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Showing 1 to 15 of 18 results Save | Export
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Ella Christiaans; So Yeon Lee; Kristy A. Robinson – Educational Psychology, 2024
Students want to learn computer science due to its usefulness for future careers, however they often meet challenges in introductory courses. In the increasingly digital world, it is important to understand some important psychological consequences of such challenges: perceived costs of pursuing computer science. This study thus investigated…
Descriptors: Undergraduate Students, Computer Science Education, Psychological Patterns, Student Attitudes
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George, Kari L.; Sax, Linda J.; Wofford, Annie M.; Sundar, Sarayu – Research in Higher Education, 2022
Computing career opportunities are increasing across all sectors of the U.S. economy, yet there remains a serious shortage of college graduates to fill these jobs. This problem has fueled a nationwide effort to expand and diversify the computing career pipeline. Guided by social cognitive career theory (SCCT), this study used logistic regression…
Descriptors: College Environment, Career Choice, College Students, School Role
Mohammed Alzaid – ProQuest LLC, 2022
Distributed self-assessments and reflections empower learners to take the lead on their knowledge gaining evaluation. Both provide essential elements for practice and self-regulation in learning settings. Nowadays, many sources for practice opportunities are made available to the learners, especially in the Computer Science (CS) and programming…
Descriptors: Learning Analytics, Self Evaluation (Individuals), Programming, Problem Solving
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Chen, Chen; Jeckel, Stuart; Sonnert, Gerhard; Sadler, Philip M. – International Journal of Computer Science Education in Schools, 2019
This study examines the relationship between students' pre-college experience with computers and their later success in introductory computer science classes in college. Data were drawn from a nationally representative sample of 10,197 students enrolled in computer science at 118 colleges and universities in the United States. We found that…
Descriptors: Computer Science Education, Programming, Academic Achievement, College Students
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Gurung, Regan A. R.; Mai, Theresa; Nelson, Matthew; Pruitt, Sydney – Teaching of Psychology, 2022
Background: Instructors and students are on a continuing quest to identify predictors of learning. Objective: This study examines the associations between self-reported exam score and study techniques among students in two courses, Introductory Psychology and Computer Science. Method: We used an online survey to measure the extent students (N =…
Descriptors: Predictor Variables, Study Skills, Thinking Skills, Metacognition
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Chen, Chen; Haduong, Paulina; Brennan, Karen; Sonnert, Gerhard; Sadler, Philip – Computer Science Education, 2019
Background and Context: The relationship between novices' first programming language and their future achievement has drawn increasing interest owing to recent efforts to expand K-12 computing education. This article contributes to this topic by analyzing data from a retrospective study of more than 10,000 undergraduates enrolled in introductory…
Descriptors: Computer Science Education, Programming Languages, College Students, Computer Attitudes
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Adkins, Joni K.; Linville, Diana R.; Badami, Charles – Information Systems Education Journal, 2020
Online textbooks allow instructors to provide interactive and engaging activities for students. In this paper, we look at how providing an interactive online textbook is utilized and valued in a beginning computer programming course. In addition, we compare the utilization of the online textbook to the student final course grade. Our findings…
Descriptors: Instructional Effectiveness, Introductory Courses, Programming, Computer Science Education
Patton, Belinda Andromeda – ProQuest LLC, 2020
The rise in demand for computer programming jobs has created a significant need for computer programming training. Online learning can be an effective tool for meeting the needs of these job demands. The challenge for universities is that computer programming is perceived as a difficult course by many students (Askar & Davenport, 2009; Baser,…
Descriptors: Student Attitudes, Undergraduate Students, Programming, Computer Science Education
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Quille, Keith; Bergin, Susan – Computer Science Education, 2019
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…
Descriptors: Computer Science Education, Introductory Courses, Programming, Student Attrition
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Brown, Michael; DeMonbrun, R. Matthew; Teasley, Stephanie – Journal of Learning Analytics, 2018
In this study, we develop and test four measures for conceptualizing the potential impact of co-enrollment in different courses on students' changing risk for academic difficulty and recovery from academic difficulty in a focal course. We offer four predictors, two related to instructional complexity and two related to structural complexity (the…
Descriptors: At Risk Students, Dropout Prevention, Difficulty Level, College Curriculum
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Silva-Maceda, Gabriela; Arjona-Villicaña, P. David; Castillo-Barrera, F. Edgar – IEEE Transactions on Education, 2016
Learning to program is a complex task, and the impact of different pedagogical approaches to teach this skill has been hard to measure. This study examined the performance data of seven cohorts of students (N = 1168) learning programming under three different pedagogical approaches. These pedagogical approaches varied either in the length of the…
Descriptors: Programming, Teaching Methods, Intermode Differences, Cohort Analysis
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Rolka, Christine; Remshagen, Anja – International Journal for the Scholarship of Teaching and Learning, 2015
Contextualized learning is considered beneficial for student success. In this article, we assess the impact of context-based learning tools on student grade performance in an introductory computer science course. In particular, we investigate two central questions: (1) does the use context-based learning tools, robots and animations, affect…
Descriptors: Introductory Courses, Computer Science Education, Context Effect, Grades (Scholastic)
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Rodrigo, Ma. Mercedes T.; Andallaza, Thor Collin S.; Castro, Francisco Enrique Vicente G.; Armenta, Marc Lester V.; Dy, Thomas T.; Jadud, Matthew C. – Journal of Educational Computing Research, 2013
In this article we quantitatively and qualitatively analyze a sample of novice programmer compilation log data, exploring whether (or how) low-achieving, average, and high-achieving students vary in their grasp of these introductory concepts. High-achieving students self-reported having the easiest time learning the introductory programming…
Descriptors: Programming, High Achievement, Introductory Courses, Qualitative Research
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Robins, Anthony – Computer Science Education, 2010
Compared to other subjects, the typical introductory programming (CS1) course has higher than usual rates of both failing and high grades, creating a characteristic bimodal grade distribution. In this article, I explore two possible explanations. The conventional explanation has been that learners naturally fall into populations of programmers and…
Descriptors: Programming, Learning Processes, Grading, Simulation
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Bennedsen, Jens; Caspersen, Michael E. – Computer Science Education, 2008
In order to better understand predictors of success and, when possible, improve the design of the first year computer science courses at university to increase the likelihood of success, we study a number of factors that may potentially indicate students' computer science aptitude. Based on findings in general education, we have studied the…
Descriptors: Computer Science Education, Academic Achievement, Mental Health, Correlation
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