Self-Efficacy and Engagement as Predictors of Student Programming Performance

Programming is a core subject introduced in the first year of an Undergraduate Computer Science programme. Since programming is a core subject, it is a major concern that high attrition and failure rates continue to be reported in such courses. Evidence from the literature suggests that programming is cognitively demanding, and the solutions proposed have had minimal impact on students in introductory programming courses. However, in the literature on learning theory, there is evidence suggesting that the self-efficacy beliefs of students affect their engagement, and that their engagement affects their performance. In the literature on introductory programming courses, there is a lack of research examining the effect of self-efficacy on engagement, and the effect of engagement on the programming performance of students. This leaves a gap in programming research that this research seeks to fill. Based on student engagement frameworks in the literature on learning theory, a conceptual model was developed. To operationalise and validate the conceptual model within the context of learning programming, a study consisting of focus group interviews and a survey on students in introductory programming courses is proposed. The results of the survey will be analysed using structural equation modelling (SEM) techniques

No Tests Required: Comparing Traditional and Dynamic Predictors of Programming Success

Research over the past fifty years into predictors of programming performance has yielded little improvement in the identification of at-risk students. This is possibly because research to date is based upon using static tests, which fail to reflect changes in a student's learning progress over time. In this paper, the effectiveness of 38 traditional predictors of programming performance are compared to 12 new data-driven predictors, that are based upon analyzing directly logged data, describing the programming behavior of students. Whilst few strong correlations were found between the traditional predictors and performance, an abundance of strong significant correlations based upon programming behavior were found. A model based upon two of these metrics (Watwin score and percentage of lab time spent resolving errors) could explain 56.3% of the variance in coursework results. The implication of this study is that a student's programming behavior is one of the strongest indicators of their performance, and future work should continue to explore such predictors in different teaching contexts

No tests required : comparing traditional and dynamic predictors of programming success.

Research over the past fifty years into predictors of programming performance has yielded little improvement in the identification of at-risk students. This is possibly because research to date is based upon using static tests, which fail to reflect changes in a student's learning progress over time. In this paper, the effectiveness of 38 traditional predictors of programming performance are compared to 12 new data-driven predictors, that are based upon analyzing directly logged data, describing the programming behavior of students. Whilst few strong correlations were found between the traditional predictors and performance, an abundance of strong significant correlations based upon programming behavior were found. A model based upon two of these metrics (Watwin score and percentage of lab time spent resolving errors) could explain 56.3% of the variance in coursework results. The implication of this study is that a student's programming behavior is one of the strongest indicators of their performance, and future work should continue to explore such predictors in different teaching contexts

Introductory programming: a systematic literature review

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research

Predicting Performance in an Introductory Programming Course by Logging and Analyzing Student Programming Behavior

The high failure rates of many programming courses means there is a need to identify struggling students as early as possible. Prior research has focused upon using a set of tests to assess the use of a student's demographic, psychological and cognitive traits as predictors of performance. But these traits are static in nature, and therefore fail to encapsulate changes in a student's learning progress over the duration of a course. In this paper we present a new approach for predicting a student's performance in a programming course, based upon analyzing directly logged data, describing various aspects of their ordinary programming behavior. An evaluation using data logged from a sample of 45 programming students at our University, showed that our approach was an excellent early predictor of performance, explaining 42.49% of the variance in coursework marks - double the explanatory power when compared to the closest related technique in the literature

Affective Characteristics Analysis: a Comparative Study between Elementary School of Indonesia and Thailand

Indonesia and Thailand have many similarities in the education system, but the learning outcome is much different. The purpose of this study is to measure the affective domain of student in both countries. Comparing the similarity and differences of both countries would figure out the certain education policy for better understanding on education system improvements. This research combines qualitative and quantitative research methods using ex-post facto approach. The result shows there was significance different on self-esteem, interest, and belief. Indonesia got the higher score than Thailand for self-esteem and interest aspect. On the contrary, Thailand got the higher score for belief aspect than Indonesia. Both, Indonesia and Thailand got no significantly different for attitude. In total, there is no significant difference between both countries on affective characteristics. Three factors considered gave effect to effective characteristics profile in both countries namely learning strategy, ICT use, and teacher and student interaction

Sequences of Frustration and Confusion, and Learning

We use sensor-free affect detection and a discovery with models approach to explore the relationship between affect over varying durations and learning outcomes among students using Cognitive Tutor Algebra. Researchers have suggested that the affective state of confusion can have positive effects on learning as long as students can resolve the confusion, and recent research seems to accord with this hypothesis. However, there is room for concern that some of this earlier work may have conflated frustration and confusion. We replicate these analyses using sensor-free automated detectors trained to distinguish the two affective states. Our analyses suggest that the effect may be stronger for frustration than confusion, but is strongest when these two affective states are taken together

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

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

Power of Near-Peers: Conceptualizing and Testing a Near-Peer Mentoring Model in Raising Youths\u27 Self-Efficacy in Computer Programming

Self-efficacy is seen as a barrier for youth, females in particular, to enter computer science (CS). In this study, I presented a near-peer mentoring model that focused on changing the mentee’s self-efficacy in CS. The present study had three objectives: (a) to design a near-peer mentoring model (i.e., a conceptual model) around the sources of information that influence self-efficacy, (b) to develop a mentor training model based on the conceptual model, and (c) to test the effectiveness of the training model in increasing mentees’ self-efficacy in the context of a summer App programming camp. The present study adopted a mixed-methods approach following a concurrent, embedded design to answer research questions. Data were collected from pre-post surveys and camper interviews. Comparison of quantitative and qualitative findings indicated that the near-peer mentoring model has a potential in increasing youth’s self-efficacy regardless of their gender. It was also found that encouragement was important for fostering self-efficacy and while they did not directly influence self-efficacy, modeling and instructive feedback enhanced campers’ learning experience, which, in turn, would boost self-efficacy. The present study also provided examples of how to train mentors to do modeling and provide instructive and encouraging feedback, which may be helpful for programs that use mentors to recruit youth to CS