machine learning in ed tech
Contributors : F M Nurul, Rohan Roney, Smruti Ranjan, Roopesh Valluru
The Internet has become one of the vital ways to make available resources for research and learning for both teachers and students to share and acquire information. Technology-based e-learning encompasses the use of the internet and other important technologies to produce materials for learning, teach learners, and also regulate courses in an organization. Typically, e-learning is conducted on the Internet, where students can access their learning materials online at any place and time.
It is flexible when issues of time and place are taken into consideration. Every student has the luxury of choosing the place and time that suits him/her. The adoption of e-learning provides the institutions as well as their students or learners the much flexibility of time and place of delivery or receipt of according to learning information.
E-learning enhances the efficacy of knowledge and qualifications via ease of access to a huge amount of information.
It is able to provide opportunities for relations between learners by the use of discussion forums. Through this, e-learning helps eliminate barriers that have the potential of hindering participation including the fear of talking to other learners. E-learning motivates students to interact with other, as well as exchange and respect different point of views. E-learning eases communication and also improves the relationships that sustain learning. E-Learning makes available extra prospects for interactivity between students and teachers during content delivery.
E-learning is cost effective in the sense that there is no need for the students or learners to travel. It is also cost effective in the sense that it offers opportunities for learning for maximum number of learners with no need for many buildings.
E-learning always takes into consideration the individual learners differences. Some learners, for instance prefer to concentrate on certain parts of the course, while others are prepared to review the entire course.
E-learning helps compensate for scarcities of academic staff, including instructors or teachers as well as facilitators, lab technicians etc.
The use of e-Learning allows self-pacing. For instance the asynchronous way permits each student to study at his or her own pace and speed whether slow or quick. It therefore increases satisfaction and decreases stress
Let us look a some of the shortcomings of E-learning :
E-learning as a method of education makes the learners undergo contemplation, remoteness, as well as lack of interaction or relation. It therefore requires a very strong motivation and time management skills in order to reduce such effects.
With respect to clarifications, explanations, and interpretations, the e-learning method may be less effective that traditional methods of learning. The learning process is much easier face-to-face with instructors or teachers.
When it comes to improvement of learner’s communication skills, e-learning may have a negative effect. Though learners might have an excellent academic knowledge, they may not possess the needed skills to deliver their acquired knowledge to others.
Since tests and assessments in e-learning are frequently supervised by proxy, it may be difficult, if not impossible, to control or regulate activities such as cheating.
E-learning may also be subject to piracy, plagiarism, cheating, inadequate selection skills, and inappropriate use of of copy and paste.
Despite the shortcomings, it is essential to use the data analytics and machine learning tools to improve the state of e-learning by personalizing content and resources to students’ needs and progress. ML has to intervene so as to make content of the e-learning platforms more fun and interactive, using the insights obtained by analyzing past user-content engagement data. With the onset of pandemic worldwide and increasing commuting restrictions, there has been an increasing demand to improve the standard of imparting online education on a mass scale. Ed-tech companies now need to put extra efforts in building their trust among students, as more and more students are going online for their studies.
Machine learning for education. So, what does it bring? It is already changing the global landscape of online education. As per the research report released by Technavio, the global market of online courses based on machine learning technologies will grow with the CAGR higher than 16% by 2022! This rapid growth is bound to happen due to affordable large volume data storage and its efficient processing. From the perspective of making the e-learning process more engaging and result-driven, machine learning and artificial intelligence are already turning it into a more convenient way of getting the knowledge as compared to usual courses or corporate training. What are the features that they can offer to both sides of the process — students and universities?
Machine learning and education processes are closely interconnected. ML algorithms analyze how the students perceive and explore the information they are given. It helps the system to either draw the user back and go through some learning points again or let them step further. ML also helps the teachers to monitor and trace the learning process individually. As compared to traditional methods in classrooms, where the goal is to deliver the course, but not ensure that everyone got it, ML gives an advantage of more profound information perception. This type of learning is offered by Edtech and MagicBox learning systems.
Using machine learning in education helps to move the industry of online learning to the new level by making the content more up-to-date and relevant to an exact request. How? ML technologies analyze the content of online courses and help to figure out whether the quality of the offered information meets the applicable standards on one hand, and on the other, it shows how the users perceive the data and do they understand what is taught. Hence users receive the information under their personal needs and abilities, and the overall learning process improves greatly.
Have a quick look at Udemy, one of the world’s largest educational marketplaces, which connects students and instructors and offers over 150K different courses. It boasts a great feature of personalized recommendations. By answering several simple questions, its users get an optimized set of different courses and relevant content upon their request. The feature is a good contribution to platform usability.
There is another application of machine learning in education that deals with scores and grading. As each online course reflects the learning abilities of a large number of students, they have a lot of experience in grading them. ML technologies turn the grading process into a couple of seconds issue. We’re speaking more of the exact sciences in this perspective. There are spheres, where machines cannot replace teachers, but even in those cases, they can help to improve the existing grading and evaluating approaches.
In addition to that, applying machine learning in education today is the easiest way to detect cases of plagiarism. The application of ML technologies in this sphere turned out to be highly revenue-generating. Would you believe that a company that uses ML technology to reveal plagiarism costs $1.735 billion? A year ago a project called Turnitin was purchased by a privately held media company exactly at this price.
Here both machine learning and artificial intelligence help to give each student a personal course. It is compiled based on the exact user’s request — rather a time-saving solution! The program gives precise answers to the users whenever they need support.
As still machines cannot replace humans, this kind of studying is applicable only for those disciplines, where there are exact answers and it will not help to explain some philosophical questions, for example. But the amount of data it can offer to precise requests is astronomical! Have a glance at the Wizcabin project, in which the e-learning automation tool converts its users’ storyboards automatically into interactive courses.
Administrative work is extremely important and extremely boring. Let the machines do this kind of task! ML helps to automate scheduling and content delivery processes and free your time and resources for other, more engaging parts of e-learning process development.
The use of machine learning for education enhancement and optimization leads to outstanding results! But there are not only benefits but also serious risks of machine learning and artificial intelligence application in e-learning. If these technologies could solve all the problems and replace the traditional system of education, it would already have happened. What are the problems of ML and AI in the education sector today?
1. Prediction accuracy
Machine learning for education algorithms analyze the interests of online students, make predictions about their further needs, and give recommendations on courses. The statistical tools here cannot always reflect real needs. Let’s imagine, we see a student, who sits in a noisy place like a cafe or some workspace, and he chooses a text format of information delivery, as right at the moment it suits him better. We cannot say that he would make the same choice at home.
Thus, the student’s preferences are not that vivid for machine learning and prediction scenario is not 100% accurate. The reason for that is that ML doesn’t have enough data for use. To make predictions, people use information that they see every day, remember from the past, dealing with it during some period. To make this information digitally accessible, we need to collect and describe almost every moment of a student’s life, which seems to be impossible in today’s world.
Furthermore, it is not that easy to recommend a course and be sure that you offer the content that is really needed. Quite often learning management systems rely on the student’s job title and role, the department he\she works in and some skills they list. Their existing experience and the true level of knowledge are not always reflected correctly by a machine and here such things as instructor observations and feedback from peers and managers cannot be done by a machine with the same accuracy a human can render.
2. Human factor and old-fashioned approach to e-learning
The methodologies used by teachers and professors are highly adaptive when used in a classroom. When it comes to the remote educational process, we face an issue of behavior patterns and individual approach. The real-life process allows us to see how two different people perceive the same data and amend the approach if needed.
IT systems can measure the progress of each student and correct the learning plan, but they cannot invent the methodology on their own, and in some cases the approach that ML and AI are taught is not the best and rather old-fashioned.
In future this issue can become a great driver of methodologies further development as many non-vivid problems of the present system will become obvious and IT systems will offer even greater education methodologies. Until it didn’t happen, the human factor here is an issue.
3. Adaptive learning development costs
This is the model when machine learning is used to run the interaction between student and e-learning platform, analyze the unique needs of each user and offer customized resources and learning activities. No doubt this is a great technology and in the nearest years it will be more widely introduced by the key e-learning web platforms.
But as for today, the development of this approach is quite costly and time-consuming. The most expensive part here is the granularity of content. This system requires constant updating and monitoring, as the developers need to understand how deep the information adaptation of each learner should be — curriculum, course, or model level? Rules and predictions also need continuous attention of developers, and these things altogether form quite a big budget.
4. Data cleansing
Speaking about technological challenges, it’s worth mentioning the problem of data cleansing. The process of figuring out which data is incorrect or unnecessary and its removal is crucial for making the educational content relevant and up-to-date.
But which data and in which volume is needed to improve the learning process? What are the criteria for its relevance? The methodologies are still not perfect and today they cannot replace humans in these processes.
Moving forward from descriptions to examples, let’s check it out, how are machine learning and education interconnected? Who is creating these applications and how do they work?
Content Technologies, Inc. — one of the world’s leading AI and ML technologies developers that render solutions for business processes automation and intelligent instruction design, is offering two platforms that are interesting to look at. The first one is called Cram101. The web platform uses ML to segment the content of textbooks and outline the most useful parts of it, create study guides, make chapter summaries, and multiple choice practice tests.
Quite a similar service is offered by JustTheFacts101 service. It also highlights the most significant parts of the text and makes the summaries of chapters. Then it archives the data and makes it available on Amazon.
The e-learning platform called Netex Learning makes it possible for the educators to create digital curriculum, and offer different content across platforms and integrate different forms of information delivery — video\audio\texts. The company has also invested in the development of personalized learning cloud platform, that is applicable in the workplaces, where employers can design customizable learning systems with apps; gamification and simulations; virtual courses; self-assessments and others.
A company called OTTER is offering a great service based on AI. They’ve developed an application that allows users to set the keywords in advance of recording a class or a course. It’s great to support for getting exactly what you need in a nice summary.
Machine learning is widely used in many spheres. Let’s have a quick look at a couple of examples of how it is applied in education projects by businesses.
A Swedish EdTech company called Hubert.ai has developed an assessment system that they call a cognitive assistant. Its goal is to turn the surveys into speaking tests by asking students follow-up questions. It uses machine learning algorithms to make a deep text analysis that automatically categorizes feedback. It helps to make an assessment in education much more engaging and effective.
Getting ready for school examinations becomes much easier with ML and AI technologies. A project called MobyMax uses these technologies to compile a highly adaptive curriculum for K-8 subjects.
It provides personalized lessons for each student based on their individual needs and helps to fix learning gaps. The users have their personal lesson plans and practice sheets. The application also features different contests, certificates, games, and daily smiles to motivate students not to stop their studies.
A popular education tools platform called Quizlet Learn has developed a service for students based on machine learning algorithms, that help not only to study but also to prepare for different tests. It does this by creating tailored study plans based on current knowledge levels and upcoming test dates. The core of this algorithm is books and paper summarization and the creation of smaller study guides for textbooks and revision materials.
The most competitive feature of any educational project today is the ability to offer customized content in an engaging format with personal guidance and support. Some 10 or 15 years ago, which is nothing for the conservative sphere of education, the online world wouldn’t be capable of offering this kind of education.
Massive implementation of machine learning in education has changed this situation and it has completely replaced humans in many spheres of the learning process. It’s not only better data processing that they can do but a real enhancement of the e-learning industry. The content becomes more relevant, place, and time do not matter anymore, students have virtual assistants that guide them all way long.
The capabilities of these technologies and their potential make them the most significant tools of transforming the whole e-learning industry and bringing the tomorrow of e-learning closer!
Our digital product development team knows how to make these technologies work for you and turn your e-learning project into the most annoying competitor to the websites that you benchmark today!