Mining Online Training Log Data

Online training has been growing in popularity, and offers many advantages for both trainers and learners. Assessing the usage and impact of online material can be difficult, especially if content is made available to anyone and is not part of a course requiring formal enrollment. The Cornell Virtual Workshop (CVW) first offered online training on topics in high-performance computing and computational science in 1994, and ten years ago we began logging usage. We are now performing our first in-depth analysis of those log data to identify patterns in usage, so that we can better understand how users access the material, which types of topics and materials result in the greatest impact, how topic usage changes over time, and what types of presentation format might be preferred. While the CVW is built around a cohesive, sequential narrative for each training topic, we find that many users access our content in a more targeted fashion, suggesting that we rethink how we package our material. We anticipate that ongoing analysis using data science and machine learning methods will enable us to produce more useful training materials, and provide the educational community with valuable information about patterns in online material usage.