Machine learning in education is a prime example of how technology strengthens human processes, especially when it comes to teaching and learning. Whatever your desired outcomes are, from creating educational software to reducing the burden of administrative tasks, machine learning is a vital tool in achieving your goals.
At Intellias, our experts work with clients all over the world to develop technological solutions for the education sector. Bring your ideas to life and improve your students’ learning outcomes with a team of experts by your side.
What is the role of machine learning in education?
Machine learning (ML) is a field of computing that enables algorithms to learn without continual reprogramming or any other external input. By analyzing new data, a system using machine learning becomes more intelligent thanks to its ability to notice and classify patterns and trends. Ultimately, this results in improved performance over time. Implementing machine learning in education helps students, teachers, and administrators improve their processes and enhance the learning journey.
Identifying learning difficulties
Machine learning uses adaptive learning to identify difficulties students may be experiencing. Due to heavy workloads or overcrowded classrooms, tutors often fail to distinguish struggling students from uninterested participants. Machine learning can help to flag learning issues and enable a tutor or program to alter teaching methods in response.
Personalizing teaching and learning processes
ML algorithms allow educational software to personalize content, schedules, and learning goals to students’ needs and capabilities, thereby improving the efficiency and quality of teaching and learning. This lets tutors shift their focus to processes that benefit from a human perspective.
Predictive analytics
Educators use ML to predict future learning outcomes and change teaching methods based on these outcomes. Predictive analytics detects patterns in student behavior and determines the probability of each student passing a course or pursuing extracurricular activities.
Identifying and predicting problems in K12 education
Machine learning is particularly beneficial at the K12 level. With ML, behavioral problems and poor academic performance can be identified or predicted with a high degree of accuracy. Educators can then intervene and address issues before it’s too late. ML is also used to improve security and to enable students and parents to access self-service tools.
Covering applications in higher education
ML can predict enrollment levels and help higher education institutions find potential applicants. There are also applications for research, as machine learning algorithms can quickly and accurately analyze large, growing datasets.
Improving marketing for EdTech companies
It isn’t just schools and colleges that can benefit from machine learning. Learning and EdTech companies also use ML to improve learning outcomes, customer service, and targeted marketing. Capabilities such as text-to-speech, translation, transcription, chatbots, and content classification are a bonus.
What is the difference between machine learning and artificial intelligence?
Machine learning is a branch of artificial intelligence (AI). AI refers to the simulation of human intelligence by a computer program. ML is the ability of a computer program to learn, improve, and adjust its performance over time in response to new information. If performance is not at the expected level, AI can identify the issue and alter its algorithm.
AI enables the autonomy of systems and data-driven decisions, while ML involves the continuous development of algorithms that find patterns and trends. Well-known examples of ML include the facial and voice recognition used by Google Assistant, Facebook, Alexa, and Siri.
There are three main types of machine learning:
- Supervised ML
- Unsupervised ML
- Reinforcement ML
Supervised ML
With supervised machine learning, data is labeled and provided to the system, which is programmed to analyze data and predict outcomes. One common example of supervised ML is email spam folders. Users give rules that govern what is sent to the spam folder. Supervised ML is also often used in marketing and advertising.
Unsupervised ML
Unsupervised machine learning works with unlabeled data and separates it into categories without human input. For example, algorithms on platforms like YouTube and TikTok identify patterns in user habits and generate recommendations.
Reinforcement ML
Reinforcement machine learning either rewards or punishes systems based on their performance and specific outcomes — for example, based on error reports. This allows systems to become increasingly capable of making and responding to decisions. A notable use case is self-driving vehicles.
What are the benefits of machine learning in education?
Using machine learning in the education sector improves teaching and learning processes in various ways. Experts predict that ML will become an increasingly significant part of the education sector over the coming years due to the many benefits of machine learning applications in education, such as:
Greater global access to online learning
eLearning courses and individual materials (video lectures, eBooks, discussion forums, etc.) can be expensive to translate into different languages. ML programs can use natural language processing and deep learning in education to translate and transcribe content quickly, accurately, and cost-effectively.
More efficient courses
Predictive analytics allows a course to adjust to individual needs. Using machine learning, skills gaps can be identified, and specific pieces of content can be delivered to learners based on their performance. Learners can also see other metrics, including how long they spend on each section and their degree of interaction with course materials. This ensures that ML-based courses are more efficient and effective than traditional one-size-fits-all courses.
Reduced administrative workload
ML-based eLearning courses can automatically carry out many administrative and management tasks including onboarding, scheduling, providing instructions, tracking attendance, and grading work. This frees up time for educators to spend on more creative, subjective, and human-focused tasks.
Personalized learning pathways
One limitation of traditional teaching processes is the homogenous nature of educational courses. Learning styles vary, and homogenous approaches give certain styles an advantage while placing others at a disadvantage. Machine learning systems enable differentiated instruction, where teaching is optimized to suit each individual learner. This allows educators to provide targeted, personalized learning pathways without the extra workload needed to do it manually.
A more effective user experience
Using ML in education can improve the experiences of all eLearning system users, including teachers, learners, and administrators. Personalization of course content and the streamlining of administrative tasks increases engagement and motivation, leading to improved learning outcomes. Self-service capabilities can also be useful for students who lack the confidence to approach teachers or other staff with concerns.
More accurate assessment and grading
While manual grading can be affected by human error and bias, machine learning systems don’t have these limitations. This results in fairer and more accurate grading and assessments. ML algorithms also automatically detect plagiarism in learners’ projects.
Faster research
Machine learning dramatically benefits higher education research departments. Deep data science capabilities (for example, text mining) of ML systems have many implications for the discovery and management of research depositories.
Improved enrolment processes
ML is used extensively in advertising and marketing to identify and reach target audiences. Educational institutions use these capabilities to attract the most suitable students. Each system can also detect and prevent fraud or security concerns during enrolment.
Machine learning in education: Case studies
There are many machine learning use cases in education. Well-known examples include:
UC Berkeley’s Gradescope
At UC Berkeley, a tool called Gradescope streamlines grading workflows during times of high demand. Teachers determine a list of criteria against which the tool compares students’ work to provide accurate performance assessments.
University of Michigan’s M-Write
The University of Michigan’s M-Write automated text analysis tool was developed using ML principles. It analyzes written assignments to determine which students may need help and aids them with drafting and revising.
UC San Diego’s adaptive tutoring platform
UC San Diego’s online bioinformatics course uses an intelligent tutoring system (ITS) to continually analyze students’ learning paths. Using data to alter course content for each student, it provides actionable data on learner performance to instructors and tutors at regular stages.
Grammarly
The grammar checking and plagiarism detection tool Grammarly has more than 30 million daily users, including teachers and students. The platform is based on machine learning, deep learning, and natural language processing algorithms. Using global data to improve suggestions, it becomes familiar with each user’s writing style and identifies common errors.
What are the challenges of using machine learning in education?
Of course, no technology provides a magic solution for every challenge. While machine learning and education are well-suited, implementing ML at its full potential requires knowledge, planning, and practice. Using ML systems carelessly introduces a new set of challenges and issues.
For example, AI-based technologies can only be as accurate as the data they are provided and trained with. This is why rigid data governance policies are vital. ML programs use instance-based learning, where data is compared to instances experienced during training. As a result, algorithms can replicate human biases (for example, around race, gender, age, or other characteristics). If a system is trained using biased data, these biases can be embedded into the system’s programming and decision-making.
Issues such as biases or inaccuracies can go unnoticed if ML models aren’t regularly audited by those with an understanding of how they learn and work. Simply delegating all tasks, processes, and decisions to education software (for example, an automated grading system) without any human input can be disastrous. Machine learning programs should be treated as useful assistants rather than replacements for human educators and other experts.
How can you start using machine learning for your eLearning course today?
The use of ML in educational processes offers many opportunities for teachers and learners. Making the most of these opportunities requires the precise application of machine learning in education. Implementing and using ML successfully means choosing the most engaging content and using a multistep model.
An effective multistep model may look like this:
- Analyzing historical data to find the attributes of successful learners.
- Setting goals for achieving success and improving learning outcomes. For example, your goal could be to raise the average grade by a particular amount in a particular time frame.
- Using historical data to construct a machine learning model that finds the attributes of unsuccessful learners.
- Comparing this ML model’s effectiveness against the institution’s existing processes.
- Defining archetypes of learners who are least likely to succeed.
- Planning interventions and actions designed for students of each different archetype.
If you’re unsure how best to do this, partnering with a service provider with expertise in ML is particularly useful. Providers of machine learning development services offer expert support and guidance on how to implement ML most effectively for specific requirements and projects.
Delivering the personal guidance and support that learners need is essential. Neglecting this is unfortunately common with distance learning and eLearning courses, regardless of the technologies that power them.
What is the future of machine learning in the education sector?
Online learning has already shaken up the education sector and removed many challenges of traditional classroom-based learning. Machine learning is the next step in the evolution of educational technology and is designed to improve teaching and learning even further.
Predictive data and analytics enable more personalized, customized, and streamlined learning pathways while increasing learner engagement. As a result, educators’ focus will shift from administrative tasks, classroom control, and other routine matters to providing direct support to learners and more effective teaching.
Machine learning provides immense benefits and opportunities to educators and institutions. However, many schools and colleges are likely to implement machine learning in ineffective or even damaging ways. Input from experts is invaluable, especially if your institution lacks the internal knowledge and skills needed to utilize ML most effectively.
At Intellias, we’ve developed eLearning solutions using AI and ML for leading universities, course providers, businesses, and online learning specialists across the globe. Contact us to discover how to use machine learning in education, and let’s create a solution together.
