How Machine Learning Can Improve Teaching Excellence
Machine learning is revolutionizing how lessons are taught and how students learn.
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 of 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 behavior and other factors. This improves the chances of better learning. Machine learning also plays an important role in assessments or evaluations by removing biases. (Big data is also playing a big role in education trends. Read more in How Big Data Can Revolutionize Education.)
Let us explore the impact of machine learning in the field of teaching excellence.
Customized teaching is the direct opposite of the one-size-fits-all methodology or philosophy. It considers individual student aptitude, learning speed, background, response and other variables. It processes the data in real time and provides feedback to the teacher, 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 overall results. Machine learning will be able to explain the concepts as well as set the goals for individual students. On the other hand, teachers will be able to track whether or not the students are able to digest the concepts. Based on that feedback, educators can change or modify the methodology, curriculum or 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 makes the decision-making process automatic and uniform.
Assessment is a major part of 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. The process is taken care of by the machines, removing human intervention and helping to remove human prejudice or bias from the process.
However, at the same time, we need to remember that the assessment is done by machine learning algorithms, based on the data feed. Therefore, some human intervention might be required on a case-to-case basis. For example, occasions such as research paper evaluation, interactive work, oral examination, etc., some human intervention is still necessary. Overall, the assessment process is made more streamlined, accurate and unbiased with the help of machine learning.
Custom Lesson Plans
To date, lesson plans have been made in a generic way, so they are the same plan for all the students. However, students have different types of learning ability, so the same lesson plan may not be ideal for all students. Imagine a scenario where a student is able to learn quickly through visual representations/figures/diagrams, but he/she is given text-based study material – the student may struggle with learning the material.
Before AI and machine learning, there wasn't a practical way to detect this and find a possible solution. As a result, it imposes a tremendous amount of pressure on the student and sometimes leads to failure, although the student might have had a good potential. If the material had only been presented differently, the student may have easily understood and learned it.
AI applications are a great solution to this situation. Custom lesson plans can potentially result in better learning because the technology can assess student data and determine the best methods in which students can learn. It will also determine a better mapping of subjects based on student interest.
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Feedback is an important part of any learning system. In teaching as well, feedback is one of the most important components. When we talk about feedback, it means 360-degree feedback. Here, it is applied to both student and teacher. Machine learning analyzes the student data (grading, interest, score, behavior, etc.) and provides feedback. Machine learning also analyzes teachers’ data (subject taught, method of teaching, acceptance, etc.) and prepares feedback. This feedback helps both parties. Students are able to get constructive feedback and act accordingly to get better results. On the other hand, teachers are able to adjust themselves to provide a better teaching experience. While the teacher does already provide student feedback, machine learning will go further and deeper. It will assess student behavior, responses and historical data, and arrive at data-based conclusions and provide objective feedback. As for assessments, it will eliminate the possibility of human prejudice while providing feedback.
Career Path Prediction
This is one area where students can get confused and make a decision that may not work out for the best. The career path of a student is very important for their future. If the path is not chosen with care, frustration and disappointment can be the result. In general, the decision for a student’s career path can be greatly influenced by a number of factors, including the family profession, parents and neighbors – and, of course, the most lucrative careers options. However, the most important thing is missing: the interest of the individual student. AI and machine learning can play a major role here. Machine learning applications for career path prediction are able to track student interest, aptitudes and dislikes. It analyzes student behavior and reactions. Based on the analysis, it can fairly predict interest areas in which the student can excel. (For more on cutting-edge education, see Education Must Turn to the Cloud.)
Artificial intelligence and machine learning are having a tremendous impact on the teaching industry. Before the introduction of AI/machine learning, a generic, one-size-fits-all type of approach was commonly used. As a result, students were forced to try to adjust their style of learning to the lesson plan, rather than the other way around. On the other hand, educators were facing a lot of trouble, trying to understand the students' needs and the possible solutions. So, the teaching experience and the success rate was not as per expectation. With the advent of machine learning and AI, it is becoming more focused, accurate and successful. Machine learning, if harnessed, can revolutionize teaching just based on data. In the near future, machine learning will be more efficient and produce even better results.