Topic Dependency Models: Graph-Based Visual Analytics for Communicating Assessment Data

Abstract

Educational environments continue to evolve rapidly to address the needs of diverse, growing student populations while embracing advances in pedagogy and technology. In this changing landscape, ensuring consistency among the assessments for different offerings of a course (within or across terms), providing meaningful feedback about student achievements, and tracking student progress over time are all challenging tasks, particularly at scale. Here, a collection of visual Topic Dependency Models (TDMs) is proposed to help address these challenges. It uses statistical models to determine and visualize student achievements on one or more topics and their dependencies at a course level reference TDM (e.g., CS 100) as well as assessment data at the classroom level (e.g., students in CS 100 Term 1 2016 Section 001), both at one point in time (static) and over time (dynamic). The collection of TDMs share a common two-weighted graph foundation. Exemplar algorithms are presented for the creation of the course reference and selected class (static and dynamic) TDMs; the algorithms are illustrated using a common symbolic example. Studies on the application of the TDM collection on datasets from two university courses are presented; these case studies utilize the open-source, proof of concept tool under development.