Browse Public Designs
Page: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58
-
Copied: New learning design
Description:
-
Exploratory data analysis with R
Description:
This design describes a hands-on, R-based, activity where students will put into practice data wrangling and plotting skills to conduct an exploratory data analysis (EDA) of the Palmer penguins data set in R. To prepare for the course students will watch short videos on exploratory data analysis and the relevant R commands as well as read a chapter from the R for Data Science 2nd edition book (Wickham et al 2023). Students will complete a short multiple-choice quiz on this material prior to the in-class activity.
The in-class activity will begin by going through the quiz providing feedback and addressing any common misunderstandings evident from responses to the questions. Following this, students will work individually while grouped into pairs or small groups; this is to allow each student to work at their own pace but have peers available to answer questions or help with problems students encounter. Students will work through a tutorial built using RMarkdown and the learnr package. As well as running R code blocks and modifying code blocks to achieve a stated aim, students will answer questions embedded in the tutorial that are based on the output from the R code.
The learnr tutorial will be run inside Posit Cloud, a cloud-based R Studio instance that provides a known computing environment.
The tutorial will guide the students through an exploratory data analysis of the Palmer Penguins data set using data wrangling, computation of summary statistics, and creation of plots with ggplot. The main new concepts introduced in this activity are measures of central tendency and spread, and appropriate plots for showing data and their distribution. This activity builds upon earlier topics where data wrangling and data visualisation are covered.
Upon completion of the tutorial, students will submit their answers to the tutorial questions in the form of a hash that they paste into a MS Forms or Google Forms form which allows the teachers to collect responses.
Intended Learning Outcomes:
- Understand how to use the summarise verb to calculate summary statistics from a data set
- Modify R code templates to display, explore, and summarise a data set
- Use basic ggplot commands to explore and summarise a data set graphically
- Apply data wrangling skills and data visualisation techniques and theory to explore a data set
-
Just-in-time feedback loop
Description:
A feedback loop to understand the competencies of course participants, adjust teaching activities, improve assessment of and for learning, and enhance intended learning outcomes (ILOs).
Intended Learning Outcomes:
- Analyse types of next generation sequencing technologies
- Perform genome-wide association study for candidate gene discovery
-
Optimized learning through structured guidance
Description:
Intended Learning Outcomes:
- Quantify and discuss digestion of nutrients and absorption in each section of the gastrointestinal tract in monogastric farm animals
- Describe scientific methods to study the effect of nutrition on physiology and production
-
Flipped classrooms for knowledge scaffolding
Description:
Learning design to help students brush up prerequisites for a course and scaffold knowledge for deeper learning.
Intended Learning Outcomes:
- Understand and explain the basic concepts and theories of formal genetics, population genetics, evolution and breeding
- Explain how characteristics of plants and animals can be changed by selection and predict this change
- Explain why preservation of genetic diversity is paramount in breeding applications
Page: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58