Research and development of new algorithms specialized in the field of education, and heading towards practical application and commercialization

Optimal algorithms for the teaching and the learning

In August 2020, we developed an algorithm for optimizing the match of teachers and learners through implementation of feature engineering based on personality factor information, orientation characteristics information, and language ability information of teachers and learners.
The algorithm would be installed in applications that collect learning behavior data of teachers and learners and expected to be applied to the evaluation analysis of impact on learning effectiveness or motivation through machine learning analysis.

Development background

Current educational models were created based on the premise of equal educational opportunity in the days when there was no Internet access.
These educational models ensure that everyone receives the same education by implementing education and its operation efficiently and lead to a systematic structure based on a systematic lesson plan. 
However, it is not a best system for those who do not adapt to the systematic structure and at the same time, this was true for both those teachers and learners. 
As the approach to solve these challenges, we concluded to develop an algorithm to achieve the concept of “Establishing a system of educational opportunities, not just a one-size-fits-all approach to education” through optimizing of matching for teachers and learners.

Areas of practical application

  • In the field of education, the algorithm will be applied to maintain and improve the learning motivation by visualizing achievement of learner, and to improve the teacher’s performance by optimizing teaching methods for individuals.
  • In the field of industry, we apply the algorithm to predict the compatibility of employee combinations within a company and plan human resource allocation, as well as to determine the criteria for hiring new graduates and mid-career employees.
  • In addition, as the contribution to the academic field through these research and development activities, Kawaijuku Group will research and propose new learning method by collecting large amounts of learning data in the real world.

Opportunity of Alliance partnership

We are looking for educational institutions, private education companies, and private companies (human resource departments, recruiting departments, training departments, etc.) in Japan and abroad that agree with our corporate philosophy and the purpose of our advanced research. 

Contact

  • KEI Advanced, Inc
  • General Planning and policy division/Alliance partner
  • Tel  +81-3-5276-2731
  • email bizinfo<at>keiadvanced.jp (Please convert <at> to @)

Alliance Case

  • Verification and demonstration in the educational field
  • Verification and demonstration in the private sector
  • Big date collaborative generation
  • Algorism collaborative research
  • Collaborative research from an academic perspective

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