Editor’s Note: This is part two of a three-part series. The first segment discusses how learning organizations need to better integrate learning capabilities within business operations in order to strengthen their value proposition. This segment discusses how emerging technologies, such as Tin Can, natural language processing, learning machines and analytics can help develop smart learning systems.
By having Tin Can API 1.0 (the Experience API) enter the learning scene, organizations can now capture information about who is accessing and contributing corporate content, online and offline, across vast information landscapes to identify usage patterns that can be correlated to on-the-job performance, such as output and sales.
Unfortunately, many companies tend to respond rather slowly to advance Tin Can, often waiting to see how other organizations leverage it and how aggressively technology platforms adopt the new standard. This approach may seem prudent, considering the outlay of finance that will likely accompany outfitting an organization with Tin Can enabled technologies. However, the updating of existing platforms or procurement of new technologies is not the first step to building smart learning systems, and stalling corporate commitment to prepare for their arrival likely limits or even eliminates the lifespan of any competitive advantage that companies can achieve.
Unless organizations immediately start designing smart learning systems by identifying the specifics of how they will support a future learning vision, they will find themselves unable to leverage such technologies once they secure them. Therefore, it is important for companies to pinpoint the types of digital transactions that provide insight to workforce competency and where such transactions take place — for example, on specific enterprise applications used to perform work or communication technologies that connect people. Further, learning professionals must anticipate the entrance of capabilities that are maturing in other industries, as well as those that are nascent or entirely nonexistent, as they start to consider their future needs.
The Power of Data
Google and LinkedIn, for example, have highlighted the power of data. No one can argue the success that Google has had by collecting data across all client touch points to improve everything from search results to direct marketing. Similarly, LinkedIn collects relationship data points and the effectiveness of its networking platform has quickly been adopted by organizations and individuals who understand the importance of broadening their community in a very intelligent manner. Organizations can look to companies such as Google and LinkedIn not only to emulate their offerings but to ask what else can be done.
The Power of the Social Network
Social network analysis provides an opportunity for learning organizations in recognizing the importance of connecting people in facilitating informal learning. Organizations are often not aware, for example, of the knowledge dams that they inadvertently erect by severing relationships during restructuring or right-sizing a workforce. Through data collection and interpretation enabled by analytic technologies, organizations can map their social network structure to effectively identify members and social structures that are critical to sustaining productivity, communication and innovation.
The Power of Technology
Natural language processing and learning machines, such as IBM’s Watson, serve as another example. Learning professionals are no longer restricted to interpreting meaning from transactional data alone. Platforms such as Watson have already demonstrated that it is possible for technology to read through and understand volumes of free text, audio and video files. Such technology is able to posture a question, examine arguments, and position an answer by understanding volumes of content. As such, the value of worker transactions can automatically be determined by understanding the content exchanged during those transactions.
Combining Tin Can, social network analysis, analytics and technologies allows companies to automatically measure and manage knowledge repositories, foster future learning and support activities as they happen. At the heart of smart learning systems, is the understanding that learning occurs for each individual within an organization (all the time across every engagement and corporate transaction) where information is passed and used. If workers are provided with relevant on-the-job assistance and have easy access to correct and actionable information then they will be able to perform their roles.
Whether business leaders orient their management practices towards process management or people management, they certainly recognize that the success of their companies is dependent on the successful interfacing of both.
Part Three of this three-part series discusses a future workplace when considering the implications of integrated smart learning systems.