We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Learning Programming: A Model Emerging from Data

by Nazir Hawi
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 100 - Number 4
Year of Publication: 2014
Authors: Nazir Hawi
10.5120/17514-8070

Nazir Hawi . Learning Programming: A Model Emerging from Data. International Journal of Computer Applications. 100, 4 ( August 2014), 24-34. DOI=10.5120/17514-8070

@article{ 10.5120/17514-8070,
author = { Nazir Hawi },
title = { Learning Programming: A Model Emerging from Data },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 100 },
number = { 4 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 24-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume100/number4/17514-8070/ },
doi = { 10.5120/17514-8070 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:29:06.337541+05:30
%A Nazir Hawi
%T Learning Programming: A Model Emerging from Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 100
%N 4
%P 24-34
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Learning computer programming is a prominent issue in the fields of computer science and education. This paper is an attempt to address this issue by investigating the experiences of undergraduate university students who studied computer programming. A total of 260 computer science and engineering students (210 males and 50 females) were recruited from three geographically distant campuses. They were surveyed with a questionnaire that exhibited good internal reliability. Eventually, a learning model emerged from the data. It consisted of three independent structures that included most of the study times under focus. The study times creatively and dynamically interact stimulated by learning needs and sustained by learning passion. In addition, while some indices of confirmatory factor analysis indicated that the learning model is an adequate fit, others suggested that it needs improvement, which should be considered by future research.

References
  1. Boyle, R. , Carter, J. , & Clark, M. (2002). What makes them succeed? Entry, progression and graduation in computer science, Journal of Further and Higher Education, 26(1), 3-18.
  2. Caspersen, M. , & Bennedsen, J. (2007). Instructional design of a programming course: A learning theoretic approach. Proceedings of the Third International Workshop on Computing Education Research (pp. 111-122). Atlanta, Georgia, USA: ACM Press.
  3. Hanks, B. , & Brandt, M. (2009). Successful and unsuccessful problem solving approaches of novice programmers. Proceedings of the 40th SIGCSE technical symposium on computing science education (pp. 24-28). Chattanooga, Tennessee, USA: ACM Press.
  4. Kinnunen, P. , & Malmi, L. ( 2008). CS minors in a CS1 course. Proceedings of the Fourth International Workshop on Computing Education Research (pp. 79-90). Sydney, Australia: ACM Press.
  5. Lahtinen, E. , Ala-Mutka, K. , & Järvinen, H. (2005). A study of the difficulties of novice programmers. Proceedings of the 10th annual SIGCSE conference on innovation and technology in computer science education (pp. 14-18). Monte de Caparica, Portugal: ACM Press.
  6. McCartney, R. , Eckerdal, A. , Moström, J. E. , Sanders, K. , & Zander, C. (2007). Successful students' strategies for getting unstuck. Proceedings of the 12th annual SIGCSE conference on innovation and technology in computer science education (pp. 156-160). Dundee, Scotland, UK: ACM Press.
  7. Proulx, V. (2000). Programming patterns and design patterns in the introductory computer science course. Proceedings of the 31st SIGCSE technical symposium on computing science education (pp. 80-84). Austin, TX, USA: ACM Press.
  8. Thomas, L. , Ratcliffe, M. , Woodbury, J. , & Jarman, E. (2002). Learning styles and performances in the introductory programming sequence. Proceedings of the 33rd SIGCSE technical symposium on computing science education (pp. 33-37). Covington, Kentucky, USA: ACM Press.
  9. Gehringer, E. , & Miller, C. (2009). Student-generated active-learning exercises. Proceedings of the 40th SIGCSE technical symposium on computing science education (pp. 81-85). Chattanooga, Tennessee, USA: ACM Press.
  10. Hanks, B. , Murphy, L. , Simon, B. , McCauley, R. , & Zander, C. (2009). CS1 students speak: Advice for students by students. Proceedings of the 40th SIGCSE technical symposium on computing science education (pp. 19-23). Chattanooga, Tennessee, USA: ACM Press.
  11. Ma, L. , Ferguson, J. , Roper, M. , & Wood, M. (2011). Investigating and improving the models of programming concepts held by novice programmers. Computer Science Education, 21(1), 57-80.
  12. Pears, A. , Seidman, S. , Malmi, L. , Mannila, L. , Adams, E. , Bennedsen, J. , Devlin, M. , & Paterson, J. (2007). A survey of literature on the teaching of introductory programming. ACM SIGCSE Bulletin, 39(4), 204-223.
  13. Bergin, S. , & Reilly, R. (2005). Programming: Factors that influence success. Proceedings of the 36th SIGCSE technical symposium on computing science education (pp. 411-415). St. Louis, Missouri, USA: ACM Press.
  14. Bergin, S. , Reilly, R. , & Traynor, D. (2005). Examining the role of self-regulated learning on introductory programming performance. Proceedings of the First International Computing Education Research Workshop (pp. 81-86). Seattle, Washington, USA: ACM Press.
  15. Hawi, N. S. (2010a). Causal attributions of success and failure made by undergraduate students in an introductory level computer programming course. Computers & Education, 54(4), 1127-1136.
  16. Hughes, J. , & Peiris, D. (2006). ASSISTing CS1 students to learn: Learning approaches and object-oriented programming. Proceedings of the 11th annual SIGCSE conference on innovation and technology in computer science education (pp. 275-279). Bologna, Italy: ACM Press.
  17. Ramalingam, V. , LaBelle, D. , & Wiedenbeck, S. (2004). Self-efficacy and mental models in learning to program. Proceedings of the 9th annual SIGCSE conference on innovation and technology in computer science education (pp. 171-175). Leeds, UK: ACM Press.
  18. Robins, A. , Rountree, J. , & Rountree, N. (2003). Learning and teaching programming: A review and discussion. Computer Science Education, 13(2), 137-172.
  19. Wiedenbeck, S. (2005). Factors affecting the success of non-majors in learning to program. Proceedings of the First International Computing Education Research Workshop (pp. 13-24). Seattle, Washington, USA: ACM Press.
  20. Begel, A. , & Simon, B. (2008). Novice software developers, all over again. Proceedings of the Fourth International Workshop on Computing Education Research, (pp. 3-14). Sydney, Australia: ACM Press.
  21. Sancho-Thomas, P. , Fuentes-Fernández, R. , & Fernández-Manjón, B. (2009). Learning teamwork skills in university programming courses. Computers & Education, 53(2), 517-531.
  22. Lui, A. K. , Kwan, R. , Poon, M. , & Cheung, Y. H. (2004). Saving weak programming students: applying constructivism in a first programming course. ACM SIGCSE Bulletin, 36(2), 72–76.
  23. Milne, I. , & Rowe, G. (2002). Difficulties in learning and teaching programming – Views of students and tutors. Education and Information Technologies, 7(1), 55-66.
  24. Schulte, C. , & Bennedsen, J. (2006). What do teachers teach in introductory programming? Proceedings of the Second International Computing Education Research Workshop (pp. 17-28), Canterbury, UK.
  25. Byrne, P. , & Lyons, G. (2001). The effect of student attributes on success in programming. Proceedings of the 6th annual SIGCSE conference on innovation and technology in computer science education (pp. 49-53). Canterbury, UK: ACM Press.
  26. Rountree, N. , Rountree, J. , & Robins, A. (2002). Predictors of success and failure in a CS1 course. ACM SIGCSE Bulletin, 34(4), 121-4.
  27. Wilson, B. C. , & Shrock, S. (2001). Contributing to success in an introductory computer science course: A study of twelve factors. Proceedings of the 32rd SIGCSE technical symposium on computing science education (pp. 184-188). Charlotte, NC, USA: ACM Press.
  28. Boyer, K. E. , Phillips, R. , Wallis, M. D. , Vouk, M. A. , & Lester, J. C. (2009). The impact of instructor initiative on student learning: A tutoring study. Proceedings of the 40th SIGCSE technical symposium on computing science education (pp. 14-18). Chattanooga, Tennessee, USA: ACM Press.
  29. Hawi, N. (2010b). The exploration of student-centered approaches for the improvement of learning programming in higher education. US-China Education Review, 7(9), 47-57.
  30. Roberts, E. (2000). Strategies for Encouraging Individual Achievement in Introductory Computer Science Courses. Proceedings of the 31st SIGCSE technical symposium on computing science education (pp. 295-299). Austin, TX, USA: ACM Press.
  31. Xiaohui, H. (2006). Improving teaching in computer programming by adopting student-centred learning strategies. The China Papers, 6, 46-51.
  32. Tavakol, M. , & Dennick, R. (2011). Making sense of Cronbach's alpha. International journal of medical education, 2, 53-55.
  33. Seitz, Lindsey (2012). Student attitudes toward reading: A case study. Journal of Inquiry and Action in Education, 3(2), 30-44.
  34. Guzdial, M. (2003). A media computation course for non-majors. ACM SIGCSE Bulletin, 35(3), 104–108.
  35. Betts, G. T. (1985). Autonomous learner model for the gifted and talented. Greeley, CO: ALPS Publishing.
  36. Gasparinatou, A. & Grigoriadou, M. (2011). Supporting students' learning in the domain of computer science. Computing Science Education, 21(1), 1-28.
  37. Kaasbøll, J. , Berge, O. , Borge, R. E. , Fjuk, A. , Holmboe, C. , & Samuelsen, T. (2004). Learning Object-Oriented Programming. Proceedings of the 16th Workshop of the Psychology of Programming Interest Group (86-96). Carlow, Ireland. Retrieved from http://www. ppig. org/ papers/16th-kaasboll. pdf
  38. Bennedsen, J. & Caspersen, M. (2012). Persistence of elementary programming skills. Computer Science Education, 22(2), 81-107.
  39. Carmines, E. G. , & McIver, J. P. (1981). Analyzing models with unobserved variables. In G. W. Bohrnstedt & E. F. Borgatta (Eds. ), Social measurement: Current issues. Beverly Hills: Sage.
  40. Griffin, K. A. (2006). Striving for success: A qualitative exploration of competing theories of high-achieving black college students' academic motivation. Journal of College Student Development, 47(4), 384-400.
  41. Hawi, N. S. (2008). An attributional approach to computer programming achievement of undergraduate business computing students in a university computer science department (Doctoral dissertation). Retrieved from British Library ETHOS. (Accession http://hdl. handle. net/2381/8219).
  42. Weiner, B. (2000). Intrapersonal and interpersonal theories of motivation from an attributional perspective. Educational Psychology Review, 12, 1-4.
Index Terms

Computer Science
Information Sciences

Keywords

Learning computer programming Higher education Educational environment

Powered by PhDFocusTM