SUBJECT: M.S. Thesis Presentation
   
BY: Danielle Saracino
   
TIME: Friday, April 16, 2021, 11:00 a.m.
   
PLACE: Virtual, WebEx
   
TITLE: Comparison of Makerspace Learning Outcomes Between Genders, Universities, and Online Communities
   
COMMITTEE: Dr. Julie Linsey, Chair (ME)
Dr. Katherine Fu (ME)
Dr. Robert Nagel (James Madison University Engineering)
 

SUMMARY

Academic makerspaces are places where students can explore, learn, build, and form relationships throughout their education. As the number of academic makerspaces continues to grow rapidly, very little empirical evidence describes students’ learning outcomes from their involvement in these spaces. Prior work investigated how university makerspaces are supporting women students as designers, learners, makers, and community members. Through this the Learning through Making Typology was developed showcasing the breadth of women students’ learning in the makerspaces. To further understand student learning and how learning outcomes compare across different populations in our makerspaces, interviews were conducted at two universities (Big City U & Comprehensive U). The universities chosen were selected based on their fundamental differences in makerspace & engineering curriculum structure. Using an in-depth phenomenologically based interviewing process we are able to gain insight into the participants’ lived experiences in the makerspace. Through a rigorous qualitative data analysis process of over 1000 pages of single-spaced transcriptions we are able to compare student learning across gender and universities. The analyses revealed our makerspaces are no escape from societal and engineering culture, and much of our data was rich in themes of gendered expectations, benevolent sexism, and competition. Throughout these analyses the significance of online learning became apparent for our participants. A model was developed showcasing how students are learning online and how this online learning supplements their in-person making.

https://gatech.webex.com/gatech/j.php?MTID=m247d7ef38ab3b64dfea5610d301a6b73