Machine learning for education: use cases, payoffs, and challenges

Machine learning for education: use cases, payoffs, and challenges

September 5, 2022

Andrea Di Stefano

Technology Research Analyst

One of the greatest technological wonders of the past three decades, specifically in the realm of artificial intelligence, has arguably been the rise of machine learning (ML), a sub-concept of AI summarizing the ability of computers to autonomously learn through experience without being explicitly programmed to do so.

But machines, like the Lannisters from Game of Thrones, "always pay their debts", and upon receiving the gift of learning, are ready to return the favor with a variety of enhancements that promise to make human learning more enjoyable and productive.

Therefore, let's explore the opportunities unlocked by machine learning consulting in the education sector, along with some real-life examples and potential challenges of its implementation.

The role of ML in education

Machine learning in education encompasses a comprehensive array of learning and training tools powered by self-improving algorithms that can process data, identify key metrics, recurring patterns, and relations among variables, and therefore understand how such factors might influence the core trends and scenarios in this sector.

EdTech service providers, corporate trainers, and educational institutions typically benefit from machine learning in two ways:

Analytical functions:

Several ML-based solutions use machine learning algorithms to analyze education-related data and provide valuable insights to enhance the learning experience. These may include eLearning platforms' recommendation engines, adaptive learning applications, data analytics software for student performance tracking, and more.

Operational functions:

AI-related technologies like natural language processing (NLP) and computer vision rely on machine learning to identify linguistic and visual patterns and autonomously learn how to replicate innate human capabilities, such as sight and communication. These represent the core of interactive tools like virtual tutors, educational robots, RPA bots, real-time translation software, etc.

The variety of use cases and learning environments positively influenced by machine learning has catalyzed massive interest in the potential impact of this sub-branch of AI in education. Indeed, Verified Market Research's 2021 report on AI in education describes machine learning, along with deep learning (a further ML subdivision) and NLP, as one of the three pillars of this market, which was valued at $1.1 billion globally in 2020 and is expected to reach $21.52 billion by 2028.

Global AI in education market trends

The benefits of machine learning in education

The pandemic-driven abrupt shift from classroom to distance education was just the straw breaking the camel's back for a sector suffering from a plethora of other structural challenges, such as the excessive paperwork handled by teachers and administrators, an over-standardization of teaching methods, and unequal access to education. What can machine learning do to mitigate such issues?

  • Personalize the learning experience through tailored content recommendations, customized learning paths, and adaptive training activities.
  • Improve learning outcomes via education analytics platforms to track student performance and adjust learning speed and personal curricula.
  • Enhance operational efficiency by automating several clerical duties, such as administrative, moderation, and evaluation tasks, via ML-powered bots.
  • Improve the profitability of your educational services and training initiatives via targeted ads, cost monitoring, course optimization, ROI analysis, etc.
  • Foster inclusivity thanks to NLP-based real-time translation and transcription of eLearning content, along with tailored learning pathways for special education.
  • Reduce school dropout rate by identifying students at risk via learning analytics, assessing their areas of weakness, and designing tailored interventions.

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10 ML use cases in education

After briefly mentioning the essential applications of machine learning in education and their resulting payoffs, let's better define their use on the ground.

1. Adaptive learning

Professor Keating from the film Dead Poets Society aspired to an education that promoted students' uniqueness. ML-based predictive data analytics is making his dream come true. This technology has been implemented into several eLearning software solutions to monitor a variety of parameters (such as exercise completion time, time spent on the platform, number of logins, and test results) and therefore assess student performance, engagement, and learning patterns or even forecast learning outcomes.

Based on such findings, adaptive platforms such as MobyMax, SchooLinks, Carnegie Learning's LiveLab, and Kidaptive can adjust individual learning activities in real time or help teachers and students plan personalized curricula. Similar tools can be particularly helpful when it comes to providing students affected by specific conditions, such as autism, with special education.

2. Dropout mitigation

A very welcome side effect of predictive analytics applied to education is the ability to track specific variables, such as poor school performance and above-average attendance, to identify students at risk of attrition and dropout and proactively set up personalized initiatives aimed at motivating them and filling their knowledge gaps.

ML-based identification of students at risk of attrition

3. AI tutor and chatbots

Nowadays, we are used to interfacing with several virtual assistants on a daily basis, and students are no exception. NLP-powered tutors and chatbots can be deployed in a variety of educational and administrative functions, such as recommending eLearning content, engaging students with quizzes and training activities, moderating online learning communities, and providing 24/7 user support to streamline student enrollment.

4. Task automation

"Two things are infinite: the universe and human stupidity". The third one, that Einstein forgot to mention, is the bureaucratic workload handled by teachers and school administrators. So much so that, according to McKinsey, educators spend half of their working hours on preparation, evaluation, and other purely clerical tasks, and lack of time is a major obstacle to implementing personalized learning.

Fortunately, most of these procedures can be automated via ML-based software, including test grading, as for the Zhejiang International Studies University in east China, and email processing, for example through Capgemini's RPA solution deployed by the UK Department for Education.

5. Robot teachers

Chatbots' tangible "colleagues" can bring virtual interactions from digital environments to the real world thanks to computer vision and NLP-powered capabilities. Educational robots are typically implemented for early childhood education to develop creative and social skills, especially among introverted kids. Here’s an example from Singapore:

6. Recommendation engines

While having built a reputation mostly in online shopping, ML-powered recommendation systems have also proved exceptionally useful for guiding eLearning platforms' users towards courses, seminars, or any other training resources actually matching their learning needs.

Similarly to their ecommerce-oriented counterparts, these powerful engines can take into account an ensemble of profile data (user information such as gender, age, and profession), activity data (behavioral patterns related to browsing history, likes and dislikes, watch time, etc.), and course metadata (including topic, difficulty, and required skills) to segment learners into different archetypes and target them with suitable learning content recommendations.

LinkedIn's course recommendation system framework

7. Targeted marketing

Targeted ads represent the step before recommendation systems in the routing journey to guide prospective students towards the learning experience that best meets their needs. With the same process adopted by the aforementioned engines, data analytics software can peer into user data collected from social media and other sources to identify learners potentially interested in specific services, be it elearning platforms or private educational institutions, and target them with personalized ads to promote subscriptions and matriculations, respectively.

Here are the results of an ML-based analytical solution adopted by a private nonprofit university and reported by McKinsey:

Conversion rates of an ML-based targeted marketing solution

8. Real-time translation

Breaking down language barriers to broaden the reach of an educational program and promote inclusivity has never been easier, thanks to the availability of real-time NLP-based translation and transcription solutions. Tools like Azure Cognitive Services Translator and Presentation Translator, as the name suggests, can easily translate video lessons, papers, and other types of eLearning content in real time to make your educational services available to a global audience.

9. Augmented and virtual reality

Fuelling technologies such as AR and VR in education, ML-powered computer vision is paving the way for gamification of the learning experience. Educational software embracing this innovative approach can range from kid-friendly apps, which combine physical play items and virtual environments, to eLearning solutions designed to help professionals train with digital simulations.

AR training platform developed by Itransition for an oil enterprise

10. Corporate training platforms

The modern challenges faced by professionals and corporations as a whole, including faster innovation cycles, higher workforce mobility, and a general shortage of specialized talent, are fostering the adoption of ML-powered EdTech solutions in the workplace.

Several online platforms specifically designed for corporate eLearning, such as SAP learning hub, LinkedIn Learning, and AstraZeneca's mentoring portal, have already implemented a mix of the aforementioned tools. Among them, we may find chatbots to moderate learning communities, adaptive learning features to offer tailored mentoring programs, and data analytics systems to optimize training budget planning, calculate the ROI of your training initiatives, and assess your workforce expertise to configure suitable upskilling plans.

ML-powered eLearning apps and platforms

Following the rise of eLearning, further catalyzed by two years of social distancing, the education sector has experienced a proliferation of apps and platforms that allow both individual users to study at their own pace and learning groups to interact in a highly collaborative virtual environment. Here are a few examples of online tools leveraging machine learning for a variety of different functions:

ALEKS

ALEKS

Powered by semi-supervised ML algorithms, McGraw-Hill's tutoring and assessment platform can frame students' knowledge and learning patterns based on test results and other parameters, therefore providing them with tailored learning modules while offering timely feedback on their progress to parents and educators.

Course Hero

Course Hero

This AWS-based eLearning platform offers a fully personalized learning experience by tapping into Amazon's machine learning-focused services. It also includes features such as smart content search and ML-based fraud detection to supervise content uploading and detect spam and copyright infringements.

LinkedIn

LinkedIn

Along with its well-known features for networking and recruiting, the leading employment-oriented social network also encompasses LinkedIn Learning, an online education platform providing professionals with personalized course suggestions thanks to its ML-based recommendation engine.

SAP Learning Hub

SAP Learning Hub

This online portal and learning community, designed to help professionals deepen their knowledge of SAP's solutions, relies on machine learning and NLP to power smart bots acting both as moderators (answering questions, facilitating student onboarding, supervising user interactions, etc.) and curators (recommending suitable training resources).

Osmo

Osmo

The peculiar educational approach embraced by this app combines digital activities and physical game pieces identified through a computer vision-powered camera, and aims at engaging children with an interactive, gamified learning experience covering subjects such as coding, math, spelling, and drawing.

Duolingo

Duolingo

Despite being more famous for its cute but peremptory owl mascot/chatbot than for the algorithms driving its operation (including logistic regression and neural networks), this successful language learning app has implemented ML, DL, and NLP to probe user skills with an adaptive placement test, customize each learning path in real time, provide AI-generated lessons, and fine-tune its courses based on user reports.

Duolingo’s ML-based analysis of user reports to prioritize course improvements

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The challenges of ML-driven education

While relying on self-learning machines to facilitate learning might sound like a funny pun, the actual implementation of this technology in education is anything but a joke. Here are some of the challenges typically faced by corporations and institutions willing to implement it in their daily operations.

Privacy and data security

Learning experience enhancements shouldn't come at the expense of transparency and personal data protection, which have always been a pain point of any ML-powered application due to the infamous appetite machine learning algorithms have for data. Balancing analytical performance and compliance with data management regulations is not an option, but an essential EdTech software development requirement.

Inclusivity vs inequality

While several embodiments of machine learning in education are aimed at promoting inclusivity, the actual requirements in terms of hardware availability, data sources, and internet reliability may turn machine learning and EdTech in general into a source of further inequalities. Filling this gap between wealthy and disadvantaged education institutions should be addressed with proper international policies.

Deployment complications

Even among institutions and companies relying on remarkable financial assets, the adoption of ML-based educational tools may be inhibited by a general lack of professional expertise, IT infrastructures, and implementation strategies required to readapt teaching methods. A mix of national policies to foster digitalization and internal guidelines to promote upskilling should help mitigate any resistance to change.

Unsupervised machines

Delegating repetitive tasks, such as grading, to ML-fuelled bots without any kind of human supervision may lead to unexpected results, as experienced by the International Baccalaureate. This educational foundation implemented an automated scoring system to replace exams in a classroom with historical data-based evaluations during the COVID-19 lockdown, resulting in a huge amount of anomalous grades and consequent outrage among students.

A new way of approaching education

With his characteristic bluntness, Aristotle used to say that "Learning is not child's play, we cannot learn without pain". Maybe he was right or, at least, realistic. But if it's true that education, in order to be effective, might end up being rather painful, at least from a conceptual perspective, we may add that it should also be fair and motivating.

Despite the challenging implications of its adoption, machine learning in education can help us in this regard, reforging our approach to this essential component of personal development and making learning a more enticing, productive, and inclusive experience.