Flipped classroom for a selected chapter in quantitative genetics course
Description:
In our masters course, Quantitative Genetics, we work through numerous exercises most of which require software usage such as R and DMU. A challenge in this course is that most students are not familiar with statistical software used to implement exercises in this course. Therefore, often lots of time is spent specially in first few classes to familiarize students with programs like R or DMU, that would have otherwise been used to discuss the subject matter in the ILO. Here a learning design is proposed to transform the first chapter of the course, the introduction part, which mainly introduces the theoretical backgrounds and familiarises the students with statistical programs like R and DMU in a small class teaching setting. The learning design will follow a flipped classroom approach where such trivial tasks as running a software and understanding command lines can best be taken by the students themselves prior to coming to class in a flipped classroom approach.
Intended Learning Outcomes:
- Be able to code with R to run statistical analysis in quantitative genetics
- Understand and interprate results from analysis of data in DMU
- Develop pipelines in R where data analysis and result compilation from DMU is incorporated
Resources | Tasks | Supports | |||
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Before class |
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Software wiki, download links |
Student download software, load nessecary packages and read through manuals ↓ |
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Educator provide list of packages to be used in the course |
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Web casts |
Students study the statistical theories and methods to be applied through out the course by watch the intro videos ↓ |
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SWebcasts presenting an introduction to statistical methods and tools to be applied in the course |
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Web casts, youtube videos |
Students understand basic coding in R and DMU to run statistical analysis applied in quantitative genetics ↓ |
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In class |
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Example codes |
Students work on applying provided codes to solve statistical problems using example datasets provided ↓ |
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Answer questions, support in code understanding when students face problems |
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Discussion forum |
Students present their results from running the codes and discuss on challeneges faced in their analyses, interprate the results and relate to the general ILO in the course ↓ |
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Educator facilitates the discussion |
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After class |
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Brightspace |
1) Students develop codes in R integrating genetic analysis with DMU using datasets provided 2) Students post their codes in Brightspace 3) Students implement codes provided by atleast one other student and post their feedback on the performance of the code |
← |
Educator uploads datasets, provides hints for each exercise |