An Approach to Skill Mapping in
Online Courses
Presenters:
Norman Bier, Carnegie Mellon University, http://oli.cmu.edu
Ross Strader, Stanford University, http://oli.stanford.edu
Dawn Zimmaro, Stanford University, http://oli.stanford.edu
Agenda

Overview of the skills map process

Theoretical learning model used to predict mastery

Current analytics reports

Next steps for future improvements

Implications for improving student learning and courses

Facilitated discussion
Overview of the skills map process
Skills linked to learning objectives
Activities (resources) linked to skills
Theoretical learning model

Bayesian hierarchical statistical model

Takes as inputs data about student performance on questions and activities
related to a certain skill, and estimates the probability that the student has
mastered that skill.
Analytics reports

Instructor dashboard gives instructors a live view of student performance as
they work
Data-driven improvement
Learning Curves in DataShop
Future directions

Delivering OLI courses on OLI, OpenEdx, and other platforms

Creating ability to author skills maps outside of a platform

Developing outcome analytics service outside of a platform to create and test
one or more learning models

Designing customizable data visualizations

Creating data analytics tools for instructors and researchers
Implications for improvements in
student learning and courses

Design process prompts instructors to think in terms of what they want their
students to know or be able to do (learning objectives and skills) and to
create activities to address those objectives

Real-time data is shared with instructors and students to guide instructional
interventions

Analytics focus on direct measures of learning versus proxies (outcome vs.
performance/event analytics)

Data is used to iteratively improve instructional materials

For a copy of the white paper “An Approach to Knowledge Component / Skill
Modeling in Online Courses”: http://oli.stanford.edu/news/
Questions?

Norman Bier: nbier@cmu.edu

Ross Strader: strader@stanford.edu

Dawn Zimmaro: dzimmaro@stanford.edu