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| astro300_f16:day8 [2016/09/29 05:59] – [Faculty Visits (40 minutes)] ccheng | astro300_f16:day8 [2016/10/03 05:03] – [Homework] bethmcbride | ||
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| * One amusing comment | * One amusing comment | ||
| * One thing you agree or disagree with | * 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: | (10 minutes) As a class: | ||
| Line 18: | Line 23: | ||
| * Eliot Quataert | * Eliot Quataert | ||
| * Jessica Lu | * Jessica Lu | ||
| - | * Eugene Chiang | ||
| Some prompts given in advance: | Some prompts given in advance: | ||
| Line 43: | Line 47: | ||
| =====Homework===== | =====Homework===== | ||
| - 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 {{: | - 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 {{: | ||
| - | - Read papers: | ||
| - Bring your lesson plan notebooks to 375 next week. | - Bring your lesson plan notebooks to 375 next week. | ||
| + | - Look through the abstracts from any of the following conference proceedings. Choose at least one paper and answer some questions. | ||
| + | * Learning @ Scale: mainly focused on learning from MOOCs (massively open online courses), but also includes research onusing student data to understand aspects of learning, http:// | ||
| + | * 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:// | ||
| + | * 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:// | ||
| + | * EDM: Educational Data Mining, mainly focused on machine learning as a method for using data to understand student learning, http:// | ||
| + | * AIED: Artificial Intelligence in Education, more comprehensive methodology than EDM (including natural language processing) as a means to understand student learning, http:// | ||
| + | * AERA: American Educational Research Association, | ||
