Big data, data mining and data analytics are the buzzwords in technology now. They are transforming everything from advertisements to crime fighting. But what do these words have to do with schools?
Information technology has also touched education sector to transform the way we learn and teach. Schools and universities across globe are collecting data to continuously evolve the way they teach and operate.
What is Data driven decision making?
Data driven decision making or DDDM is the method of using data to take decisions. Decisions in DDDM methodology are backed by actual data rather than intuition. This is different from trial and error method because decision makers rely on hard evidence. DDDM is commonly applied in manufacturing and Machine learning.
Data is not new to the education sector. Schools and universities have been collecting data (attendance, demographics and test scores) since the formalization of education. Technology can help schools to capture data better and gain insights to improve every day functions. Timely and correct data collection can empower schools. Decisions can be taken on insights gained into the data collected rather than relying on instincts
How can school management software help in DDDM?
School management software capture information on multiple data points. Data on students such as demographics, attendance, test scores, learning progress and interests can be used to take decision on teaching methods. Operational data on fee payment cycle, transportation, staff recruitment, teacher effectiveness, expenses and costs can be used to take decision on allocation of resources.
School management systems must be used to capture more qualitative data. For example, data relating to social circles of students, informal interaction between teacher and student, hobbies of students and after school activities can give a comprehensive picture while analyzing data.
Here are the ways data collected by school management software can help in data driven decisions:
1. Learning Analytics
Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning. This is data mining in education sector.
Analyzing data can give us insight into how students learn and how each student differs in learning patterns. Tracking student log ins, search history, social networks and learning discussions can help in adapting the course for better learning.
Teachers can change pace and method of instruction based on real time feedback rather than following a rigid predetermined teaching method.
The graph above plots average grade in relation to a group of students. Such analysis can be used to plan intervention for students who need more attention.
2. Develop flexible curriculums
Every student comes with unique learning capabilities and interest levels for different subjects. Moreover, not everyone is on the same page when they start off on a topic. Some students come with more knowledge on the subject and may find the course repetitive. All this calls for a personalized approach to teaching.
Assessment software can be used to collect data on the student’s prior knowledge, learning capabilities and motivation. A flexible curriculum can then be developed to maximize learning efforts.
It should be noted that the utility of data is maximum when data from multiple points is used to build a relevant context. For example, a medical crisis in family may have the student hard-pressed for time and affect his grades. Lessons in audio format may help him to learn while travelling or waiting in hospital. Software should hence capture as much data as possible including family background, after school activities, social networks and informal learning interactions.
It is important to check your state or country’s policy on student data privacy and ownership while collecting such personal data.
3. Optimum allocation of resources
Data based decision will also help in supporting functions of the school. Data on fee payment cycle, library books issuance pattern, transportation routes, defaulters list, etc can help the administration to plan ahead. This will help in employing resources where they are needed and avoid duplication of work.
4. Reduce Operational Burden
Machine learning and artificial intelligence can automate a lot of work. Assignment reminders can be sent in advance. Report cards with detailed analysis can be generated by software.
Communication can be made very easy with parents, teachers and students on the same platform.
Data analysis and automated software are not replacement of a teacher. Data driven decision making only allows teachers to manage their time and allocate it to activities that are most beneficial to student learning. It only empowers educators to maximize their impact in a scientific way.
Data driven decisions also reduce operational burden on school and to allocate time, cost and efforts where required.