Week 3
July 4 – 10



WELCOME to the online section of the course!There are some important dates to keep in mind for each online week of the course.

  • Base Group Checkin – Due Wed
  • Live Meetings – Thurs @9pm Eastern
  • Everything else – Sunday night at 11:59pm Eastern
  • Readings – Are discussed online via Piazza (just follow links)

Also, by now we hope that you can turn things in via your portfolio. If not, you can still use the dropbox.

IMPORTANT: There are several big projects ahead, including several article critiques, and a research proposal. You cannot wait until the week they are due to be successful, please look ahead and plan ahead!



Check-in Question: What is the best thing that happened to you last weekend?You do not have permission to view this form.



This week and next you will work on developing an audio interview about what motivates people. For details, see the audio interview page. Please read the whole assignment.For this week, complete the first two bullet points (at a minimum). Next week, the remaining bullet points are due.


Your third article critique is due. Remember the importance of this in your grade (see syllabus). Submit your Article Critique #3 here!



Research Reports #5 and #6 are due for your RDP. From now on, you only need to complete a written report.Submit RR#5 here, and RR#6 here. As you have in the past, please save them as follows: “LASTNAME_RR#_MM.DD.YY”.



Cognitivism & Motivation

Schunk, D. H. (2011). (Chapter 7) Cognitive learning processes. Learning theories: An educational perspective (6th Edition) (pp. 278-344). Boston, MA: Addison Wesley. [discussion: Q1, Q2, Q3 ]

Schunk, D. H. (2011). (Chapter 8 ) Motivation. Learning theories: An educational perspective (6th Edition) (pp. 345-398). Boston, MA: Addison Wesley. [discussion: Q1, Q2, Q3 ]

Ed-Tech

Koehler, M.J., & Mishra, P. (2009). What is technological pedagogical content knowledge? Contemporary Issues in Technology and Teacher Education, 9(1), 60-70. [discussion: Q1, Q2, Q3 ]

[**OPTIONAL**] Baker, R., & Siemens, G. (2014). Educational data mining and learning analytics. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 253-272). doi.org/10.1017/CBO9781139519526.016




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