Educational Data Analysis and Mining Workstation

Collect & Connect University Data on Students and their learning!

malt•lab partners with the Office of Information Technology, UNLV Analytics, and instructors from large undergraduate courses to understand how students of different backgrounds learn in undergraduates STEM courses. Here’s a brief video produced by Splunk, our software partner.

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Slides and full audio from a Splunk .conf 2016 presentation recapping the partnership are here.

We use Splunk to build data models collecting students’ demographics and achievement from surveys and university systems and pair them with students’ learning behaviors on their  university learning management system course sites. We anonymize these data and use them to research learning processes and build prediction models so we can support students via early alerts, advice, and training. A feature story was recently published in UNLV magazine.

RESEARCHERS!: We welcome secondary data analysis projects! Interested researchers should contact the lab director.

 

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In summer 2015, the malt•lab moved to the second floor of the Carlson Education Building. The move afforded the opportunity to consolidate lab members into a more communal environment, and to create a new resource for the College of Education’s research community. As of this fall, the malt•lab will host a new Educational Data Analysis and Mining Workstation, that is freely available for use by members of the COE research community. Acquired with a mix of university, department, and external support, the station features a PC workstation with the substantial processing power & RAM, and the software packages researchers need in order to answer complex research questions that require sizable datasets.

  • For research questions requiring structural equation modeling (SEM), hierarchical linear modeling (HLM), latent growth curve analyses (LGCM) and other complex quantitative approaches, the workstation is equipped with Mplus (with combination add-on package for multi-group SEM and more).
  • For research questions that use Big Data to answer questions pertaining to education, the lab has acquired an academic license of RapidMiner, and members have developed resources to support a set of educational data mining analyses.

Students and faculty who wish to make use of this new resource are encouraged to contact Dr. Bernacki to reserve time and be connected with colleagues who can provide support.