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Teaching Using Canvas: Canvas Beyond the Basics

Welcome to Canvas Beyond the Basics

Welcome to the Canvas Beyond the Basics series, a selection of professional development resources created to help you advance your knowledge and skills on the instructional use of Canvas and the UW-System Digital Learning Environment. Many of these topics were covered during the summer of 2021 Superior Learning Experience (SLE 2.0) as weekly self-paced course content, focusing on some of the tools in Canvas related to instructional technology. The areas of Canvas professional development focused on as part of the SLE 2.0  include using:

  • Canvas Analytics for quality improvement,
  • applying rubrics,
  • using Mastery Paths,
  • designing for mobile
  • and course setting and publishing.

Inside the section you will find many of these same self-paced instructional technology focus resources. Each themed resource section has tabs for specific areas with content will be laid out very similar. You will also find many of the resources are curated from Canvas including online guides and videos. Please note as you work through this content, if you have additional questions or would like some additional support in this particular area –remember that through the Center for Learning, Innovation and Collaboration (CLIC), you can request one-to-one consultation for course design, working with OERs and support from our librarians. We have great library and staff that will help you, as well as Canvas administrative support  available through our Canvas administrators. You can always reach out to the CLIC if you need some additional help. Thanks.

Using Analytics for Quality Course Improvement

Moving beyond Backwards Design for Continuous Quality Course Improvement 

Book cover image of Teaching in a Digital Age by A.W. BatesCanvas as part of our Digital Learning Environment (DLE) offers a number of useful analytic tools, each tool can provide valuable information that can inform us on areas in the course design where improvements can be made. During the 2020 Superior Learning Experience the course design model applied was Backwards Design, a great approach for rapid course development from scratch. However, once a course has been designed, taught and you are ready make quality improvements – an instructors should consider other instructional design models such as ADDIE to support the effort.

Applying the ADDIE model (Analysis – Design – Develop – Implementation – Evaluate) to a revision or course redesign is both a good use of Canvas analytics and the principles of ADDIE to perpetuate continuous quality improvement in your courses.

If you are not already using analytics to monitor student engagement with your course content, Canvas usage, or discussions interactions, a great place to get started is to watch the brief video on the analytic tools in Canvas tabbed page linked above as an introduction.

Recommended Readings on Instructional Design

The Addie Model Chapter 4: Methods of teaching with an online focus – 4.3 The ADDIE model

(Chapter 4: Methods of Teaching with an online Focus) – Teaching in a Digital Age - Second Edition by Anthony William (Tony) Bates, October 10, 2019, is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, except where otherwise noted.

Optional Full Book Download to have a copy of Teaching in the Digital Age as a great reference resource.

Using Pocket Data Analytics

This is a brief article introducing the concept of using "pocket data analytics" to guide you in course design and instruction decisions. Using Student Analytics for Online Course Improvement  –  Jennifer Patterson Lorenzetti  – Faculty Focus (online) May 16, 2016

Using Analytic Tools in Canvas

Course and Student Analytics

Canvas provides course instructors with an Access Report for each student which shows how many times a student has viewed or participated in a particular item. However, it is unlikely that course instructors will compile the report for each student to better understand overall student content engagements in a course. In this unit, we will talk about how to obtain, analyze and leverage Canvas access reports to inform overall student content engagements.

New Analytics Overview

New Analytics provides access in-course viewable access to data collected (which can be downloaded as CSV files) for missing assignments, late assignments, excused assignments, the class roster, and course activity data. Using APIs, all reports provide real-time data except for the Course Activity report, which includes data that may be delayed up to 24 hours.

Course Activity —displays a list of daily user interactions in course resources, where each report entry captures a summary of user views and participations. The calendar filter only accommodates up to the past 14 days. Data includes User ID, User Name, Section ID, Section Name, Course ID, Course Name, Content ID, Content Type, Content Name, Times Viewed, Times Participated, First Viewed Date, and Last Viewed Date.


Related Online Canvas Guides

Please Note: These guides are maintained by the Canvas Documentation Team and are available on the Canvas Community site. Some of the information in these guides may not apply to the UW-System (UW-Superior) instance of Canvas. If you have a question about a feature mentioned in these guides, please review Getting Help page for details on who to contact at the Canvas Support Team.


New Analytics

Using Quiz Analytics

You can view quiz statistics for quizzes that have been published and have at least one submission. You can also download comma separate value (CSV) files to view Student Analysis or Item Analysis for each quiz question. For more detailed information about item analysis limitations and calculations, please refer to the Item Analysis PDF linked below.

For optimum course performance in the Canvas interface, quiz statistics will only generate for quizzes with 100 or fewer unique questions or 1000 total attempts. For instance, a quiz with 200 questions will not generate quiz statistics. However, a quiz with 75 questions will generate quiz statistics until the quiz has reached 1000 attempts. Results greater than these maximum values can be viewed by downloading the Student Analysis report and viewing the CSV file.

Using Analytics for Course Design

In this module you have learned that from the course home you can click on New Analytics to view the associated course data on student access to course content, grades, and student-instructor communications. Analytics can evaluate individual components of a course and evaluate student performance. Course Analytics takes a three pronged approach to creating substantive data for Canvas users, this data can be used for the following:

  • Justification: focuses on system reports and how the system is being used – access and use of course content.
  • Intervention: looks to predict at-risk students and how to meet their needs – check student progress, direct message those needing intervention or early alert reporting.
  • Learning: focuses on learning outcomes, the effectiveness of the teaching style, and the division of time between students achieving competence and those falling behind.

Suggested Reading and a Case Study

Online Article UW-Madison - What are the pedagogical uses of learning analytics?

This article updated September 15, 2020 published by University of Wisconsin-Madison Academic Technology (Division of Informations Technology) reviews, "What is Learning analytics?" and "What can I use learning analytics for?" and provides a framework for accessing a variety of data sets using Canvas and related Kaltura (MyMedia) analytics.

Online Article Educause - 7 Things You Should Know About First-Generation Learning Analytics

Learning analytics (LA) applies the model of analytics to the specific goal of improving learning outcomes. LA collects and analyzes the “digital breadcrumbs” that students leave as they interact with various computer systems to look for correlations between those activities and learning outcomes. The type of data gathered varies by institution and by application, but in general it includes information about the frequency with which students access online materials or the results of assessments from student exercises and activities conducted online. Learning analytics tools can track far more data than an instructor can alone, and at their best, LA applications can identify factors that are unexpectedly associated with student learning and course completion.

Case Study - Using Learning Analytics to Evaluate Course Design and Student Behaviour in an Online Wine Business Course

Kerry Wilkinson, Imogen McNamara, David Wilson and Karina Riggs – International Journal of Innovation in Science and Mathematics Education, 27(3), 97–108, 2019 Special Issue: Agricultural Education

Abstract This case study describes the use of learning analytics to evaluate the transition of a postgraduate wine business course from face-to-face to online delivery using e-learning course design principles. Traditionally, Foundations of Wine Science lectures were delivered face-to-face, however the decision to transition the course from semester to trimester format presented an opportunity for online delivery of lectures. This was initially achieved through audio recordings, then video lectures, supported by a range of digital learning resources intended to engage, support and enhance student learning and the student experience. Descriptive analysis of learning analytics, comprising assessment results, student evaluations of learning and teaching, and data sourced from the Learning Management System, was performed to evaluate the impact of online delivery of course content on student performance, satisfaction and engagement. The use of audio lecture recordings negatively impacted students’ perception of the overall quality of the course (including course organization, learning strategies and learning resources). The subsequent implementation of e-learning designed video lectures was considered superior to audio recordings, albeit final grades were not significantly different between the delivery modes. However, student engagement was equal to, or better than face-to-face delivery, when content was designed specifically for an eLearning environment.

Applying what you have learned about Analytics

In this unit we took a high level view of both the analytic tools available in Canvas, touched on concepts around learning analytics and how one may use analytics to find learning patterns or gaps in student learning within a course. If you can look back at the available analytics for a previous version of the course, chances are you will be able to identify areas for improvement. In the chapter 4.3 The ADDIE Model, from Teaching in a Digital Age – you are introduced to Analyze, Design, Develop, Implement, Evaluate as the cycle for designing, revising or redeveloping a course. Leveraging available course analytics to "evaluate" the results of your recently taught course is a key step in continuous course improvement and supporting student success. 

If you have access to a previously taught version of the course you are working on, take some time to access and review the analytics for that course. Look for patterns or trends in the course like page and course content access, time spend in the course, specific page views for content, assignment instructions, and media content like videos or podcasts. If students are returning to specific content or course pages multiple times, perhaps that content can be reviewed to see if it can be scaffolded or chunked down further to make it easier to use or more understandable. Are your videos too long? Do your students access all the content in the course? 

Review your course using analytics, look for areas of improvement, build a strategy for a re-design, develop and implement your changes. After the course is taught again, evaluate the analytics to see if your re-design has produced the desired results or if you can still make additional improvements. 

Kaltura offers you the ability to view analytics for your course videos in a couple of different ways, depending on whether you are using the Course Media Gallery feature or simply sharing individual Kaltura videos via Canvas pages.

The Entry Level analytics dashboard enables you to discover how your users engage with a specific entry, where they watched it from and with what devices so you can track and optimize your content. The dashboard can be used to explore engagement in different time periods and even compare between different periods. You can also filter based on a variety of parameters, including location and category. To learn more see Working with the Entry Level Analytics Dashboard

Entry Level Analytics for Kaltura