Its multi-phase joint development effort with IBM leverage the power of big data in enterprise learning
Skillsoft has revealed new capabilities as part of its multi-phase joint development effort with 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.
John Ambrose, Senior Vice President (Strategy, Corporate Development and Emerging Business), Skillsoft, said, “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.”
With these assets in place, companies will be enabled to increase training utilization and completion rates through targeted learning and improved engagement, proving the investment value in a learning program.
This big data-driven solution will facilitate higher engagement and better outcomes for individual learners through a customized, seamless program that melds their preferences with the successful experiences of users with similar learning objectives. Enterprises will benefit by providing employees with an adaptive learning system that grows smarter and more powerful with each interaction, allowing them to develop and manage talent more effectively.
According to Anshul Sheopuri, Manager (Consumer Modeling), IBM Research, “The objective of this collaboration with Skillsoft is to significantly improve user engagement by creating personalized learning recommendations that are delivered at times preferred by the user, and delivered visually to communicate the rationale behind the recommendations. We plan to pilot these big data technologies with select Skillsoft customers and measure usage outcomes, prior to deployment.”
