As the quote by Peter Drucker goes, “You can’t manage what you don’t measure.”
What he meant by this is that success first has to be defined and tracked before it can be achieved. Ask any teacher and they will tell you that they plan backwards. They begin lesson planning with what they would like their students to learn (learning outcomes) and work backward.
Measuring student’s progress throughout their academic career is an important part of ensuring their long-term success. How does one measure progress? Through student learning data. Here, we discuss how learning analytics can guide teachers and help students succeed.
Although the term “data” has earned somewhat of a negative reputation amongst educators (read: they dislike it very, very much), what we’re referring to here is simply measurable progress. Things like test scores, grades, class performance, and other student achievement metrics can all be measured and tracked as students work through their educational careers.
The purpose of tracking student learning data is to find what works best for a particular student. This can help them optimize that for the highest probability of success.
For example, if a student demonstrates an affinity for project-based learning, more opportunities can be given for that style of assessment where student outcomes would be best.
In order to achieve success, educators must collect the right historical 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.
This refers to personal data collected about the individual student. This can include demographic information such as age, nationality, socioeconomic status, and any documented special educational needs, as well as data collected referring to past academic performance — grades, scholarships, disciplinary incidents, etc.
Collecting personal data about students is one way that teachers can begin to establish a relationship with that child. After all, students are far more likely to learn from a teacher who shows an interest in them than with someone who they aren’t able to connect with.
It is important to always make sure this sensitive information is safe and secure so that it doesn’t fall into the wrong hands.
Learning data refers to data collected about a student’s active learning both inside and outside of the classroom. This historical data can range from test scores and classwork grades to the level of class participation and any data collected from learning resources like online platforms (think Imagine Learning, Prodigy, etc.) This kind of data and predictive analytics is invaluable in guiding instruction.
How are learning analytics used?
Teachers can use student learning data to determine what concepts have been mastered and which ones need to be reviewed. They can then determine if an entire re-teach is necessary or if a handful of students need extra practice in a certain area.
Talk about a time-saver! Knowing what students need to work on makes lesson planning, instruction and even assessments much more effective.
Teachers and school leaders can also use personal and learning data to plan for student success before the student even steps foot in the classroom. Of course, students change and evolve from year to year but, at the minimum, teachers get a good idea of the types of activities students will have the most success with. They can also use those diagnostic analytics as a starting point for their plans for the year.
Imagine being a student. You walk into class on the first day of school and your teacher has already planned a writing activity for you to introduce yourself to your class – your favorite!
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. That is the way to achieve positive results and ensure district-wide student success.
At LINQ, we are experts in helping educators automate and improve their district processes . However, we also understand that just like for learning, there is no one-size-fits-all approach to teaching. If you would like to check out further information about how predictive 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 data analytics about your district’s processes? Schedule a demo at LINQ today!
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