CI 5371: Learning Analytics: Theory and Practice
DRAFT - Aug 30, 2018
Fall 2018 - Online - 3 Credits
Bodong Chen, Assistant Professor
Learning analytics as a nascent field is broadly defined as 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.” This course aims to provide a general survey of learning analytics emphasizing its application in various educational contexts, rather than its underlying algorithmic details. In particular, we will discuss foundations of learning analytics, survey pertinent education theories, discuss emerging forms of assessment, explore popular data analytic techniques, review learning analytical tools and case studies, and design analytics for our own contexts. Given the breadth of this field, personalized support is provided for deeper dives in special interest areas. Overall, this course provides a comprehensive, theory-driven overview of learning analytics to orient students to this nascent field and prepare them for advanced research/practice in learning analytics.
The course is designed for a broad audience. All graduate students interested in learning analytics and its application in specific educational areas (e.g., STEM learning, literacies, online learning, workplace learning, learning in informal settings) are welcomed.
Prerequisites: None. Prior knowledge in learning theories, assessment, and data science is helpful but not required.
By the end of the course, students will:
This is a Knowledge Building course, meaning all participants (including the instructor) are collectively producing ideas and knowledge of value to the community, in order to solve authentic learning analytics problems. Our top-level goal in this course will be to work as a knowledge building team, living and exploring the capacity of learning analytics in supporting learning in various domains. This overarching goal will be interwoven throughout this course. We will advance this goal through social annotation of readings, design activities, and group projects.
The course design also follows facets of Open Pedagogy. Instructional content (produced by the instructor) will be made open on the web as Open Educational Resources (OERs). All members of the community are encouraged to create and openly share artifacts -- e.g., web annotations, blog posts, tweets, essays, computer codes -- at any stage of the course.
This is an online class. The course website will be the hub, and most course readings will be listed on the site.
For the semester:
Digital tools and practices are important for this online course. Supporting tools include but are not limited to the following:
You will need a functional web camera and a microphone to participate in Zoom meetings. Please consult with the Office of Information Technology (OIT) in advance if you need additional support. If you’re new to virtual meetings, familiarize yourself with basic virtual meeting etiquettes.
This is a 3-credit course, with an expected weekly workload of 9 hours.
Assessment and Grades
Attendance & Deadlines
Attendance requirements and penalties for missing class: Attendance are required. Missed classes will lead to lower grades (see section Grading).
All graded work in class must be completed by the due dates listed below in the Course Schedule section of this syllabus.
If you find a specific deadline not working for you or you need more time for an assignment, you can establish a new deadline if you contact the instructor in advance prior to the deadline, provided that the new deadline would not disrupt your peers' work. However, if the instructor receives no prior communication from you and you submit an assignment late, the assignment will be penalized at the rate of 10% per day.
* No extra credit is allowed in the course.
Class participation involves active and constructive participation in sync and async discussions. Evaluation will be based on both numeric metrics reflecting participation efforts and qualitative assessment of one's discussion contribution.
SIG presentation. Each SIG will design a session to engage the whole class in exploring a theme in learning analytics. When one group presents/leads, other groups will participate and assess the session following a given rubric. Students in a same group will get a same score as other peers. Each SIG member will also be assessed by group members.
Each WG will tackle a real-world problem of their choice, and will be expected to produce a project artifact and present it to the whole class.
Reflection essay or e-portfolio. Students would have the choice between a reflective essay (not exceeding 2,000 words excluding references) or preparing an e-portfolio reflecting on one's journey in the course.
Class Schedule with Weekly Readings and Activities
Learning Analytics: A Brief Overview
Explore WG group ideas
Ethics, Algorithmic Accountability, and System Integrity
Theory and Learning Analytics
WG project ideas share-out
Hidden Assumptions: Epistemology, Pedagogy, and Assessment
SIG and WG signup
Educational Data Mining: An Overview
Cases and Examples of Learning Analytics
"Fun with Data" Hands-on
Social Networks (theme 1)
To be designed by SIG 1
Predictive Models (theme 2)
To be designed by SIG 2
Text and Discourse Analytics (theme 3)
To be designed by SIG 3
AI and Text Mining (theme 4)
To be designed by SIG 4
Visual Learning Analytics (theme 5)
To be designed by SIG 5
WG Presentations and Reflection
WGs present their group projects
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 See https://tekri.athabascau.ca/analytics/.