Various studies have noted computer programming challenges faced by novice learners in upper secondary schools. Apart from the existing teaching strategies authors recommend different modes of assessments to strengthen both feedback to students and improve teaching of computer programming. This paper suggests the use of a programming grid to record and analyse programming errors from students’ written-based programming solutions. It reports the empirical findings of a study that used a pre-formulated grid to analyse a total of ninety scripts corresponding to three problem-solving questions answered by a batch of thirty students (17-19 ages) of upper secondary school in Mauritius. The results not only identify the exact errors made by students but also reveal conceptual difficulties. The grid based approach serves as a tool for teachers to identify learning and conceptual difficulties and revisit teaching strategies at the individual level and collectively as a class. The conclusion notes the relevance of the grid based approach as an easily accessible assessment in schools that can improve computer programming education. The grid is part of a work in progress for a doctoral research.
Keywords: Computer Programming, Programming Grid, Written-Based Programming Codes, Conceptual Difficulties.
References:
[1] Altadmri, A. & Brown, N. C. C., 2015. 37 Million Compilations: Investigating Novice Programming Mistakes in Large-Scale Student Data. kansas City, MO, USA, IGCSE’15.
[2] Altun, A. & Mazman, S. G., 2015. Identifying latent patterns in undergraduate Students’ programming profiles. Springer Open Journal, 2(13), pp. 1-16.
[3] Barry J, 2006. The Effect of socio-economic status on academic achievement, Kansas: Wichita State University.
[4] Bennedsen, J., Caspersen, M. E. & Kölling, M., 2008. Reflections on the Teaching of Programming. 1st ed. Berlin: Springer-Verlag Berlin Heidelberg.
[5] Cuniah, C. & Panchoo, S., 2015. Computer Programming Difficulties at Higher Secondary School: A Content Analysis. Dubai, Fifth International Conference on Industrial Engineering and Operations Management (IEOM 2015).
[6] Esteves, M., Fronseca, B., Leonel, M. & Martins, P., 2008. Contextualization of Programming Learning: A Virtual Environment Study. New York, 38th ASEE/IEEE Frontiers in Education Conference, pp. 17-22.
[7] Examiners, 2015. Principal Examiner Report for Teachers, Cambridge: Cambridge International Advanced Subsidiary Level and Advanced Level.
[8] Gries, D., 2008. Foreword. In: Reflections on the Teaching of Programming: Methods and Implementations.. Berlin: Springer, p. V.
[9] Kölling, M., 2008. Introduction to Part IV Assessment. In: Reflections on the Teaching of Programming: Methods and Implementations. Berlin: Springer, p. 209.
[10] Saeli, M., Perrenet, J., Jochems, W. M. G. & Zwaneveld, B., 2011. Teaching Programming in Secondary School: A Pedagogical Content Knowledge Perspective. Informatics in Education, 10(1), pp. 73-88.
[11] Sarkar, A., 2015. Confidence, command, complexity: metamodels for structured interaction with machine intelligence. Bournemouth, Psychology of Programming Interest Group.
[12] Wang, Y. et al., 2012. Assessment of programming language learning based on peer code review model: Implementation and experience report. Elsevier, Volume 59, pp. 412-422.
Pixel International Conferences
