As more aspects of everyone’s daily lives are integrated with technology, the way people amend their ways of attaining knowledge and recording that information has become the prime key to the e-learning industry today. With evolution of e-learning technologies, audience is no more confined to the brick-and-mortar classrooms. Rather, they are embracing new strategies to shift towards effective digital learning. Learning Analytics is not a new concept in digital learning. From comparing learners’ performance and administering appropriate attention to setting test benchmarks, analytics is fundamental in learning mechanisms. With digitization, there is a rising demand of familiarizing with cutting-edge technologies to be able to adapt them easily. Analytics focuses on the use of learning data to benefit the training outcomes. Using tools such as predictive analytics, machine learning and multi-source mapping, it is easy to design a system that caters to the needs of efficient Learning Analytics.
Automating E-Learning Content Delivery
Automating the entire learning process is taken by adaptive systems. Such systems can change its response to inputs based on context and result. The efficiency of an adaptive system is calculated by its ability to improved results by effecting these changes. They are created to gather data, analyze and formulate decisions in order to optimize a specific criterion.
In customized recommendation through Machine Learning, system uses neural network algorithms approach to attain the following learning goals:
► Recommendations on course materials, time to spend on completing a specific course.
► Creating dynamic course structures by collecting data from peer learners.
► Providing Interventions to educators/mentors based on individual’s learning progress.
► Awards and badges given automatically when a learner jumps to the next level.
Analysis of Social Learning
Online knowledge in the form of wikis, discussion forums, blogs and videos shared by contributors is increasing becoming significant learning resource for corporate learners. Mapping learners’ interactions with social knowledge go through the phases of Search, Study, Rating, Recommendation and Contribution.
► Search can be made more targeted to the learners’ curriculum.
► Study can be tracked and then tested to evaluate retention rate and understanding.
► Ratings are aggregated to help learners find the right content at the right time.
► Contribution can be rewarded as a fulfillment of entire social learning cycle.
There are multiple factors that have enhanced the interest of organizations in Learning Analytics. One of the factors is the latest trend for improved accountability in all levels of training. Analytics provides varied methods to monitor learners’ performance as well as provide tools that encourage continuous improvement. It has always been in pedagogy since mass education allowed instructors to collect learners’ data and use it to refine training strategies. The prime focus of Learning Analytics is on developing structures that help adjusting content, learning support levels and other customized services by capturing, processing, reporting and acting on data.
So, despite of gamut of benefits of Data Analytics in e-learning, there are several challenges too. For instance, the inability of learning platforms to capture learning experiences accurately. Another factor is to find the correlation between patterns of data and learners’ behavior. In addition, managing data privacy is also a big concern. The bottom line is that Data Analytics provides ample opportunities for organizations from different industry domains. As evolving technologies have a huge impact on e-learning, there exists a wide scope of new possibilities. With big data changing the facet of almost every industry, workplace training niche is positioned to benefit in an efficient manner. To know more, click here to download the white paper for free.