How Data Analytics Improves Your Corporate Training Experience

Impact Of Data Analytics On Corporate Training

Data analytics, when embedded in eLearning, enables learners to have more effective and engaging learning experiences. It has become popular in pedagogy since mass training enabled facilitators to collect performance data and use it to redefine their training strategies. To compare learners’ performance, seek appropriate attention and set test benchmark, analytics plays a major role in learning mechanisms. Learning analytics helps educators to understand diverse learning styles and preferences. Trainers can use the data to design more efficient training courses based on specific learning needs. With the evolution of modern technology, digital learning is being distributed via desktops and personalized mobile devices. In addition, eLearning happens via social media, wiki, emails, chats and more.

With the successful utilization of predictive analysis, automation, and multi-source knowledge mapping, a system is designed per the needs of modern learners. A lucrative way to find the optimal learning path is through classification and correlation. If we consider a learning system, there exists actors (learners, trainers, courses, external information), actions (study, review, share, apply, etc.) and results (points, skill, retention, etc.). The main aim is to chunk down these attributes, organize, and map them in a way that answers different questions, including:

  • What courses to enroll to accomplish goals?
  • Which learners need mentor intervention and which need refreshers?
  • Which components should be included in a program?
  • What content characteristics (video, audio, text, or interactivity) drive employee retention and external attribute development?

Impact Of Learning Analytics For Better Learning

Data analytics in eLearning provides the ability to categorize learning elements using attributes and statistical tools to evaluate the mapping between these classifications and expected outputs. Let us consider an example of automated learning for content delivery, which is taken by adaptive systems. Such systems change responses to inputs based on data and circumstances. Their efficiency is calculated by their ability to collect data, analyze, evaluate, and process decisions to empower learning.

Through Machine Learning, the system uses neural network algorithms to provide personalized learning paths for learners aligned to their business objectives:

  • Designing dynamic courses which take historical data into account from other learners, as well as facilitators.
  • Recommendations on training courses to study, time invested, and refreshers based on learners’ performance level.
  • Providing feedback to learners based on their learning capability.
  • Providing interventions for educators/mentors based on the progress of employees.
  • Giving awards and certificates when a learner jumps to the next level, from slow tracker to fast learner.

For organizations, whether big or small, training is a vital tool to boost employee performance and business productivity. Companies from different industry verticals consider workplace learning as a significant investment to maximize ROI (Return On Investment). The most lucrative way to drive ROI is through implementing a knowledge competency management system, improving the overall skill development cycle.

Data analytics provides trainers with relevant information, along with providing learners with ample benefits. For instance, if eLearning helps educators to develop better training materials according to learning patterns, then learners also get motivated and engaged during the entire learning process. Given the large amount of data available to the organizations, it is possible to proceed with sufficient knowledge that allows to improve the entire structures. The perfect blend of latest learning tactics with new paradigms of processing (Big Data) enables content to be changed in order to optimize the existing training methods.

Learning analytics helps organizations to invest wisely and distribute training budget by informing educators about the areas of greatest need. This way, analytics holds an important niche in maintaining cost-effectiveness and productivity within employee training. In today’s modern learning environment, responses and interactions of every employee during the learning phase, such as engaging in forum discussions, employee assessment matrix, and engagement in online assessments, produce data which is recorded in the log file. This data is further analyzed and used during the training process to improve the overall workforce training approach. With the utmost utilization of data analytics, effective course completion, time spent to complete it, scores, badges, feedback, and assessments can be evaluated. So, analytics incorporated in eLearning imparts a highly interactive learning experience with effective delivery of online training programs.