3 Matching Annotations
- Nov 2018
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carpentries.github.io carpentries.github.io
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What’s missing, either in your list or the one below
Missing on the list below:
- grit,
- minute card
- unconscious incompetence, conscious incompetence, conscious competence, unconscious competence
- learning objectives
- value, expectancies
- knowledge organization
- rubric
- ground rules
- self-directed learner
- course climate
- student development
- prior knowledge, declarative knowledge, procedural knowledge
- concept inventory
- component skills
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What’s missing, either in your list or the one below
Missing on the list below:
- grit,
- minute card
- unconscious incompetence, conscious incompetence, conscious competence, unconscious competence
- learning objectives
- value, expectancies
- knowledge organization
- rubric
- ground rules
- self-directed learner
- course climate
- student development
- prior knowledge, declarative knowledge, procedural knowledge
- concept inventory
- component skills
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-
carpentries.github.io carpentries.github.io
-
People learn best when they care about a topic and believe they can master it. This presents us with a problem because most scientists don’t want to program: they want to do science. In addition, their early experiences with computers are often demoralizing, and believing that something will be hard to learn is a self-fulfilling prophecy.
From the revelations in How Learning Works (p. 79) that value and expectancies drive motivation and how these interact derives from the learning environment, I see that in this situation, we need to build a positive learning environment more via the third and fourth factors listed above (Encouraging learners to learn from each other and acknowledging confusion). Setting learning goals that show the relevance of the coding skills to the learners' future professional existence and our own enthusiasm for the coding, will help us create value and design assessments and activities that are in alignment with the goals. Thus, learners' expectations can be enhanced.
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