2023 ASEE Annual Conference & Exposition

Leveraging Social Media Analytics in Engineering Education Research

Presented at Disciplinary Engineering Education Research – Session 2

The purpose of this method paper is to explore how social media can be leveraged in engineering education research and provide a step-by-step method for social media analytics. Social media platforms serve as a space for people to share their thoughts, feelings, and experiences. These platforms give space to marginalized voices (although censorship of these voices still occur) and are underutilized data sources to understand marginalized groups in engineering education research. For example, in our larger research project focusing on neurodivergent (e.g., ADHD, autism, dyslexia, anxiety) engineering students, published knowledge on what it means to be neurodivergent is limited to deficit framing and language developed by researchers and clinicians. However, a plethora of knowledge and first-hand accounts of neurodivergent experiences exist on social media where emancipatory– as opposed to deficit– language is used to describe their experiences. Researchers should immerse themselves in these online communities as a way to join their conversations and to understand the experience of being neurodivergent and other marginalized experiences in the larger social context rather than just academic contexts.
To leverage social media in research, data mining and natural language processing techniques are necessary. In this paper, we detail a natural language processing technique called the latent Dirichlet allocation (LDA) method, which is a probabilistic topic modeling tool that considers various hidden text elements using machine learning. LDA identifies themes and text structures from large amounts of text-based data sets, such as data mined social media content, to categorize the data. We then present an example of how we used LDA in engineering education research. Our work utilized LDA to identify neurodivergent themes discussed on TikTok, a video-based, social media platform. Our analysis leveraged video hashtags (e.g, ADHD, neurodivergent, and neurospicy) to train the LDA model and visualize the topic clusters from the data produced. We then compared our results to our smaller scale qualitative thematic analysis which aligned with our themes generated. These themes included neurodivergent classifications, neurodivergent manifestations, and societal misconceptions.

Authors

Ms. Sakshi Solanki Utah State University [biography]

Sakshi Solanki is a PhD student in the Engineering Education department at Utah State University. She earned a bachelor's degree in Electrical and Electronic Engineering from ITS Engineering College, India and a master's degree in Data Science from University at Albany, New York. She worked as a Data Analyst during one of her summer internships in 2020, where she learned and gained experienced in data evaluating and validating company’s huge data using the techniques based on Excel, Python, and R. She is currently working with Dr. Marissa Tsugawa on Neurodiversity Research and Education. She believes that neurodiversity can help her better understand her younger brother's condition (Asphyxiation) and respond to his basic needs because his mind works differently from everybody else’s due to which he unable to express his feelings and pain.
kiana kheiri
Dr. Marissa A Tsugawa http://orcid.org/0000-0001-6009-8810 Utah State University [biography]

Marissa Tsugawa is an assistant professor at Utah State University who leverages mixed-methods research to explore neurodiversity and identity and motivation in engineering. They completed their Ph.D. in Engineering Education where they focused on motivation and identity for engineering graduate students.
Hamid Karimi Utah State University [biography]

I completed my Ph.D. in Computer Science at Michigan State University (MSU) in 2021, with my primary research focus on artificial intelligence (AI) for social good. During my doctoral studies, I explored several intriguing areas, such as AI in education, computational politics, and misinformation detection. As a member of the interdisciplinary Teachers in Social Media project, I concentrated on creating innovative and efficient data mining and machine learning algorithms to enhance the quality of PK-12 education. Throughout my academic journey, I have been honored with multiple awards. These include the Best Paper Award at the IEEE-ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2018), the Outstanding Graduate Student Service Award (2019), the Dissertation Completion Award (2020), and the International Faculty Recognition Award at Utah State University (2022). In August 2021, I joined Utah State University as an Assistant Professor (tenure-track) in Computer Science, where I now lead the Data Science and Applications lab (dsa.cs.usu.edu).

Download paper (2.35 MB)

Are you a researcher? Would you like to cite this paper? Visit the ASEE document repository at peer.asee.org for more tools and easy citations.

» Download paper

« View session