Assessing Student Behavior in Computer Science Education with an fsQCA Approach

This study uses complexity theory to understand the causal patterns of factors that stimulate students’ intention to continue studies in computer science (CS). To this end, it identifies gains and barriers as essential factors in CS education, including motivation and learning performance, and proposes a conceptual model along with research propositions. To test its propositions, the study employs fuzzy-set qualitative comparative analysis on a data sample from 344 students. Findings indicate eight configurations of cognitive and noncognitive gains, barriers, motivation for studies, and learning performance that explain high intention to continue studies in CS. This research study contributes to the literature by (1) offering new insights into the relationships among the predictors of CS students’ intention to continue their studies and (2) advancing the theoretical foundation of how students’ gains, barriers, motivation, and learning performance combine to better explain high intentions to continue CS studies.

[1]  Kaitlyn J. Bunker,et al.  University Studies of Student Persistence in Engineering , 2012 .

[2]  Amber Settle,et al.  Undergraduate Students’ Perceptions of the Impact of Pre-College Computing Activities on Choices of Major , 2016, TOCE.

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

[4]  Karl R. B. Schmitt,et al.  Student expectations from CS and other stem courses: they aren't like CS- majors! or (CS !=Stem-CS) , 2013 .

[5]  Sandra Lowe,et al.  Redesigning Social Inquiry Fuzzy Sets And Beyond , 2016 .

[6]  Petra Kaufmann,et al.  Experimental And Quasi Experimental Designs For Research , 2016 .

[7]  Michael Hewner,et al.  Undergraduate conceptions of the field of computer science , 2013, ICER.

[8]  Vassilis Kostakos,et al.  Applying configurational analysis to IS behavioural research: a methodological alternative for modelling combinatorial complexities , 2017, Inf. Syst. J..

[9]  Chin-Chung Tsai,et al.  Students' perceptions of collaboration, self-regulated learning, and information seeking in the context of Internet-based learning and traditional learning , 2011, Comput. Hum. Behav..

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

[11]  Johanna Pirker,et al.  Motivational active learning: engaging university students in computer science education , 2014, ITiCSE '14.

[12]  Charles C. Ragin,et al.  Set Relations in Social Research: Evaluating Their Consistency and Coverage , 2006, Political Analysis.

[13]  Allison Elliott Tew,et al.  A fresh look at novice programmers' performance and their teachers' expectations , 2013, ITiCSE -WGR '13.

[14]  Gerd Gigerenzer,et al.  Homo Heuristicus: Why Biased Minds Make Better Inferences , 2009, Top. Cogn. Sci..

[15]  Jiyeon Kim,et al.  Locus of control, self-efficacy, and task value as predictors of learning outcome in an online university context , 2013, Comput. Educ..

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

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

[18]  Roy D. Pea,et al.  Remedying misperceptions of computer science among middle school students , 2014, SIGCSE.

[19]  Michail N. Giannakos,et al.  Adaptable Learning and Learning Analytics: A Case Study in a Programming Course , 2016, EC-TEL.

[20]  Laurie Williams,et al.  Computer Science Attitude Survey , 2003 .

[21]  W. Shadish,et al.  Campbell and Rubin: A primer and comparison of their approaches to causal inference in field settings. , 2010, Psychological methods.

[22]  Katariina Salmela-Aro,et al.  Task value profiles across subjects and aspirations to physical and IT-related sciences in the United States and Finland. , 2012, Developmental psychology.

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

[24]  Michail N. Giannakos,et al.  Making Sense of Learning Analytics with a Configurational Approach , 2016, SE@VBL@LAK.

[25]  Edward J. Maloney.  What Web 2.0 Can Teach Us about Learning. , 2007 .

[26]  Panos E. Kourouthanassis,et al.  Explaining online shopping behavior with fsQCA: The role of cognitive and affective perceptions , 2016 .

[27]  Athanassios Jimoyiannis,et al.  Analysing students’ engagement and learning presence in an educational blog community , 2012 .

[28]  Daniel P Keating,et al.  Gendered motivational processes affecting high school mathematics participation, educational aspirations, and career plans: a comparison of samples from Australia, Canada, and the United States. , 2012, Developmental psychology.

[29]  John C. Smart,et al.  If and When Money Matters: The Relationships Among Educational Expenditures, Student Engagement and Students’ Learning Outcomes , 2011 .

[30]  John C. Smart,et al.  Do Institutional Characteristics Affect Student Gains from College? , 2001 .

[31]  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.

[32]  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..

[33]  D. Sathianathan,et al.  Effects of faculty interaction and feedback on gains in student skills , 2002, 32nd Annual Frontiers in Education.

[34]  Scott B. MacKenzie,et al.  Common method biases in behavioral research: a critical review of the literature and recommended remedies. , 2003, The Journal of applied psychology.

[35]  A. Parasuraman,et al.  When the Recipe Is More Important Than the Ingredients , 2014 .

[36]  Michail N. Giannakos,et al.  Understanding student retention in computer science education: The role of environment, gains, barriers and usefulness , 2016, Education and Information Technologies.

[37]  Melissa Sobel,et al.  An examination of belongingness and confidence among female computer science students , 2015, CSOC.

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

[39]  Paul Dalziel,et al.  Education and Skills , 2013 .

[40]  Hariharan Swaminathan,et al.  Development of a Classification System for Engineering Student Characteristics Affecting College Enrollment and Retention , 2009 .

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

[42]  N. Venkatraman,et al.  The Concept of Fit in Strategy Research: Towards Verbal and Statistical Correspondence , 2018 .

[43]  Rose M. Marra,et al.  Leaving Engineering: A Multi‐Year Single Institution Study , 2012 .

[44]  J. Walkup.  Fuzzy-set social science , 2003 .

[45]  J. Blickenstaff.  Women and science careers: leaky pipeline or gender filter? , 2005 .

[46]  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.

[47]  Michail N. Giannakos,et al.  The interplay of online shopping motivations and experiential factors on personalized e-commerce: A complexity theory approach , 2017, Telematics Informatics.

[48]  Radhika Suresh,et al.  The Relationship between Barrier Courses and Persistence in Engineering , 2006 .

[49]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[50]  Michail N. Giannakos,et al.  Investigating Factors Influencing Students' Intention to Dropout Computer Science Studies , 2016, ITiCSE.

[51]  Barbara Vis,et al.  The Comparative Advantages of fsQCA and Regression Analysis for Moderately Large-N Analyses , 2012 .

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

[53]  G. Fitzsimons.  Death to Dichotomizing , 2008 .

[54]  Susan E. Walden,et al.  “What's to keep you from dropping out?” Student Immigration into and within Engineering , 2008 .

[55]  Jacob Clark Blickenstaff*,et al.  Women and science careers: leaky pipeline or gender filter? , 2005 .

[56]  Arch G. Woodside,et al.  Embrace•perform•model: Complexity theory, contrarian case analysis, and multiple realities , 2014 .

[57]  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.

[58]  Peer C. Fiss.  Building Better Causal Theories: A Fuzzy Set Approach to Typologies in Organization Research , 2011 .

[59]  Jerry M. Mendel,et al.  Charles Ragin's Fuzzy Set Qualitative Comparative Analysis (fsQCA) used for linguistic summarizations , 2012, Inf. Sci..

[60]  C. Fornell,et al.  Evaluating structural equation models with unobservable variables and measurement error. , 1981 .

[61]  A. Woodside.  Moving beyond multiple regression analysis to algorithms: Calling for adoption of a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting theory , 2013 .

[62]  R. O’Brien,et al.  A Caution Regarding Rules of Thumb for Variance Inflation Factors , 2007 .

[63]  Alexander Leischnig,et al.  Employee Adaptive Behavior in Service Enactments , 2015 .

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

[65]  Judith Glaesser,et al.  Gender, parental education, and ability: their interacting roles in predicting GCSE success , 2012 .

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