A Tag-Based Automated Framework for Collecting, Tagging, Preprocessing, and Interactively Visualizing Student Learning Outcomes

• In Event: Friday Roundtable Session 9:45 am
  In Roundtable Session: Innovation in Statistical Methods (Table 31)

Abstract

The Mechanical Engineering faculty at a public four-year, comprehensive university in the Northeast region are developing and piloting a tag-based framework to systematically identify, collect, process, and interactive visualize large volumes of student learning data for course- and program-level outcomes assessments. Student learning outcome identifier tags are used to link the questions on assignments to course outcomes and overall program outcomes. The data collected from grading a given assessment is imported into a Python-designed web-based dashboard to be de-identified, cleaned, stored in an SQL database, and visualized interactively. This paper aims to present the tag-based framework, which includes data source, rubrics design, digital tagging, pre-processing, and interactive visualization, as well as demonstrate a dashboard prototype for class-level and program-level assessments.

Authors

• Tonghui Xu, University of Houston

• Hsien-Yuan Hsu, University of Massachusetts - Lowell

• Melissa Nemon, University of Massachusetts - Lowell

• Christopher Hansen, University of Massachusetts - Lowell

• Hunter Mack, University of Massachusetts - Lowell

• David Willis, University of Massachusetts - Lowell