Guidelines for using learning analytics
We use learning analytics to improve instructional practice, and students are our partners in this process. Take a thoughtful approach, and ensure that your use of learning analytics aligns with ethical principles and educational goals.
Keep in mind, learning analytics must:
- Benefit the learner. Learning analytics should not focus solely on deficit models that target underperforming students or those considered to be "at risk." These efforts should be focused on student learning and support, including improvement of the teaching and learning environment.
- Respect the dignity of all learners. Learners should not be defined by the data collected about them. Students should be actively engaged with their learning analytics data and resulting decision-making. Equity and inclusion are also important considerations.
- Protect the privacy of individuals. Personally identifiable information gleaned from learning analytics should only be shared with properly authorized individuals; and used to inform or benefit the specific individual.
- Be transparent. Learners should have a clear understanding about how their learning analytics data is collected, used, analyzed, and reported.
Building blocks for creating an action plan
The first four weeks of class are an opportunity to establish the tone, expectations, and healthy habits that will help your students succeed. Using Canvas tools and data, instructors can create communication and feedback channels to help students start strong. Use these 3 building blocks to create a strong learning path for your students.
1. Cultivate transparency and trust.
Learning analytics are records of student behavior, so the data are literally personal. Your students might feel vulnerable being "observed" in this way. Start by adding a statement about learning data to your course syllabus. This is the beginning of a conversation!
Use this template language for a learning analytics syllabus statement. Make a copy of the editable syllabus statement.
I am committed to your success in this course, and one way I will be supporting your learning is by reviewing student learning analytics data on our course site. Learning analytics are generated from the digital footprints you create as you access and interact with course materials and are used with the intent to support your learning and improve your learning environment, including my instruction. These analytics are part of our secure, approved learning tools at our institution, such as Canvas and Kaltura. Learning analytics data might include:
- what day(s), how long, and how often you access course materials;
- your assessment scores; and/or
- responses to anonymous surveys.
This information will be used only to improve instruction, for example, to provide supplemental resources, invite to office hours, set more practice sessions, offer self-assessment opportunities, etc.
Learning analytics data do not tell the full story of your learning experience and may not always accurately represent your activity. As such, I will not use learning analytics data to make any decisions about your course work. Your learning analytics data are only useful when combined with feedback from you.
In general, I approach learning analytics with respect for the dignity and privacy of individuals and in accordance with the Family Educational Rights and Privacy Act (FERPA). For more information, please review University of Minnesota FERPA resources (Students' rights).
Please contact me about any questions you may have about how I am using learning analytics in this course.
2. Provide opportunities for feedback
Low-stakes assessment data give instructors and students important insights into students’ preparedness. Try to offer a low-stakes assessment each week at the beginning of the semester and use them as an opportunity to communicate with and support your students.
3. Deliver timely and effective messages
Students tell us that effective messages are focused, non-judgmental, frequent and actionable. Create an effective communication plan by identifying what students need to know and do at important points in the semester.
- When creating your communication plan, it’s useful to consider three types of messages:
- Supporting messages offer tips and advice and are generally delivered at the beginning of the term.
- Timed messages are published to coincide with course events, like exams.
- Targeted messages address a subset of your students in order to address specific needs. Canvas makes it easy for instructors to reach out to students grouped by performance/grades.
- Learn more about effective messaging in support of teaching and learning, including information about Canvas tools that support messaging.
- Use and adapt these sample messages to support students in the first weeks of the semester.
These basic building blocks are the components of your action plan.
A blueprint for action
When designing your course, consider what kinds of questions you might need to ask at different points in time. Is your course designed in a way to have those questions answered?
--Colin DeLong, Director of University Data & Institutional Reporting, University of Minnesota
At the end of the first week of class, you might wonder if your students have accessed the syllabus and other pages that tell them how the course will operate. You can review course analytics to determine if students are accessing important course resources, and if you find that some students haven’t yet met this expectation, you might send those students a gentle nudge. As the semester progresses, you’ll have different questions. Learning data can provide actionable insights if you’ve designed your course to anticipate these questions.
Use the Beginning of the Term Blueprint to planfully make these connections. The Blueprint is populated with examples that
- provide foundational instructional questions,
- Identify relevant Canvas learning data and instructional strategies, and
- suggest appropriate actions.
Make a copy of the document and customize the Blueprint to map out the first weeks of the semester and create your own action plan. You’ll notice that the downloaded version contains a blank template for your use.
In this post we have focused on questions and learning data that are particularly important at the beginning of the term. But the practices we've identified are relevant throughout the semester. To continue the process of learning about data, we recommend that you identify a Canvas course that you have taught in the past (one that contains student data) and plan to teach in the future: look at the Canvas tools you used to deliver content and support activities and assignments, and what learning data was generated by those tools. Your exploration might be guided by questions that you have about your course.
Resources
Acknowledgments
This post was co-written with Yelena Yan, Instructional Designer, Academic Technology Support Services.Special thanks to Jackie Olsson, Director, SMART Learning Commons, for contributing sample messages to support students in the first weeks of the semester.
Unizin Learning Analytics Guiding Principles (adapted in this post) were developed for the Unizin consortium by Robin Pappas Ph.D., Oregon State University; Kimberly Arnold Ph.D., University of Wisconsin-Madison; Marcia Ham Ph.D., The Ohio State University; Doug Johnson Ph.D., University of Florida.