The 20th century management consultant, educator, and author Peter Drucker once quoted, “you can’t manage what you don’t measure.” What he meant by this quote is that success cannot be achieved if it is not properly defined and tracked. Drucker’s management theories have been widely used throughout the business world for almost a century, and alongside many other long-standing theories has begun to make its way into education.
Measuring student’s progress throughout their academic career is extremely important in ensuring the student’s long-term success. This article will act as a simple guide for educators to begin exploring learning analytics and understanding the positive impact they can have on their students.
What is learning analytics?
According to the Society for Learning Analytics Research , or SOLAR, learning analytics is defined as “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.” Essentially, learning analytics seeks to track and understand a student’s progress throughout their academic career in a way that allows for the highest probability of success for that student.
What does learning analytics measure?
In order to achieve success, educators must collect the right data about their students. Boiled down to its most simple form, the data being collected will fall into one of two categories; student data and learning data.
Student data refers to personal data collected about the individual student. This can include demographical data such as age, nationality, socioeconomic status, and any documented special educational needs, as well as data collected referring to past academic performance such as grades, enrollments, scholarships, transcripts and past disciplinary incidents and actions.
Collecting personal data about your students is important for discovering and tracking trends across your student population and can be used to make inferences and predictions about future students sharing similar traits. However, it is important that this data is collected and stored in a way that both safe and secure in order to remain compliant and prevent it from getting into the wrong hands.
Learning data refers to data collected about a student’s active learning both inside and outside of the classroom. Examples of learning data could include level of class participation, the rate at which a student utilizes educational resources such as extra help sessions or online tools, as well as current exam and assignment grades. This information is extremely important in understanding which lessons and theories students find easier or more troublesome to grasp, as well as the effectiveness of various teaching styles across the entire student population.
How is learning analytics used?
Once the proper data has been collected, learning analytics can provide educators with the information they need to make some pretty remarkable improvements at an individual student level. For example, educators may collect data indicating that several students are in danger of failing a particular class. On the surface it may seem that these students are not understanding any of the course material and may not have been paying attention or taking the class seriously. However, learning analytics has the ability to show educators that in fact, these students understand much of the course material but are having trouble with a particular concept that has a hand in much of the testing material. Understanding the deeper issue, the teacher may choose to adapt their teaching style with regards to that concept, or reach out to the students directly and offer some extra help or external resources that will allow them to catch up with the rest of the class and achieve success moving forward.
Even more impressive is the predictions learning analytics can provide educators about individual student success before the student even steps foot in the classroom. For example, after collecting the appropriate student and learning data it may become apparent that students sharing certain demographic traits have historically had poorer grades across a particular subject. With this information, teachers now have the ability to be proactive and provide extra attention to those students from day one, making the probability of success much higher for those students.
The most important thing to remember when applying learning analytics to your school district is that communication is key . Everyone involved in this process from upper administration to teachers and even parents must work together in order to achieve positive results and ensure district-wide student success.
Resources for educators
At Script, we are experts in helping educators automate and improve their district processes . However, if you would like to seek further information about how learning analytics can help your students succeed, we encourage you to read the literature made available by the experts at SOLAR and the United States Department of Education Office of Educational Technology.
Want to learn how to collect the same analytics about your district’s processes? Schedule a demo at Script today!