Fostering Student Success: Leveraging Canvas Analytics for face-to-face, hybrid, and online courses Welcome February 16, 2018

Pat Fellows Instructional Designer & Technologist pfellows@uoregon Pat Fellows Instructional Designer & Technologist pfellows@uoregon.edu Dave Furjanic Graduate Employee dfurjan2@uoregon.edu Introductions

Introduce yourself... Name, department, expectations

Learning Outcomes: Understand the role of Learning Analytics in student success & retention Learn how to access Canvas Analytics Understand what the Data is saying Learn how to use data to support students Apply concepts to a Winter Term course

What are Learning Analytics? Learning Analytics: 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.1 1. Ferguson, Rebecca (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6) pp. 304–317. International Journal of Technology Enhanced Learning

Types of Analytics Learning analytics – academia Predictive analytics What aspects of the course did students master? What topics are students struggling with that may require further discussion, attention or presentation? Predictive analytics How often do students view pages in a course? How close to the due date do students submit their assignments? Academic analytics Action analytics Predictive analytics: How often do students view pages in a course? How close to the due date do students submit their assignments? Academic analytics: What are the characteristics of at-risk students? Does recruiting tactics match institutional aid? Action analytics: Which recruiting practices improves retention and completion rates? What is the use of mobile devices on Campus and their demand on WiFi infrastructure?

What the Research is Saying Data models may be able to predict student final grade as early as week 3 (of 10) Increased time spent online does not accurately predict achievement Discussion posts, messages sent, and assessments completed may be more accurate Implication: quality over quantity? Procrastination associated with lower achievement This effect is stronger in e-learning than traditional courses Implication: we should explore proactive strategies – for example, break tasks into chunks How often a student looks at their grades may be the strongest predictor of achievement Students who never access grades are more likely to fail

Canvas Learning Analytics Course Analytics Predict how students react to course activities See which students are at-risk and need help View how effective your teaching strategies are in allowing students to learn See a quick view of what your students are achieving in your course *Currently does not measure activity on mobile devices Learner analytics How well a particular student is doing in your course AND what can we learn about the design of our course in Canvas. Keep this in mind for later

What to look at/for? Patterns Color Red No data points Whole class From student to student Color Red Missing Poor No data points Activity by date Communication At end of slide – let’s take a look at Canvas Course Analytics and data Go to a Winter term course and click on the Course Analytics button on the right top of page

Canvas Course Analytics Zoom, laser, pen Use flowchart for navigation Use word doc for definitions

Student Interactions Report: (aka: Teacher Activity Report) People>Settings>SI Report on menu Note, last interaction date, scores, and assignments Flip order

Course Analytics Next: Student analytics Go to Course Analytics and scroll down Sortable Numeric presentation

Student Analytics Activity by Date includes – joining a web conference, posting a new comment in discussions or an announcement, submit a quiz or start a quiz, submit an assignment, create a wiki page. Communication – All conversations, individual or group/class. Submissions – green line, submitted early, yellow line, submitted late, red box, missing.

Student Access Report Course Navigation Menu clicked – Course Home, Course Grades, Course Syllabus, Course Modules, Course People Course participation – Course Assignments, Course Discussions, Course Quizzes, Course Conferences, Course Pages, Course Files

Page Views and Participation B Difference between Page Views and Participation A, B, C students – Patterns? C

Submissions A B C A student, B student, C student – what do you see?

Grades A B Same idea Patterns? C

Let’s try it! https://giphy.com/stickers/forever-young-7N9Y5qu2X0KXe Think about showing what you find to the group. We can keep student identity Go into your Winter course and identify, using the student and class information, student that might be at risk, and if you see anything that stands out with respect to specific content and course activities and design of the course. https://giphy.com/stickers/forever-young-7N9Y5qu2X0KXe

Working with Students Conversations Announcements Discussions – Posts Grades Message students who…Assignment feedback Written Audio Video Attendance Calendar How to work with students at risk, or doing great work

Quiz Statistics Quiz Summary Question Breakdown Export Student Analysis Export Item analysis Not a lot of time, but let’s talk briefly about quizzes, as there are statistics there.

True/False and Multiple Choice quiz questions include an item discrimination index, which attempts to look at a spread of scores and reflect differences in student achievement. This metric provides a measure of how well a single question can tell the difference (or discriminate) between students who do well on an exam and those who do not. +.25 or higher is good

Course Statistics Statistics for course site Totals Assignments Students – last Log-in time File storage FYI

Questions?

library.uoregon.edu/cmet Thank you! Educational Technology Support Center for Media & Educational Technologies (CMET) 19, Knight Library lms@ithelp.uoregon.edu 541-346-1942 library.uoregon.edu/cmet