The study uses the partial least squares-structural equation modeling (PLS-SEM) algorithm to predict the factors affecting the programming performance (PPE) (low, high) of the students receiving computer programming e...
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The study uses the partial least squares-structural equation modeling (PLS-SEM) algorithm to predict the factors affecting the programming performance (PPE) (low, high) of the students receiving computer programming education. The participants of the study consist of 763 students who received programming education. In the analysis of the data, the PLS-SEM method was used with the help of the SmartPLS 4 program. In addition, multigroup SEM was used to examine the differentiation of models between groups with low and high PPE. According to the research results, the percentage of explanation of the model is relatively high in the group with high PPE compared to the group with low performance. According to the findings of the study, age, education level, general academic achievement, and PPE scores were found to be related. In addition, programming experience, attitude, and programming empowerment are related to PPE. The most important of some of the limitations of this study is that the data collected from the participants are based on their self-reports. The results of this study may have important contributions to the differentiation of approaches toward low and high-performing students in supporting programming education. This type of research can help design relevant interventions for students experiencing failures in programming education.
This study used the ARCS approach to investigate the effects of university students' motivation, including attention, relevance, confidence, and satisfaction, to use the programming Teaching Assistant (PTA) on the...
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This study used the ARCS approach to investigate the effects of university students' motivation, including attention, relevance, confidence, and satisfaction, to use the programming Teaching Assistant (PTA) on their programming Problem-Solving Skills (PPSS). Previous studies have shown that PTA features enhance learners' programming performance, but relevant studies have not empirically evaluated the factors influencing learners' problem-solving skills in PTA-based programming language practice. Thus, the current study used the ARCS model to investigate the effects of its core factors, Attention (A), Relevance (R), Confidence (C), and Satisfaction (S), on the learners' PPSS. A total of 99 students major in computer science and engineering (CSE) participated in this study. Multiple linear regression analysis was conducted and the results indicated that these motivational factors influenced learners' PPSS in PTA-based programming language practice. The PTA has proven to be a motivating platform for programming language practice. Finally, relevant implications are offered.
This study aims to explore and reveal profiling patterns in the measurement of cognitive and noncognitivecharacteristics of undergraduate students’ programming performances. Spatial skills, workingmemory, perceived p...
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Background and Context: Computer Science attrition rates (in the western world) are very concerning, with a large number of students failing to progress each year. It is well acknowledged that a significant factor of ...
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Background and Context: Computer Science attrition rates (in the western world) are very concerning, with a large number of students failing to progress each year. It is well acknowledged that a significant factor of this attrition, is the students' difficulty to master the introductory programming module, often referred to as CS1. Objective: The objective of this article is to describe the evolution of a prediction model named PreSS (Predict Student Success) over a 13-year period (2005-2018). Method: This article ties together, the PreSS prediction model;pilot studies;a longitudinal, multi-institutional revalidation and replication study;improvements to the model since its inception;and interventions to reduce attrition rates. Findings: The outcome of this body of work is an end-toend real-time web-based tool (PreSS#), which can predict student success early in an introductory programming module (CS1), with an accuracy of 71%. This tool is enhanced with interventions that were developed in conjunction with PreSS#, which improved student performance in CS1.
Background. Assessing a software engineer's ability to solve algorithmic programming tasks has been an essential part of technical interviews at some of the most successful technology companies for several years n...
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Background. Assessing a software engineer's ability to solve algorithmic programming tasks has been an essential part of technical interviews at some of the most successful technology companies for several years now. We do not know to what extent individual characteristics, such as personality or programming experience, predict the performance in such tasks. Decision makers' unawareness of possible predictor variables has the potential to bias hiring decisions which can result in expensive false negatives as well as in the unintended exclusion of software engineers with actually desirable characteristics. Methods. We conducted an exploratory quantitative study with 32 software engineering students to develop an empirical theory on which individual characteristics predict the performance in solving coding challenges. We developed our theory based on an established taxonomy framework by Gregor (2006). Results. Our findings show that the better coding challenge solvers also have better exam grades and more programming experience. Furthermore, conscientious as well as sad software engineers performed worse in our study. We make the theory available in this paper for empirical testing. Discussion. The theory raises awareness to the influence of individual characteristics on the outcome of technical interviews. Should the theory find empirical support in future studies, hiring costs could be reduced by selecting appropriate criteria for preselecting candidates for on-site interviews and potential bias in hiring decisions could be reduced by taking suitable measures.
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