For years schooling has been typified by its aspect of the physical grind on the part of both students and their teachers: teachers cull and prepare educational materials, manually grade students’ homework, and provide feedback to the students (and the students’ parents) on their learning progress. They may be burdened with an unmanageable number of students, or a wide gulf of varying student learning levels and capabilities in one classroom. Students, on the other hand, have generally been pushed through a “one-size-fits-all” gauntlet of learning, not personalized to their abilities, needs, or learning context. I’m always reminded by this quote by world-renowned education and creativity expert Sir Ken Robinson
What is Machine Learning?
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.ML is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look Machine learning works especially well prediction and estimation when the following are true:
- eThe inputs are well understood. (You have a pretty good idea of what is important but not how to combine them.)
- The output is well understood. (You know what you are trying to model.)
- Experience is available. (You have plenty of examples to train the data.)
Application of Machine Learning in Education
A few years ago, Sotiris Kotsiantis, mathematics professor at the University of Patras, Greece presented a novel case study. describing the emerging field of educational data mining, where he explored using students’ key demographic characteristic data and grading data in a small number of written assignments as the data set for a machine learning regression method that can be used to predict a student’s future performance.
Process efficiency: Scheduling, grading, organization
Elsewhere, several Machine Learning for Education ICML (international machine learning conference) workshops have explored novel machine learning applications designed to benefit the education community, such as:
- Learning analytics that build statistical models of student knowledge to provide computerized and personalized feedback on learning the students’ progress and their instructors
- Content analytics that organize and optimize content items like assessments, textbook sections, lecture videos, etc.
- Scheduling algorithms that search for an optimal and adapted teaching policy that helps students learn more efficiently
- Grading systems that assess and score student responses to assessments and computer assignments at large scale, either automatically or via peer grading
- Cognitive psychology, where data mining is becoming a powerful tool to validate the theories developed in cognitive science and facilitate the development of new theories to improve the learning process and knowledge retention
- Active learning and experimental design, which adaptively select assessments and other learning resources for each student individually to enhance learning efficiency
