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astro300_f16:day8 [2016/09/01 19:01]
ccheng
astro300_f16:day8 [2016/10/03 05:26] (current)
ccheng [Homework]
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 =====Section Recap (20 minutes)===== =====Section Recap (20 minutes)=====
  
-=====Faculty Visits (45 minutes)=====+(10 minutes) In groups, share from your midsemester evaluations:​ 
 +  * One consistent comment 
 +  * One amusing comment  
 +  * One thing you agree or disagree with 
 +If you have not yet administered your evaluations:​ 
 +  * One thing you are particularly interested in learning about from your eval 
 +  * One thing you expect that your students will say 
 +If you are not teaching this semester: 
 +  * Share one of the questions you wrote on your eval 
 + 
 +(10 minutes) As a class: 
 +  * What were some things you learned from your midsemester evaluations?​ 
 +  * Were there any comments that stood out? Was anything surprising?​ 
 +  * How were sections this week? 
 +=====Faculty Visits (40 minutes)=====
  
 Professors visiting at 7pm: Professors visiting at 7pm:
-   * Eliot +   * Eliot Quataert 
-   * Jessica ​ +   * Jessica ​Lu
-   * Eugene+
  
 Some prompts given in advance: Some prompts given in advance:
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 =====Break (5 minutes)===== =====Break (5 minutes)=====
  
-=====Ethics (40 minutes)=====+=====Ethics (45 minutes)===== 
 + 
 +(5 minutes) Explanation of activity. 
 + 
 +(10 minutes) Group preparation. 
 + 
 +(30 minutes) Six scenarios (5 minutes each).
  
  
 =====Homework===== =====Homework=====
-  - Read papers:+  - Section video-taping is happening this week. We will send you a link to your section video by the end of the week. Watch it, and complete the {{:video.pdf|Section Video Recap Worksheet}} (due 10/12, next week!).
   - Bring your lesson plan notebooks to 375 next week.    - Bring your lesson plan notebooks to 375 next week. 
 +  - Look through the abstracts from some of the following conference proceedings. Choose one paper to read (it should be at least four pages long) and answer the questions at the bottom of this page.
 +  * Learning @ Scale: mainly focused on learning from MOOCs (massively open online courses), but also includes research on using student data to understand aspects of learning: http://​dl.acm.org/​citation.cfm?​id=2876034
 +  * ICLS: International Conference of the Learning Sciences, top research conference in the field and mainly focuses on STEM education, includes some research in higher education: https://​www.isls.org/​icls/​2016/​index.html (see proceedings,​ volumes 1 & 2 on homepage)
 +  * NARST: National Association of Research in Science Teaching, mainly focused on practitioners and research that comes from classrooms or is being applied to classrooms: http://​narst.org/​annualconference/​2016_Narst_International_Conference_Program.pdf
 +  * EDM: Educational Data Mining, mainly focused on machine learning as a method for using data to understand student learning: http://​www.educationaldatamining.org/​EDM2016/​proceedings/​edm2016_proceedings.pdf
 +  * AIED: Artificial Intelligence in Education, more comprehensive methodology than EDM (including natural language processing) as a means to understand student learning: http://​www.springer.com/​us/​book/​9783319197722
 +  * AERA: American Educational Research Association,​ the biggest conference for presenting new work in all areas of education (including higher education, STEM education, etc.), but the proceedings are a pain to sift through: http://​www.aera.net/​Publications/​Online-Paper-Repository/​AERA-Online-Paper-Repository
 +
 +Answer these questions about your articles:
 +  * What is the title?
 +  * What were the main take-aways (conclusions) from the article?
 +  * What methods did the authors use to come to their conclusions?​
 +  * Do you agree with the conclusions?​ What critiques do you have about the paper?
 +  * What did you find most interesting about your article or about the proceedings in general?