Machine learning can potentially redefine not only how education is delivered but also foster quality learning on the students’ part. Probably, the most important part in the role of machine learning in teaching is customized teaching. With machine learning, we are moving away from the one-size-fits-all methodology. Machine learning promises to deliver custom in-class teaching by providing real-time feedback based on individual student behaviour, response, background, aptitude, interests and other factors. This improves the chances of better learning. Machine Learning also plays an important role in assessments or evaluations by removing bias prejudices though it plays a supporting role here. This article discusses a few ways machine learning can improve teaching excellence.
Let us explore the impact of machine learning in the field of teaching excellence.
Custom teaching is directly opposite to the one-size-fits-all methodology or philosophy. It considers individual student aptitude, learning speed, background, response and other variables. It processes the data real-time and provides the teacher feedback, so that the teacher can recognize flagging student attention or poor response immediately and take corrective actions. This can potentially improve student participation and, in the process, the results. Machine learning will be able to explain the concepts, set the goals for individual students. On the other hand, teachers will be able to track, if the students are able to digest the concept or not. Based on that feedback, educators can change or modify the methodology, curriculum, topics accordingly. And, the result is more accurate and targeted for individuals. In simple terms, machine learning does the analytics based on individual student data, and make the decision making process automatic and perfect.
Assessment is one of the major part in the teaching industry. Machine learning technology can help teachers assess or evaluate tests objectively and provide feedback. Machine learning applications can do the assessment and provide scores. So, the mechanical process is taken care by the machines and removing human intervention. It almost takes out human prejudice or bias of the process .But, at the same time, we need to remember that the assessment is done by some machine learning algorithms, based on the data feed. So, some human intervention might be required from case to case basis. For example, occasions such as research paper evaluation, interactive work, oral examination etc., some human intervention is necessary. Overall, the assessment process is made more streamlined, accurate, un-biased with the help of machine learning.
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Custom learning plan
Till date, the learning plan is made in a generic way. So, it is the same plan for all the students. But, the students have different types of learning ability. So, it cannot be same for all. Imagine a scenario where, a student is able to learn quickly through visual representations/figures/diagrams and, he/she is given a text based study material. The result will be horrible. Before AI and machine learning, there was no other way to detect this and find a possible solution. As a result, it imposes a tremendous pressure on the student and sometime leads to failure, although the student might had a good potential. Only, the route to learn was incorrect. AI applications are a great relief to this situation. Custom learning plan can potentially result in better learning because the technology can assess student data and find out the best methods individual students can learn. It will also find out a better mapping of subjects based on student interest.
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Feedback is an important part of any learning system. In teaching also, feedback is one of the most important component. When we talk about feedback, it means 360 degree feedback. Here, it is applied to student and teacher. Machine learning analyses the student data (grading, interest, score, behaviour etc.) and provides feedback. Machine learning also analyses teachers’ data (subject taught, way of teaching, acceptance etc.) and prepares a feedback. This feedback helps both the parties. Students are able to get constructive feedback and act accordingly to get better result. On the other side, teachers are able to adjust themselves to provide better teaching experience. While the teacher does already provide student feedback, but machine learning will go beyond and deeper. It will assess student behaviour, responses, historical data and arrive at data-based conclusions and provide an objective feedback. As in assessments, it will eliminate the possibility of human prejudice while providing feedback.
Career path prediction
This is one area where students get confused and take wrong decision. The career path of a student is very important for the entire life time. If the path is not correctly chosen, the life can be a failure. In general, the decision for a student’s career path is greatly influenced by the family profession, parents and neighbours. And, of course the lucrative careers options. But, the most important thing is missing, that is the ‘INTEREST’ of the individual student. AI and machine learning plays a major role here. The machine learning applications (for career path prediction) are able to track the student interest and their dis-likes. It analyses the repetitive nature of the student behaviour and their reactions. Based on the analysis, it can fairly predict the interest area where the student can excel.
AI and machine learning is having a tremendous impact on the teaching industry. Before the introduction of AI/machine learning, it was a generic and one-size fits all types of approach. As a result, students were not able to understand their interest, select correct learning path and follow wrong directions. One the other hand, educators were facing a lot of trouble, understanding the students need and their possible solution. So, the teaching experience and the success rate was not as per expectation. With the advent of machine learning and AI, it becomes more focused, accurate and successful. Student and teacher are also happy to collaborate and move forward. Machine learning, if harnessed, can revolutionize teaching just based on data. In the near future, machine learning will be more efficient and produce better result. As in other fields, machine learning will produce amazing results in the field of teaching also.