Personalized Education

Cognitive-based learning strategies crafted for each student’s unique success.
A category of technologies that uses natural language processing and machine learning to enable people and machines to interact more naturally to extend and magnify human expertise and cognition. These systems will learn and interact to provide expert assistance to scientists, engineers, lawyers, and other professionals in a fraction of the time it now takes.

The idea of personalized learning (sometimes referred to as adaptive learning or differentiated learning) is by no means new. In fact, in the physical world this simply means a one-to-one tutorship between teacher and student. However, this model is not practical nor is it cost effective. What if a ‘system’ could perform a similar task? What if a ‘system’ could understand the learner, recognize where they are failing to grasp a concept and knowing all possible learning options can direct their learning pathway accordingly?

In a one-to-one setting, this is meat and drink to an experienced teacher as they draw on their years of experience and skills to explain topics in a variety of ways. But in a class of 30 students, there is a wide array of abilities and there are simply too many variables and too little time for a personalized approach. Invariably, a ‘personalization proxy’ takes place whereby the teacher differentiates student abilities along a bell curve, effectively teaching to 3 or 4 cohorts of varying aptitudes. This is not ideal, and the problem is further exacerbated because learning is sequential. If a student fails to ‘get’ algebra 101, there is little hope they will ever come to terms with simultaneous equations. What if a cognitive system could support a teacher to prevent such learning roadblocks for each and every child?

Teacher: Karin, you did OK on your latest mathematics test, you got 72%. It looks like the algebra questions were areas where you struggled. Is that a fair assessment?
Karin: Yes, I’m not sure I really get algebra. Are there any particular areas where I could improve?
Teacher: Well, let’s see what my assistant suggests.
Cognitive-enabled teacher assistant:
From an analysis of Karin learning profile and her last five tests, algebra is a relatively weak area for her in mathematics. Based against learning outcomes of 1.2 million similar Year-8 students with matching learning characteristics, her understanding could be improved by either reviewing algebra module 2.3 or looking at instructional video 7.
Teacher: Karin , start with that and then we’ll see how you get on

Teacher: Karin , start with that and then we’ll see how you get on

Challenges For Personalized Education


Personalized Learning are seen as a means to:

  • Learning is driven by learner interests
  • Reduce drop-out rates by creating better candidate selection processes based on more robust data
  • Identify students who may need extra help
  • Provide a richer analysis of why students fail tests
  • Insure students are at the optimal level of attainment
  • Helps Students Adaptive Learning(Human Or digital), Individualized learning(pace) , and Differentiated Learning(Approach)

Traditional Approach & Personalized Education

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