1 / 14

Learning Analytics: Process & Theory

Learning Analytics: Process & Theory. January 22, 2014. Welcome to Learning Analytics: Process & Theory. Seminar-style course where we’ll discuss several perspectives on what learning analytics is Educational Data Mining Learning Analytics Big Data

ohio
Download Presentation

Learning Analytics: Process & Theory

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Learning Analytics: Process & Theory January 22, 2014

  2. Welcome to Learning Analytics: Process & Theory • Seminar-style course where we’ll discuss several perspectives on what learning analytics is • Educational Data Mining • Learning Analytics • Big Data • Connecting these perspectives to philosophy and theory on the nature of design and inquiry • I hope you’ll come away from this class with an appreciation for what each of these communities brings to the discussion and broader field

  3. Welcome to Learning Analytics: Process & Theory • We will consider • what it means for a learning analytics analysis or model to be valid • the key challenges to the effective and appropriate use of learning analytics

  4. Overall Course Sweep • Paradigms • Sciences of the Artificial • Educational Data Mining • Learning Analytics • Big Data • Evidence-Centered Design • General Concerns • Validity and Generalizability • Uses • Automated Intervention (EDM, BD) • Reporting (LA, ECD) • Discovery with Models (SOTA, EDM, LA, ECD)

  5. Format • Discussion course rather than lecture or laboratory/studio course

  6. Format • I won’t lecture at you very much • So please read the readings and be ready to discuss them as a group

  7. Course times • Monday 11am-12:40pm • Wednesday 11am-12:40pm

  8. Course times • If we met every single time, this would be double the typical amount for HUDK classes • Instead, we’ll meet about half the time • There will also be occasional option class sessions on topics that are not in the main thread of the class, but may be of interest • The online syllabus will always have the most up-to-date info on future class sessions • I’ll also remind you in class about what’s coming up the next couple weeks

  9. Course Prerequisite • None

  10. Assignments • Theoretical Paper Prospectus 10% (Feb 19) • Midterm Exam 25% (Mar 10-12, open book) • Theoretical Paper 20% (Apr 21) • Final Exam 25% (May 10-12, open book) • Class Participation 20%

  11. Required Books • Simon, H.A. (1996) Sciences of the Artificial • Needed for class Feb. 3 • Trochim, W.M.K., Donnelly, J.P. (2007) The Research Methods Knowledge Base. • Needed for class Mar. 31

  12. Other Readings • I will make them available to you in class

  13. Introductions • Everyone please • Say your name • Say what program you’re studying in (if you’re in program) • Say what your current job is (if you have one) • Is your intellectual background: statistics, data mining, EDM, learning analytics, education research? (multiple or none of the above are fine; please explain) • Say why you’re interested in the material in this class (if you are)

  14. Upcoming Classes • 1/27: Methodological Pluralism • 1/29: Attend EdLab Seminar at noon by Professor Baker (topic: the Learning Analytics grad program at TC; will indicate where this course falls in overall learning progression) • 2/3: Sciences of the Artificial, Part One • 2/5: Sciences of the Artificial, Part Two

More Related