Identifying learning dimensions in CS project descriptions

For this study, we conducted a qualitative content analysis of a random sample of 94 English-language project descriptions stored in the CS Track database with the goal of determining which dimensions of learning are reflected most prominently in these texts. Using a slightly modified version of the model of individual learning outcomes developed by Phillips et al. in 2018 as a coding rubric, two members of the research team independently coded all project descriptions by manually assigning phrases, sentences and short paragraphs to eight main categories and 21 subcategories. From these text snippets, distinctive and frequently occurring keywords were extracted, which have since been used in follow-up studies. Our analysis revealed that some learning dimensions (such as data collection or using technology) are very prominently discussed in the project descriptions we studied, while others (e.g. experimenting, study design, community action) are clearly underrepresented. In other words, the project descriptions analysed only partially reflect the educational potential of participation in CS. Based on these findings, we suggested possible explanations and ways in which the issue could be addressed on the level of both project design and project communication. This study profited immensely from the kind support of Tina Phillips and her colleagues, who agreed to share parts of their coded dataset with us. Details related to the analysis procedure are provided in a paper which is currently under review (on the date of submission of this deliverable - November 2022), no link to a repository is available. Contact the main authors if you have interest to receive further information). References: Phillips, T., Porticella, N., Constas, M., & Bonney, R. (2018). A framework for articulating and measuring individual learning outcomes from participation in citizen science. Citizen Science: Theory and Practice, 3(2). More information on this research can be found in D2.2 section 5.1. Content and grouping: coding rubric used (Main category, Subcategory, Definition (Draft) and Inclusion/exclusion criteria) columns) complete coding (i.e. including CS Track projects title) calculation of rate of agreement between coders list of keywords extracted list of projects titles and the website URL from where we extracted the informationOther related information about CS project descriptions and data source websites can be found in the following: https://doi.org/10.5281/zenodo.7310096 and https://doi.org/10.5281/zenodo.7310295