scholarly journals Effect of Self-efficacy and Emotional Engagement on Introductory Programming Students

2019 ◽  
Vol 23 ◽  
Author(s):  
Geetha Kanaparan ◽  
Rowena Cullen ◽  
David Mason
Keyword(s):  
Self Efficacy ◽  
Online Survey ◽  
Emotional Engagement ◽  
Failure Rates ◽  
Introductory Programming ◽  
Programming Students ◽  
Negative Effect ◽  
The Relationship ◽  
Introductory Programming Courses ◽  
Programming Courses

High failure rates appear to be a norm in introductory programming courses. Many solutions have been proposed to improve the high failure rates. Surprisingly, these solutions have not lead to significant improvements in the performance of students in introductory programming courses. In this study, the relationship between self-efficacy, emotional engagement and the performance of students in introductory programming courses were examined. Enjoyment, interest, and gratification were identified as three factors contributing to emotional engagement in introductory programming courses from a review of existing literature and from focus groups. An online survey of 433 students in introductory programming courses showed that the students’ programming self-efficacy beliefs had a strong positive effect on enjoyment, while gratification and interest had a negative effect on programming performance. These findings have implications for course instructors who design and deliver introductory programming courses.

scholarly journals Self-efficacy and engagement as predictors of student programming performance: An international perspective

2021 ◽  
Author(s):  
◽  
Geetha Kanaparan
Keyword(s):  
Deep Learning ◽  
Focus Groups ◽  
Self Efficacy ◽  
Help Seeking ◽  
Research Model ◽  
Introductory Programming ◽  
Novice Programmer ◽  
Introductory Programming Courses ◽  
Programming Courses ◽  
Survey Questionnaires

<p>High attrition and failure rates are a common phenomenon in introductory programming courses and are a major concern since course instructors are not able to successfully teach novice programmers the fundamental concepts of computer programming and equip them with skills to code solutions to programming problems. Existing solutions that attempt to minimise the high failure and attrition rates have had little impact on improving the performance of the novice programmers. However, the behaviour of the novice programmer has received little attention from introductory programming course instructors although the literature on learning theory suggests that self-efficacy and engagement are two behavioural factors that affect a student’s performance. This study fills the gap in existing research by examining the effect of programming self-efficacy on the engagement of novice programmers, and the effect of their engagement on their programming performance.  A research model that proposes a link between programming self-efficacy and the indicators of engagement that are specific to the context of introductory programming courses, and a link between the indicators of engagement to the programming performance of the novice programmer was developed. A three-phased mixed methods approach which consists of two survey questionnaires and focus groups was used to validate the research model. Data was collected in New Zealand and in Malaysia with 433 novice programmers participating in the survey questionnaires while 4 focus groups were held to refine and validate the indicators of engagement in introductory programming courses. The findings of the focus groups confirmed that participation, help-seeking, persistence, effort, deep learning, surface learning, trial and error, interest, and enjoyment were indicators of engagement while gratification emerged as a new indicator of engagement in introductory programming courses.  The data from the survey questionnaires were analysed using Partial Least Squares Structural Equation Modeling (PLS-SEM). This study found that the programming self-efficacy beliefs of novice programmers had a strong influence on their engagement behaviour with the exception of help-seeking, while effort, enjoyment, deep learning, and surface learning were predictors of programming performance. These findings have implications for introductory programming course instructors and the recommendations emerging from this study include making clear behavioural expectations, designing courses which stimulate and support effective behaviour, and making novice programmers aware of the engagement behaviour that does not lead to better programming performance. This study contributes to the theory of teaching computer programming, and to the practice of designing and delivering introductory programming courses.</p>

scholarly journals Machine Learning Prediction and Recommendation Framework to Support Introductory Programming Course

2021 ◽  
Vol 16 (17) ◽  
pp. 42
Author(s):  
Ijaz Muhammad Khan ◽  
Abdul Rahim Ahmad ◽  
Nafaa Jabeur ◽  
Mohammed Najah Mahdi
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Early Stage ◽  
Introductory Programming ◽  
Machine Learning Classifiers ◽  
Programming Students ◽  
New Students ◽  
Precautionary Measures ◽  
Introductory Programming Courses ◽  
Programming Courses

The new students struggle to understand the introductory programming courses, due to its intricate nature, which results in higher dropout and increased failure rates. Despite implementing productive methodologies, the instructor struggles to identify the students with distinctive levels of skills. The modern institutes are looking for technology-equipped practices to classify the students and prepare personalized consultation procedures for each class. This paper applies decision tree-based machine learning classifiers to develop a prediction model competent to forecast the outcome of the introductory programming students at an early stage of the semester. The model is then transformed into an adaptive consultation framework which generates three types of colored signals; red, yellow, and green which illustrates whether the student is performing low, average, or high respectively. This provides an opportunity for the instructor to set precautionary measures for low performing students and set complicated tasks that help the highly skilled students to improve their skills further. The experiments compare a set of decision tree-based classifiers and conclude J48 as an efficient model in classifying students in all classes with high accuracy, sensitivity, and F-measure. Even though the aim of the research is to focus on introductory programming courses, however, the framework is flexible and can be implemented in other courses.

scholarly journals Self-efficacy and engagement as predictors of student programming performance: An international perspective

2021 ◽  
Author(s):  
◽  
Geetha Kanaparan
Keyword(s):  
Deep Learning ◽  
Focus Groups ◽  
Self Efficacy ◽  
Help Seeking ◽  
Research Model ◽  
Introductory Programming ◽  
Novice Programmer ◽  
Introductory Programming Courses ◽  
Programming Courses ◽  
Survey Questionnaires

<p>High attrition and failure rates are a common phenomenon in introductory programming courses and are a major concern since course instructors are not able to successfully teach novice programmers the fundamental concepts of computer programming and equip them with skills to code solutions to programming problems. Existing solutions that attempt to minimise the high failure and attrition rates have had little impact on improving the performance of the novice programmers. However, the behaviour of the novice programmer has received little attention from introductory programming course instructors although the literature on learning theory suggests that self-efficacy and engagement are two behavioural factors that affect a student’s performance. This study fills the gap in existing research by examining the effect of programming self-efficacy on the engagement of novice programmers, and the effect of their engagement on their programming performance.  A research model that proposes a link between programming self-efficacy and the indicators of engagement that are specific to the context of introductory programming courses, and a link between the indicators of engagement to the programming performance of the novice programmer was developed. A three-phased mixed methods approach which consists of two survey questionnaires and focus groups was used to validate the research model. Data was collected in New Zealand and in Malaysia with 433 novice programmers participating in the survey questionnaires while 4 focus groups were held to refine and validate the indicators of engagement in introductory programming courses. The findings of the focus groups confirmed that participation, help-seeking, persistence, effort, deep learning, surface learning, trial and error, interest, and enjoyment were indicators of engagement while gratification emerged as a new indicator of engagement in introductory programming courses.  The data from the survey questionnaires were analysed using Partial Least Squares Structural Equation Modeling (PLS-SEM). This study found that the programming self-efficacy beliefs of novice programmers had a strong influence on their engagement behaviour with the exception of help-seeking, while effort, enjoyment, deep learning, and surface learning were predictors of programming performance. These findings have implications for introductory programming course instructors and the recommendations emerging from this study include making clear behavioural expectations, designing courses which stimulate and support effective behaviour, and making novice programmers aware of the engagement behaviour that does not lead to better programming performance. This study contributes to the theory of teaching computer programming, and to the practice of designing and delivering introductory programming courses.</p>

Creativity in CS1: A Literature Review

2022 ◽  
Vol 22 (2) ◽  
pp. 1-26
Author(s):  
Sadia Sharmin
Keyword(s):  
Science Education ◽  
Literature Review ◽  
Computer Science ◽  
Creative Thinking ◽  
Computer Science Education ◽  
Project Based Learning ◽  
Retention Rates ◽  
Introductory Programming ◽  
Introductory Programming Courses ◽  
Programming Courses

Computer science is a fast-growing field in today’s digitized age, and working in this industry often requires creativity and innovative thought. An issue within computer science education, however, is that large introductory programming courses often involve little opportunity for creative thinking within coursework. The undergraduate introductory programming course (CS1) is notorious for its poor student performance and retention rates across multiple institutions. Integrating opportunities for creative thinking may help combat this issue by adding a personal touch to course content, which could allow beginner CS students to better relate to the abstract world of programming. Research on the role of creativity in computer science education (CSE) is an interesting area with a lot of room for exploration due to the complexity of the phenomenon of creativity as well as the CSE research field being fairly new compared to some other education fields where this topic has been more closely explored. To contribute to this area of research, this article provides a literature review exploring the concept of creativity as relevant to computer science education and CS1 in particular. Based on the review of the literature, we conclude creativity is an essential component to computer science, and the type of creativity that computer science requires is in fact, a teachable skill through the use of various tools and strategies. These strategies include the integration of open-ended assignments, large collaborative projects, learning by teaching, multimedia projects, small creative computational exercises, game development projects, digitally produced art, robotics, digital story-telling, music manipulation, and project-based learning. Research on each of these strategies and their effects on student experiences within CS1 is discussed in this review. Last, six main components of creativity-enhancing activities are identified based on the studies about incorporating creativity into CS1. These components are as follows: Collaboration, Relevance, Autonomy, Ownership, Hands-On Learning, and Visual Feedback. The purpose of this article is to contribute to computer science educators’ understanding of how creativity is best understood in the context of computer science education and explore practical applications of creativity theory in CS1 classrooms. This is an important collection of information for restructuring aspects of future introductory programming courses in creative, innovative ways that benefit student learning.

scholarly journals Structural Relationship among Mobile Phone Dependence, Self-Efficacy, Time Management Disposition, and Academic Procras-tination in College Students

2021 ◽  
Author(s):  
Peng Sheng Chen ◽  
Jing Li ◽  
Seung-Yong Kim
Keyword(s):  
Factor Analysis ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Time Management ◽  
Self Efficacy ◽  
Academic Procrastination ◽  
Self Regulated Learning ◽  
Regulated Learning ◽  
Negative Effect ◽  
The Relationship

Background: We aimed to investigate the relationship among mobile phone dependence, self-efficacy for self-regulated learning, time management disposition, and academic procrastination in Chinese students majoring in physical education. In addition, we explored the mediating roles of self-efficacy for self-regulated learning and time management disposition in the relationship between mobile phone dependence and academic procrastination. Methods: We adopted a random sampling method to identify 324 physical education majors at five universities in Shaanxi Province, China in 2020. Data were analyzed via exploratory factor analysis, confirmatory factor analysis, correlation analysis, structural equation model analysis, and path analysis. Results: Mobile phone dependence had significant positive effects on academic procrastination (P<0.001) and self-efficacy for self-regulated learning (P<0.05) but a significant negative effect on time management disposition (P<0.001). Self-efficacy for self-regulated learning had a significant positive effect on academic procrastination (P<0.001), while time management disposition had a significant negative effect on academic procrastination (P<0.01). Notably, self-efficacy for self-regulated learning and time management disposition mediated the relationship between mobile phone dependence and academic procrastination (P<0.05). Conclusion: In addition to its direct effect on academic procrastination, mobile phone dependence exerts an indirect effect via time management disposition and self-regulated learning efficacy. Reducing students’ dependence on mobile phones is necessary for attenuating academic procrastination on university campuses. Thus, universities should aim to restrict the use of mobile phones in the classroom, actively cultivate students’ confidence in their self-regulated learning ability, and educate them regarding appropriate time values.

scholarly journals Learners Programming Language a Helping System for Introductory Programming Courses

2016 ◽  
Vol 35 (3) ◽  
pp. 347-358
Author(s):  
Muhammad Shumail Naveed ◽  
Muhammad Sarim ◽  
Kamran Ahsan
Keyword(s):  
Programming Languages ◽  
Programming Language ◽  
Retention Rate ◽  
Introductory Programming ◽  
Source Program ◽  
Definition Of ◽  
High Level ◽  
Novice Students ◽  
Introductory Programming Courses ◽  
Programming Courses

Programming is the core of computer science and due to this momentousness a special care is taken in designing the curriculum of programming courses. A substantial work has been conducted on the definition of programming courses, yet the introductory programming courses are still facing high attrition, low retention and lack of motivation. This paper introduced a tiny pre-programming language called LPL (Learners Programming Language) as a ZPL (Zeroth Programming Language) to illuminate novice students about elementary concepts of introductory programming before introducing the first imperative programming course. The overall objective and design philosophy of LPL is based on a hypothesis that the soft introduction of a simple and paradigm specific textual programming can increase the motivation level of novice students and reduce the congenital complexities and hardness of the first programming course and eventually improve the retention rate and may be fruitful in reducing the dropout/failure level. LPL also generates the equivalent high level programs from user source program and eventually very fruitful in understanding the syntax of introductory programming languages. To overcome the inherent complexities of unusual and rigid syntax of introductory programming languages, the LPL provide elementary programming concepts in the form of algorithmic and plain natural language based computational statements. The initial results obtained after the introduction of LPL are very encouraging in motivating novice students and improving the retention rate.

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