One of the real benefits of data science in education is that it offers an optimal and personalized learning experience that is tailored to the individual needs of each student.
In education, the practice of using data and learning analytics to drive decision making is rapidly being adopted. Despite being a practice that is still in development, administrators and teachers around the world are increasingly looking for information to help them make better decisions and meet goals.
Although personalized learning is one of the most exciting benefits of data-driven technologies and learning analytics, institutions must understand the differences between personalized learning and prescriptive learning, and ensure that both faculty and students explore a wide range of skills.
HData would assist you with the collection and analysis of essential data in the education sector
eLearning Industry is a platform that enables computer-aided learning and knowledge sharing. It describes decision-making in the education system that is based on data as follows: "A systematic collection, analysis, investigation and interpretation of data in order to provide methods and strategies in educational institutions with information ".
In other words, education data is collected and analyzed to improve education in schools. Large amounts of educational data are collected every day, especially in the university system, from various data sources in a wide variety of formats. The different types of education data are noisy eLearning Industry:
Student related data such as demographics and past academic achievements.
Data on the teaching staff, such as skills and professional experience.
Data generated during class, study, and assessment. This data can be collected both in the physical classroom and through the collection of curricula, assessment methods and classroom management.
Staff, infrastructure, financial plans, educational and non-educational staff, hardware and software, and expenses.
Important education data that can be collected via data science
• Registration metadata: data is automatically collected on the website. This includes information such as: visitors, interactions, IP address of the visitors, cookies and browser settings, which can help to identify causes of technical problems.
• Application statistics: Automatic collection of information about interactions with the functions of the website in order to identify useful functions.
• Assessment data: It consists of test results and observations to check the students' learning progress.
• Attendance: data on attendance at school and in courses; Suspensions and expulsions from school can be used to test for truants.
• Demography: Personal data of the students such as date of birth, gender, ethnicity, language, socio-economic position, impairments, special school support, etc. These data are used to understand the needs of a diverse group of pupils and to develop personal teaching units.
• Enrollment Information: Grade Levels, Classroom Engagement, Mentor, Courses Chosen, and Graduation Year.
• Information about the students: student name, ID number and contact information.
Using the collected data efficiently
Because there is so much data available, you end up using only a fraction of the collected learning data. This data includes a wide variety of information, such as engagement data, time spent on task, performance in activities and achievements. And everything should be analyzed in the context of the student and considering their specific circumstances and needs.
Any special assistance situation, disability, cultural differences and other influencing factors should always be considered.
Data on their own are not very valuable and can be easily misinterpreted. When we consider the context, apply objectives and strategies, and evaluate those elements, the data becomes valuable information and knowledge that allows us to take actions to achieve results.
Our top predictive analytics tools at HData Systems
SAS Advanced Analytics
Amazon Web Services