Browse Public Designs
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Small class  ttest 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 handins, contributing to alignment on the course.
The etivity 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 etivity reaches stage 4 in the 5stage model, as it is intended to help students construct knowledge from hands on experience.Intended Learning Outcomes:
 Carry out a ttest in Excel
 Evaluate hypotheses based on data similarity to known mean or other data sample assessed by a ttest

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.

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

Thesis introduction writing with AIgenerated 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 plagiarismchecking tools. By others, AI generated text  when supervised and qualitychecked 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 AIgenerated 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 AIgenerated text for University assignments and the ethical considerations

Hydrogeophysics Instrument Introductions
Description:
The Hydrogeophysics course gives students an overivew of a number of different geophysical technologies and how they can be used to study groundwater systems. The lectures focus on understanding the theory behind each measurement  how measurements are linked to Earth properties, understanding the physics of the measurements, and how the results can help better understand groundwater systems. To improve students more practical understandings  including things such as how does field equipment look, operate, and get used in practice  an online component is added to the course featuring 360 videos and accompanying quizzes.
The goal of the videos is to introduce students to the various insturmentation discussed in class by giving a walkthrough of the equipment in a field setting  where each component of the systems are discussed and the role that they play elaborated. The intent is that the videos, by filming them in the field, give a much better sense of how the systems work in practice  than could be achieved purely in the lecture.
The goal is that each week students will watch a brief 1015 minute video discussing the relevant geophysical equipment and then complete a short quiz that touches on the main takeaways from the video.
Intended Learning Outcomes:
 Develop familiarity with hydrogeophysical field instrumentation
 Understand how each instrument is used in practice
 Articulate the role of each component in the instrumentation
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