It’s already 9 am, and Abby is in no rush. She doesn’t need to run to college first thing in the morning because her online program allows her to take classes in a laidback home setting. For this, Abby uses a digital platform that integrates machine learning in education to guide students in their studies.
While sipping her coffee, Abby is taking a quiz at her own pace. As soon as she’s done, a chatbot informs her she’s moved six steps up in the class ranking. Not bad! Abby is starting a new course today that she’ll select from a list of suggested courses. Landscape photography? Sounds perfect! And as she browses new learning content, she gets recommendations for additional projects she might want to take on to brush up her skills.
Just like Abby, an increasing number of students are taking classes remotely. On average, over 30% of all university students take at least one course online each semester according to the US National Center for Education Statistics. And with the COVID-19 pandemic showing no signs of stopping, this number keeps growing, making the online learning industry boom both in academic and corporate environments.
The global corporate eLearning market is estimated to grow to $30 billion at a 13% CAGR through the forecast period.
Source: Market Research Future
Educators have been grappling with the waves of change by adopting AI- and ML-powered eLearning technology. Along with artificial intelligence, machine learning in education sector is poised to transform the online classroom experience for distant learners, just like it has for Abby. The engagement, motivation, and fun in her learning process is just a drop in the bucket of what this technology can bring to distance education. Let’s see how beneficial machine learning can be for both students and teachers and how you can start preparing for fundamental changes in the eLearning landscape today.
Can computers become more like students?
Machine learning is a branch of AI that makes use of computer algorithms designed to identify patterns, provide insights, make decisions, and predict outcomes based on analysis of vast data sets. The strength of this technology lies in enabling systems to automatically evolve and increase their accuracy without being explicitly programmed. Simply put, ML allows computers to learn on their own, without human aid or coding. With every new bit of information, the ML program becomes more intelligent and progressively improves its performance on specific tasks by relying on patterns and inferences.
Algorithms, data analytics, and predictions all come together for greater personalization in online learning environments. With machine learning, education technology can reinvent itself to cater to the ever-changing needs of learners. More and more training programs are being adapted to the abilities and knowledge of each student rather than relying on a one-size-fits-all approach. Based on past performance, training platforms can deliver insightful data intelligence to predict students’ needs, create customized, learner-centered experiences, and achieve better learning outcomes.
ML for eLearning software development by Intellias
Consolidate your education software with advanced technologies, lean design, and an emphasis on learners
How is machine learning different from artificial intelligence?
Though artificial intelligence and machine learning are closely tied to each other, they’re used for different tasks. While AI enables computers to simulate intelligent human behavior, ML trains and modifies the system as it’s exposed to more data. The more information you feed into an algorithm, the better the results you can expect. If the algorithm fails to deliver accurate outcomes, AI tries to locate the problem and find a solution by remodeling and retraining its algorithm to refine the results.
In short, if AI enables machines to learn autonomously and make data-driven decisions, ML trains those machines to develop algorithms that can more precisely recognize patterns and trends.
Implementing artificial intelligence and machine learning in education is already creating an adaptive, individualized, and student-centric learning environment. Advances in AI and ML will allow educators to automate behind-the-scenes tasks of analyzing performance to improve student learning outcomes, provide instant feedback to steer students in their studies, and address hidden knowledge gaps.
Advantages of applying machine learning for education
In the years to come, online training experts expect an even greater increase in the use of machine learning in education sector, as it can boost both learning and teaching by bringing tremendous benefits to the classroom.
Improved course efficiency via predictive analytics
The major boon of machine learning in education is its ability to track learner progress and adjust courses to respond to students’ actual needs, thus increasing engagement and delivering high quality training. Feedback from ML algorithms allows instructors to understand learners’ potential and interests, identify struggling students, spot skill gaps, and provide extra support to help students overcome learning challenges.
These are just a few questions that machine learning can answer:
- Are learners interacting with the course materials?
- How long do students spend on each section?
- Did students get stuck or did they just skim the content?
- How long did students take to complete the test?
Automation of intensive administrative tasks
Machine learning education technology implemented in an eLearning course can free training professionals from mundane and time-consuming routines, allowing them to focus on creative and satisfying work that requires a human touch. Machine learning can take care of tedious but critical learning management tasks such as organizing curriculum, onboarding, providing instructions, scheduling classes, delivering content, and grading. By taking over these operations, machine learning technology makes educators more efficient in mentoring students and crafting engaging, valuable, and up-to-date content. Learn how Intellias created an AI/ML-based solution for language learning that uses the power of NLP to provide rapid feedback on progress
Learn how Intellias created an AI/ML-based solution for language learning that uses the power of NLP to provide rapid feedback on progress
Personalized and adaptive learning
Machine learning in education is creating new hyper-personalized experiences where each student is taken on a customized learning path through an online program. A learning path is a series of courses and materials offered to a student based on that student’s previous educational history, knowledge and skills, current progress, preferences, and learning style.
Such a high level of customization helps educators dynamically deliver targeted and learner-centric content to their audiences. In other words, by using ML algorithms, you can make your content smarter and more intuitive in a matter of seconds. Advanced corporate training platforms also feature learning paths to reskill employees and equip them with the expertise they need for workplace success.
Learner motivation boost
Having to go through dull and redundant parts of a program is one of the most demotivating aspects of generic eLearning courses. Introducing machine learning in education sector can change all that. Machine learning algorithms monitoring learner progress can refine curriculum and provide tailored content that resonates with students. By giving learners exactly what they need to achieve their learning goals, online platforms allow them to quickly fill knowledge gaps and build vital skills. Knowing a course offers an individualized experience can motivate students to take it up, while the use of gamification and chatbots can boost their engagement and retention rates.
Advanced assessment and grading
Students often complain about human biases in assessments. Educators, in turn, point to the need for more precise and fairer grading systems. Automated test scoring has been around for a while but incorporating machine learning in education enables smart assessments that can instantly evaluate multiple formats, including written assignments such as papers, essays, and presentations.
Innovative grading tools can evaluate style, structure, and language fluency, analyze narrative depth, and detect plagiarism. ML technologies turn assessment into a matter of a few seconds, ensure accurate measurement of students’ academic abilities, and eliminate the chance of human error.
Find out how Intellias built an engaging AI/ML-enabled learning chatbot that serves as a virtual teaching assistant to learners
Machine learning in education: Success stories
Machine learning education technology is becoming an inherent part of the academic environment, revolutionizing many aspects of teaching and learning. Here are a few notable use cases from prominent US universities:
The University of Michigan builds an automated text analysis solution
Students learn complex material better through writing, and M-Write is an automated text analysis (ATA) tool that analyzes writing assignments for large-enrollment introductory courses to prioritize students who need help. The system guides students through their writing assignments, from drafting to revisions and feedback and right up to the final version of the text.
To analyze students’ papers, M-Write looks for specific words, ideas, and topics to give faculty members personalized insights into the level of students’ understanding of course concepts. If the software finds many students have difficulty with certain material, the professor can make changes to the program to expand on a particular topic and create better learning outcomes.
UC Berkeley creates an ML-assisted scoring and grading tool
Gradescope is designed to automate the workflow for grading exams and assignments in large courses. In a class of several hundred students, to eliminate the grading rush, instructors often use teaching assistants, which may lead to inconsistencies in assessment. The Gradescope software is rubric-based, which means a teacher creates a list of evaluation criteria for each question to provide for more consistent and uniform grading. While students get immediate feedback on their results, teachers save time grading and get an accurate picture of how their students are doing.
UC San Diego launches an adaptive intelligent tutoring system
UC San Diego designed an online advanced course in bioinformatics as an adaptive tutoring platform that leads students through personalized learning paths, provides individualized guidance, and adjusts the course according to a student’s needs. The goal of the intelligent tutoring system (ITS) is to analyze digital learning paths of thousands of students and use this data to tweak course content to address individual challenges of each learner. The system offers students a personalized experience with one-on-one tutoring and allows instructors to evaluate students at every learning stage and take steps to maximize their performance and retention.
How can you start using machine learning for your eLearning course today?
These are exciting times for the world of eLearning. The massive implementation of machine learning in education is rapidly transforming online classroom experiences and becoming one of the most competitive features of any training program. Place and time no longer matter — it’s all about delivering engaging and customized content along with personal guidance and support, making the time spent on training worthwhile for learners. Now the time has come for you to explore and experience how machine learning can benefit your eLearning courses.
At Intellias, we’ve been building eLearning solutions with AI and ML for years and are excited to make these technologies work for leading universities, organizations, and online learning providers. Contact us to find out more about how you can use machine learning for education and let us make it happen for you!