Self-efficacy Program Research Articles

Differentiated measurement of cognitive loads in computer programming

AbstractThis study had two objectives: (1) to evaluate the validity of an instrument for measuring differentiated cognitive loads in its Spanish version; and (2) to evaluate the three types of cognitive loads and their relationship with self-efficacy, self-concept, and interest in programming of students in an introductory course. Understanding and assessing cognitive loads when learning computer programming is key to supporting student learning. While there are instruments in English and German assessing the different types of cognitive loads, there is no validated instrument in Spanish. This study took place during the implementation of an online training program in basic programming, with a sample of 1162 students. We used Exploratory Factor Analysis and Confirmatory Factor Analysis to validate the structure of the instrument. The results allowed us to establish a factorial structure of the subjective scale of differentiated cognitive loads, managing to measure the germane, intrinsic, and extraneous cognitive loads. The bivariate correlation analysis allowed identifying statistically significant associations between the study variables, including (a) the negative relationship between extraneous cognitive load and germane cognitive load and (b) the negative relationship between extraneous cognitive load and self-efficacy in programming.

Growth mindset mediates the relationship between computational thinking and programming self-efficacy

Growth mindset mediates the relationship between computational thinking and programming self-efficacy

Relationships between middle school students’ digital literacy skills, computer programming self-efficacy, and computational thinking self-efficacy

This study aims to explain the relationships between secondary school students' digital literacy, computer programming self-efficacy and computational thinking self-efficacy. The study group consists of 204 secondary school students. A relational survey model was used in the research method and three different data collection tools were used to collect data. The structural equation model was used in data analysis to reveal a model that explains and predicts the relationships between variables. According to the results of the research, it was determined that digital literacy of secondary school students affected their computer programming self-efficacy, digital literacy affected their computational thinking self-efficacy, and computer programming self-efficacy affected their computational thinking self-efficacy. It was also found that digital literacy skills have an indirect effect on secondary students' computational thinking self-efficacy on computational thinking self-efficacy.

Unlocking the Code: Exploring Predictors of Future Interest in Learning Computer Programming Among Primary School Boys and Girls

The integration of programming into primary and secondary education has gained substantial attention globally, particularly in recent years. Despite efforts to showcase its benefits, there remains a gap in understanding whether students embrace programming and intend to persist in learning it. This study investigates the factors influencing the intention of primary school students to learn programming. It explores eight variables like perceived enjoyment, utility, ease of learning, social influence, creative and programming self-efficacy, and collaboration attitude, hypothesized to positively affect students’ behavioral intention to learn programming. Demographic factors such as age and gender were also considered. Data from 235 Serbian primary school students were collected via self-report questionnaires. Structural equation modeling revealed that the proposed model significantly explained students’ behavioral intention, accounting for 65.9% of the variance. Social influence and perceived enjoyment emerged as the most influential factors. The study discusses teaching implications for programming.

Examining Factors Predicting Programming Self-Efficacy for Computer Information Systems Students

Examining Factors Predicting Programming Self-Efficacy for Computer Information Systems Students

Programming Self-Efficacy in Higher Education Research: A Systematic Literature Review

The goal of the systematic literature review (SLR) was to provide an overview of the concepts and factors associated with programming self efficacy (PSE) from 2017 to 2021 in Scopus (215) and the Web of Science (310) academic indexes. From the initial 525 articles identified, 405 were left after duplication identification. Using further criteria such as article years must be between 2017 and 2021 in English, and through a peer-review process, 60 remained. These 60 papers were then analyzed using a set of questionnaires, from which 36 articles were excluded. Finally, 24 articles were selected for further analysis and to answer the study’s research questions. Results revealed that student success in information communication technology (ICT) and computer programming courses was from making the learners aware of their programming abilities. Therefore, higher education (HE) institutions should be encouraged to review learners’ abilities in programming and their related perceptions. Finally, institutions should create a relevant learning strategy using tools appropriate for improving education quality.

African American Students’ Academic and Web Programming Self-Efficacy, Learning Performance, and Perceptions towards Computer Programming in Web Design Courses

Computer programming has been included in computer literacy education in many countries in the last decade. This study examined the effects of gender and the prior programming experience of computer programming on academic and web programming self-efficacy and learning performance in the web design course among African American students, as well as their perceptions towards computer programming. This study’s 14-week web design course taught African American students multiple web programming languages, including HTML, CSS, and JavaScript, in order. A one-group pretest–posttest design was adopted in the experiment. The quantitative method was primarily used in data analysis. This study revealed that African American students’ academic and web programming self-efficacy significantly increased after the web design course. Most of the African American students’ perceptions of computer programming became positive after attending the web design course. This study also found that male African American students had a significantly higher level of web programming self-efficacy than female students before the web design course. Interestingly, this difference disappeared after the course. Additionally, both gender and prior experience in computer programming did not significantly affect students’ learning performance in the web design course. The findings of this study not only contribute to the understanding of the feasibility of teaching multiple programming languages in web programming courses for African American students, they also provide evidence of the positive influence of web programming on African American students’ perceptions of computer programming.

How Do Computational Thinking and Logical and Math Thinking Skills Predict Programming Self-Efficacy?

The aim of this study is to examine the effects of high school students’ perceptions of computational thinking skills and perceptions of logical and mathematical intelligence on their programming self-efficacy. Descriptive survey model as a quantitative research design was used in the research. The data were collected face to face on a voluntary basis from 506 students aged 14-16 studying at various high schools in Türkiye. Thirteen incorrect or missing data items were removed. The remaining 496 data items were analyzed. Three scales were completed by the students: “Computational Thinking Scale,” “Logical and Mathematical Intelligence Perception Scale” and “Programming Self-Efficacy Scale.” Structural equation modeling was used to test the relationship between the variables. The results indicate that high school students’ computational thinking skills and mathematical and logical thinking skills have an effect on their programming self-efficacy. In addition, students’ mathematical and logical thinking skills predicted their computational thinking skills.

Students’ beliefs about computer programming predict their computational thinking and computer programming self-efficacy

ABSTRACT While research has shown that students’ epistemic beliefs can be a strong predictor of their academic performance, cognitive abilities, or self-efficacy, studies of this topic in computer education are rare. The purpose of this study was twofold. First, it aimed to validate a newly developed questionnaire for measuring students’ epistemic beliefs about computer programming at middle school level. Second, the study aimed to understand how students’ epistemic beliefs predict their computational thinking disposition and computer programming self-efficacy. A total of 406 middle school students completed a survey consisting of three research instruments: the Computer Programming Epistemic Belief Inventory (CPEBI), the Computational Thinking Scale (CTS), and the Computer Programming Self-Efficacy Scale (CPSES). The results indicated that the epistemic belief of “uncertainty” was a significant and positive predictor of the “abstraction” and “generalization” dimensions of computational thinking dispositions. In addition, the belief of “complexity” was an antecedent to three dimensions of computational thinking, namely, “algorithmic thinking,” “evaluation,” and “generalization.” Only the “evaluation” dimension of CT disposition had significant and positive relationships with “programming self-efficacy.” The roles of “uncertainty” and “complexity” of computer programming epistemic beliefs are discussed. Implications and suggestions for future computer programming education, especially for young students, are provided.

CS1: Intrinsic Motivation, Self-Efficacy, and Effort

This research investigates university students’ success in their first programming course (CS1) in relation to their motivation, mathematical ability, programming self-efficacy, and initial goal setting. To our knowledge, these constructs have not been measured in a single study before in the Finnish context. The selection of the constructs is in line with the statistical model that predicts student performance (“PreSS”) (Quille and Bergin, 2018). The constructs are compared with various demographic and background variables, such as study major, prior programming experience, and average weekly working hours. Some of the main results of this study are as follows: (1) students generally entered with a high interest in programming and high motivation, but these factors did not increase during the course, i.e., interest in programming did not increase. (2) Having prior experience yielded higher initial programming self-efficacy, grade expectations, and spending less time on tasks, but not better grades (although worse neither). While these results can be seen as preliminary (and alarming in some parts), they give rise to future research for investigating possible expectation–performance gaps in CS1 and later CS studies. As our dataset accumulates, we also hope to be able to construct a valid success prediction model.

A study on the effects of using gamification with the 6E model on high school students’ computer programming self-efficacy, IoT knowledge, hands-on skills, and behavioral patterns

A study on the effects of using gamification with the 6E model on high school students’ computer programming self-efficacy, IoT knowledge, hands-on skills, and behavioral patterns

Effect of software development course on programming self-efficacy

Negative attitudes and perceptions on programming impair the effectiveness of learning programming skills. In this study the attitude related to programming, problem solving, and self-views on importance of IT/programming knowledge were assessed by pre- and post-test completed at the beginning and at the end of a software development course. The study was conducted using an online questionnaire and four different dimensions were measured by a survey consisting 23 items. The results show positive moderate associations between self-commitment in problem solving and algorithmic and problem solving ability and negative weak relationship with lack of self-confidence in programming. K-means algorithm showed that the students could be classified into two main groups stronger and weaker self-confidence in programming. In the case of both clusters, it was possible to achieve a positive change in attitudes related to programming. In the case of weaker self-confidence in programming, a greater change can be observed in the attitudes, which can be considered an important result from the point of view of the effectiveness of the software development course. The research presented in the article proves that attitudes related to programming can be influenced in a positive direction both in the case of those with stronger, but even more so in the case of those with weaker attitudes.

An Investigation of Affective Factors Influencing Computational Thinking and Problem-Solving

This study investigated the effects of affective factors on computational thinking and problem-solving. Computer science subjects are becoming part of the regular curricula in K-12 and higher education to enhance computational problem-solving skills. However, affective factors influencing computational thinking skills and computational thinking components predicting problem-solving skills have yet to be fully explored. This paper proposed a conceptual model to predict (a) four affective factors that influence computational thinking self-efficacy and (b) six computational thinking components that affect problem-solving self-efficacy. Structural equation modeling was used to analyze self-report data from college students to examine the direct relationships among study variables. The findings showed that two affective factors (i.e., programming self-efficacy and computer science usefulness) significantly predicted computational thinking self-efficacy and influenced problem-solving self-efficacy. Also, two computational thinking components (i.e., algorithm and debugging) were the significant determinants of problem-solving self-efficacy. The results validate the importance of affective factors in computer science education and suggest specific computational thinking activities that should be emphasized in computer science curricula to facilitate problem-solving skills.

The effect of generative artificial intelligence (AI)-based tool use on students' computational thinking skills, programming self-efficacy and motivation

The effect of generative artificial intelligence (AI)-based tool use on students' computational thinking skills, programming self-efficacy and motivation

크래치를 활용한 수학수업에서 도구화 수준과 컴퓨터 프로그래밍 자기효능감에 관한 연구

The study investigated students’ instrumentalization levels and computer programming self-efficacy in mathematics classrooms while using Scratches, to understand the properties of equality. 32 of 7th-grade students from D middle school in Gyeonggi-do participated in the program consisting of 7 lesson units. To investigate individual students' levels of instrumentalization, each worksheet they worked on using Scratches was saved into computers after each lesson. Questionnaires measured self-efficacy regarding computer programming at the study's beginning and the end. The level of students' instrumentalization was revealed to be variously from level 0 to 4. In the beginning, 9% of students corresponded to level 3 or 4, but more than 80% of students reached level 3 or above at the end. In addition, computer programing self-efficacy was improved significantly.

Learning Performance of Different Genders’ Computational Thinking

While the role of computational thinking (CT) has been widely reported in technology applications and further integrated into interdisciplinary learning, the integration of pedagogy-supported interdisciplinary activities for the empowerment of girls’ learning must not solely emphasise CT problem-solving skills. Rather, it must scaffold them with interactive learning that supports their characteristics while catering to gender equality. In this study, a gender-balanced interdisciplinary activity, integrating CT with Mandarin learning (ML), was designed for an elementary school in the Mandarin as a Second Language learning context using Social Robots (SRs). It sought to verify the results of the proposed method along with focused activities and interaction in an SR-integrated activity on the CT abilities and target-language learning of young learners. A total of 46 Grade 5 students, 26 boys and 20 girls, participated in the experiment. The study used a quasi-experimental method by examining the result of pre- and post-tests on language acquisition, programming self-efficacy, the educational robot attitude, and learning perceptions in the activity. The results indicated that there were no gender differences in terms of ML, self-efficacy in programming, or attitudes toward using SRs in the SR-integrated interdisciplinary activities. However, the boys and girls had different perceptions of learning. Suggestions for conducting SR-integrated interdisciplinary learning are given, along with pedagogical implications for the further promotion of women in technology.

Increasing Programming Self-Efficacy (PSE) Through a Problem-Based Gamification Digital Learning Ecosystem (DLE) Model

The researchers initially undertook a systematic literature review concerning problem-based learning (PBL), gamification, digital learning ecosystems (DLE), and programming self-efficacy (PSE). Using recent studies and papers from Google Scholar, a preliminary gamified and DLE model was proposed to help Thai students PSE. Further analysis was conducted on gamification mechanics, dynamics, and aesthetics (MDA). After an assessment by nine educational experts using four educational assessment tools from the Joint Committee on Standards for Educational Evaluation (JCSEE) of the proposed seven-step A3S3R (advise, assign, analyze, search, synthesize, summarize, and report) DLE Management Model, the model’s suitability was judged to be ‘very high’ (mean = 4.40, SD = 0.59). Consequently, the development of this learning management model will result in learners recognizing their abilities better in programming and having better programming skills. It can also potentially increase students’ critical and analytical thinking skills and computational thinking and allow them to apply new knowledge or skills to new situations. Finally, researchers can develop new practical learning styles for use in teaching and learning.

Sure I can code (but do I want to?). Why boys' and girls’ programming beliefs differ and the effects of mandatory programming education

In our increasingly digitalized society, it is important to have a grasp of basic programming and therefore, programming was recently made mandatory in Swedish schools. We investigated how the mandatory programming education affects self-efficacy and interest in programming in a sample of 12-year-old children (N = 373). Data was collected at the start of the school year, directly after the education (mainly consisting of block programming), and the end of the school year. Boys had higher self-efficacy and interest than girls at all these three timepoints. Interestingly, as opposed to girls with Swedish background, girls with foreign background did not differ from the boys in programming self-efficacy. The gender difference in programming self-efficacy was completely mediated by gender differences in the children's previous mastery experience, social persuasion, and vicarious experience of computer use. Self-efficacy was strongly related to interest, but despite increasing the children's self-efficacy, programming education surprisingly reduced their interest in programming. The results regarding predictors of self-efficacy lend support to Bandura's social cognitive theory. The intersectional (gender x background) differences could be interpreted in terms of the gender-equality paradox. Possible ways of encouraging children's programming efforts and boosting the effects of interventions are discussed.

Exploring the factors of students' intention to participate in AI software development

PurposeAlthough many universities have begun to provide artificial intelligence (AI)-related courses for students, the influence of the course on students' intention to participate in the development of AI-related products/services needs to be verified. In order to explore the factors that influence students' participation in AI services and system development, this study uses self-efficacy, AI literacy, and the theory of planned behaviour (TPB) to investigate students' intention to engage in AI software development.Design/methodology/approachThe questionnaire was distributed online to collect university students' responses in central Taiwan. The research model and eleven hypotheses are tested using 151 responses. The testing process adopted SmartPLS 3.3 and SPSS 26 software.FindingsAI programming self-efficacy, AI literacy, and course satisfaction directly affected the intention to participate in AI software development. Moreover, course playfulness significantly affected course satisfaction and AI literacy. However, course usefulness positively affected course satisfaction but did not significantly affect AI literacy and AI programming self-efficacy.Originality/valueThe model improves our comprehension of the influence of AI literacy and AI programming self-efficacy on the intention. Moreover, the effects of AI course usefulness and playfulness on literacy and self-efficacy were verified. The findings and insights can help design the AI-related course and encourage university students to participate in AI software development. The study concludes with suggestions for course design for AI course instructors or related educators.

Investigating the effects of a programming course using flipped learning

ABSTRACT In recent years, flipping classrooms has become a popular topic of discussion. However, few previous studies have focused on the effect of the flow experience on programming self-efficacy. To address this gap, the present researchers developed a model that included six research hypotheses. The study applied a flipped classroom model to a programming course in order to examine the relationship between self-efficacy, flow experience, and learning performance in a flipped programming course. The research model was empirically examined using a survey to collect data from 46 college students in Taiwan. Programming courses were conducted for 180 minutes once a week, with six weeks of total teaching time. The findings of this study indicate that there is a positive relationship between programming self-efficacy and enjoyment, engagement, and flow control. The study also revealed that certain important implications and factors to improve the design and delivery of such courses.