An Approach to Skill Mapping in Online CoursesPresenters:

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

Activities (resources) linked to skills

Skills linked to learning objectives

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