Adaptive learning, or a few words about Knewton
If you are interested in modern technology in education, then you probably already know about Knewton. If not, then the information below will be useful to you!
Why is it important?
Knewton is known for being one of the first to actively apply data analysis technologies in the field of education. As a result of this work, an adaptive educational platform was created that can be connected to any modern learning management system (LMS).
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The idea that an educational application adapts to a student’s unique “learning curve” has great potential. Moreover, the stack of data analysis technologies that allow you to build a similar system is in a fairly mature stage. Despite this, such technologies remain closed to most players in the educational market due to the high cost of development. Only organizations with large resources can afford such a system, for example, Khan Academy, which received multi-million grants from The Gates Foundation, Google and others.
A ready-made platform that allows any educational institution to introduce personalized learning is a big step forward in the development of educational technologies.
As the head of the London office of Knewton Charlie Harrington writes:
Charlie Harrington speaks at EdCrunch conference in Moscow
Adaptive learning
The idea of ​​adaptive and personalized learning originated in the 1950s and goes back to the “learning machines” of the psychologist B.F. Skinner, the founder of behaviorism, was a professor at Harvard University at the time. Based on the principles of learning that he developed during experiments with pigeons, Skinner created a mechanical device that resembles a box that “fed” questions to students. Correct answers were rewarded with new academic material; incorrect - led to the repetition of the old question. “The student quickly learned to answer correctly,” Skinner noted.
"Educational machine" B.F. Skinner
The movement became popular in the 70s on the wave of interest in artificial intelligence technologies. Then scientists believed that sooner or later a computer could adapt to the external environment as well as a human being. The use of machine learning mechanisms in education has become a fashionable topic in scientific circles, but the cost and size of computers of that time deprived this undertaking of any practical meaning.
It was only at the end of the 2000s that the idea began to take real shape, and adaptive learning became fashionable again. Systems like Knewton today have a wide range of functions, such as sophisticated skill development tracking, instant feedback, personalized prompts, and something that was not available to Harvard Skinner students — a computer-like interface!
Knewton interface
How Knewton Works
Knewton founder Jose Ferreira has been engaged in educational technology all his life. Since 1991, he has worked for Kaplan, one of the largest players in the market for paid educational services. In 1993, he tried to introduce ideas of adaptive learning to the company, but he could not move the sluggish corporation from its place, which in general was not surprising - in 1993 only a few people had computers! Jose was ahead of his time, and when technology reached the desired level of development, in 2008 he founded Knewton.
Knewton founder and CEO Jose Ferreira
The Knewton methodology is built around two basic concepts: educational trajectory planning technology and a complex student assessment model. This approach is fundamentally different from most “adaptive applications,” which essentially apply an adaptive approach to the only point where students' knowledge is measured. An example of such a “weakly adaptive” approach is the diagnostic exam, according to the results of which the computer determines what content will be shown to the student in the future. Data mining and personalization technologies are used here minimally or not at all.
Knewton's adaptive learning must respond in real time to the results of an individual student and his actions in the system. This approach increases the likelihood that a student will receive the right educational content at the right time and achieve their goals. For example, if a student does not cope well with a certain set of questions, Knewton will be able to guess which topics covered in this list of questions turned out to be incomprehensible and offer him content that will help increase the level of understanding of these topics.
Individual educational trajectories of two students
Knewton calls itself an additional level of educational application at which data is analyzed. That is why any educational institution or project can work with Knewton. The data that the adaptive platform uses is collected by the educational application itself and transmitted to the Knewton server using the API. To start collecting a certain type of data, for example, when a student has started watching a video or the result of answering a question, it is enough to add one line of code that will transmit this data to Knewton. The adaptive platform analyzes the collected data and returns it to the application in the form of recommendations to the teacher or indicating which block of content should be shown to the student as follows.
Arizona Dream
Arizona State University is the largest US state institution in terms of the number of enrolled students (annual recruitment is 70,000). Its president, Michael Crow, a rebel and troublemaker in the academic world, called his school a “new American university” and chose a strategy of actively introducing modern technologies in the field of e-learning. It was within the walls of the University of Arizona in the fall semester of 2011 that an experiment began on the introduction of adaptive learning, in which Knewton took part together with its partner, Pearson, a giant in the world of paid educational services.
The role of Knewton and Pearson in the project of the University of Arizona
What was the experiment? It was decided to introduce an adjective system for training first-year students in the field of mathematics. The system of adaptive learning had a double focus and, on the one hand, helped the teachers, on the other, helped the student in autonomous work on the material. She used the data to understand the student’s level of knowledge and which learning method is most effective for him. Based on the analysis of these data, the system made a recommendation about the sequence of studying topics. On the other hand, Knewton provided instructors with real-time reports that helped them identify weaknesses in preparing students, create an adapted curriculum for everyone, and pay particular attention to the topics that students learned the worst.
The preliminary results of the experiment showed that the results improved by 18%, and the percentage of deductions fell by 47%. These results inspired The Gates Foundation in 2013 to launch a special program to accelerate the spread of adaptive learning technologies. The experiment also had a significant economic result: this program helped the University of Arizona to earn an additional $ 12 million in additional tuition. As noted in an interview with Jose Ferreira, the University of Arizona pays Knewton $ 150 per student who used an adaptive platform.
Data "before" and "after" the experiment
Knewton Criticism
Despite the enormous and obvious successes of adaptive learning, many skeptics and critics of this approach remain. The first “stone” in the Knewton garden is thrown by specialists in pedagogy. They argue that the Knewton approach, comes from the fact that there is always the right answer. Is this approach universal? And wouldn't the availability of tools that work well for the exact sciences impose this model on other disciplines?
Another question that often arises in connection with Knewton is personal data. Knewton says it does not store student personal data. According to Jose Ferreira: “We help the student to understand his educational history without saving his identification information.” Despite this, questions continue to arise. For example, according to the well-known blogger and journalist in the field of educational technology, Audrey Watters, “What does personalization mean if we cannot identify a person?”.
Knewton in Russia
Taking this opportunity, we will make an announcement - on December 1, at 19:30, Digital October Knewton holds an open lesson “Analyze it: how big data will revolutionize education” . Speaker will be the head of the London office of Knewton Charlie Harrington. The open lesson will be held as part of the educational program “Producer of Online Courses” at the Laboratory for New Professions .
Also - a video of Charlie Harrington's speech at the EdCrunch conference on November 18 in Moscow.
Why is it important?
Knewton is known for being one of the first to actively apply data analysis technologies in the field of education. As a result of this work, an adaptive educational platform was created that can be connected to any modern learning management system (LMS).
')
The idea that an educational application adapts to a student’s unique “learning curve” has great potential. Moreover, the stack of data analysis technologies that allow you to build a similar system is in a fairly mature stage. Despite this, such technologies remain closed to most players in the educational market due to the high cost of development. Only organizations with large resources can afford such a system, for example, Khan Academy, which received multi-million grants from The Gates Foundation, Google and others.
A ready-made platform that allows any educational institution to introduce personalized learning is a big step forward in the development of educational technologies.
As the head of the London office of Knewton Charlie Harrington writes:
Imagine that a teacher can, with the help of a couple of mouse clicks, assess a student’s individual knowledge of his subject at any one time. This will help teachers to easily and quickly identify topics in which a gap in knowledge is only beginning to emerge and change the learning process in such a way as to eliminate this gap. Teachers will have more time to do what they do best - to inspire and teach.
Charlie Harrington speaks at EdCrunch conference in Moscow
Adaptive learning
The idea of ​​adaptive and personalized learning originated in the 1950s and goes back to the “learning machines” of the psychologist B.F. Skinner, the founder of behaviorism, was a professor at Harvard University at the time. Based on the principles of learning that he developed during experiments with pigeons, Skinner created a mechanical device that resembles a box that “fed” questions to students. Correct answers were rewarded with new academic material; incorrect - led to the repetition of the old question. “The student quickly learned to answer correctly,” Skinner noted.
"Educational machine" B.F. Skinner
The movement became popular in the 70s on the wave of interest in artificial intelligence technologies. Then scientists believed that sooner or later a computer could adapt to the external environment as well as a human being. The use of machine learning mechanisms in education has become a fashionable topic in scientific circles, but the cost and size of computers of that time deprived this undertaking of any practical meaning.
It was only at the end of the 2000s that the idea began to take real shape, and adaptive learning became fashionable again. Systems like Knewton today have a wide range of functions, such as sophisticated skill development tracking, instant feedback, personalized prompts, and something that was not available to Harvard Skinner students — a computer-like interface!
Knewton interface
How Knewton Works
Knewton founder Jose Ferreira has been engaged in educational technology all his life. Since 1991, he has worked for Kaplan, one of the largest players in the market for paid educational services. In 1993, he tried to introduce ideas of adaptive learning to the company, but he could not move the sluggish corporation from its place, which in general was not surprising - in 1993 only a few people had computers! Jose was ahead of his time, and when technology reached the desired level of development, in 2008 he founded Knewton.
Knewton founder and CEO Jose Ferreira
The Knewton methodology is built around two basic concepts: educational trajectory planning technology and a complex student assessment model. This approach is fundamentally different from most “adaptive applications,” which essentially apply an adaptive approach to the only point where students' knowledge is measured. An example of such a “weakly adaptive” approach is the diagnostic exam, according to the results of which the computer determines what content will be shown to the student in the future. Data mining and personalization technologies are used here minimally or not at all.
Knewton's adaptive learning must respond in real time to the results of an individual student and his actions in the system. This approach increases the likelihood that a student will receive the right educational content at the right time and achieve their goals. For example, if a student does not cope well with a certain set of questions, Knewton will be able to guess which topics covered in this list of questions turned out to be incomprehensible and offer him content that will help increase the level of understanding of these topics.
Individual educational trajectories of two students
Knewton calls itself an additional level of educational application at which data is analyzed. That is why any educational institution or project can work with Knewton. The data that the adaptive platform uses is collected by the educational application itself and transmitted to the Knewton server using the API. To start collecting a certain type of data, for example, when a student has started watching a video or the result of answering a question, it is enough to add one line of code that will transmit this data to Knewton. The adaptive platform analyzes the collected data and returns it to the application in the form of recommendations to the teacher or indicating which block of content should be shown to the student as follows.
Arizona Dream
Arizona State University is the largest US state institution in terms of the number of enrolled students (annual recruitment is 70,000). Its president, Michael Crow, a rebel and troublemaker in the academic world, called his school a “new American university” and chose a strategy of actively introducing modern technologies in the field of e-learning. It was within the walls of the University of Arizona in the fall semester of 2011 that an experiment began on the introduction of adaptive learning, in which Knewton took part together with its partner, Pearson, a giant in the world of paid educational services.
The role of Knewton and Pearson in the project of the University of Arizona
What was the experiment? It was decided to introduce an adjective system for training first-year students in the field of mathematics. The system of adaptive learning had a double focus and, on the one hand, helped the teachers, on the other, helped the student in autonomous work on the material. She used the data to understand the student’s level of knowledge and which learning method is most effective for him. Based on the analysis of these data, the system made a recommendation about the sequence of studying topics. On the other hand, Knewton provided instructors with real-time reports that helped them identify weaknesses in preparing students, create an adapted curriculum for everyone, and pay particular attention to the topics that students learned the worst.
The preliminary results of the experiment showed that the results improved by 18%, and the percentage of deductions fell by 47%. These results inspired The Gates Foundation in 2013 to launch a special program to accelerate the spread of adaptive learning technologies. The experiment also had a significant economic result: this program helped the University of Arizona to earn an additional $ 12 million in additional tuition. As noted in an interview with Jose Ferreira, the University of Arizona pays Knewton $ 150 per student who used an adaptive platform.
Data "before" and "after" the experiment
Knewton Criticism
Despite the enormous and obvious successes of adaptive learning, many skeptics and critics of this approach remain. The first “stone” in the Knewton garden is thrown by specialists in pedagogy. They argue that the Knewton approach, comes from the fact that there is always the right answer. Is this approach universal? And wouldn't the availability of tools that work well for the exact sciences impose this model on other disciplines?
Another question that often arises in connection with Knewton is personal data. Knewton says it does not store student personal data. According to Jose Ferreira: “We help the student to understand his educational history without saving his identification information.” Despite this, questions continue to arise. For example, according to the well-known blogger and journalist in the field of educational technology, Audrey Watters, “What does personalization mean if we cannot identify a person?”.
Knewton in Russia
Taking this opportunity, we will make an announcement - on December 1, at 19:30, Digital October Knewton holds an open lesson “Analyze it: how big data will revolutionize education” . Speaker will be the head of the London office of Knewton Charlie Harrington. The open lesson will be held as part of the educational program “Producer of Online Courses” at the Laboratory for New Professions .
Also - a video of Charlie Harrington's speech at the EdCrunch conference on November 18 in Moscow.
Source: https://habr.com/ru/post/244539/
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