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July 22, 2022

Automation in online education

Online delivery is rightly seen as a valid approach. However, we must consider the students’ consumer expectations for educational experiences that are as good as experiences in other sectors, such as retail or online services, along with the goal of ensuring students are treated as individuals. Automation is key to meeting these challenges.

 

Tension between scale and individualised learning

In recent decades, higher education (HE) has scaled rapidly. More people can access education than ever before, which is for the greater good – access to HE has been widened and more and more of our global society can view higher degrees as an achievable goal.

 

However, universities are striving to meet growing demand on ever-decreasing budgets alongside increasingly competitive pricing structures. At the same time, today’s student expects their educational experience to reflect the kinds of experiences provided by online retailers and service providers – efficient, high-quality, responsive, and personalised. In this context, the need to meet scale, and still support each student as an individual, is a huge driver for change in learning, teaching, and assessment.

 

Without doubt, technology is fundamental to addressing this challenge, but automation will be pivotal in achieving these goals while remaining within the budgets available to most universities.

 

What do we mean by automation in education?

Automation is a process by which we aim to reduce manual work, usually on repetitive tasks, that can be more easily carried out by computers or other digital technologies.

 

In this sense, automation is already commonplace, and used across all education settings. Think of automatic mail lists for student and staff information, management of student data in the records system, attendance tracking systems and the use of learning management systems/virtual learning environments (LMS/VLE) to share teaching resources.

 

Where this becomes interesting is when education begins to use automation to improve the learning experience and enhance the individualisation of learning.

 

In approaching this we need to ask questions such as:

  • Can data be used to help every student be more successful?
  • Can the experience of each student be adapted to what they most need to learn?
  • Can effective, meaningful, and personal responses to student work be generated without the need for a teacher?

 

Concurrent with this, we also need to recognise concerns that may be raised, such as:

  • Are we doing teachers out of a job?
  • Will students continue to get value for money if the number of interactions with their teachers are reduced?
  • Can all teaching and assessment scenarios be adequately supported though automated processes?

 

Perhaps these questions have not been fully answered yet, but the important thing to do currently is to consider the available solutions and see how they fit into the quality of education that we all want to provide.

 

Types of automation

Learning analytics

The activities of a student generate vast amounts of data. On campus, students have attendance records, visits to the library and library loans logged. Online, students have their visits to the LMS/VLE, levels of participation in online learning activities, scores in online tests and quizzes and grades for all assessments logged.

 

This data can be analysed in a variety of ways, and many universities are providing students and their teachers with dashboards that can be used to show them how well they are performing, how their performance compares to their classmates, and even to predict the likelihood of students to pass their programme.

 

Adaptive learning

Learning analytics can be used to provide personalised learning at scale through adaptive learning.  Adaptive learning works by using technology to monitor a student’s prior knowledge/competency and adjusts the curricula to accommodate that individual.

 

It also allows for monitoring a student’s progress through a course/module and then directing them to relevant learning resources specific to their needs, also known as “just-in-time” teaching.

 

The potential of this is incredibly powerful in helping to reduce attainment gaps and for catering to the developmental needs of students from different degree backgrounds entering postgraduate education. It also greatly enhances, without the direct intervention of teaching staff, the individualisation of each student’s learning journey.

 

Intelligent tutoring systems

Providing personalised tutoring at scale is impossible without large numbers of staff to support this, which is often beyond the budgetary scope of many institutions. Potentially, this hinders their ability to grow the kind of scale they need to remain viable in the competitive HE market.

 

One potential solution is to supplement the human tutoring resource with artificial intelligence (AI) – intelligent tutoring systems (ITS) that create AI tutors. One example of this is using chatbots as tutors (tutor bots) to provide immediate responses to learners’ questions. Machine learning can be applied to direct learners to relevant content, as part of their adaptive learning experience. The scale of ITS is currently fairly limited, but it is without doubt that AI is going to be used increasingly to improve the students experience and reduce the faculty burden in much the same way that it is used to improve consumer experiences across online and tele-sales.

 

Automated assessments

Assessing students is the single biggest challenge in HE. The challenge is two-fold; first is providing authentic and meaningful assessments and the second is providing timely and useable feedback on assessments.

 

The ubiquitous use of LMS/VLE in university education have enabled the common use of automated assessment practices such as multiple-choice questions tests that provide immediate answers and directive feedback. However, automation is becoming much more subtle and complex. For instance, systems have been developed that use NLP – natural language processing – to grade essays and reports against grading criteria.

Are we there yet?

Automation is already prevalent in many functional aspects of higher education, and the technologies are becoming more readily available to carry out tasks of greater complexity that will have a direct impact on the quality of a student’s experience.

 

Some of these, such as automated essay scoring using NLP, are at developmental stages and currently are perhaps out of the budget of most institutions. However, technologies in the education sector are developing at an incredibly rapid pace, and we should expect to see solutions such as these increasingly enabling and enhancing life of the student, teacher, and the institution.

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