Data Science Applications In The Education Sector In Schools

The goal of this article is to use data science to address some of the urgent issues plaguing the school education industry for a long time. Different machine-learning methods have been used to address four challenges that are of great concern. We are able to come up with a workable solution for each issue and a desired course of action, even though some of the techniques (usage of photographs and data) employed for data collecting are quite ambitious.

The four issues we looked at are:

• A high rate of student dropouts across all levels of education;
• The current educational system's inability to recognize a child's learning disability;
• One-size-fits-all pedagogy and an ineffective traditional method of evaluation; and
• A lack of emphasis on evaluating teachers' performance and instructional techniques.

Classification, grouping, and regression are a few machine-learning techniques utilized to address the issues. The findings indicate that data science has a lot of potential for creating original answers to problems facing the higher education and school sectors.

1. The high percentage of student dropouts across all academic levels:

Our nation loses a lot of resources when a child quits school. Finding a method to lower the dropout rate will benefit society and the nation. To help the government address the causes of dropout, data scientists have tried to forecast the number of students who will leave school in a given year and the number of students who will leave a specific school or region.

Data on the many aspects that affect a student's decision to drop out must be gathered for the same. The information needed to predict dropout rates must include a variety of variables, such as the family's financial situation, educational background, family history of illness, the child's gender, and academic performance.

In order to forecast whether a student will drop out of the current academic year or not, a classification algorithm is utilized. Decision trees, Naive Bayes, and KNN are a few potential models. (Refer to the data science course online to learn these ML concepts in detail.)

A regression method also forecasts the number of dropouts in a region or school for the following year. Polynomial regression and linear regression are both potential models.

2. The current educational system's incapacity to recognize a child's learning problem in its early stages:

One of the most overlooked, untreated, and unpredictably occurring issues in children is learning disability. This is mainly due to 4 factors:

• A lack of awareness of such issues among older people.
• The inability of a child with such a disability to express his situation appropriately.
• A lack of a suitable mechanism to observe or measure such disabilities directly in children.
• The problem of over-identification in the current method of faculties observation and various checklists.

The success of people with learning difficulties over the long run depends on early detection and assistance. Early identification includes the assessment and counseling offered to families with children under three who have a handicap or are at risk of developing one.

Dyscalculia, Dysgraphia, Dyslexia, Non-Verbal Learning Disorders, Oral/Written Language Disorder, Specific Reading Comprehension Deficit, ADHD, and Dyspraxia are a few examples of learning disabilities. A multiclass classification technique can be used to divide pupils into various categories of disabilities. Classes include things like dyslexia, dysgraphia, dyscalculia, etc.

Other difficulties include the considerable processing power needed to analyze the data that was gathered and the analysis of data that contained photos and movies using complicated, opaque models and algorithms. Lack of openness is a problem since school administrators must respond to parents if their children are found to have learning difficulties.

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3. One-size-fits-all instruction and inefficient, traditional evaluation techniques:

The lack of methods to identify curious and creative minds, the minimal weight given to extracurricular activities and soft skills, and the inability to adapt teaching strategies based on a student's aptitude and interest or student's strengths and weakness are some of the flaws in the current educational system's pedagogy and evaluation. All of this has placed an unwarranted strain on students' shoulders, causing them to lose sight of the fundamental purpose of education in favor of earning high marks at all costs.

The goal of the educational system has to be the growth of students' abilities, knowledge, skills, and perspectives within a single framework. Mark-based evaluation is being replaced with "Continuous and thorough evaluation," according to the New Education Policy 2020. Additionally, critical thinking and more all-encompassing, inquiry-based, discovery-based, discussion-based, and analysis-based learning are receiving a lot of attention. The policy also emphasizes developing moral character and producing whole, well-rounded people. This article explores the role data science can play in enhancing student evaluation and, in turn, building student-tailored education.

Each subject is evaluated on numerous parameters, which is done frequently, as opposed to evaluating a subject as a whole in a written exam. The evaluation is conducted using a scientifically created evaluation rubric and a scale of 1 to 5. Written exams, practical exercises, projects, group discussions, and class participation are all ways that data is gathered. The same methodology as described before is used to evaluate the students' behavior. Using an evaluation rubric, teachers rate the parameters on a scale of 1 to 10. Group activities, athletics, debating, and other activities are some data collection sources. For evaluating both academic performance and behavior, a clustering technique is applied.

4. Lack of attention paid to evaluating teachers' performance and instructional strategies:

Teachers are now, more than ever, dangerously overworked. They need more than only their typical responsibilities for education, socialization, assessment, and classroom administration to be effective. Additionally, there are difficulties in the present day that teachers in the past never faced. They are dealing with a changing educational environment full of several challenging scenarios.

The main issue is that teachers are only evaluated on their capacity for problem-solving. Additionally, experience is given more weight during teacher evaluations without considering whether or not the teacher has improved, and character attributes are not assessed.

Teachers' evaluations frequently have two objectives: to gauge their proficiency and promote their professional advancement. A teacher assessment system will also provide teachers with helpful input on the needs of their classrooms, enabling them to learn new teaching strategies and adapt their instruction accordingly. As a result, the goal of teacher evaluation in bringing about improvement is now broadly acknowledged and embraced. The primary goal of the evaluation is the ongoing enhancement of the instructional environment.

A multiclass classification algorithm categorizes a teacher's performance into many groups. Classes include excellent, decent, moderate, poor, and extremely poor ratings.

Conclusion

Four of the major issues we found in the education sector's concerns are addressed in this study by the use of data science. As an illustration, we might observe that kids with a high likelihood of dropping out throughout an academic year could be anticipated if several causes causing school dropout could be identified and the data could be collected using an acceptable mechanism. Similarly to this, if the right machine learning algorithm is applied, a student's learning difficulty can be accurately detected.

Although the evaluation of teachers and students using data science has some limits, greater research in the field and more sophisticated and scientific data collection techniques could provide more insight into how to address such issues. In this article we saw how data science might be used to address some of the problems in the education sector. This work should be viewed as a preliminary step before more extensive research on this topic is done in the near future. Furthermore, if you are someone interested in learning more about tools used by data scientists in various sectors, join Learnbay. It offers the best data science courses in India for working professionals of all backgrounds.