Within the last 10 years, learning analytics has become an important field with stakeholders beginning to look at how data can be used to assist education. Instructors may not be used to thinking about data that comes from student learning, so here are some basics to get started.
Learning analytics is referred to as the collection and analysis of student data to
provide insight into courses and student education. It is often used with the goal of better optimizing learning. This process is not new as you may have already been doing a similar process in the form of observations. Through relying on visual data such as the number of students paying attention, you can determine how well students are learning and make necessary changes to the course. Learning analytics is similar as it gathers data through online resources. It allows for a deeper insight by collecting data that is often not visual. However, due to the recent development of the field, instructors are not as familiar with using learning analytics. You should be thinking about learning analytics for 3 main reasons.
Scenario #1: By observing student data regarding engagement and whether they are on track to pass the course, we can create assignments designed to compel them to access the course material for certain amounts of time.
Scenario #2: Through noticing trends within the student data, we can observe what students know from prior experience in order to prioritize what topics they still need to learn, updating the course design in real-time to provide a better learning model specifically around those topics.
Scenario #3: Through collecting data over many runs of a course, we can identify the connection between a student doing poorly on the first exam and whether additional resources or particular assignments would help them pass the second exam.
Understanding and using the data to its full extent is often not an easy task. Once you’ve accessed this data, working with an instructional designer can help you recognize which data has the highest importance. Furthermore, instructional designers can assist in determining which adjustments to your courses would best achieve improvements in learning outcomes for your students. If you are SOLS faculty, write to tic@asu.edu to get started on this process.
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Isaac Gray is an undergraduate student at Pacific University. He has been interning at ASU, shadowing the Teaching Innovation Center within the School of Life Sciences. His interests include data analysis and how it can be incorporated into higher education. He is currently working towards a bachelor’s in Mathematics.
provide insight into courses and student education. It is often used with the goal of better optimizing learning. This process is not new as you may have already been doing a similar process in the form of observations. Through relying on visual data such as the number of students paying attention, you can determine how well students are learning and make necessary changes to the course. Learning analytics is similar as it gathers data through online resources. It allows for a deeper insight by collecting data that is often not visual. However, due to the recent development of the field, instructors are not as familiar with using learning analytics. You should be thinking about learning analytics for 3 main reasons.
1. Understand how your students are engaging in the course
Learning analytics allows for a glimpse into the activities students are engaging in. For example, a course might show how often students are viewing a page or the amount of time a student spends on an assignment. This data is often used to connect how engagement in a course affects the student’s outcome. However, on an instructional level, this data can inform you on whether the design of a course is engaging to the students and thus promoting their learning. Likewise, it can guide you on changes that will produce desired student participation.Scenario #1: By observing student data regarding engagement and whether they are on track to pass the course, we can create assignments designed to compel them to access the course material for certain amounts of time.
2. Create better learning models to help students be successful
The use of learning analytics can inform you about course design that would allow for more students to be successful. It is a fact that there is not one model that works best for everyone. Further, assuming an activity or assignment that does work well for one group will also not work well for a different group is sometimes incorrect. As such, having data regarding the students' past performance and experiences would help accurately guide the development of more personalized courses. Thus, the design of the course would be around the students, allowing better learning to occur.Scenario #2: Through noticing trends within the student data, we can observe what students know from prior experience in order to prioritize what topics they still need to learn, updating the course design in real-time to provide a better learning model specifically around those topics.
3. Proactively support students that might be struggling in your course
Learning analytics allows instructors to identify students that are struggling in the course. Through noticing patterns in the student’s performance, you can see if a student is starting to fall behind. Further, it details the specific areas where a student needs help. One way this can be easily done is by having students answer a question asking whether or not they understand the concept after a section. Having this information would allow you to make quick real-time modifications to the course. Further, this would give you feedback on the specific revisions to the course that would help these students succeed.Scenario #3: Through collecting data over many runs of a course, we can identify the connection between a student doing poorly on the first exam and whether additional resources or particular assignments would help them pass the second exam.
How can instructors find this data?
Learning analytics relies on data that is gathered and viewed online. This means students would need to use online tools for class activities in order to have data generated. Nearly all our ASU technologies generate data including Canvas, Cogbooks, Labster, Yellowdig, Mediamp and Zoom. Being part of ASU, means some data is viewed through Canvas’ analytics. Canvas provides information about students' participation such as the number of page views, interactions on a page, and even the last time they participated. You can find a guide to Canvas’s analytics here.Understanding and using the data to its full extent is often not an easy task. Once you’ve accessed this data, working with an instructional designer can help you recognize which data has the highest importance. Furthermore, instructional designers can assist in determining which adjustments to your courses would best achieve improvements in learning outcomes for your students. If you are SOLS faculty, write to tic@asu.edu to get started on this process.
References
- de Freitas, S., Gibson, D., Du Plessis, C., Halloran, P., Williams, E., Ambrose, M., Dunwell, I. and Arnab, S. (2015), Foundations of dynamic learning analytic: Using university data to increase retention. Br J Educ Technol, 46: 1175-1188. https://doi-org.ezproxy1.lib.asu.edu/10.1111/bjet.12212
- Muljana, P.S., Luo, T. (2020), Utilizing learning analytics in course design: voices from instructional designers in higher education. J Comput High Educ 33, 206–234. https://doi.org/10.1007/s12528-020-09262-y
- Naujokaitienė, J., Tamoliūnė, G., Volungevičienė, A., & Duart, J. (2020), Using learning analytics to engage students: Improving teaching practices through informed interactions. Journal of New Approaches in Educational Research, 9(2), 231-244. doi:http://dx.doi.org/10.7821/naer.2020.7.561
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