Dataset can be downloaded here: https://drive.google.com/file/d/1QzQ6P0ewgx_hgHC-qJ3nCBRAr1139w6N/view?usp=sharing

Data in learning analytics research (e.g. SIS, clickstream, log-files) are often rich in temporal features that could allow us to explore the dynamic changes in learning behavior at different time granularities (e.g. seconds, days, weeks, semesters). This workshop will introduce participants to several common temporal/sequential analysis methods and techniques using R. During the workshop, we will discuss how temporal analysis can be applied to answer RQs in learning analytics and which learning constructs are relevant to temporal analysis. Next, we will go through techniques to explore and visualize temporal/sequential data. Participants will learn and apply two types of temporal models: a) explanatory models using statistical techniques, such as Sequence Analysis with application to identify common patterns of learning activities from log data and b) association rule mining (apriori) with application to detect courses that are frequently taken together and create association rules for course recommendations.

Activities:

• Learn the foundation and intuition of temporal/sequential analysis
• Apply temporal/sequential analysis on educational datasets using R
• Discuss in groups how to use temporal analysis to answer research questions in education

Target Audience:

This workshop is designed for anyone interested in temporal/sequential analysis. No experience in temporal and sequential analysis is required. To get the best learning experience, participants should familiarize themselves with basic statistics and machine learning concepts (e.g. regression, variance, autocorrelation, classification, cross-validation, overfitting).

Bring a laptop with RStudio installed. More information on the packages will be provided before the workshop

Special issues worth checking out:

• Knight, S., Friend Wise, A., & Chen, B. (2017). Time for Change: Why Learning Analytics Needs Temporal Analysis. Journal of Learning Analytics, 4(3), 7–17. https://doi.org/10.18608/jla.2017.43.2

• Chen, B., Knight, S., & Wise, A. F. (2018). Critical Issues in Designing and Implementing Temporal Analytics. Journal of Learning Analytics, 5(1), 1–9. https://doi.org/10.18608/jla.2018.53.1

Instructor: Quan Nguyen, University of Michigan