%0 Journal Article %1 Oliver2020 %A Oliver, Kevin M. %A Houchins, Jennifer K. %A Moore, Robert L. %A Wang, Chuang %D 2020 %J International Journal of Science and Mathematics Education %K assessment learninganalytics linguisticanalysis makerspace outcomes %R 10.1007/s10763-020-10060-2 %T Informing Makerspace Outcomes Through a Linguistic Analysis of Written and Video-Recorded Project Assessments %U https://doi.org/10.1007/s10763-020-10060-2 %X A growing body of research focuses on what outcomes to assess in makerspaces, and appropriate formats for capturing those outcomes (e.g. reflections, surveys, and portfolios). Linguistic analysis as a data mining technique holds promise for revealing different dimensions of learning exhibited by students in makerspaces. In this study, student reflections on makerspace projects were gathered in 2 formats over 2 years: private written assessments captured in the 3D GameLab gamification platform, and semi-public video-recorded assessments posted in the more social FlipGrid platform. Transcripts of student assessments were analyzed using Linguistic Inquiry Word Count (LIWC) to generate 4 summary variables thought to inform makerspace outcomes of interest (i.e. analytical thinking, authenticity, clout, and emotional tone). Comparative findings indicate that written assessments may elicit more analytical thinking about maker projects compared with less analytical conversation in videos, while video assessments may elicit somewhat higher clout scores as evidence of social scaffolding along with a much more positive emotional tone. Recommendations are provided for layering assessment approaches to maximize the potential benefits of each format, including reflective writing for social spaces, in social groups, and about design processes and procedures.

@article{Oliver2020, abstract = {A growing body of research focuses on what outcomes to assess in makerspaces, and appropriate formats for capturing those outcomes (e.g. reflections, surveys, and portfolios). Linguistic analysis as a data mining technique holds promise for revealing different dimensions of learning exhibited by students in makerspaces. In this study, student reflections on makerspace projects were gathered in 2 formats over 2 years: private written assessments captured in the 3D GameLab gamification platform, and semi-public video-recorded assessments posted in the more social FlipGrid platform. Transcripts of student assessments were analyzed using Linguistic Inquiry Word Count (LIWC) to generate 4 summary variables thought to inform makerspace outcomes of interest (i.e. analytical thinking, authenticity, clout, and emotional tone). Comparative findings indicate that written assessments may elicit more analytical thinking about maker projects compared with less analytical conversation in videos, while video assessments may elicit somewhat higher clout scores as evidence of social scaffolding along with a much more positive emotional tone. Recommendations are provided for layering assessment approaches to maximize the potential benefits of each format, including reflective writing for social spaces, in social groups, and about design processes and procedures.}, added-at = {2020-02-18T21:35:21.000+0100}, author = {Oliver, Kevin M. and Houchins, Jennifer K. and Moore, Robert L. and Wang, Chuang}, biburl = {https://www.bibsonomy.org/bibtex/2fb48ac2947255e4aca45f9dcc913a382/ereidt}, day = 03, doi = {10.1007/s10763-020-10060-2}, interhash = {74a8555ee057c69510b31b8d693b702b}, intrahash = {fb48ac2947255e4aca45f9dcc913a382}, issn = {1573-1774}, journal = {International Journal of Science and Mathematics Education}, keywords = {assessment learninganalytics linguisticanalysis makerspace outcomes}, month = feb, timestamp = {2020-02-18T21:35:21.000+0100}, title = {Informing Makerspace Outcomes Through a Linguistic Analysis of Written and Video-Recorded Project Assessments}, url = {https://doi.org/10.1007/s10763-020-10060-2}, year = 2020 }