The why’s and how’s of machine learning in education
Machine Learning(ML) and Artificial Intelligence(AI) are transforming many industries including education. The need for more efficient and engaging education models is growing and numerous innovative edtech solutions emerge. Global Artificial Intelligence, the field of automation which contains machine learning, in the education market exceeded $1 bln in 2020 and is expected to reach a staggering $20 bln by 2027.
In this article, we will discuss the advantages of machine learning in education and how exactly ML can be used in this sector. You will find out more about the tools and services for ML development as well as the challenges you might face. Read on to find out how to use technology, changing the landscape of the education industry.
Benefits of ML in education
More and more colleges, universities, and corporate institutions grasp the advantages of Artificial Intelligence in education. Specifically, machine learning can make the learning process more efficient and enjoyable for both students and teachers. Here are the main benefits of employing ML in this sector.
The advance of personalized learning
Traditional educational methods are based on a one-size-fits-all approach. In other words, all students are taught the same way. Machine learning can make the education process flexible enough to be tailored to each student.
Algorithms help to explain how students learn and consume information. A Student’s educational history, preferences, current progress, and learning style are taken into account. Therefore, each learner can proceed at their individual pace. There is a much lesser chance that someone will be left behind. The teachers can monitor each student’s progress, paying attention to those areas where they experience difficulties.
Such an approach boosts student engagement and retention rates. Refined curriculums and tailored content resonate with learners more than standardized ones. Gamification and chatbots can also serve to keep them interested. Even the fact that some courses offer personalized experiences can persuade students to take it.
“An AI system with rich data can also provide a more personalized learning experience. For example, if an art history student is struggling with a certain topic, an AI engine can recommend materials that past art history students have benefitted from. It may also recommend classes that other art history students have taken and liked. This could allow students to exercise more autonomy by learning at their own pace and playing an active role in what they learn”, says Sameer Maskey, Founder and CEO at Fusemachines, Adjunct Associate Professor at Columbia University.
Routine tasks automation
Tracking class attendance, organizing curriculum, providing instructions, and onboarding are essential parts of the educational routine. However, they are tedious and repetitive, eating up a substantial amount of time and effort. Using machine learning in education, teachers can reduce that burden, focusing on creative and satisfying tasks. As a result, they get more efficient in actually teaching their students.
Faster and more accurate grading
Teachers strive to access their students’ work objectively and quickly to facilitate evaluations and grading. Students, teachers and administrators are interested in removing human biases from this process. Automated test scoring isn’t something new, but the spread of machine learning in edtech boosts intelligent assessments while potentially mitigating biases. State-of-the-art grading tools can evaluate written assignments such as presentations, essays, and papers allowing teachers more time to focus on students.
Algorithms can evaluate style, language fluency, and structure, look for plagiarism and analyze narrative depth. Moreover, they can do it in a matter of seconds. The teachers will remain in control of the grading process, but ML will help them be more efficient and impartial.
How can machine learning be used in education?
ML and AI have already found numerous applications in this sector. Let’s look at different machine learning use cases in education and find out which tools can be utilized to build ‘smart’ edtech.
Automated text analysis
As we’ve already mentioned, ML can become a valuable tool in analyzing written assignments. For instance, the University of Michigan created its own instrument for that, called M-Write. While analyzing students’ papers, it looks for specific words, and topics providing insights on how well each student understands the course. Depending on the results, professors can focus on helping some students, move those who show proficiency through or amend the program altogether as needed.
Predicting student outcomes
Teachers no longer have to rely only on their ‘gut feeling’ while determining which students will graduate successfully. Prediction analytics systems can help to spot students at high risk of dropout. For instance, Taiwanese researchers proved that machine learning algorithms could predict dropouts with 80% accuracy. Armed with that information, universities can step in early, to support, help and guide learners at risk.
Closing learning gaps
ML algorithms can also help to find and fix missing skills. MobyMax solution for US schools offers personalized curriculums for each student depending on their individual needs. The application identifies learning gaps during diagnostics and suggests where the focus of particular students should be. Various games, contests, and certificates should motivate learners to keep on.
Choosing where to study
ML can be helpful even for would-be students. There are a lot of educational institutions marketing their services. The CollegeAI tool makes the task of choosing the best college easier. A student feeds relevant information into the algorithm and gets some insights about the most suitable options. Users can find out why a certain educational institution is or isn't a good choice for them.
Available ML technologies, frameworks, and services
There are plenty of tools you can use to put machine learning and education together. Here are some of the most popular ones:
Python is a programming language, which is often used for machine learning algorithms. It is simple, consistent, flexible, and offers access to great libraries and frameworks for AI and machine learning and has a wide, active user community.
Go, or Golang is another programming language, which can be used for ML. Speed is one of its advantages. Unlike Python, Go allows the asynchronous execution of multiple algorithms without affecting the outcome.
TensorFlow is an open-source software library for ML and AI. It focuses on creating deep neural networks used for image and video analysis, voice recognition, and text processing.
PyTorch is another open-source ML library. Its applications include natural language processing and computer vision. While both TensorFlow and PyTorch are great libraries, the latter is for production ML models and scalability. At the same time, PyTorch is easier to work with and, hence, better suited for building prototypes.
Keras is one more open-source library, acting as an interface for TensorFlow. It provides access to the building blocks of artificial networks. With its help, one can work with image and text data.
Calligo is a company offering Machine Learning as a Service. A team of data scientists will build, fine-tune, and monitor the performance of algorithms for you.
Google Cloud ML or Vertex AI is a service that simplifies creating, deploying, and scaling machine learning models. It offers pre-trained solutions for computer vision, video, and natural language processing, and more. Vertex AI supports all open source ML frameworks.
Computer Vision API, provided by Azure, is an AI service for image and video analysis. It allows users to extract handwritten and printed text from images, detect concepts and objects, and analyze the movement of people in space.
AWS Lex is a service for building chatbots that use voice and text like Amazon's Alexa. It uses deep learning for converting speech to text as well as natural language understanding.
The challenges of using machine learning for education
Education can benefit from the use of machine learning and artificial intelligence. Like any technology, it should be applied with caution. Some inherent restrictions, as well as external factors, hinder the penetration of machine learning in education. We listed the main challenges for you.
Organizing data collection and processing
Machine learning algorithms rely on the quality as well as quantity of available digital data to provide accurate predictions. However, there are some practical and ethical limitations to the amount of information we can collect about each student. Finding the balance is important to make the algorithms as reliable as possible without violating students’ privacy.
Some information, such as the student's job role and title, listed skills, etc., can be easily used by the e-learning system. Other data like existing experience and the real level of knowledge aren’t always easy to measure and factor in. Starting your ML project, you should figure out how to collect and digitize all necessary data.
Data hygiene and cleansing are essential for accuracy and quality of analysis. The information we collect may be partly incorrect or incomplete. Moreover, it may come in different formats from a variety of data sources. Synchronizing and unifying all those data flows as well as dealing with the data flaws is a cornerstone for any ML project.
“Machine learning models aren’t designed to distinguish between good data and insufficient data. Instead, algorithms take the training dataset provided, no matter how deficient or irrelevant, and use it as a precedent that determines all resulting analyses and conclusions. Without data comprehensiveness or the proper quality standard, the old dilemma of “garbage in, garbage out” comes home to roost,” says Srinivas Arasada, Chief Technology Advisor at Evolutyz Corp.
Training machine learning model
The right data should be combined with the right algorithm to provide the best results. Finding the one that suits your needs and fine-tuning it may require a lot of time and effort. For the best results, consider the trade-offs and constraints of all available options. The option you choose should work well enough to justify the use of the whole system. It requires testing and rigorous analysis.
The problem with machine learning algorithms is that there are a lot of things that may go wrong. Deciding which parameters to tweak requires some intuition born from experience. If you want to build an effective ML-based software for education, look for skillful and knowledgeable engineers to do the job.
Ensuring the effective use of new instruments
Aligning complex ML systems solutions with the business/education processes is essential to realize the value in the solution. For instance, an algorithm pointing out students at high risk of dropping out isn’t enough to improve the graduation rate. It should be combined with a strategy on how to help those students and potentially prevent the dropping out proactively.
There are questions about education techniques and how ML-powered systems can assess the progress of each student and change their learning plan. However, they don’t design the improvement strategy on their own. Humans do it for them — and they may employ methodologies, which work well in classrooms, but not necessarily for online education.
As the technologies improve, algorithms may come as far as choosing the most suitable education strategies. Until then, even the smartest models are as good as the guidance of educators who ‘teach’ and use them. Therefore, teachers and professors should be open-minded about the techniques they use considering what the data may suggest.
Dealing with high development costs
The advancement of machine learning in the education industry can result in complex education platform solutions. Those platforms have the potential to revolutionize the education process. They can monitor students’ interactions with the learning material, analyze it, and provide suggestions for improvement.
However, building, fine-tuning, and maintaining such edtech requires time and money. Skillful and experienced developers are needed to fulfill the task. Educators should work alongside engineers to tailor the new systems to the needs of their institutions. For instance, developers have to understand the depth of the information adaptation for each student. Should it be curriculum or course level? Rules and predictions also have to be adjusted. Dealing with that and many other details may result in significant capital costs and budgets.
The future of ML in education: Menklab’s vision
Menklab has enough knowledge and experience to enhance the education process with machine learning tools. Our team has already built some sophisticated software systems for clients from this industry. We have some perspective ideas about using ML in education.
For instance, employing this technology for scheduling in Nurture, our education application, can be the next big thing. In pre-school institutions, children are assigned to different classrooms according to their age (and, hence, abilities). A director has to decide whether a particular child is ready to graduate from one classroom to another. As a result, new children from the waiting list may be admitted.
If the number of children and classrooms is large enough, choreographing such movements requires tons of experience. New directors may be inefficient at this task and ML-based systems, analyzing the information about each child, could make their job easier. Educational institutions could potentially maximize facility and staff efficacy and thus their profits, admitting new children as swiftly as possible.
Another idea of employing ML in classrooms includes computer vision. Algorithms can be trained to analyze camera feeds, assigning names to particular faces. It could help to automate some routine tasks such as attendance checks. Moreover, this technology could boost security.
Change the future of education together with Menklab
The role of machine learning in education is unlimited today. This technology has the potential to revolutionize the industry, advancing adaptable, personalized learning. ML-based education systems can help to improve students’ engagement, at the same time boosting teachers’ efficiency. Therefore, they are beneficial for the student performance, retention rates as well as educators’ ability to teach more one-to-one and their own job satisfaction.
Machine learning is a sophisticated tool which requires knowledgeable and experienced specialists to design, integrate and deploy effectively. Menklab’s team can do the job combining both our experience and passion. We will gladly help you to leverage the power of ML and AI, using all our skills and years of experience. Our company has successfully completed complex projects for clients from various industries, including education. Contact us. We’d love to hear your ideas and make them a reality!