Browse Public Designs
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Implementing online learning in a genetic course
Description:
Sometimes teaching quantitative genetics can be boring for some students, as we see mainly numbers and formulas. By doing an small video with a virtual visit to a farm, where we will get the data to estimate genetic parameters, we could help the student to be curious and more interested in the topic. Methane emissions and feed efficiency in dairy cattle are two of the topics I currently investigate, and I consider they are both crucial for livestock production. The idea is to take a video of the farm, explain a bit about how it work the measuring of the phenotypes, and the identification of the animal, and take 2 or more cows as individuals we are interested on. In the exercise the students will have time to get familiar with the database, and the final idea to calculate some genetic parameters and rank the cows (that were introduce to them) on which one is more efficient and/or produce less methane. With this information the students will get familiar on how selection is being done in real life, and they will get the tools needed to take decisions. For this exercise we follow the STREAM methodology where we will have the out of class activity (watching the video) and the in class activity to solve the exercises, then out-class activity (mentimeter) and in-class feed-back.
Intended Learning Outcomes:
- Recognize the basic principles of animal breeding and genetics
- Identify the importance and use of animal breeding selection
- Use the basic concepts of animal breeding to estimate genetic parameters and EBV
- Conclude based on the results which cows are more convenient to keep
- Reflect on the outcomes and results
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Small class - t-test in practice
Description:
In a first semester introductory course for the bachelor of chemical engineering program we notice that students have a hard time grasping the concept of statistical tests for deciding if data are in agreement with a known, true mean, or if two sample means are significantly different. To us teachers it seems like problem sessions with teacher guidance (TØ) are great, but that teacher resources are easily consumed by the tedious task of showing students which buttons to press in Excel.
To free up teacher time in class deeper interaction with the students about statistics and and how mean and standard deviation relate to terms like accuracy and precision, I have created a learning design based on the STREAM model using screencast resources and an online quiz to construct an out of class loop and make sure that students know the basics when they arrive for the in class teaching. The screencast resources should also be useful for the students in connection with later laboratory exercises and lab report hand-ins, contributing to alignment on the course.
The e-tivity included in the first out of class phase is a quiz which serves a dual purpose. 1) The quiz has a deadline before the in class teaching date, assuring student participation in the out of class activities. 2) The quiz serves an assessment purpose to evaluate the effectiveness of the out of class activities. This e-tivity reaches stage 4 in the 5-stage model, as it is intended to help students construct knowledge from hands on experience.Intended Learning Outcomes:
- Carry out a t-test in Excel
- Evaluate hypotheses based on data similarity to known mean or other data sample assessed by a t-test
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Data Structures and Algorithms
Description:
This mini course basically furnishes the students with the tool to answer the question: how many resources will I need for my algorithm?
It provides the basic tooling to compare algorithms in an idealized way, regardless of how fast computers have become: the bigOh notation.
The bigOh notation is essentially an algebra of worst case estimates.
Intended Learning Outcomes:
- Explain the rationale behind the definition of the bigOh notation
- Demonstrate graphically why some simplifications can be made in the bigOh notation
- Demonstrate mathematically, applying the definition, what the bigOh of a given algorithm is.
- Know the most common bigOh expressions for the most common algorithms.
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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
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Thesis introduction writing with AI-generated text
Description:
The use of AI for generating text is becoming increasingly widespread. Using AI generated text for university assignments and projects is viewed by some as cheating. This form of ‘cheating’ is hard to detect because it does not directly involve plagiarizing existing text and thus will not be flagged by plagiarism-checking tools. By others, AI generated text - when supervised and quality-checked by the user - is viewed as the future of writing. This exercise aims at i) introducing the student to AI generated text (if they do not know of it already), ii) writing and editing a draft of the thesis introduction with the use of AI generated text, iii) discussing and critically assessing the thesis introduction as a genre, and iv) evaluating and discussing the pros and cons of the method and the ethical borderland with the student.
The pedagogical rationale is to enable the student to critically reflect on writing, thesis content, plagiarism, ethics, and the future of the borderland between human and artificial intelligence.
Intended Learning Outcomes:
- Basic knowledge on AI-generated text
- Write and edit a draft of the thesis introduction with the use of AI generated text
- Discuss and critically assess the thesis introduction as a genre
- Evaluate and discuss the pros and cons of AI-generated text for University assignments and the ethical considerations
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