Exploring Bimodality in Introductory Computer Science Performance Distributions

This study examines student performance distributions evidence bimodality, or whether there are two distinct populations in three introductory computer science courses grades at a four-year southwestern university in the United States for the period 2014-2017. Results suggest that computer science course grades are not bimodal. These findings counter the double hump assertion and suggest that proper course sequencing can address the needs of students with varying levels of prior knowledge and obviate the double-hump phenomenon. Studying performance helps to improve delivery of introductory computer science courses by ensuring that courses are aligned with student needs and address preconceptions and prior knowledge and experience.

[1]  Anthony V. Robins,et al.  Illustrating performance indicators and course characteristics to support students’ self-regulated learning in CS1 , 2015, Comput. Sci. Educ..

[2]  Janet Rountree,et al.  Learning and Teaching Programming: A Review and Discussion , 2003, Comput. Sci. Educ..

[3]  Tenzin Doleck,et al.  Exploring computer science students’ continuance intentions to use Kattis , 2017, Education and Information Technologies.

[4]  Daniel Zingaro.  Examining Interest and Grades in Computer Science 1: A Study of Pedagogy and Achievement Goals , 2015, TOCE.

[5]  Raad A. Alturki.  Measuring and Improving Student Performance in an Introductory Programming Course , 2016, Informatics Educ..

[6]  Raymond Lister,et al.  Geek genes, prior knowledge, stumbling points and learning edge momentum: parts of the one elephant? , 2013, ICER.

[7]  D. Royce Sadler,et al.  Interpretations of criteria‐based assessment and grading in higher education , 2005 .

[8]  Yasmin B. Kafai,et al.  Computer Programming Goes Back to School , 2013 .

[9]  Marc Roper,et al.  Investigating and improving the models of programming concepts held by novice programmers , 2011, Comput. Sci. Educ..

[10]  Raymond Lister,et al.  COMPUTING EDUCATION RESEARCHGeek genes and bimodal grades , 2010, INROADS.

[11]  Gregory V. Wilson,et al.  On the difficulty of replicating human subjects studies in software engineering , 2008, 2008 ACM/IEEE 30th International Conference on Software Engineering.

[12]  Richard Bornat.  Camels and humps: a retraction , 2014 .

[13]  Kasper Green Larsen,et al.  Mental models and programming aptitude , 2007, ITiCSE '07.

[14]  Anna Eckerdal,et al.  On the Bimodality in an Introductory Programming Course: An Analysis of Student Performance Factors , 2015, 2015 International Conference on Learning and Teaching in Computing and Engineering.

[15]  Malcolm W. Corney.  Designing for engagement : building IT systems , 2009 .

[16]  Yizhou Qian,et al.  Students’ Misconceptions and Other Difficulties in Introductory Programming , 2017, ACM Trans. Comput. Educ..

[17]  J. Hartigan,et al.  The Dip Test of Unimodality , 1985 .

[18]  Fabio Q. B. da Silva,et al.  The Effect of Reasoning Strategies on Success in Early Learning of Programming: Lessons Learned from an External Experiment Replication , 2010, EASE.

[19]  Frederick W. B. Li,et al.  Failure rates in introductory programming revisited , 2014, ITiCSE '14.

[20]  Stuart Wray,et al.  SQ Minus EQ can Predict Programming Aptitude , 2007, PPIG.

[21]  Sze Yee Lye,et al.  Review on teaching and learning of computational thinking through programming: What is next for K-12? , 2014, Comput. Hum. Behav..

[22]  Anthony V. Robins,et al.  Learning edge momentum: a new account of outcomes in CS1 , 2010, Comput. Sci. Educ..

[23]  Chris Morgan,et al.  Seeking quality in criterion referenced assessment , 2002 .