Home

Text Mining in STEM Ed Research

The transition to digital learning has made available new sources of data, providing researchers new opportunities for understanding and improving STEM learning. Data sources such as digital learning environments and administrative data systems, as well as data produced by social media websites and the mass digitization of academic and practitioner publications, hold enormous potential to address a range of pressing problems in STEM Education, but collecting and analyzing text-based data also presents unique challenges. The Text Mining (TM) Learning Labs will provide LASER Institute scholars hands-on experience with popular techniques for collecting, processing, and analyzing text-based data—including mining data from application programming interfaces or APIs, screen-scraping, sentiment analysis, topic modeling, text networks, and more.

SUMMER INSTITUTE

TM Learning Lab 1: An Introduction to Text Mining in STEM Ed

Part 1: Tidy Text, Tokens, & Twitter. We take a closer look at the literature and research questions that will be guiding our analysis; importing data through Twitter's developer API; and wrangling our data into a one-token-per-row tidy text format.

Part 2: Word Counts, Clouds & Correlations. For our second lab, we use simple summary statistics, data visualization, and word correlations to explore our data and see what insight they provides in response to our questions.

Part 3: Sentiment Analysis & School Reform. We focus on the use of lexicons to compare the sentiment of tweets about the NGSS and CCSS state standards in order to better understand public reaction to these two curriculum reform efforts.

Part 4: Select, Polish, & Narrate. We wrap our look at public sentiment around STEM state curriculum standards by selecting an analysis that provides some unique insight; refining or polishing a data product, such as a graph or table in static or interactive form; and writing a brief narrative explaining the ways in which the data product helps answer the research question.

ONLINE COMMUNITY

TM Learning Lab 2: STEM Ed in the News

Part 1: Screen Scraping & STEM Reporting. Lorem ipsum dolor...

Part 2: Topic Models & Tea Leaves. In Part 2 we introduce an approach to identify "topics" by examining how words cohere into different latent themes based on patterns of co-occurrence of words within documents.

Part 3: Examining & Refining Our Models. Lorem ipsum dolor...

Part 4: Select, Polish, & Narrate. We wrap our look at public reporting around STEM education by selecting an analysis that provides some unique insight; refining or polishing a data product, such as a graph or table in static or interactive form; and writing a brief narrative explaining the ways in which the data product helps answer the research question.

TM Learning Lab 3: Text Classification & Prediction

Part 1: TBD. Lorem ipsum dolor...

Part 2: TBD. Lorem ipsum dolor...

Part 3: TBD. Lorem ipsum dolor...

Part 4: TBD. Lorem ipsum dolor...