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Merge pull request swcarpentry#346 from swcarpentry/lex-19-objectives
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Update lesson objectives of lesson on them
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Greg Wilson authored Nov 14, 2016
2 parents 19079dd + 8fd9ccf commit 257a937
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11 changes: 5 additions & 6 deletions _episodes/19-lessons.md
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Expand Up @@ -6,18 +6,17 @@ questions:
- "How can I design more effective lessons?"
- "What lessons do Software and Data Carpentry currently contain?"
objectives:
- "Describe the four steps in reverse instructional design and explain why following them is an efficient way to create good teaching materials."
- "Follow the steps in the reverse instructional design process to create a short lesson."
- "Analyze a lesson by comparing it to the elements of WHERETO."
- "Write a learner profile describing a typical member of the lesson's intended audience."
- "Describe the characteristics of a good learning objective and correctly state whether a learning objective meets those criteria."
- "Classify the level of a learning objective in terms of Bloom's taxonomy and similar cognitive hierarchies."
- "Write a learner profile describing a typical member of their intended audience."
- "Critically analyze a SWC/DC lesson's objectives."
- "Describe the four steps in reverse instructional design and explain why following them is an efficient way to create good teaching materials."
- "Summarize the existing Software Carpentry and Data Carpentry lessons."
keypoints:
- "Use reverse instructional design to create lessons: concepts, summative assessment, formative assessments, teachings."
- "Write learner profiles to clarify audience for a lesson."
- "Communicate lesson goals by writing specific, verifiable learning objectives."
- "Bloom's Taxonomy classifies levels of understanding."
- "Write learner profiles to clarify audience for lesson."
- "Use reverse instructional design to create lessons: concepts, summative assessment, formative assessments, teachings."
- "Software Carpentry's lessons cover the Unix shell, version control, programming, SQL, and Make."
- "Data Carpentry's lessons cover data cleanup, management, analysis, and visualization in a variety of fields."
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