Computing Educational Activities Involving People Rather Than Things Appeal More to Women (CS1 Appeal Perspective)

Melissa Høegh Marcher, Ingrid Maria Christensen, Pawel Grabarczyk, Therese Graversen, Claus Brabrand

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review


Prior research on recruitment of women to computing has established that computing tasks involving People rather than Things have been perceived as much more appealing by female high-school students (potentially recruitable as university computing students). This paper changes the focus from prospective to current university students and presents the results of a new experiment that advances and moves beyond earlier research in two crucial respects. First of all, the participants of the experiment are N=152 university students, who already study computing, rather than general high-school students. Second of all, the choice between a People-themed versus an isomorphic Things-themed version of an educational task now pertains to real (in fact, mandatory) assignments that the students had to perform, rather than hypothetical tasks. The change of experimental context, design, and methodology allows us to complement previous findings related to recruitment with suggestions significant for computing educational activities. The overall findings of the new experiment are consistent with that of the previous one. We find that, also at university, there is a visible preference for choosing People themed over Things themed computing tasks amongst women. The results also expose considerable variation between tasks in the effect of gender observed. At the same time, male students, in general, seem to be either indifferent to the themes or to slightly prefer People versions. This suggests that educators should consider favoring People themed assignments over ones involving Things.
Original languageEnglish
Title of host publicationProceedings of the 17th ACM Conference on International Computing Education Research (ICER 2021)
PublisherAssociation for Computing Machinery
Publication date17 Aug 2021
ISBN (Electronic)9781450383264
Publication statusPublished - 17 Aug 2021


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