2021 journal article

Informing Makerspace Outcomes Through a Linguistic Analysis of Written and Video-Recorded Project Assessments

International Journal of Science and Mathematics Education, 19(2), 333โ€“354.

By: K. Oliver nโ€‰, J. Houchins n, R. Mooreโ€‰* & C. Wangโ€‰*

co-author countries: China ๐Ÿ‡จ๐Ÿ‡ณ United States of America ๐Ÿ‡บ๐Ÿ‡ธ
author keywords: Assessment; Data mining; Learning analytics; Linguistics; LIWC; Makerspace; Reflection
Source: ORCID
Added: February 4, 2020

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 2ย 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.