Investigating Factors Influencing Students' Intention to Dropout Computer Science Studies

Research in the area of Computer Science (CS) education, has focused on identifying the reasons that students do not finish their studies in CS. Although there is increasing demand for CS professionals, there is not enough knowledge to explain the high dropout rates in CS education. This study aims to empirically examine how students' intention to complete their studies (retention) in CS is affected by variables playing a key role in higher education. By identifying which variables contribute to dropout in CS studies, we will be able to focus on how to improve aspects related with them in order to reduce dropout rates. To do so we identified the following variables: Year of studies, Gender, Age, Students' Effort, Absence from Classes, Expected Grade point average (GPA), and Current GPA, and tested their effect on retention, based on the responses collected from 241 CS student. Year of studies and Effort have positive effects on students' intention to finish their studies in CS. Interestingly, the expected GPA has a negative effect on students' intentions to finish their studies. The findings contribute to theory and practice, as they offer CS educators and policy makers insights that may aid towards increased student retention and reduced dropout rates.

[1]  Alberto Salguero,et al.  Factors influencing university drop out rates , 2009, Comput. Educ..

[2]  I. Warner,et al.  Hierarchical Mentoring: A Transformative Strategy for Improving Diversity and Retention in Undergraduate STEM Disciplines , 2011, Journal of Science Education and Technology.

[3]  J. Jacobs,et al.  Twenty-five years of research on gender and ethnic differences in math and science career choices: what have we learned? , 2005, New directions for child and adolescent development.

[4]  L. Cronbach Coefficient alpha and the internal structure of tests , 1951 .

[5]  Wiji Arulampalam,et al.  Am I Missing Something? The Effects of Absence from Class on Student Performance , 2012, SSRN Electronic Journal.

[6]  Michail N. Giannakos,et al.  Investigating teachers’ confidence on technological pedagogical and content knowledge: an initial validation of TPACK scales in K-12 computing education context , 2015 .

[7]  Mary Beth Rosson,et al.  Orientation of Undergraduates Toward Careers in the Computer and Information Sciences: Gender, Self-Efficacy and Social Support , 2011, TOCE.

[8]  Joint Task Force on Computing Curricula Computer Science Curricula 2013: Curriculum Guidelines for Undergraduate Degree Programs in Computer Science , 2013 .

[9]  Panayiotis E. Pintelas,et al.  A survey on student dropout rates and dropout causes concerning the students in the Course of Informatics of the Hellenic Open University , 2002, Comput. Educ..

[10]  W. Mau,et al.  Factors that Influence Persistence in Science and Engineering Career Aspirations. , 2003 .

[11]  Lori Carter Why students with an apparent aptitude for computer science don't choose to major in computer science , 2006, SIGCSE '06.

[12]  Il-Hyun Jo,et al.  Educational technology approach toward learning analytics: relationship between student online behavior and learning performance in higher education , 2014, LAK '14.

[13]  E. Seymour,et al.  Talking About Leaving: Why Undergraduates Leave The Sciences , 1997 .

[14]  Christopher Lynnly Hovey,et al.  Results of a large-scale, multi-institutional study of undergraduate retention in computing , 2014, 2014 IEEE Frontiers in Education Conference (FIE) Proceedings.

[15]  Tim Gramling How Five Student Characteristics Accurately Predict For-Profit University Graduation Odds , 2013 .

[16]  Sally Fincher,et al.  Computer Science Curricula 2013 , 2013 .

[17]  Tuba Yilmaz,et al.  Student perceptions of computer science: a retention study comparing graduating seniors with cs leavers , 2008, SIGCSE '08.

[18]  Zohreh R. Eslami,et al.  Understanding Why Students Drop Out of High School, According to Their Own Reports , 2013 .

[19]  Lecia Jane Barker,et al.  Exploring factors that influence computer science introductory course students to persist in the major , 2009, SIGCSE '09.

[20]  G. Ruxton The unequal variance t-test is an underused alternative to Student's t-test and the Mann–Whitney U test , 2006 .

[21]  R. Layton,et al.  Persistence, Engagement, and Migration in Engineering Programs , 2008 .

[22]  Suzanne G. Brainard,et al.  IDENTIFYING DETERMINANTS OF ACADEMIC SELFCONFIDENCE AMONG SCIENCE, MATH, ENGINEERING, AND TECHNOLOGY STUDENTS , 2001 .

[23]  Xianggui Qu,et al.  Multivariate Data Analysis , 2007, Technometrics.

[24]  J. Olsen,et al.  The European Commission , 2020, The European Union.

[25]  William Aspray,et al.  Just Get Over It or Just Get On with It: Retaining Women in Undergraduate Computing , 2008 .

[26]  S E P T E M B,et al.  U.S.BUREAU OF LABOR STATISTICS , 2004 .