Article Preview
TopIntroduction
The global coronavirus outbreak has significantly impacted academic programs. Several schools had to be shut down due to the coronavirus outbreak, which had a devastating effect on educational settings around the world. It was disruptive to both educational and occupational pursuits (Onyema, 2020).
Predicting how students will perform on upcoming assignments is a common requirement for bettering classroom instruction and developing personalized plans to help students struggling with schoolwork. The use of text mining comes into play at this point. Methods of data analysis investigate and collect data to transform it into structures that can be used in some way (Rastrollo-Guerrero et al., 2020).
In education, one of the primary goals is the cultivation of each student's strengths. There is a widespread misunderstanding that each student's educational pursuits are unique. Thus, it is essential to tailor instruction to each learner's unique strengths. However, under the standard education model, only one method is used, and the emphasis is always on the student's education. Almost no educator has the time or resources to create unique instructional materials for each pupil (Luan & Tsai, 2021).
Most studies in the field of education are conducted using learning communities, wherein most participants are affiliated with the same institution and have similar levels of expertise. With so many students spread out over the country, it might be challenging to get to know them all personally (Kidziński et al., 2016).
Most machine learning's business uses are in instructional settings. Several of the more engaging ones are included below (Kučak et al., 2018):
- •
Forecasting Student Success Machine learning has found great use in the education field, specifically in determining student outcomes. Each learner's weaknesses might be “learned” by the machine learning algorithm, which would then “recommend” ways to remedy the situation (such as supplementary material or additional study time) (Kučak et al., 2018).
- •
There needs to be a level playing field when grading student work. (Machine learning can aid in making automated adaptive testing better.) With the help of machine learning analysis, teachers and students may determine whether a student has grasped a concept, how much additional assistance they require, and how far along they are in their course of study (Kučak et al., 2018).
- •
Assisting educators (machine learning-based methods can help classify students' handwritten evaluation papers (Kučak et al., 2018).
Online education, like traditional education, needs teacher evaluation. However, they discovered the following issues when attempting to evaluate online education in a virtual environment (Ma et al., 2018): Online education, like traditional education, needs teacher evaluation. But when they tried to evaluate online education in a virtual setting (Ma et al., 2018), they found the following problems:
- •
There is a dearth of research into the feature method for assessing online learning.
- •
How can the existing approach for evaluating online learning be made more efficient and reliable?
- •
How can we tell if an e-learning platform is any good? How should machine learning algorithms be used?
Many people nowadays, including students, workers, and business owners, are interested in expanding their knowledge and developing their abilities. Educational institutions are undergoing tremendous changes due to the widespread adoption of continuous learning, and virtual learning is becoming extremely frequent. The number of online programs offering free or paid degree programs and extracurricular activities has exploded as a direct result of this demand. To analyze the copious amounts of data that provides, machine learning techniques have been developed. The best way to utilize this powerful new technology to enhance e-learning is worth exploring (Farhat et al., 2020).