What is meant by personalization and contextualization?
Each person is unique. While an enterprise may require uniformity of processes, procedures, corporate culture, behaviors, or core knowledge, we recognize that the organization is made up of unique individuals to whom the knowledge, skills and experiences will be transferred. Each individual has their own prior experiences, mental scaffolding, preferences and needs at the point of learning.
The strength of the team is in leveraging diversity to collaboratively create something beyond what any one person brings to the table. Personalization and contextualization meet the needs of the individual, so that they can better contribute to the team.
One of the keys to embracing transparency in learning is personalizing and contextualizing the learning experience– transparently adapting the learning experience to match the environment, conditions and needs of the learner. Contextualizing in this sense is related to all of the context data that can be gathered in relation to the learner. (This is not referring to “contextual learning” which “involves the presentation of information in a way that allows students to construct meaning based on their own experiences.”)
Data available today can be used to predict learner needs.
Given the proliferation of data and the emergence of the Internet of Things, more data than ever is available to help draw insight and intelligently adapt user and learner experiences. A learner’s mobile device may be able to tell the location of the person, their heart rate, the temperature of the surroundings, how bright the surroundings are, whether the person is in a moving vehicle and more. Their calendar can indicate if they are about to go into a meeting. Email and chat data can tell who they commonly communicate with. Browser history gives clues regarding topics of interest. Analysis of the contextual data can help predict what learning experiences in what formats would be of value and when they would be of value.
Personalization and contextualization are used extensively outside of learning today.
There are numerous examples of personalization and contextualization outside of the learning field. For example, Google’s search algorithm can take into consideration your search history to provide “better search results and suggestions.” Netflix quietly gathers information related to a user’s interaction with Netflix to improve the quality of service, such as providing subtitles or closed captioning. Microsoft Excel’s autocomplete for formulas and Eclipse’s content assist feature for code syntax are other examples of using context to adapt the user’s experience.
Personalization and contextualization are underutilized in learning today.
Some of the examples of contextual learning include museum applications that display content to a mobile device or play recordings on a mobile device depending on QR code or RFID readings. Some learning systems, such as Think Through Math, adapt the content that is displayed to the learner based on how they perform on pretests. Though the body of literature is growing in the space of adaptive, personalized and contextualized learning, implementation is lagging the rhetoric and lagging behind what other industries have shown is possible.
Personalization and contextualization transparently optimize learning.
Personalization enables organizations cut through the time sink of people unnecessarily taking formal training that does not meet their needs or does not meet the context of what they need, when they need it and how they need it.
“Personalization and Contextualization” is a key characteristics of Dark Learning.
Personalization and Contextualization are a key approach to Dark Learning, as they transparently reduce the distance between the learner and the new knowledge, skills and abilities that they are trying to acquire.