Skillsoft today revealed new capabilities as part of its multi-phase joint development effort with IBM (NYSE: IBM) that leverage the power of big data in enterprise learning. The current phase of the program has produced algorithms to predict optimal engagement times, a content recommendation engine and a visualization framework to provide the foundation of a next-generation adaptive learning solution.
Skillsoft is a pioneer in the field of technology-delivered learning with a long history of innovation and delivering solutions for its customers worldwide, ranging from global enterprises, government, and education to mid-sized and small businesses. Skillsoft has an unrivaled environment of 19 million users across 60,000 learning assets. Skillsoft partnered with IBM, a leader in big data technology, to mine its rich customer base’s usage data and co-invent new customer experience analytics which are expected to make their way into the Skillsoft offerings. The initial results of this work will be presented to attendees next week at the 2014 Global Skillsoft Perspectives learning industry event.
“We are excited by the early capabilities we are developing with IBM,” said John Ambrose, Senior Vice President, Strategy, Corporate Development and Emerging Business, Skillsoft. “We’re building a powerful new big data engine that will enable us to optimize learning experiences and uncover new learning patterns that can be applied immediately so that the system is continually improving. This is the perfect application of big data – harness it and apply it to improve individual and organizational performance.”
The key components of the joint Skillsoft/IBM effort unveiled today include:
- Engagement engine – mine usage patterns to understand users’ evolving interaction preferences in order to identify optimal times and channels to engage with users
- Recommendations engine – create personalized learning recommendations leveraging user-content interactions, content ontologies and temporal consumption patterns
- Visualization techniques – to provide visual context for the recommendations to explain to the user why certain personalized content recommendations are being made along three different dimensions: people similar to the user liked this content, content similar to content the user liked, and popular content