The influence of a game-making project on male and female learners’ attitudes to computing

Abstract

There is a pressing need for gender inclusive approaches to engage young people in computer science. A recent popular approach has been to harness learners’ enthusiasm for computer games to motivate them to learn computer science concepts through game authoring. This article describes a study in which 992 learners across 13 schools took part in a game-making project. It provides evidence from 225 pre-test and post-test questionnaires on how learners’ attitudes to computing changed during the project, as well as qualitative reflections from the class teachers on how the project affected their learners. Results indicate that girls did not enjoy the experience as much as boys, and that in fact, the project may make pupils less inclined to study computing in the future. This has important implications for future efforts to engage young people in computing.

Keywords:

• gender
• game authoring
• teacher education

Acknowledgements

The author is grateful to Cathrin Howells and Andrew Macvean for their help in data gathering, to Maurits Kaptein for his assistance with the R scripts used for statistical analysis, to Katy Howland for her comments on a draft and to the pupils and teachers who took part in this project. Thanks also, to the anonymous reviewers who suggested very valuable improvements.

Notes

1. http://www.ltscotland.org.uk/learningteachingandassessment/curriculumareas/technologies/eandos/index.asp.

2. All teacher resources can be downloaded from http://www.adventureauthor.org.

3. See “Part 2: Teaching and Learning” at http://judyrobertson.typepad.com/adventure_author/teaching-materials.html.

4. Despite the low return of post-test data, the power of the study is still good at 0.91 for the questions relating to the project itself, and 0.81 for the attitude questions. Power was calculated using the pwr package documented at http://www.statmethods.net/stats/power.html.

5. The pre-test data was retained as it was relevant to the benchmarking research question.

6. Using the R code documented in http://cran.r-project.org/doc/contrib/Bliese_Multilevel.pdf.

7. Complete descriptive and inferential statistical results can be found at http://www.macs.hw.ac.uk/~judy/AdventureAuthor/mgisdata/FurtherDetailsOfStatisticalTests.pdf.

8. These are considered in the text as raw differences in percentage points because it is easier to reason about the real world impact this way. For estimated relative treatment effect size statistics, see http://www.macs.hw.ac.uk/~judy/AdventureAuthor/mgisdata/.