Machine learning in education is redefining the way educational institutions function and impart knowledge and train learners. Young minds are learning about multiple crucial worldly and bodily topics and concepts, using technology. This would shape the mind in a way that would be ready to take the tech-driven world as it comes.
Using machine learning in education has allowed educators to make the learning and training process more personalized and adaptive for the students, among other things. However, some infrastructural ad accessibility issues still make the use of machine learning in education a bit challenging.
Thus, in this blog, we would discuss the role of machine learning in education, its benefits as well as challenges of ML in education.
Machine learning in education, along with other technologies, has expanded the impact and reach of education through localization, transcription, text-to-speech, and personalized learning. ML algorithms do continuous learning, without having to reprogramme again and again or use any external input.
It enables the EdTech software to analyze student data and identify behavior, patterns, and trends. These analyses are then used to generate suitable results, recommendations, and suggestions that help the institution offer better learning and teaching experience to the student.
As the ML algorithms receive more data over time, they start generating more accurate results, enhancing the productivity and efficiency of the education system gradually.
The role played by machine learning in education involves:
Identification of learning difficulties faced by students through adaptive learning
Personalizing teaching and learning processes through data analytics and feedback
Facilitating predictive analytics for future learning patterns/outcomes and suggesting present improvements required
Enabling EdTech companies to improve learning outcomes and do targeted marketing.
Let’s now look at the many benefits of machine learning in education.
Benefits of Machine Learning in Education
Just like AI, machine learning also brings a lot to the table in the educational sector. It is improving the entire student and educator interaction, with the latter being able to offer more personalized learning through data analytics facilitated by ML technology.
There are some more detailed benefits that come out of this changed interaction. Let’s look at them.
Some benefits of machine learning in education are:
1. More Accessible And Cost-Effective Online Learning
ML programs make use of natural language processing (NLP) and deep learning that translates and transcribes content easily and accurately. This saves learners and educators the costs, time, and efforts of translation while accessing remote learning or eLearning courses. This also encourages an inclusive education system for people with hearing or visual disabilities and those who are international students.
2. Identification of Skill Gaps And More Effective Courses
With the help of machine learning, student data is analyzed to identify skill gaps, among other things, and appropriate additional resources, courses, or extra hours, are recommended. Learners can view their metrics, and progress, and work on the weak concepts or topics accordingly.
With the help of ML, the courses are more targeted and effective, rather than the one-size-fits-all approach.
3. Takes Care of Administrative Tasks
Many administrative and management tasks are automated through digitalization in the education sector, freeing up time for educators to utilize for teaching purposes. Moreover, eLearning courses that are ML-based take care of multiple such tasks. These tasks include onboarding, scheduling, providing setting up instructions, monitoring attendance, and grading assignments and tests. This increases the operational efficiency of the entire system.
4. Effective Feedback And Personalized Learning
With the use of machine learning in education, educational institutions are able to offer effective and actionable feedback by analyzing students’ present and historical performance data. This further helps educators to cater to individual needs and offer personalized learning. This is bringing a massive change in the way students approach learning. They are more involved due to higher levels of targeted teaching and training.
Additionally, this allows educational institutions to reduce student dropout rates. At-risk students are identified, assessing their weak areas, and recommending the required course of action, all in a timely manner.
5. Improvement of The Overall Education System
The above factors and some additional ones such as better grading systems, deeper research and expanded reach, and higher quality of learning content, all lead to better education experiences and outcomes.
ML-based systems more effectively and comprehensively grade assignments and tests, taking into account plagiarism and internet usage. Furthermore, deep data science capabilities allow better discovery and research depository management.
Through extensive yet targeted advertising and marketing, EdTech institutions are able to attract the most suitable student crowd. It additionally, streamlines the administrative process while considering all the security concerns during enrolment.
Now let’s look at some of the challenges faced by ML in the education sector.
Machine learning in education, in particular, also comes with a set of its own challenges. Some of them are:
1. Data-Related Issues
Data is the most crucial aspect of the machine learning technology. Therefore, the challenges faced around it can hamper the performance of ML algorithms and thus, the quality of results might go down.
Data Quality: Data quality is of utmost importance for ML. Low-quality data defeat the entire purpose of using ML in any sector. This is because the low-quality data would hamper the quality of predictions and as a result, those results would be highly inaccurate and unreliable.
Thus, the data addition should be done carefully, with the appropriate amount of time devoted to the data preprocessing process. Suitable ML algorithms such as regression, classification, dimensionality reduction, etc., should be applied to data before modeling.
Data Collection: Before modeling, educators or educational institutions should carefully check and set the accessibility of the collected data. The accessibility should be given to only a few selected employees, and the entire system should be transparent for data security and privacy concerns.
Data Training: It is extremely important to understand the data requirements and timelines of the machine learning models before putting them to work. These models generally require massive amounts of data to train on and achieve results accuracy over time.
The educational institution should provide sufficient amounts of data for the model to do its comparisons and carry out its analysis. The more data there is, the more accurate and detailed the analysis would be.
Additionally, object/ input classification or speech recognition may take more than millions of samples to offer reliable results.
Machine learning is being increasingly adopted by businesses across various industries. However, it is still in its nascent phase. Thus, there is a shortage of qualified resources or professionals to manage deep learning and analytics.
3. Data Privacy And Security
Since ML technology runs on massive amounts of data, it is more prone to cyberattacks and security breaches. Any ML-based software or app goes through this risk since it has a high-volume of data sets.
Thus, strict data security and privacy measures and provisions have to be put in place by educational institutions to overcome this challenge.
Examples of Machine Learning in Education
Some popular software that uses ML in the education sector is as follows:
Grammarly needs no introduction. It is one of the most used software around the world. It is a checking and plagiarism detection tool that is used by millions of students and professionals globally every day to better their essays, emails, articles, etc.
The platform uses machine learning, deep learning, and NLP to identify common and repetitive errors such as spelling, grammatical, and punctuation mistakes and improve suggestions in a written piece. It takes users’ behavior, patterns, and actions into account to enhance the literature.
2. UC Berkeley’s Gradescope
This ML-based learning tool streamlines grading tasks for both students and teachers. Students use Gradescope to access each other’s work and offer feedback, while teachers can assess and grade online assignments and tests by providing the platform with a list of criteria for grading.
This ML-based tool, developed by the University of Michigan, analyses text through automation to enhance students’ writing skills. The ML algorithms, trained on past data, recognize varying levels of knowledge and skills.
Diverse methods are applied to analyze the text and give appropriate results by M-Write. Additionally, the in-build prompts of the program help with the differentiation between weak and strong answers. Peer-to-peer review is a bonus feature of this tool.
There are more such ML-based tools that are enhancing the quality of educational content and learner’s skills such as ALEKS, Duolingo, Echo360, Weka, Orange, etc.
While machine learning is highly beneficial for learners, it also helps educators and institutions to offer their best in an educational setup. Machine learning personalizes the learning process, enabling teachers to offer more targeted and effective feedback and enhancing the entire learning and training experience.
There are certain challenges that the education sector faces while adopting machine learning technology. However, all those challenges could be overcome by careful data preparation before modeling and focusing on data privacy and security.
Furthermore, machine learning is still a relatively new technology. Thus, it should be adopted under the guidance of experts or professionals who are well-versed in the concepts of machine and deep learning and data analytics.
BigOhTech is one such expert in the AI/ML field. With years of experience and expertise, we help clients to adopt ML technology effectively, optimizing resources and results.
Our dedicated development team ensures that the data is pre-processed meticulously, and all the right ML algorithms are applied, exactly matching the project needs.
Get in touch with us today to take your business to another level with fruitful predictive analysis!