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The potential for machine learning to improve various aspects of higher education is considerable. Read about the possibilities and the limitations of this emerging technology, by Heath Yates, PhD candidate and Software Engineer at Biosecurity Research Institute at Kansas State University and Craig Chamberlain, Manager of Institutional Enrollment and Budget Planning at Pacific Lutheran University.
Software is eating the world, so said Marc Andreesen in 2011.1 These days it seems that machine learning and its specialized algorithms are eating the software world.2 Is it thus a foregone conclusion that machine learning will play a significant role in disrupting technology and shaping our future?
Machine learning concerns teaching machines to learn about something without explicit programming. At the core of machine learning is the idea of modeling and extracting useful information out of data. Societal trends clearly point to data as the resource of the future. Colleges and universities are already swimming in data, and there is much more on the way. Imagine a future in which computers are everywhere and interconnected with everything from clothes to refrigerators, phones, vending machines, and more. Some people have even proposed equipping toilets with sensors that collect data.3 Storing those data will be very cheap.4 These interconnected devices will produce quantities of data that are too large human analysis, requiring us to teach computers to look for patterns in the data, identify predictor variables, and even try to predict for those variables.
Organizations that adapt and adopt machine learning will have a bright future. Machine learning is a new tool in the box, and it is worth learning how to use.5 Colleges, universities, and other educational institutions often adopt disruptive technologies in novel ways and are therefore in a good position to use machine learning to improve higher education. Adopting a machine learning–centric data-science approach as a tool for administrators and faculty could be a game changer for higher education.
Before we discuss machine learning further, it is important to briefly discuss analytics and traditional statistics. It is true that not all predictive analytics needs to be done with machine learning. The traditional methods here are statistical methods such as time series forecasting or various forms of regression. These have been used successfully in many fields for several years. In this article, from a very high overview, we refer to analytics as the subfield of machine learning that is predictive analytics and relies on training algorithms with a labeled training set, otherwise known as supervised learning. A common example is weather.6 Suppose we are interested in predicting sunny days. We can do this by observing our entire data set and feed the conditions into an algorithm that will look at days that were sunny and days that were not. This model is then trained and then can be fed new data and make guesses about whether it is sunny. For our purposes, we are interested in using supervised methods to make predictions and unsupervised methods such as classification to find patterns in the data that we might not have seen.
It is important to discuss the potential benefits and recommendations for pursuing machine learning as a tool for educational experts. In addition, it is important to note potential limitations and ethical considerations. Although an in-depth discussion is beyond the scope of this article, our hope is to start a conversation among higher education administrators, faculty, and IT specialists regarding the potential of machine learning to help make more-informed and better decisions — in other words, get people interested in machine learning to try it and see how things go. We are practicing what we advocate in this article. Heath Yates is actively exploring new algorithmic approaches to machine learning, while Craig Chamberlain is applying machine learning to data in higher education.
Potential Benefits of Machine Learning in Higher Education
Our interest in machine learning began by doing some very simple clustering analysis parallel to k-nearest neighbor (kNN). Such techniques as kNN can assist in finding patterns in larger data for analysts. During the 2016–17 year, Chamberlain was approached by his university to look at a question posed by a donor: "Can we identify a group of students who need an additional scholarship that would eventually lead to increased retention?" After spending time with several data sets and after a lot of research, Chamberlain and his team identified a group of students who needed additional money to remain enrolled. At the time, many believed that increasing retention for this group was a long shot. However, after awarding these students additional scholarships, retention rose from approximately 64% to about 90%. This effort has had two distinct benefits. The most important is that it contributed to the continued success of those students. The second is that it resulted in about $200,000 in additional net tuition revenue from an investment of about $50,000 in scholarships. By conducting basic machine learning to find patterns in the data and testing hypotheses, Chamberlain and his team were able to help students and the university. Although this use case is simple and nascent and relied on some traditional statistical inference, once machine learning and education begin interacting more often, this simple example can evolve into larger data sets with large solutions...
Heath Yates and Craig Chamberlain writes in the conclusion, "Machine learning shows great potential to disrupt how we process and consume data and use software. Serious ethical considerations and limitations must be considered. However, higher education is naturally and uniquely positioned to capitalize on the promise of machine learning by using it as a tool for social and moral good. Higher education has the opportunity not only to use machine learning to help transform itself to make better decisions but also to explore how it might apply machine learning as a force for good. How can machine learning relate to and benefit higher education?"...
Read more...
Source: EDUCAUSE Review
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| Photo: Storyblocks.com |
Software is eating the world, so said Marc Andreesen in 2011.1 These days it seems that machine learning and its specialized algorithms are eating the software world.2 Is it thus a foregone conclusion that machine learning will play a significant role in disrupting technology and shaping our future?
Machine learning concerns teaching machines to learn about something without explicit programming. At the core of machine learning is the idea of modeling and extracting useful information out of data. Societal trends clearly point to data as the resource of the future. Colleges and universities are already swimming in data, and there is much more on the way. Imagine a future in which computers are everywhere and interconnected with everything from clothes to refrigerators, phones, vending machines, and more. Some people have even proposed equipping toilets with sensors that collect data.3 Storing those data will be very cheap.4 These interconnected devices will produce quantities of data that are too large human analysis, requiring us to teach computers to look for patterns in the data, identify predictor variables, and even try to predict for those variables.
Organizations that adapt and adopt machine learning will have a bright future. Machine learning is a new tool in the box, and it is worth learning how to use.5 Colleges, universities, and other educational institutions often adopt disruptive technologies in novel ways and are therefore in a good position to use machine learning to improve higher education. Adopting a machine learning–centric data-science approach as a tool for administrators and faculty could be a game changer for higher education.
Before we discuss machine learning further, it is important to briefly discuss analytics and traditional statistics. It is true that not all predictive analytics needs to be done with machine learning. The traditional methods here are statistical methods such as time series forecasting or various forms of regression. These have been used successfully in many fields for several years. In this article, from a very high overview, we refer to analytics as the subfield of machine learning that is predictive analytics and relies on training algorithms with a labeled training set, otherwise known as supervised learning. A common example is weather.6 Suppose we are interested in predicting sunny days. We can do this by observing our entire data set and feed the conditions into an algorithm that will look at days that were sunny and days that were not. This model is then trained and then can be fed new data and make guesses about whether it is sunny. For our purposes, we are interested in using supervised methods to make predictions and unsupervised methods such as classification to find patterns in the data that we might not have seen.
It is important to discuss the potential benefits and recommendations for pursuing machine learning as a tool for educational experts. In addition, it is important to note potential limitations and ethical considerations. Although an in-depth discussion is beyond the scope of this article, our hope is to start a conversation among higher education administrators, faculty, and IT specialists regarding the potential of machine learning to help make more-informed and better decisions — in other words, get people interested in machine learning to try it and see how things go. We are practicing what we advocate in this article. Heath Yates is actively exploring new algorithmic approaches to machine learning, while Craig Chamberlain is applying machine learning to data in higher education.
Potential Benefits of Machine Learning in Higher Education
Our interest in machine learning began by doing some very simple clustering analysis parallel to k-nearest neighbor (kNN). Such techniques as kNN can assist in finding patterns in larger data for analysts. During the 2016–17 year, Chamberlain was approached by his university to look at a question posed by a donor: "Can we identify a group of students who need an additional scholarship that would eventually lead to increased retention?" After spending time with several data sets and after a lot of research, Chamberlain and his team identified a group of students who needed additional money to remain enrolled. At the time, many believed that increasing retention for this group was a long shot. However, after awarding these students additional scholarships, retention rose from approximately 64% to about 90%. This effort has had two distinct benefits. The most important is that it contributed to the continued success of those students. The second is that it resulted in about $200,000 in additional net tuition revenue from an investment of about $50,000 in scholarships. By conducting basic machine learning to find patterns in the data and testing hypotheses, Chamberlain and his team were able to help students and the university. Although this use case is simple and nascent and relied on some traditional statistical inference, once machine learning and education begin interacting more often, this simple example can evolve into larger data sets with large solutions...
Heath Yates and Craig Chamberlain writes in the conclusion, "Machine learning shows great potential to disrupt how we process and consume data and use software. Serious ethical considerations and limitations must be considered. However, higher education is naturally and uniquely positioned to capitalize on the promise of machine learning by using it as a tool for social and moral good. Higher education has the opportunity not only to use machine learning to help transform itself to make better decisions but also to explore how it might apply machine learning as a force for good. How can machine learning relate to and benefit higher education?"...
Read more...
Source: EDUCAUSE Review
