adopt or use coding in their future teaching. It is in alignment with previous research indicating the significant roles of perceived ease of use and perceived usefulness in predicting users’ intention to use technology (Davis, 1989; Tao et al., 2020), specifically for the application of TAM in identifying teachers’ willingness to use new technology (Scherer et al., 2019; Teo et al., 2011). The result also illuminates the critical role that perceived ease of use and perceived usefulness play in addressing pre-service teachers’ intention to adopt coding or coding technology. Involving pre-service teachers in more coding activities, or providing them with opportunities to use other types of coding tools, may help to develop their perceived ease of use and perceived usefulness for coding. In addition, perceived usefulness was found to be the strongest predictor of pre-service teachers’ intention to adopt coding, corresponding to the finding in the study of Scherer et al. (2019) that examined pre- and in-service teachers’ adoption of digital technology.

6.4. More Positive Perspectives Towards Coding than Negative Perspectives

While the quantitative analyses focus on the relationships of proposed variables, the qualitative analysis, which is the content analysis in this study, provides further views of pre-service teachers about their coding experiences with the project beyond the quantitative findings. Based on the content analysis, more positive perspectives towards coding were generated than negative ones among pre-service teachers, and positive changes in their views on coding were greater than negative and no changes. These results add to the significant effect of emotions on pre-service teachers’ coding acceptance from the quantitative analysis, and provide further insights for the changes in their perspectives, either positive, negative, or no changes. They imply that most pre-service teachers’ perceptions towards coding through their participation in the coding activity were positive, and that the coding activity contributed to pre-service teachers’ learning experiences with coding using Scratch. In alignment with the findings from previous research (Ari et al., 2022; Yukselturk & Altiok, 2017), pre-service teachers’ positive attitudes increased after their participation in coding or programming activities. Pre-service teachers identified coding as an essential skill to possess, as it develops students’ thinking and problem-solving skills, which confirms the important role that coding plays in enhancing students’ ability to solve problems or think critically (Schmidt-Crawford et al., 2019; Zha et al., 2019). The coding tool, Scratch, was recognized by pre-service teachers as an easy-to-use tool to learn coding, and it made their learning process with coding easier. This result supports the claim that using block-based coding applications is beneficial in assisting students to learn coding or programming (Timur et al., 2021).
The negative perspectives pre-service teachers had were about their frustrating or struggling feelings with coding or making codes work in the way they expected. Some indicated that coding is just hard for them and they do not enjoy or like coding. As suggested by pre-service teachers, additional support or more practice time is needed for them to continue the journey of learning to code beyond this activity, which conforms to the suggestion of Zha et al. (2019) that a sustained scaffolding approach is needed to support students’ practice or continuous learning of coding. Overall, the content analysis provides more in-depth information about pre-service teachers’ perspectives towards coding, which are not captured by quantitative analyses.

7. Conclusions

This study investigated pre-service teachers’ experience with the coding activity, as well as their perceptions towards coding and the coding tool. The findings of this study have increased our understanding of pre-service teachers’ perceptions of coding and their intention to use coding for future teaching, with a focus on TAM (i.e., ease of use, usefulness, and intention) and affective factors (i.e., positive and negative emotions, situational interests). This study not only adds to the limited studies addressing factors affecting non-CS pre-service teachers’ intention to adopt coding in teaching, but also provides insights into the application of TAM in coding for pre-service teachers, and the influence of affective and personal variables on pre-service teachers’ intention to integrate coding in teaching. This study provides evidence of the importance of TAM in addressing pre-service teachers’ intention for coding, indicating ease of use and usefulness as significant predictors of intention. Personal variables, including gender and coding skills, were found to have an impact on pre-service teachers’ intention for coding. Pre-service teachers’ coding experiences were positive overall, with most of them identifying coding as an essential skill to possess and Scratch being a useful tool to learn or teach coding. In addition, the coding activity led to more of the positive changes in perceptions of coding than the negative and no changes in perceptions among pre-service teachers.
There are several limitations for this study, and some of them are linked to research implications. The findings of this study may not be generalized to other groups of pre-service teachers from other countries or with different cultures. The pre-service teachers in this study were from different subject areas, and they were expected to teach at different grade levels (e.g., early childhood, elementary or secondary schools). We did not consider how subject areas or grade levels might play a role in pre-service teachers’ acceptance to coding, which could be further explored in future research. This study focused on TAM, affective, and personal factors in relation to pre-service teachers’ intention to use or teach coding. There may be other factors (e.g., self-efficacy, school support, the design of activities, etc.) associated with pre-service teachers’ acceptance for coding, and we encourage future researchers to include them. In addition, pre-service teachers’ changes in perceptions towards coding were found based on the qualitative analysis. We did not perform a pre–post design to verify pre-service teachers’ changes in perceptions. It is suggested that future studies adopt pre–post surveys to examine changes in pre-service teachers’ perceptions of coding.
As for the practical implications, this study suggests that teacher educators should (a) provide pre-service teachers with opportunities to engage in coding activities to enhance their awareness of the importance of coding, as well as their willingness to adopt or integrate coding into teaching (Ertmer & Ottenbreit-Leftwich, 2010; Hunsaker & West, 2019); (b) offer additional support or practice opportunities to female pre-service teachers and those with no or basic coding skills; (c) identify pre-service teachers who may possess negative emotions or low levels of interests about coding, and pair them up with those who show positive emotions towards coding or have higher levels of coding skills or interests in coding (Ari et al., 2022); (d) share best practices or successful examples about teachers’ journeys from learning to code to integrating coding into teaching, as well as how coding significantly enhances students’ learning outcomes, to enhance pre-service teachers’ coding acceptance (Y. C. Kuo et al., 2023); and (e) provide pre-service teachers with relevant resources for coding, such as useful, highly rated coding tools or platforms (Yukselturk & Altiok, 2017).

Author Contributions

Conceptualization, Y.-C.K.; methodology, Y.-C.K. and Y.-T.K.; formal analysis, Y.-C.K. and Y.-T.K.; investigation, Y.-C.K.; data curation, Y.-C.K.; writing—original draft preparation, Y.-C.K. and Y.-T.K.; writing—review and editing, Y.-C.K. and Y.-T.K.; supervision, Y.-C.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Rowan University (protocol code PRO-2022-381 and date of approval: 19 December 2022).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Please contact first author for information about data availability.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Prentice-Hall. [Google Scholar]
  2. Al-Adwan, A. S., Li, N., Al-Adwan, A., Abbasi, G. A., Albelbisi, N. A., & Habibi, A. (2023). Extending the technology acceptance model (TAM) to predict university students’ intentions to use metaverse-based learning platforms. Education and Information Technologies, 28(11), 15381–15413. [Google Scholar] [CrossRef]
  3. Alasmari, T., & Zhang, K. (2019). Mobile learning technology acceptance in Saudi Arabian higher education: An extended framework and a mixed-method study. Education and Information Technologies, 24, 2127–2144. [Google Scholar] [CrossRef]
  4. Almaiah, M. A., Al-Khasawneh, A., Althunibat, A., & Almomani, O. (2021). Exploring the main determinants of mobile learning application usage during COVID-19 pandemic in Jordanian universities. In I. Arpaci, M. Al-Emran, M. M. Al-Sharafi, & G. Marques (Eds.), Emerging technologies during the era of COVID-19 pandemic (1st ed., pp. 275–290). Springer. [Google Scholar] [CrossRef]
  5. Almaiah, M. A., Jalil, A. M., & Man, M. (2016). Preliminary study for exploring the major problems and activities of mobile learning system: A case study of Jordan. Journal of Theoretical and Applied Information Technology, 93(2), 580–594. [Google Scholar]
  6. Anderman, E. M., & Wolters, C. A. (2006). Goals, values, and affect: Influences on student motivation. In P. Alexander, & P. Winne (Eds.), Handbook of educational psychology (pp. 369–389). Erlbaum. [Google Scholar]
  7. Anderson, S. (2016). Approaches to achieving equity of outcomes in computational thinking and coding education. In G. Chamblee, & L. Langub (Eds.), Proceedings of society for information technology & teacher education international conference (pp. 61–66). Association for the Advancement of Computing in Education (AACE). Available online: https://www.learntechlib.org/primary/p/171651/ (accessed on 30 October 2024).
  8. Ari, F., Arslan-Ari, I., & Vasconcelos, L. (2022). Early childhood preservice teachers’ perceptions of computer science, gender stereotypes, and coding in early childhood education. TechTrends, 66(3), 539–546. [Google Scholar] [CrossRef]
  9. Basarmak, U. (2021). Views of pre-service teachers in different disciplines about coding with Arduino. Malaysian Online Journal of Educational Technology, 9(2), 1–14. [Google Scholar] [CrossRef]
  10. Bers, M. U. (2018). Coding as a playground: Programming and computational thinking in the early childhood classroom. Routledge. [Google Scholar] [CrossRef]
  11. Blackley, S., & Howell, J. (2019). The next chapter in the STEM education narrative: Using robotics to support programming and coding. Australian Journal of Teacher Education, 44(4), 51–64. [Google Scholar] [CrossRef]
  12. Broley, L., Buteau, C., & Sardella, J. (2023). When preservice and inservice teachers join forces: A collaborative way to support the enactment of new coding curricula in mathematics classrooms. Journal of Pedagogical Research, 7(2), 21–40. [Google Scholar] [CrossRef]
  13. Brooke, S. (2024). Programmed differently? Testing for gender differences in Python programming style and quality on GitHub. Journal of Computer-Mediated Communication, 29(1), zmad049. [Google Scholar] [CrossRef]
  14. Butler, D., & Leahy, M. (2021). Developing pre-service teachers’ understanding of computational thinking: A constructionist lens. British Journal of Educational Technology, 52, 1060–1077. [Google Scholar] [CrossRef]
  15. Chang, Y. H., & Peterson, L. (2018). Pre-service teachers’ perceptions of computational thinking. Journal of Technology and Teacher Education, 26(3), 353–374. [Google Scholar]
  16. Chen, A., Darst, P. W., & Pangrazi, R. P. (1999). What constitutes situational interest? Validating a construct in physical education. Measurement in Physical Education and Exercise Science, 3(3), 157–180. [Google Scholar] [CrossRef] [PubMed]
  17. Childs, J., Zarch, R., Dunton, S., Madkins, T. C., Trautmann, K., Taylor, Z. W., Jacobson, M., Ozturk, S., & Ottenbreit-Leftwich, A. (2024). Advancing equity and access: Addressing the side effects of broadening participation in computer science K–12 education. Review of Research in Education, 48(1), 121–153. [Google Scholar] [CrossRef]
  18. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–339. [Google Scholar] [CrossRef]
  19. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 928–1003. [Google Scholar] [CrossRef]
  20. Davletova, A., Myrzanova, A., & Sabyrgalieva, M. (2026). Understanding how gamified programming environments shape pre-service teachers’ engagement and instructional readiness in Kazakhstan. Frontiers in Education, 11, 1749823. [Google Scholar] [CrossRef]
  21. Dumpit, D. Z., & Fernandez, C. J. (2017). Analysis of the use of social media in Higher Education Institutions (HEIs) using the technology acceptance model. International Journal of Educational Technology in Higher Education, 14(5), 1–16. [Google Scholar] [CrossRef]
  22. Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42, 255–284. [Google Scholar] [CrossRef]
  23. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior. Longman Higher Education. [Google Scholar]
  24. Gleasman, C., & Kim, C. (2020). Pre-service teacher’s use of block-based programming and computational thinking to teach elementary mathematics. Digital Experiences in Mathematics Education, 6(1), 52–90. [Google Scholar] [CrossRef]
  25. Gokoglu, S., & Kilic, S. (2023). Programming learning and teaching of pre-service computer science teachers: Challenges, concerns, and solutions. E-Learning and Digital Media, 20(5), 498–518. [Google Scholar] [CrossRef]
  26. Hamiti, M., Kosarenko, N. N., Shabanov, G. A., Tantsura, T. A., & Shchennikova, N. V. (2021). Opinions of computer and instructional technologies students about the robotic coding course. International Journal of Emerging Technologies in Learning (iJET), 16(19), 206. [Google Scholar] [CrossRef]
  27. Han, J. H., & Sa, H. J. (2022). Acceptance of and satisfaction with online educational classes through the technology acceptance model (TAM): The COVID-19 situation in Korea. Asia Pacific Education Review, 23(3), 403–415. [Google Scholar] [CrossRef]
  28. Hunsaker, E., & West, R. E. (2019). Designing computational thinking and coding badges for early childhood educators. TechTrends, 64(1), 7–16. [Google Scholar] [CrossRef]
  29. Islamoglu, H., Yurdakul, I. K., & Ursavas, O. F. (2021). Pre-service teachers’ acceptance of mobile-technology-supported learning activities. Educational Technology Research and Development, 69, 1025–1054. [Google Scholar] [CrossRef]
  30. Karaahmetoglu, K., & Korkmaz, Ö. (2019). The effect of project-based arduino educational robot applications on students’ computational thinking skills and their perception of basic stem skill levels. Participatory Educational Research, 6(2), 1–14. [Google Scholar] [CrossRef]
  31. Kelly, H. (2014). A path analysis of educator perceptions of open educational resources using the technology acceptance model. The International Review of Research in Open and Distributed Learning, 15(2). [Google Scholar] [CrossRef]
  32. Klonou, P., Karatrantou, A., & Trantas, P. (2025). Kindergarten teachers’ acceptance of educational robotics online tools for computational thinking. International Journal of Technology in Education and Science, 9(4), 579–596. [Google Scholar] [CrossRef]
  33. Kuo, Y. C. (2015). Affective factors in learning, instruction, and technology. In J. M. Spector (Ed.), The sage encyclopedia of educational technology (pp. 12–16). SAGE Publications. [Google Scholar]
  34. Kuo, Y. C. (2026). Linking technological pedagogical content knowleKuodge of games (TPACK-G) and digital game acceptance among pre-service teachers. Education Sciences, 16(1), 7. [Google Scholar] [CrossRef]
  35. Kuo, Y. C., & Kuo, Y. T. (2025). An exploratory study of K-12 teachers’ perceptions of adopting open educational resources in teaching. Education Sciences, 15(4), 468. [Google Scholar] [CrossRef]
  36. Kuo, Y. C., Kuo, Y. T., & Abi-El-Mona, I. (2023). Mobile learning: Pre-service teachers’ perceptions of integrating iPads into future teaching. Education and Information Technologies, 28, 6209–6230. [Google Scholar] [CrossRef]
  37. Kuo, Y. C., Kuo, Y. T., & Tseng, H. (2024). Exploring the factors that influence K-12 teachers’ use of open educational resources. Education Sciences, 14(3), 276. [Google Scholar] [CrossRef]
  38. Kuo, Y. C., Kuo, Y. T., & Walker, A. (2026). Pre-service teachers’ perceptions of integrating digital games into future teaching. Research and Practice in Technology Enhanced Learning, 21, 7. [Google Scholar] [CrossRef]
  39. Kuo, Y. T., & Kuo, Y. C. (2025). Learning programming: Exploring the relationships of self-efficacy, computational thinking, and learning performance among minority students. Frontiers in Education, 10. [Google Scholar] [CrossRef]
  40. Lee, J., So, H. J., Ha, S., Kim, S., & Park, K. (2021). Unpacking academic emotions in asynchronous video-based learning: Focusing on Korean learners’ affective experiences. Asia-Pacific Education Researcher, 30(3), 247–261. [Google Scholar] [CrossRef]
  41. Li, X., Gao, Z., & Liao, H. (2024). An empirical investigation of college students’ acceptance of translation technologies. PLoS One, 19(2), e0297297. [Google Scholar] [CrossRef] [PubMed]
  42. Linnenbrink-Garcia, L., & Pekrun, R. (2011). Students’ emotions and academic engagement: Introduction to the special issue. Contemporary Educational Psychology, 36, 1–3. [Google Scholar] [CrossRef]
  43. Lloyd, M., & Chandra, V. (2020). Teaching coding and computational thinking in primary classrooms: Perceptions of Australian preservice teachers. Curriculum Perspectives, 40(2), 189–201. [Google Scholar] [CrossRef]
  44. Marcelino, M. J., Pessoa, T., Vieira, C., Salvador, T., & Mendes, A. J. (2018). Learning computational thinking and scratch at distance. Computers in Human Behavior, 80, 470–477. [Google Scholar] [CrossRef]
  45. Mason, S. L., & Rich, P. J. (2019). Preparing elementary school teachers to teach computing, coding, and computational thinking. Contemporary Issues in Technology and Teacher Education, 19(4), 790–824. [Google Scholar]
  46. Moura, I. V., de Almeida, L. B., da Silva, W. V., da Veiga, C. P., & Costa, F. (2020). Predictor factors of intention to use technological resources: A multigroup study about the approach of technology acceptance model. SAGE Open, 10(4), 1–17. [Google Scholar] [CrossRef]
  47. Ni, Y., Zhou, D. H., Cai, J., Li, X., Li, Q., & Sun, I. S. (2018). Improving cognitive and affective learning outcomes of students through mathematics instructional tasks of high cognitive demand. The Journal of Educational Research, 111(6), 704–719. [Google Scholar] [CrossRef]
  48. Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic. [Google Scholar]
  49. Park, E. S., & Park, M. S. (2020). Factors of the technology acceptance model for construction IT. Applied Sciences, 10(22), 8299. [Google Scholar] [CrossRef]
  50. Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18(4), 315–341. [Google Scholar] [CrossRef]
  51. Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. (2011). Measuring emotions in students’ learning and performance: The Achievement Emotions Questionnaire (AEQ). Contemporary Educational Psychology, 36, 36–48. [Google Scholar] [CrossRef]
  52. Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students’ self-regulated learning and achievement: A program of qualitative and quantitative research. Educational Psychologist, 37(2), 91–105. [Google Scholar] [CrossRef] [PubMed]
  53. Pekrun, R., Lichtenfeld, S., Marsh, H. W., Murayama, K., & Goetz, T. (2017). Achievement emotions and academic performance: Longitudinal models of reciprocal effects. Child Development, 88(5), 1653–1670. [Google Scholar] [CrossRef]
  54. Poth, R. D. (2019). Thinking about skills of the future: How to get preservice teachers started with coding. Journal of Digital Learning in Teacher Education, 35(1), 2–3. [Google Scholar] [CrossRef]
  55. Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., Millner, A., Rosenbaum, E., Silver, J., Silverman, B., & Kafai, Y. (2009). Scratch: Programming for all. Communications of the ACM, 52(11), 60–67. [Google Scholar] [CrossRef]
  56. Rosli, M. S., & Saleh, N. S. (2024). Predicting the acceptance of metaverse for educational purposes in universities: A structural equation model and mediation analysis of the extended technology acceptance model. SN Computer Science, 5(6), 688. [Google Scholar] [CrossRef]
  57. Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education, 128, 13–35. [Google Scholar] [CrossRef]
  58. Schmidt-Crawford, D. A., Lindstrom, D., & Thompson, A. D. (2019). Coding for teacher education: A recurring theme that requires our attention. Journal of Digital Learning in Teacher Education, 34(4), 198–200. [Google Scholar] [CrossRef]
  59. Sekyere-Asiedu, D., Kosarenko, N., Yergazina, A. A., Petunina, N. A., Yarullina, A. S., & Sidorenko, G. G. (2020). Opinions of future prospective teachers on coding technique. International Journal of Emerging Technologies in Learning (iJET), 15(23), 63. [Google Scholar] [CrossRef]
  60. Sun, J. C., & Rueda, R. (2012). Situational interest, computer self-efficacy and self-regulation: Their impact on student engagement in distance education. British Journal of Educational Technology, 43, 191–204. [Google Scholar] [CrossRef]
  61. Talukder, M. (2012). Factors affecting the adoption of technological innovation by individual employees: An Australian study. Procedia—Social and Behavioral Sciences, 40, 52–57. [Google Scholar] [CrossRef]
  62. Tao, D., Wang, T., Wang, T., Zhang, T., Zhang, X., & Qu, X. (2020). A systematic review and meta-analysis of user acceptance of consumer-oriented health information technologies. Computers in Human Behavior, 104, 106147. [Google Scholar] [CrossRef]
  63. Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52, 302–313. [Google Scholar] [CrossRef]
  64. Teo, T. (2013). Influences of contextual variables on the intention to use technology in education: A latent variable modeling approach. Campus-Wide Information Systems, 30(2), 95–105. [Google Scholar] [CrossRef]
  65. Teo, T. (2014). Unpacking teachers’ acceptance of technology: Tests of measurement invariance and latent mean differences. Computers & Education, 75, 127–135. [Google Scholar] [CrossRef]
  66. Teo, T., Fan, X., & Du, J. (2015). Technology acceptance among pre-service teachers: Does gender matter? Australasian Journal of Educational Technology, 31(3), 235–251. [Google Scholar] [CrossRef]
  67. Teo, T., Luan, W. S., & Sing, C. C. (2008). A cross-cultural examination of the intention to use technology between Singaporean and Malaysian pre-service teachers: An application of the Technology Acceptance Model (TAM). Educational Technology & Society, 11(4), 265–280. [Google Scholar]
  68. Teo, T., Ursavas, O. F., & Bahcekapili, E. (2011). Efficiency of the technology acceptance model to explain pre-service teachers’ intention to use technology: A Turkish study. Campus-Wide Information Systems, 28(2), 93–101. [Google Scholar] [CrossRef]
  69. Timur, S., Timur, B., Güvenç, E., Us, İ., & Yalçinkaya-Önder, E. (2021). Pre-service pre-school teachers’ opinions about using block-based coding/scratch. Acta Didactica Napocensia, 14(2), 299–317. [Google Scholar] [CrossRef]
  70. Vasconcelos, L., & Kim, C. (2022). Preservice science teachers coding science simulations: Epistemological understanding, coding skills, and lesson design. Educational Technology Research and Development, 70(4), 1517–1549. [Google Scholar] [CrossRef]
  71. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longi-tudinal field studies. Management Science, 46(2), 186–204. [Google Scholar] [CrossRef]
  72. Venkatesh, V., Morris, M. G., & Davis, F. D. (2014). Individual-level technology adoption research: An assessment of the strengths, weaknesses, threats, and opportunities for further research contributions. In H. Topi, & A. Tucker (Eds.), Computing handbook: Information systems and information technology (3rd ed., pp. 38-1–38-25). CRC Press. [Google Scholar]
  73. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. [Google Scholar] [CrossRef]
  74. Wu, K., Zhao, Y., Zhu, Q., Tan, X., & Zheng, H. (2011). A meta-analysis of the impact of trust on technology acceptance model: Investigation of moderating influence of subject and context type. International Journal of Information Management, 31(6), 572–581. [Google Scholar] [CrossRef]
  75. Yukselturk, E., & Altiok, S. (2017). An investigation of the effects of programming with Scratch on the preservice IT teachers’ self-efficacy perceptions and attitudes towards computer programming. British Journal of Educational Technology, 48(3), 789–801. [Google Scholar] [CrossRef]
  76. Zha, S., Jin, Y., Moore, P., & Gaston, J. (2019). Hopscotch into coding: Introducing pre-service teachers’ computational thinking. TechTrends, 64(1), 17–28. [Google Scholar] [CrossRef]
Table 1. Background information of pre-service teachers.
Table 1. Background information of pre-service teachers.
Characteristicn%
Gender
   Male612.8
   Female4187.2
Age
   18–191429.8
   20–212451.1
   22–23510.6
   24 and above48.5
Grade level
   Freshman612.8
   Sophomore2042.6
   Junior1940.4
   Senior24.2
Prior experience with coding
   Yes1531.9
   No3268.1
Coding Skills
   None2757.4
   Basic level1736.2
   Medium level24.3
   High level12.1
Used Scratch prior to the class
   Yes24.3
   No4595.7
Table 2. Instruments.
Table 2. Instruments.
ScalesNumber of ItemsRangeCronbach’s Alpha
Perceived ease of use41–70.96
Perceived usefulness 31–70.95
Intention31–70.94
Positive emotions31–50.87
Negative emotions41–50.93
Situational interest21–50.90
Table 3. T-test analysis for gender and proposed variables.
Table 3. T-test analysis for gender and proposed variables.
MalesFemales
MSDMSDt(45)p
Perceived ease of use6.130.444.861.204.87 ***0.000
Perceived usefulness5.560.894.761.231.520.135
Intention5.330.824.111.591.840.073
Note. *** p < 0.001.
Table 4. ANOVA analysis for age and proposed variables.
Table 4. ANOVA analysis for age and proposed variables.
18–1920–2122 and Above
MSDMSDMSDFp
Perceived ease of use5.110.994.691.295.780.982.950.063
Perceived usefulness5.071.334.681.215.001.120.520.598
Intention4.261.564.031.654.891.310.990.380
Table 5. T-test analysis for prior coding experience and proposed variables.
Table 5. T-test analysis for prior coding experience and proposed variables.
YesNo
MSDMSDt(45)p
Perceived ease of use5.431.104.821.221.630.110
Perceived usefulness5.071.394.761.140.800.427
Intention4.491.824.161.450.680.503
Table 6. Descriptive information for prior experience with scratch and proposed variables.
Table 6. Descriptive information for prior experience with scratch and proposed variables.
YesNo
MSDMSD
Perceived ease of use5.250.355.011.23
Perceived usefulness5.550.714.831.23
Intention5.000.004.231.59
Table 7. T-test analysis for coding skills and proposed variables.
Table 7. T-test analysis for coding skills and proposed variables.
None to BasicMedium to High
MSDMSDt(45)p
Perceived ease of use4.951.226.000.00−5.69 ***0.000
Perceived usefulness4.781.226.000.00−6.63 ***0.000
Intention4.121.536.220.38−2.35 *0.023
Note. * p < 0.05; *** p < 0.001.
Table 8. T-test analysis for perceived levels of difficulties for coding and proposed variables.
Table 8. T-test analysis for perceived levels of difficulties for coding and proposed variables.
LowHigh
MSDMSDt(45)p
Perceived ease of use5.370.854.891.301.220.231
Perceived usefulness5.330.834.681.301.690.098
Intention5.100.863.941.663.12 **0.003
Note. ** p < 0.01.
Table 9. T-test analysis for levels of positive emotions and proposed variables.
Table 9. T-test analysis for levels of positive emotions and proposed variables.
Low PEHigh PE Cohen’s
d
MSDMSDt(45)p
Perceived ease of use3.641.045.610.68−6.49 ***0.0002.46
Perceived usefulness4.191.375.141.04−2.60 *0.0130.83
Intention3.001.354.801.34−4.20 ***0.0001.34
Note. * p < 0.05; *** p < 0.001.
Table 10. T-test analysis for levels of negative emotions and proposed variables.
Table 10. T-test analysis for levels of negative emotions and proposed variables.
Low NEHigh NE Cohen’s
d
MSDMSDt(65)p
Perceived ease of use5.490.664.571.442.83 **0.0080.82
Perceived usefulness5.001.154.721.290.780.4400.23
Intention4.541.554.001.571.180.2450.35
Note. ** p < 0.01.
Table 11. T-test analysis for levels of situational interest and proposed variables.
Table 11. T-test analysis for levels of situational interest and proposed variables.
Low SIHigh SI Cohen’s
d
MSDMSDt(45)p
Perceived ease of use4.131.325.580.71−4.31 ***0.0001.47
Perceived usefulness3.891.085.460.87−5.50 ***0.0001.64
Intention2.931.325.091.06−6.21 ***0.0001.85
Note. *** p < 0.001.
Table 12. Correlations among variables.
Table 12. Correlations among variables.
Perceived Ease of UsePerceived UsefulnessIntention
Perceived ease of use-0.61 **0.68 **
Perceived usefulness -0.79 **
Intention -
Note. ** p < 0.01.
Table 13. Multiple regression model: intention to use scratch explained by two predictor variables.
Table 13. Multiple regression model: intention to use scratch explained by two predictor variables.
VariablesBSE Bβtp
Perceived ease of use0.4110.1350.3163.048 **0.004
Perceived usefulness0.7800.1340.6065.843 ***0.000
Note. ** p < 0.01; *** p < 0.001.
Table 14. Pre-service teachers’ coding experiences.
Table 14. Pre-service teachers’ coding experiences.
Changes in Perspectives (15)Positive (10)Positive changes in perspective towards coding (10)
Negative (3)Negative changes in perspective towards coding (3)
None (2)No changes in perspective towards coding (2)
Positive Perspectives (68)Coding (34)Coding is easy (4)
Coding is an important skill (18)
Practice thinking or problem solving (10)
Exposed to it at a younger age (2)
The coding activity (19)The activity is helpful (5)
The increased confidence, ability, and a better understanding of coding (14)
Scratch tool (15)Scratch is fun and easy to use (5)
A helpful tool to learn coding. It makes learning coding easier (10)
Negative Perspectives (9)Coding (9)Difficult, struggling, frustrated (5)
Do not enjoy or like coding (4)
Others (6)Practice (4)More practice or time (4)
Support (2)Help needed (2)
Note. The number in the parentheses shows the frequency of information indicated by pre-service teachers.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Kuo, Y.-C.; Kuo, Y.-T. An Exploratory Study of Pre-Service Teachers’ Perceptions of Using a Block-Based Coding Tool: Acceptance and Experiences of Coding Using Scratch. Educ. Sci. 2026, 16, 729. https://doi.org/10.3390/educsci16050729

AMA Style

Kuo Y-C, Kuo Y-T. An Exploratory Study of Pre-Service Teachers’ Perceptions of Using a Block-Based Coding Tool: Acceptance and Experiences of Coding Using Scratch. Education Sciences. 2026; 16(5):729. https://doi.org/10.3390/educsci16050729

Chicago/Turabian Style

Kuo, Yu-Chun, and Yu-Tung Kuo. 2026. "An Exploratory Study of Pre-Service Teachers’ Perceptions of Using a Block-Based Coding Tool: Acceptance and Experiences of Coding Using Scratch" Education Sciences 16, no. 5: 729. https://doi.org/10.3390/educsci16050729

APA Style

Kuo, Y.-C., & Kuo, Y.-T. (2026). An Exploratory Study of Pre-Service Teachers’ Perceptions of Using a Block-Based Coding Tool: Acceptance and Experiences of Coding Using Scratch. Education Sciences, 16(5), 729. https://doi.org/10.3390/educsci16050729

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop