Five opportunities for transformation in higher education
How to be part of the exciting future of education?
In the last blog on Higher Education we wrote about empowering educators with technology to manage and scale content delivery, leverage real time assessments, etc., and provide effective lifelong learning opportunities to a new generation of students. This is the fourth and concluding blog in the series. Here, we will attempt to examine the areas where technology can create revolutionary interventions and up the game in Higher Education.
We know that educators are keen to improve their methods and deliver better learning outcomes. But, historically, higher education has been more cautious than other industries when it comes to change. However, it cannot push back on digital much longer -- it cannot continue to use yesterday’s techniques to prepare students for tomorrow. Fortunately, an entire revolution in education is starting to slowly brew. Modern classrooms are increasingly seeing interactive smart boards, digital projectors, laptops, tablets, surface tables, smart phones and display headgear. Backing them are fresh new digital applications, libraries of rich media content, high-speed networks, cloud compute and storage infrastructure. Somewhere in between the two are systems capturing a vast amount of student data, learning preferences and classroom progress. Sitting inside the data are vital insights that can change education forever by telling us how education should be delivered so that it can be dynamically aligned with student needs, their capabilities, the desired target outcomes while making education more engaging, efficient and cost effective.
Institutions of learning, have spent centuries building infrastructure, and will now turn their attention to nifty technology that is already so much a part of their student’s lifestyle. One forecast by EdTechXGlobal and IBIS Capital says that the global EdTech market will be $252b by 2020, growing at 17% per annum [i]. Expect a tsunami of change at the hands of that kind of investment.
Digital has already begun to play a transformative role in two key areas: Universities and colleges now have extended reach and can penetrate practically any market using remote delivery methodologies and, more importantly, they are becoming acutely student-centric, with the ability to deliver lifelong learning with future employment as the goal.
Today, for a number of streams, students anywhere in the world can access the finest Ivy League education online. Today’s education systems allowing students to access learning from anywhere. -- their home, a café or even a campus across the country—by pushing content across mobile and data networks using cloud technologies. Students can even deliver assignments, take tests and be evaluated online. Among the more significant advantages of this model is the fact that a student can repeat a lecture as often as desired, take tests as many times as necessary and be better prepared.
To fine tune the outcomes of these system, it is data that is the fundamental building block. When a student takes the same test many times over, what does it tell us? How can that data – along with other critical information such as time taken to complete previous classes, submit assignments, type of device used to access education, preferred language of interaction, socio-economic background, age, peer performance, faculty interactions, etc. – be used to calibrate the delivery of education and create interventions? In other words, how can data and analytics help create pro-active and prescriptive education systems to improve outcomes?
There are 5 areas where there the opportunities for transformation are significant:
- Adaptive Learning: This approach moves away from the traditional, linear, one-size-fits-all methodology that has dominated education for centuries. Adaptive Learning uses a student’s strengths, weaknesses, interest levels and engagement patterns to re-shape content for a truly learner-centric experience. The methodology depends on Data Sciences, Artificial Intelligence (AI), Machine Learning (ML), Cognitive Sciences and Predictive Analytics in various permutations and combinations to create dynamic and scalable personalized content, mimicking one-on-one learning, allowing students to master a topic before moving on to the next. There is nothing new about the exceptional results that Adaptive Learning can deliver. In 1984, education researcher Benjamin Bloom, well-known for his ‘2 Sigma Problem’, had shown how students in one-to-one learning situation performed two standard deviations better than the learner in a conventional setting [ii]. Adaptive Learning simply uses technology to scale the idea of one-on-one learning, without linear costs.
- Predictive Analytics: Students drop out of university programs at various points due to a number of complex reasons. These could range from their socio-economic imperatives, selecting the wrong course, long commutes and the inability to manage curriculum schedules, faculty relationships, etc. While each of the problems has a solution, it is predicting and identifying students that are most likely to drop out that is difficult. Often, students leave before the solution can be applied. The challenge of retention can be addressed by analyzing student data and using it to predict which of the students are most likely to drop out. Interventions can then be applied before the student drops out. Predictive analytics has another important role to play – that of assessing academic performance and identifying students that may need interventions before their grades fall. One college developed a mobile application that predicts a student’s grade point average (GPA) based on attendance, study duration and focus, frequency and duration of partying, bed time, wake up time, sleep duration, face-to-face conversations, physical activity, location-based information, and other such inputs [iii]. The application used auto-sensing technologies and algorithms to arrive at its predictions, allowing educators to take timely and appropriate action.
- Smart Machines or BOTs: Once a predictive analytics engine is in place, smart machines or BOTs can be used to provide the necessary interventions in terms of adaptive learning measures as well as assisting in student and faculty advice. Fundamentally, Smart Machines or BOTS focus on delivering solutions that improve outcomes.
- AI-Enabled Conversational Assistants: It is almost impossible to have faculty members available all the time to answer questions from students. In such instances, AI-enabled conversational assistants can prove to be handy. These domain-specific applications, built on powerful speech technology such as Natural Language Processing (NLP) and AI, help provide the answers that students are looking for. In addition, these applications can be deployed to work as a teacher’s assistant—they can be trained to analyze and grade students, provide instant feedback and even virtual tutoring. Students are already using such technologies in the form of Siri, Alexa, Cortana and Mycroft [iv] to manage their lives and they expect it to help with their education too.
- Virtual Reality: Decades of work have gone behind creating classrooms with dependable course material. However, providing experiential learning has always been a challenge. It is resource and cost intensive. Virtual Reality (VR) can change that. VR can transform learning through interactive, immersive, and dynamic experiences that enable educators and students to engage with each other through a wide spectrum of interactive resources. Once recent study found that 2% of teachers have used VR , 60% of teachers are interested in using it, 93% sayy their students would be excited by the prospect of using VR and 83% say it will improve learning outcomes [v]. VR has some remarkably interesting applications in higher education. One university, for example, is using it to send law students chasing clues to create a murder case. Instead of reading the case – as would happen at a traditional law schools – students here visit the actual crime scene and discover their own evidence with the aid of VR combined with a gaming platform [vi].
There are a number of areas where technology can be used to enhance student learning, empower educators and optimize operations. It is almost impossible for a university or college to take on the task of complete 3600 transformation. But it could identify areas where it is simple to apply technology, do a small pilot, see if it works – if it doesn’t, simply turn to the next small experiment. And in the event that the experiment results in better academic performance and cost management, just scale the experiment. Sweeping changes should not be the goal – rather, like good academicians, the goal should be to test and try before transforming.
Would you agree that the five technologies have discussed above will be the key to change in Higher Education? Or are there other technologies that should gain precedence? Please do leave your comments – they will help us think about more appropriate solutions to meet the emerging needs of educators, students and other stakeholders in education.
Check out the post ClassTech—linking classrooms to virtually any place in the cosmos the first in a series of posts on education.
Check out the second post in this series - Engaging Digitally Native Students with Contextual and Immersive Experience powered by Insights
Check out the third post in this series - Tech hacks to empower educators
This blog is co-authored by -
Shriharsha Imrapur, Global Head of Media and Education, Mindtree
Shriharsha is helping some of the world’s premier education institutions in their transformation towards the digital future. He lives at the intersection of business and technology and speaks at industry and client forums on topics such as digital constituent experience propelled by data and cloud. He heads Mindtree’s Media and Education business globally and is based out of London.
Sriram Jayaraman, Head Digital Solutions and Consulting, Mindtree
Sriram comes with over 20 years of experience in designing solutions at the nexus of devices, social, mobile, analytics and cloud technology to transforms the way organizations deliver customer experience. Prior to Mindtree, His experience focuses on designing technology solutions that are aligned to create a compelling customer experience especially for education, technology, retail, travel, e-commerce, BFSI, betting and gaming industries around the world. Sriram is a frequent speaker at tech forums such as TechEd, Ignite, Microsoft user groups and ASUG.
[i] https://www.prnewswire.com/news-releases/global-report-predicts-edtech-spend-to-reach-252bn-by-2020-580765301.html [ii] http://web.mit.edu/5.95/readings/bloom-two-sigma.pdf [iii] http://www.cs.dartmouth.edu/~campbell/smartGPA.pdf [iv] An open source voice assistant - https://mycroft.ai/ [v] https://www.businesswire.com/news/home/20160627005621/en/Survey-Finds-Teachers-Virtual-Reality-Reality-Classroom [vi] https://www.westminster.ac.uk/news-and-events/news/2016/university-of-westminster-academics-collaborate-in-developing-cutting-edge-games-for-criminal-law-students