Xenonstack Recommends

What is Learning Analytics Platform, Best Practices and Tools

Acknowledging Data Management
          Best Practices with DataOps


What is Learning Analytics?

"Data is new the fuel." This sentence influences every field. Not even the area of learning and teaching is untouched. Nowadays every field is coming on the digital platform which means the rise of the generation of data is here. But it is also true that data never lie, the practice of analyzing data comes into existence to predict and prescribe the future. The integration of this technique with the field of Education and learning give rise to "Learning Analytics" defined as -

The calculation, accumulation, and reporting of data about learners and contexts after the analysis process for the goal of understanding and optimizing the process of learning, teaching and occurrence in the ecosystem.By leveraging learning analytics Learners, Instructors, Businesses can enhance learning and course outcomes

  • Educators can analyse engagement trends and education material practicality in their respective areas.
  • Businesses can examine whether thier performance increased because of learning.
  • Improving future E-Learning courses.

How Learning Analytics Works?

The steps which should be followed to implement analytics in education and to make it work are -

Learning & Teaching Activity - Homo-sapiens profoundly influence this step. It is the primary step which involves "Formulation of the problems." The critical components of this steps are -

  • To identify the procedure to plan Learning Analytics in education.
  • To Identify the core advantages of Learning Analytics in a specific use case.
  • To identify the challenges in Learning Analytics for a use case.

Data Collection - The primary target of collecting the data is to discover the quantitative and qualitative aspects of the use case. It is the backbone of the whole procedure.

Data Processing and Storing - This step is very use-case specific, i.e., the subcomponents changed according to the problem statement. It includes cleaning of data and handling inappropriate data.

Analyzing - This step is the main game of Data Science. It requires the procedures under the umbrella of Data Science. The main sub-processes of this step are -

  • Clustering
  • Relationship mining
  • Prediction
  • Discovery with models
  • Data Disentanglement in the process of Decision-Making.

Visualization - This step is related to the output procurement. It includes eminently advanced calculative methods and graphics to demonstrate the patterns and trends in vast and complex dataset.

Benefits of Enabling Learning Analytics Methods

Learning Analytics is beneficial for -

  • Students
  • Educators
  • Organizations
Sections Benefits
  • The predictions are helpful to improve the individual's performance.
  • Provide E-Learning experience.
  • The evaluation of learning outcome, behavior and course curriculum is easy.
  • Helps educator in increasing learning's retention rates.
  • Improving future E-Learning courses.
  • Identify target courses.
  • Raises cost efficiency.
  • A process of Post Educational Employment.
  • Easy to train practitioners and community of research.

Why Analytics Matters?

Discussion of data brings analytics into the picture itself. The technologists are already applying Artificial Intelligence in different fields such as Healthcare, Finance and Market and Sales. Why does the education field should keep untouched by the revolution of Artificial Intelligence (AI)?

For bringing AI in any area, apply Data Science to analyze the data.

E-Learning Platform provides teachers and students to connect online providing a virtual classroom to teach thousands of students at the same time. -From the Article,E-Learning Analytics Platform for Enhanced User Experience

How to Adapt Learning Analytics?

The scope of insights in respect of the information analyzed at three levels -

  • Details on doing a particular task. (for which data required)
  • Details to execute the job. (Predictions)
  • Information to perform a particular task better? (Prescriptions)

But a model can not be considered as model till it does not come on the production level. A model with Artificial Intelligent is a team game. It also consists of different production level components.

Learning Analytics Best Practices

The biggest challenge for any Artificial Intelligent related model is to bring the theoretical aspects of that model to production level. Consider every critical dimension while institutionalising Learning Analytics. You can also read this blog based on IoT Analytics Platform for real-time data ingestion. You may also reach us for Big Data Analytics consulting.

Learning Analytics Tools

Tools to implement Learning Analytics are -

  • The Social Networks Adapting Pedagogical Practice (SNAPP)
  • Connect for Success (C4S)
  • Automated Wellness Engine (AWE)
  • Personalized Adaptive Study Success(PASS)

Compressive Approach to Analytics

Analytics is all about optimizing the learning situations to lay emphasis on how learners learn and how teachers or advisors guide. Interestingly, it can reveal student responses that can lead to hypotheses which affect student success. To know more about Learning Analytics, take a look at below pointers.

Related blogs and Articles

Scaling Up RPA for Process Automation in an Organization

Cognitive Automation

Scaling Up RPA for Process Automation in an Organization

What is Robotics Process Automation (RPA)? Robotics Process Automation (RPA) uses software tools to automate the business process at a repetitive, structured, and rules-based scale. RPA is part of a continuum of automation that begins with basic, local tasks and expands to enterprise-wide, smart automation powered by machine learning and artificial intelligence. Successful scaling RPA in a...