NotesFAQContact Us
Search Tips
Back to results
Peer reviewed Peer reviewed
PDF on ERIC Download full text
ERIC Number: EJ1199911
Record Type: Journal
Publication Date: 2018
Pages: 18
Abstractor: As Provided
ISSN: EISSN-1929-7750
Topic Dependency Models: Graph-Based Visual Analytics for Communicating Assessment Data
Khosravi, Hassan; Cooper, Kendra M. L.
Journal of Learning Analytics, v5 n3 p136-153 2018
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.
Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail:; Web site:
Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A