In search of self-efficacy: development of a new instrument for first year Computer Science students

References

• Allan, J. F., Mckenna, J., & Dominey, S. (2014). Degrees of resilience: Profiling psychological resilience and prospective academic achievement in university inductees. British Journal of Guidance & Counselling, 42, 9–25.10.1080/03069885.2013.793784
   Web of Science ®Google Scholar
• Askar, P., & Davenport, D. (2009). An investigation of factors related to self-efficacy for Java programming among engineering students. TOJET: The Turkish Online Journal of Educational Technology, 8, 26–32.
   Google Scholar
• Atkinson, P. A., Martin, C. R., & Rankin, J. (2009). Resilience revisited. Journal of Psychiatric and Mental Health Nursing, 16, 137–145.10.1111/jpm.2009.16.issue-2
   PubMed Web of Science ®Google Scholar
• Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52(1), 1–26.10.1146/annurev.psych.52.1.1
   PubMed Web of Science ®Google Scholar
• Bandura, A. (2006). Guide for creating self-efficacy scales. In F. Pajares & T. Urdan (Eds.), Self-efficacy beliefs of adolescents (pp. 307–337). Greenwich, CT: Information Age Publishing.
   Google Scholar
• Bartimote-Aufflick, K., Bridgeman, A., Walker, R., Sharma, M., & Smith, L. (2016). The study, evaluation, and improvement of university student self-efficacy. Studies in Higher Education, 41, 1918–1942.10.1080/03075079.2014.999319
   Web of Science ®Google Scholar
• Beck, L., & Chizhik, A. (2013). Cooperative learning instructional methods for CS1: Design, implementation, and evaluation. ACM Transactions on Computing Education (TOCE), 13, 10.
   Web of Science ®Google Scholar
• Berland, M., Davis, D., & Smith, C. (2015). AMOEBA: Designing for collaboration in computer science classrooms through live learning analytics. International Journal of Computer-Supported Collaborative Learning, 10, 425–447.10.1007/s11412-015-9217-z
   Web of Science ®Google Scholar
• Brooman, S., & Darwent, S. (2014). Measuring the beginning: A quantitative study of the transition to higher education. Studies in Higher Education, 39, 1523–1541.
   Web of Science ®Google Scholar
• Cassidy, S. (2015). Resilience building in students: The role of academic self-efficacy. Frontiers in psychology, 6, 1–14.
   PubMed Web of Science ®Google Scholar
• Charmaz, K. (2006). Constructing grounded theory. London: Sage.
   Google Scholar
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
   Google Scholar
• Collie, R. J., Martin, A. J., Bottrell, D., Armstrong, D., Ungar, M., & Liebenberg, L. (2017). Social support, academic adversity and academic buoyancy: a person-centred analysis and implications for academic outcomes. Educational Psychology, 37, 550–564.
   Web of Science ®Google Scholar
• Collings, R., Swanson, V., & Watkins, R. (2016). Peer mentoring during the transition to university: Assessing the usage of a formal scheme within the UK. Studies in Higher Education, 41, 1995–2010.
   Web of Science ®Google Scholar
• Compeau, D., & Higgins, C. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly19, 189–211.10.2307/249688
   Web of Science ®Google Scholar
• Connor, K. M., & Davidson, J. R. T. (2003). Development of a new resilience scale: The Connor-Davidson Resilience Scale (CD-RISC). Depression and Anxiety, 18, 76–82.10.1002/(ISSN)1520-6394
   PubMed Web of Science ®Google Scholar
• Dweck, C. S., & Leggett, E. L. (1988). A social-cognitive approach to motivation and personality. Psychological Review, 95, 256.10.1037/0033-295X.95.2.256
   Web of Science ®Google Scholar
• Flanigan, A. E., Peteranetz, M. S., Shell, D. F., & Soh, L. K. (2015). Exploring changes in Computer Science students’ implicit theories of intelligence across the semester. Proceedings of the eleventh annual International Conference on International Computing Education Research (pp. 161–168). Seoul: ACM.
   Google Scholar
• Gibbs, G. (2007). Analysing qualitative data. London: Sage. Retrieved from https://doi.org.ezproxy.napier.ac.uk/10.4135/9781849208574
   Google Scholar
• Gordon, N. A. (2016). Issues in retention and attainment in Computer Science. York: Higher Education Academy.
   Google Scholar
• Higher Education Funding Council for England. (2016). “The course has helped me to present myself with confidence” (Definitely agree/Agree). Retrieved February 26, 2017, from 2016 NSS results split by teaching institution, level and JACS subject level 1: https://www.hefce.ac.uk/lt/nss/results/2016/
   Google Scholar
• Higher Education Statistics Agency. (2016a). Table SN1 – Percentage of UK domiciled young entrants to full-time first degree courses in 2013/14 who are no longer in HE in 2014/15. Retrieved July 21, 2016, from https://www.hesa.ac.uk/pis/noncon
   Google Scholar
• Higher Education Statistics Agency. (2016b). UKPIs: Non-continuation rates (including projected outcomes) Table SN2 – Percentage of UK domiciled mature entrants to full-time first degree courses in 2013/14 who are no longer in HE in 2014/15. Retrieved July 21, 2016, from https://www.hesa.ac.uk/pis/noncon
   Google Scholar
• Higher Education Statistics Agency. (2016c). Table 12 – HE qualifications obtained by sex, subject area** and level of qualification obtained 2010/11 to 2014/15. Author. Retrieved August 24, 2016, from https://www.hesa.ac.uk/stats
   Google Scholar
• Higher Education Statistics Agency. (2017, January 12). Higher education student enrolments and qualifications obtained at higher education providers in the United Kingdom 2015/16. Retrieved February 15, 2017, from HESA: https://www.hesa.ac.uk/news/12-01-2017/sfr242-student-enrolments-and-qualifications
   Google Scholar
• Hillman, N., & Robinson, N. (2016). Boys to men: The underachievement of young men in higher education–and how to start tackling it. Higher Education Policy Institute. Retrieved July 10, 2016, from https://www.hepi.ac.uk/wp-content/uploads/2016/05/Boys-to-Men.pdf
   Google Scholar
• Israel, M. (2015). Effectiveness of integrating MOOCs in traditional classrooms for undergraduate students. The International Review of Research in Open and Distributed Learning, 16, 102–118.
   Web of Science ®Google Scholar
• Jones, R., & Thomas, L. (2012). Promoting social engagement: Improving STEM student transition, retention and success in higher education. Birmingham: National HE STEM Programme, University of Birmingham.
   Google Scholar
• Kinnunen, P., & Simon, B. (2012). My program is ok–am I? Computing freshmen’s experiences of doing programming assignments. Computer Science Education, 22(1), 1–28.10.1080/08993408.2012.655091
   Google Scholar
• Kinnunen, P., Marttila-Kontio, M., & Pesonen, E. (2013). Getting to know computer science freshmen. Proceedings of the 13th Koli Calling International Conference on Computing Education Research (pp. 59–66). Koli, Finland: ACM.
   Google Scholar
• Kirkpatrick, K. (2016). Coding as sport. Communications of the ACM, 59, 32–33.10.1145/2930840
   Web of Science ®Google Scholar
• Lang, C., Fisher, J., Craig, A., & Forgasz, H. (2015). Outreach programmes to attract girls into computing: How the best laid plans can sometimes fail. Computer Science Education, 25, 257–275.
   Web of Science ®Google Scholar
• Lewis, C., Anderson, R., & Yasuhara, K. (2016). I don’t code all day: Fitting in Computer Science when the stereotypes don’t fit. Proceedings of the 2016 ACM Conference on International Computing Education Research (pp. 23–32). Melbourne: ACM.
   Google Scholar
• Lin, G. (2016). Self-efficacy beliefs and their sources in undergraduate computing disciplines: An examination of gender and persistence. Journal of Educational Computing Research, 53, 540–561.10.1177/0735633115608440
   Web of Science ®Google Scholar
• Lishinski, A., Yadav, A., Good, J., & Enbody, R. (2016). Learning to program: Gender differences and interactive effects of students’ motivation, goals, and self-efficacy on performance. Proceedings of the 12th Annual International ACM Conference on International Computing Education Research (ICER’16), Melbourne.
   Google Scholar
• Martin, A. (2013). Academic buoyancy and academic resilience: Exploring ‘everyday’and ‘classic’resilience in the face of academic adversity. School Psychology International, 34, 488–500.10.1177/0143034312472759
   Web of Science ®Google Scholar
• Martin, A., & Marsh, H. (2008). Academic buoyancy: Towards an understanding of students’ everyday academic resilience. Journal of School Psychology, 46, 53–83.10.1016/j.jsp.2007.01.002
   PubMed Web of Science ®Google Scholar
• Masten, A. S., & Tellegen, A. (2012). Resilience in developmental psychopathology: Contributions of the project competence longitudinal study. Development and Psychopathology, 24, 345–361.10.1017/S095457941200003X
   PubMed Web of Science ®Google Scholar
• McPhail, R. (2015). Pre-university prepared students: A programme for facilitating the transition from secondary to tertiary education. Teaching in Higher Education, 20, 652–665.
   Web of Science ®Google Scholar
• Morgan, M. (Ed.). (2013). Improving the student experience: A practical guide for universities and colleges. Abingdon: Routledge.
   Google Scholar
• Mountain, A. (2015). Thematic coding. Retrieved July 16, 2015, from www.betterevaluation.org: https://betterevaluation.org/evaluation-options/thematiccoding
   Google Scholar
• Nightingale, S. M., Roberts, S., Tariq, V., Appleby, Y., Barnes, L., Harris, R. A., & Qualter, P. (2013). Trajectories of university adjustment in the United Kingdom: Emotion management and emotional self-efficacy protect against initial poor adjustment. Learning and Individual Differences, 27, 174–181.10.1016/j.lindif.2013.08.004
   Web of Science ®Google Scholar
• Paciello, M., Ghezzi, V., Tramontano, C., Barbaranelli, C., & Fida, R. (2016). Self-efficacy configurations and wellbeing in the academic context: A person-centred approach. Personality and Individual Differences, 99, 16–21.
   Web of Science ®Google Scholar
• Pintrich, P., Smith, D., Garcia, T., & Mckeachie, W. (1993). Reliability and predictive validity of the Motivated Strategies for Learning Questionnaire (Mslq). Educational and Psychological Measurement, 53, 801–813.10.1177/0013164493053003024
   Web of Science ®Google Scholar
• Pollack, W. S. (2006). Sustaining and reframing vulnerability and connection, creating genuine resilience in boys and young males. In S. Goldstein & R. Brooks (Eds.), Handbook of resilience in children (pp. 65–77). New York, NY: Springer.
   Google Scholar
• Putwain, D. W., Daly, A. L., Chamberlain, S., & Sadreddini, S. (2016). ‘Sink or swim’: Buoyancy and coping in the cognitive test anxiety–academic performance relationship. Educational Psychology, 36, 1807–1825.
   Web of Science ®Google Scholar
• Ramalingam, V., & Wiedenbeck, S. (1998). Development and validation of scores on a computer programming self-efficacy scale and group analyses of novice programmer self-efficacy. Journal of Educational Computing Research, 19, 367–381.10.2190/C670-Y3C8-LTJ1-CT3P
   Web of Science ®Google Scholar
• Ramalingam, V., LaBelle, D., & Wiedenbeck, S. (2004). Self-efficacy and mental models in learning to program. ACM SIGCSE Bulletin, 36, 171–175.10.1145/1026487
   Google Scholar
• Rowan, R. M. (2015). Technical communication as teaching: A grounded theory study of cognitive empathy and audience engagement among computer science majors in a technical communication classroom (PhD Thesis). Illinois State University, Normal.
   Google Scholar
• Sanders, L. D., Mair, C., & James, R. (2016). Detecting uncertainty, predicting outcome for first year students. Journal of Applied Research in Higher Education, 8, 346–359.
   Web of Science ®Google Scholar
• Schnell, K., Ringeisen, T., Raufelder, D., & Rohrmann, S. (2015). The impact of adolescents’ self-efficacy and self-regulated goal attainment processes on school performance – Do gender and test anxiety matter? Learning and Individual Differences, 38, 90–98.10.1016/j.lindif.2014.12.008
   Web of Science ®Google Scholar
• Sinclair, J., Butler, M., Morgan, M., & Kalvala, S. (2015). Measures of student engagement in Computer Science. Proceedings of the 2015 ACM Conference on Innovation and Technology in Computer Science Education (pp. 242–247). Vilnius: ACM.
   Google Scholar
• Sobel, M., Gilmartin, J., & Sankar, P. (2016). Class size and confidence levels among female STEM Students. IEEE Technology and Society Magazine, 35, 23–26.10.1109/MTS.2016.2518251
   Web of Science ®Google Scholar
• Stewart, J. L., Lynn, M. R., & Mishel, M. H. (2005). Evaluating content validity for children’s self-report instruments using children as content experts. Nursing Research, 54, 414–418.10.1097/00006199-200511000-00008
   PubMed Web of Science ®Google Scholar
• Stoliker, B., & Lafreniere, K. (2015). The influence of perceived stress, loneliness, and learning burnout on university students’ educational experience. College Student Journal, 49, 146–160.
   Google Scholar
• Sullivan, J. (2010). Preliminary psychometric data for the academic coping strategies scale. Assessment for Effective Intervention, 35, 114–127.10.1177/1534508408327609
   Google Scholar
• Thornberg, R., & Charmaz, K. (2014). Grounded theory and theoretical coding. In U. Flick (Ed.), The Sage handbook of qualitative data analysis (pp. 153–170). London: Sage.10.4135/9781446282243
   Google Scholar
• Ulriksen, L., Madsen, L. M., & Holmegaard, H. T. (2015). The First-Year Experience: Students’ Encounter with Science and Engineering Programmes. In E. Henriksen, J. Dillon, & J. Ryder (Eds.), Understanding Student Participation and Choice in Science and Technology Education, (pp. 241–257). Dordrecht: Springer.
   Google Scholar
• Veilleux, N., Bates, R., Jones, D., Allendoerfer, C., & Crawford, J. (2012). The role of belonging in engagement, retention and persistence in computer science. Proceedings of the 43rd ACM technical symposium on Computer Science Education (pp. 707–707). Raleigh, NC: ACM.
   Google Scholar
• Wagner, I. (2016). Gender and performance in Computer Science. ACM Transactions on Computing Education (TOCE), 16, 11.
   Web of Science ®Google Scholar
• Yorke, M. (2016). The development and initial use of a survey of student ‘belongingness’, engagement and self-confidence in UK higher education. Assessment & Evaluation in Higher Education, 41, 154–166.
   Web of Science ®Google Scholar
• Zarb, M., & Hughes, J. (2015). Breaking the communication barrier: Guidelines to aid communication within pair programming. Computer Science Education, 25, 120–151.
   Web of Science ®Google Scholar