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Tuesday, 02 April 2019 08:02

Bloom's 2-Sigma Problem is Solved (Part 2)

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 By Sam S. Adkins

Benjamin Bloom is a household name in the training and education industry. He made two invaluable contributions to the industry: Bloom's Taxonomy and the famous 2-Sigma study. This article focusses on his 2-sigma research and the dramatic impact of AI-based Learning on what he called "the 2-sigma problem."

A Refresher

In his seminal 1984 study, Bloom and two of his doctoral students compared the effectiveness of learning transfer achieved by groups of students being taught in a traditional classroom setting, using so-called Mastery Learning, or being tutored by grad students in small cohorts of one to three children. "Most striking were the differences in final achievement measures under the three conditions," wrote Bloom. "Using the standard deviation (sigma) of the control (conventional) class, it was typically found that the average student under tutoring was about two standard deviations above the average of the control class (the average tutored student was above 98% of the students in the control class)." This is the infamous 2-sigma, or two standard deviations of achievement compared to conventional classroom instruction.

Bloom also wrote “The tutoring process demonstrates that most of the students have the potential to reach this high level of learning.  I believe an important task of research and instruction is to seek ways of accomplishing this under more practical and realistic conditions than the one-to-one tutoring, which is too costly for most societies to bear on a large scale.  This is the 2-sigma problem."


2-Sigma Solution

Artificial Intelligence-based Learning has proven to be quite effective at one-to-one personalized instruction and is tremendously scalable. Properly designed, AI-based Learning categorically solves the 2-sigma problem.

Intelligent Tutoring Systems (ITS) have been used by the US military for decades. All the ITS implementations included comprehensive statistical analysis of mastery outcomes.  I worked with J.D. Fletcher at the US government's Institute of Defense Analyses to compile the mastery metrics for eighteen intelligent tutoring systems. We found three empirical studies that showed that the use of next-generation Cognitive Tutors exceeded the 2-sigma deviation.


There is now rapid global adoption in all six buying segments across all seven regions tracked by Metaari. AI-based Learning is now going mainstream in developed economies and gaining traction in the developing economies, particularly in Southeast Asia. AI-based Learning truly is the answer to solving Bloom’s 2-signma problem. Look at the newest AI-based Learning applications for proof.

For more on this subject, view “How AI-Based Learning is Solving Bloom’s 2-Signma Problem” by Sam S. Adkins.


About the Author

Sam Adkins is Chief Researcher at Metaari. Metaari is a market research firm that identifies revenue opportunities for learning technology suppliers. Metaari principals have refined a sophisticated learning technology product categorization schema. Their research taxonomy is the backbone of their quantitative data repository.  Contact the author at This email address is being protected from spambots. You need JavaScript enabled to view it.


Part 1 of this 2 part article can be found HERE.


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