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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

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Learning Analytics: Process & Theory

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  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

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