“Big Data is becoming a key part of the educational landscape, too. The same kind of learning machines that share our lives with us on social media, on our smartphones and on the web take on a special importance as they begin to occupy the educational field.”
– Ben Williamson in Big Data in Education
Artificial intelligence is changing industries across the globe – from retail, to healthcare and manufacturing. The education industry had long been extremely manual, thereby making the collection and use of data extremely difficult. The advent of online learning has changed these dynamics and leading education-focused Venture Capital investment firms such as LearnCapital and OwlVentures focus their investment decisions primarily around the use of Machine Learning (“ML”). The impact areas of machine learning in education are multifold and can be clustered into three key areas :
All of these areas create significant learning efficacy as well as profit improvement opportunities for organizations.
UpGrad, an online higher education company based out of Mumbai, India, with +250,000 learners across Emerging Markets currently leverages ML in two distinct ways:
- Improvement of grader performance (Operations) – whenever a student submits an assignment, his/her assignment will be evaluated by an independent grader from UpGrad’s pool of +300 industry experts, who give feedback to students on any assignment submitted. An ML algorithm has been trained to identify and filter poor-quality feedback such as “Good job” or “Not good”, therefore, encouraging graders to provide more detailed feedback to students. This, in return, increases learning efficacy and customer satisfaction among students who appreciate detailed feedback.
- Improvement of Lead-Scoring Mechanisms (Learner Acquisition) – in order to improve marketing performance, the company leverages ML algorithms to score prospective studentson the likelihood of enrolling into a course through data points such as website behavior, search behavior and attendance in events. This allows UpGrad to optimize marketing spends across different channels.
In the near future, as part of the job-placement product initiative, the company plans to implement further ML algorithms to improve career-support by matching open job descriptions with student skillsets. The algorithm is supposed to learn from the companies’ rejection or acceptance of a suggested candidate profile.
In the long-term, the company plans to use ML to provide students with content suggestions based on their skill-profile and performance in the online program. In this case, the algorithm will predict the likelihood of any student to pass or fail a certain assignment and learn from the actual performance. This data point will then be used to suggest relevant additional readings or assignments prior to the exam.
Looking at the current and future plans of the company to leverage ML, I would recommend expanding the usage, especially in the Learning Efficacy category. Companies such as VIPKid in China have partnered with Microsoft to implement an ML platform that uses the learner’s webcam to assess his/her facial reaction to a new concept and sends prompts to the instructor on how the learner responded in order to improve the learning experience. Similar technologies could be adopted by UpGrad to send personalized content recommendations. In addition to that, I recommend using feedback of companies who hired UpGrad’s learners to redefine the curriculum as well as the assignments used to test certain skillsets. The algorithm could use data collected from partner companies on the performance of candidates and ex-students in certain skill-areas and use these to improve learning efficacy further, thereby improving the ROI for both companies who hired from UpGrad as well as learners.
One key question that will influence technological advancements in education is the degree to which human interaction in learning can actually be replaced by machines – how much human touch is needed to teach skills? Can a machine replace the human relationship element, or will education always require the human touch to offer its full potential?
Ben Williamson, “Big Data in Education – The digital future of learning, policy and practice”, (London, UK: Sage Publications, 2017), p. 10
M.I. Jordan, T. M. Mitchell, “Machine Learning: Trends, perspectives, and prospects”, Science (Jul 2015), Vol. 349, Issue 6245, 255 – 260
R. T. Kompen, P. Edirisingha, X. Canaleta, M. Alsina, “Personal learning environments based on web 2.0 services in higher education”, Telematics and Informatics (2018)
Author’s interview with Tom Costin, Managing Partner at Owl Ventures, New York/ NY, November 12, 2018
Maud Chassigol, Aleksandr Khoroshavin, Alexandra Klimova, Anna Bilyatdivona, “Artificial Intelligence trends in education: a narrative overview”, Procedia Computer Science 136 (2018), 16-24
Tom Mitchell, “What can machine learning do? Workforce implications”, Science (Dec 2017), Vol. 385, pp. 1530 – 1534
Author’s interview with Ravijot Chugh, Head of Product at UpGrad. New York, NY/ Mumbai, India, November 11, 2018
Henry Kronk, “VIPKid is making moves,” eLearning Inside, August 30, 2018, https://news.elearninginside.com/vipkid-is-making-moves/, accessed November 2018