Through the eyes of instructors: a phenomenographic investigation of student success

ABSTRACT

In this paper we present a phenomenographic analysis of computer science instructors' perceptions of student success. The factors instructors believe influence student success fell into five categories which were related to: 1) the subject being taught, 2) intrinsic characteristics of the student, 3) student background, 4) student attitudes and behaviour and 5) instructor influence on student development. These categories provide insights not only into how instructors perceive students, but also how they perceive their own roles in the learning process. We found significant overlap between these qualitative results, obtained through analysis of semi-structured interviews, and the vast body of quantitative research on factors predicting student success. Studying faculty rather than students provides an alternative way to examine these questions, and using qualitative methods may provide a richer understanding of student success factors.

REFERENCES

• 1: Mordechai Ben-Ari, Constructivism in computer science education, Proceedings of the twenty-ninth SIGCSE technical symposium on Computer science education, p.257-261, February 26-March 01, 1998, Atlanta, Georgia, United States
• 2: Jens Bennedsen , Michael E. Caspersen, An investigation of potential success factors for an introductory model-driven programming course, Proceedings of the 2005 international workshop on Computing education research, p.155-163, October 01-02, 2005, Seattle, WA, USA [doi>10.1145/1089786.1089801]
• 3: Jens Bennedsen , Michael E. Caspersen, Abstraction ability as an indicator of success for learning object-oriented programming?, ACM SIGCSE Bulletin, v.38 n.2, June 2006 [doi>10.1145/1138403.1138430]
• 4: Susan Bergin , Ronan Reilly, Programming: factors that influence success, ACM SIGCSE Bulletin, v.37 n.1, 2005
• 5: Susan Bergin , Ronan Reilly , Desmond Traynor, Examining the role of self-regulated learning on introductory programming performance, Proceedings of the 2005 international workshop on Computing education research, p.81-86, October 01-02, 2005, Seattle, WA, USA [doi>10.1145/1089786.1089794]
• 6: Anders Berglund , Mattias Wiggberg, Students learn CS in different ways: insights from an empirical study, Proceedings of the 11th annual SIGCSE conference on Innovation and technology in computer science education, June 26-28, 2006, Bologna, Italy
• 7: J. Biggs. What the student does: teaching for enhanced learning. Higher Education Research & Development, 18(1):57--75, 1999.
• 8: C. Bishop-Clark and D. D. Wheeler. The Myers-Briggs personality type and its relationship to computer programming. Journal of Research on Computing in Education, 26(2):358--371, Spring 1994.
• 9: R. Boyle, J. Carter, and M. Clark. What Makes Them Succeed? Entry, progression and graduation in Computer Science. Journal of Further and Higher Education, 26(1):3--18, 2002.
• 10: Pat Byrne , Gerry Lyons, The effect of student attributes on success in programming, Proceedings of the 6th annual conference on Innovation and technology in computer science education, p.49-52, June 2001, Canterbury, United Kingdom
• 11: S. Dehnadi and R. Bornat. The camel has two humps (working title). Draft paper. Retrieved at http://www.cs.mdx.ac.uk/research/PhDArea/saeed/paper1.pdf, 2/23/07, February 2006.
• 12: E. Dijkstra. On the cruelty of really teaching computer science. Commun. ACM, 32(12):1398--1404, 1989.
• 13: Anna Eckerdal , Michael Thuné , Anders Berglund, What does it take to learn 'programming thinking'?, Proceedings of the 2005 international workshop on Computing education research, p.135-142, October 01-02, 2005, Seattle, WA, USA [doi>10.1145/1089786.1089799]
• 14: Anna Eckerdal , Michael Thuné, Novice Java programmers' conceptions of "object" and "class", and variation theory, Proceedings of the 10th annual SIGCSE conference on Innovation and technology in computer science education, June 27-29, 2005, Caparica, Portugal
• 15: D. Fox. Personal theories of teaching. Studies in Higher Education, 8(2):151--163, 1983.
• 16: Annagret Goold , Russell Rimmer, Factors affecting performance in first-year computing, ACM SIGCSE Bulletin, v.32 n.2, p.39-43, June 2000 [doi>10.1145/355354.355369]
• 17: O. Hazzan. How Students Attempt to Reduce Abstraction in the Learning of Mathematics and in the Learning of Computer Science. Computer Science Education, 13(2):95--122, June 2003.
• 18: B. Johnson and L. Christensen. Educational Research. Quantitative, qualitative, and mixed approaches. Pearson, Boston, 2004.
• 19: P. Kansanen. Teaching as teaching-studying-learning interaction. Scandinavian Journal of Educational Research, 43(1):81--89, 1999.
• 20: P. Kansanen. Studying -- the realistic bridge between instruction and learning. an attempt to a conceptual whole of the teaching-studying-learning process. Educational Studies, 29(2/3):221--232, 2003.
• 21: P. Kansanen and M. Meri. The didactic relation in the teaching-studying-learning process. In B. Hudson, F. Buchberger, P. Kansanen, and H. Seel, editors, Didaktik/Fachdidaktik as Science(-s)of the Teaching profession, pages 107--116. TNTEE Publications, 1999.
• 22: Jeff Kramer, Is abstraction the key to computing?, Communications of the ACM, v.50 n.4, p.36-42, April 2007 [doi>10.1145/1232743.1232745]
• 23: Raymond Lister , Anders Berglund , Ilona Box , Chris Cope , Arnold Pears , Chris Avram , Mat Bower , Angela Carbone , Bill Davey , Michael de Raadt , Bernard Doyle , Sue Fitzgerald , Linda Mannila , Cat Kutay , Mia Peltomäki , Judy Sheard , Simon , Ken Sutton , Des Traynor , Jodi Tutty , Anne Venables, Differing ways that computing academics understand teaching, Proceedings of the ninth Australasian conference on Computing education, p.97-106, January 30-February 02, 2007, Ballarat, Victoria, Australia
• 24: Raymond Lister , Ilona Box , Briana Morrison , Josh Tenenberg , D. Suzanne Westbrook, The dimensions of variation in the teaching of data structures, Proceedings of the 9th annual SIGCSE conference on Innovation and technology in computer science education, June 28-30, 2004, Leeds, United Kingdom
• 25: R. Mancy and N. Reid. Aspects of cognitive style and programming. In E. Dunican and T. Green, editors, Proc. of the 16th Workshop of the Psycology of Programming Interest Group, pages 1--9, 2004.
• 26: F. Marton and S. Booth. Learning and Awareness. Lawrence Erbaum Assoc., Mahwah, NJ, USA, 1997.
• 27: Laurie Murphy , Brad Richards , Renée McCauley , Briana B. Morrison , Suzanne Westbrook , Timothy Fossum, Women catch up: gender differences in learning programming concepts, Proceedings of the 37th SIGCSE technical symposium on Computer science education, March 03-05, 2006, Houston, Texas, USA
• 28: B. T. Pioro, Introductory computer programming: gender, major, discrete mathematics, and calculus, Journal of Computing Sciences in Colleges, v.21 n.5, p.123-129, May 2006
• 29: A. Robins, J. Rountree, and N. Rountree. Learning and teaching programming: A review and discussion. Computer Science Education, 13(2):137--172, 2003.
• 30: Nathan Rountree , Janet Rountree , Anthony Robins , Robert Hannah, Interacting factors that predict success and failure in a CS1 course, ACM SIGCSE Bulletin, v.36 n.4, December 2004
• 31: Carsten Schulte , Johannes Magenheim, Novices' expectations and prior knowledge of software development: results of a study with high school students, Proceedings of the 2005 international workshop on Computing education research, p.143-153, October 01-02, 2005, Seattle, WA, USA [doi>10.1145/1089786.1089800]
• 32: Simon Simon , Sally Fincher , Anthony Robins , Bob Baker , Ilona Box , Quintin Cutts , Michael de Raadt , Patricia Haden , John Hamer , Margaret Hamilton , Raymond Lister , Marian Petre , Ken Sutton , Denise Tolhurst , Jodi Tutty, Predictors of success in a first programming course, Proceedings of the 8th Austalian conference on Computing education, p.189-196, January 16-19, 2006, Hobart, Australia
• 33: H. G. Taylor and L. C. Moundfield. Exploration of the relationship between prior computing experience and gender on success in college computer science. Educational Computing Research, 11(4):291--306, 1994.
• 34: Lynda Thomas , Mark Ratcliffe , John Woodbury , Emma Jarman, Learning styles and performance in the introductory programming sequence, Proceedings of the 33rd SIGCSE technical symposium on Computer science education, February 27-March 03, 2002, Cincinnati, Kentucky
• 35: S. Turkle and S. Papert. Epistemological pluralism and the revaluation of the concrete. Journal of Mathematical Behavior, 11:3--33, 1992.
• 36: Phil Ventura , Bina Ramamurthy, Wanted: CS1 students. no experience required, ACM SIGCSE Bulletin, v.36 n.1, March 2004
• 37: P. R. Ventura. Identifying Predictors of Success for an Objects-First CS1. Computer Science Education, 15(3):223--243, 2005.
• 38: Susan Wiedenbeck, Factors affecting the success of non-majors in learning to program, Proceedings of the 2005 international workshop on Computing education research, p.13-24, October 01-02, 2005, Seattle, WA, USA [doi>10.1145/1089786.1089788]
• 39: B. C. Wilson. A study of factors promoting success in computer science including gender differences. Computer Science Education, 12(1-2):141--164, 2002.
• 40: Brenda Cantwell Wilson , Sharon Shrock, Contributing to success in an introductory computer science course: a study of twelve factors, ACM SIGCSE Bulletin, v.33 n.1, p.184-188, March 2001
• 41: Jack M. Wolfe, Perspectives on testing for programming aptitude, Proceedings of the 1971 26th annual conference, p.268-277, January 1971 [doi>10.1145/800184.810494]
• 42: M. Xenos, C. Pierrakeas, and P. Pintelas. A survey on student dropout rates and dropout causes concerning the students in the Course of Informatics of the Hellenic Open University. Computers & Education, 39:361--194, 2002.
• 43: G. S. Åkerlind. Variation and commonality in phenomenographic research methods. Higher Education Research & Development, 24(4):321--334, 2005.

INDEX TERMS

Primary Classification:
  K. Computing Milieux
  K.3 COMPUTERS AND EDUCATION
      K.3.2 Computer and Information Science Education
          Subjects: Computer science education

General Terms:
Experimentation, Human Factors

Keywords:
instructor perceptions, phenomenography, qualitative analysis, student performance