ABSTRACT
Multiple studies have shown that when novice and experienced programmers are enrolled in the same introductory programming course, the novice programmers are negatively impacted. We have two entry points into our course sequence for majors. One course is intended for students with little or no programming experience, while the alternate course is intended for students who have had previous programming experience. In 2015 we discovered that many students with programming experience were enrolling in the course for novice programmers. A change in our placement strategy in 2016 resulted in a greater portion of the students with programming experience actually enrolled in the course intended for students with programming experience. Last year we reported on the impact this change had on the courses and the students enrolled in these introductory courses. Although student performance improved only slightly, many fewer students with little or no previous programming experience reported that their first programming course was unreasonably difficult in 2016. In this paper we examine how this change in placement strategy and resulting changes in the courses is impacting student persistence in the major. Initial indications are that a greater percentage of students with little or no previous programming experience are persisting in their computing major when these students begin in an introductory course that does not also include students who have substantial programming experience.
- {n. d.}. NCWIT Extension Services for Undergraduate Programs. https://www. ncwit.org/project/extension-services-undergraduate-programsGoogle Scholar
- Glen Archer, Briana Bettin, Leonard Bohmann, Allison Carter, Christopher Cischke, Linda M. Ott, and Leo Ureel. 2017. The Impact of Placement Strategies on the success of students in introductory computer science. In Proceedings of Frontiers in Education Conference (FIE) 2017. 1–9.Google ScholarCross Ref
- Glen Archer, Leonard Bohmann, Allison Carter, Christopher Cischke, Linda M. Ott, and Leo Ureel. 2016. Understanding Similarities and Differences in Students Across First-Year Computing Majors. In Proceedings of Frontiers in Education Conference (FIE) 2016. 1–8.Google ScholarCross Ref
- Jan Chong and Tom Hurlbutt. 2007. The Social Dynamics of Pair Programming. In Proceedings of the 29th international conferenceon Software Engineering (ICSE ’07). IEEE Computer Society, 354–363. Google ScholarDigital Library
- Sanda L. Christenson, Amy L. Reschly, and Cathy Wylie. 2012. Handbook of Research on Student Engagement. Springer-Verlag, New York, NY, USA.Google Scholar
- James P. Cohoon. 2007. An introductory course format for promoting diversity and retention. In Proceedings of the 38th SIGCSE technical symposium on Computer science education (SIGCSE ’07. ACM, 395–399. Google ScholarDigital Library
- 1227450Google Scholar
- Penelope M Huang and Suzanne G. Brainard. 2001. Identifying Determinants of Academic Selfconfidence among Science, Math, Engineering, and Technology Students. Journal of Women and Minorities in Science and Engineering 7, 4 (2001), 315–337.Google ScholarCross Ref
- Neha Katira, Laurie Williams, and Jason Osborne. 2005. Towards increasing the compatibility of student pair programmers. In Proceedings of the 27th international conference on Software engineering (ICSE ’05). ACM, 625–626. 1145/1062455.1062572 Google ScholarDigital Library
- Päivi Kinnunen and Beth Simon. 2011. CS majors’ self-efficacy perceptions in CS1: results in light of social cognitive theory. In Proceedings of the seventh international workshop on Computing education research (ICER ’11). ACM, 19–26. Google ScholarDigital Library
- Michael S. Kirkpatrick and Chris Mayfield. 2017. Evaluating an Alternative CS1 for Students with Prior Programming Experience. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE ’17). ACM, 333–338. Google ScholarDigital Library
- Robert W. Lent, Steven D. Brown, and Kevin C. Larkin. 1984. Relation of selfefficacy expectations to academic achievement and persistence. Journal of Counseling Psychology 31, 3 (July 1984), 356–362. 3.356Google ScholarCross Ref
- Colleen M. Lewis, Ken Yasuhara, and Ruth E. Anderson. 2011. Deciding to major in computer science: a grounded theory of students’ self-assessment of ability. In Proceedings of the seventh international workshop on Computing education research (ICER ’11). ACM, 3–10. Google ScholarDigital Library
- Keith Quille, Natalie Culligan, and Susan Bergin. 2017. Insights on Gender Differences in CS1: A Multi-institutional, Multi-variate Study. In Proceedings of the 2017 ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE ’17). ACM, 263–268. Google ScholarDigital Library
- Christine Rogerson and Elsje Scott. 2010. The Fear Factor: How It Affects Students Learning to Program in a Tertiary Environment. Journal of Information Technology Education 9 (2010), 147–171.Google ScholarCross Ref
- Nathan Rountree, Janet Rountree, Anthony Robins, and Robert Hannah. 2004. Interacting factors that predict success and failure in a CS1 course. In Working group reports from ITiCSE on Innovation and technology in computer science education (ITiCSE-WGR ’04). ACM, 101–104. Google ScholarDigital Library
- Nanette Veilleux, Rebecca Bates, Cheryl Allendoerfer, Diane Jones, Joyous Crawford, and Tamara Floyd Smith. 2013. The relationship between belonging and ability in computer science. In Proceeding of the 44th ACM technical symposium on Computer science education (SIGCSE ’13). ACM, 65–70. Google ScholarDigital Library
- Ian Ward. 1972. The study habits of college students. The Vocational Aspect of Education 24, 58 (1972), 101–104.Google ScholarCross Ref
- Laurie Williams and Robert Kessler. 2002. Pair Programming Illuminated. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA. Google ScholarDigital Library
- Laurie Williams, Lucas Layman, Jason Osborne, and Neha Katira. 2006. Examining the Compatibility of Student Pair Programmers. In Proceedings of the conference on AGILE 2006 (AGILE ’06). IEEE Computer Society, 411–420. 1109/AGILE.2006.25 Abstract 1 Introduction 2 Background 2.1 Related Research 2.2 Key Findings from Previous Study 3 IMPACT ON PERSISTENCE 4 Discussion 5 CONCLUSIONS AND FUTURE WORK Acknowledgments References Google ScholarDigital Library
Index Terms
- The impact of placement in introductory computer science courses on student persistence in a computing major
Recommendations
Performance of python CS1 students in mid-level non-python CS courses
SIGCSE '10: Proceedings of the 41st ACM technical symposium on Computer science educationIf you change the CS1 language to Python, what is the impact on the rest of the curriculum? In earlier work we examined the impact of changing CS1 from C++ to Python while leaving CS2 in C++. We found that Python-prepared CS1 students fared no ...
The impact of placement strategies on the success of students in introductory computer science
2017 IEEE Frontiers in Education Conference (FIE)Studies have shown that novice programmers face unnecessary obstacles when they are enrolled in an introductory course alongside students with previous programming experience. Thus a recommended best practice is to have separate courses or separate ...
A games first approach to teaching introductory programming
SIGCSE '07: Proceedings of the 38th SIGCSE technical symposium on Computer science educationIn this paper we argue for using a "Game First" approach to teaching introductory programming. We believe that concerns over whether an OO approach or a procedural approach should be used first are secondary to the course assignment and example content. ...
Comments