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
-
STREAM'LINING THE LIMITS OF MACHINE LEARNING
Description:
This learning design attempts to apply the STREAM model to part of an introduction to machine learning, which focuses on machine learning hype versus technical limits.
The motivation and rationale is two-fold, first the for the topic itself, second for the use of STREAM:
A: New students of machine learning typically buy into much of the hype presented in the media and elsewhere, and as a result have unrealistic expectations. At the same time it is difficult to realize the limitations of machine learning independently if not specifically told where to look, and in many cases even skilled practitioners are also not aware of the real-world technical limits; and as a results may take on tasks or make promises that are simply technically impossible to fulfill.
B: A previous version of the learning design did not feature out-of-class activities, and as a result many students seemed to have difficulties relating cases covered in lectures to other and new cases (part of the ILOs). The STREAM model affords out-of-class activities to strengthen this aspect, for example by way of moderated forum discussions and Mentimeter assessments for learning.
Learning Design Structure:
1. Four short lectures provides background information, and sets up a number of general questions. The lectures are captured for later access and review.
2. In-class group discussions of specific lead-on questions. Lecturer floats.
3. Out-of-class forum discussion on new case study.
4. Out-of-class Mentimeter assessment for learning and evaluation of process.
5. In-class follow up with examples from forum discussion and Mentimeter results.
Intended Learning Outcomes:
- Define the differences between hype and technical reality in machine learning.
- Use machine learning case studies for comparison with new cases.
- Evaluate feasibility of new machine learning use cases.
-
New learning design
Description:
The learning design described here is to be used in the 10 ECTS course RNA molecular biology, which is a master course at the Department of Molecular Biology and Genetics, Aarhus University. About 30 students attend each year. Specifically, the learning design described here is intended for the part of the course covering RNA modifications (1 of 14 weeks). This part of the course includes a lecture, which cover the gamut of the field and is supported by the students reading a recent review article prior to the lecture, and three hours of small class teaching. The final assessment is a written exam where the list of questions is given beforehand.
Specifically, the learning design described here, is intended for the part of the course covering RNA modifications (1 out of 14 weeks). This part of the course includes a lecture, which covers the gamut of the field and is supported by the students reading a recent review article prior to the lecture. In addition, three hours of small class teaching will conclude the week.The objective for the learning design described here, is to try an alternative approach to facilitate a greater student participation in the in-class discussions and that the students enter the small-class teaching room better prepared. Many student currently use part of the in-class time, which is meant for group discussions, for reading the original article. With the learning design described here, I am hoping that a greater fraction of the students will read the article before the session and that more interaction with the instructor will facilitate more discussion and a deeper learning.
Intended Learning Outcomes:
- To facilitate a deeper learning of the students
- To create a better alignment between the teaching and the final exam
- To facilitate more peer to peer interactions
-
Exam preparation with playing cards as a motivational factor for oral participation
Description:
-
Digitale IIR-filtre
Description:
Lektion 8-11 handler om digitale filtre.
De studerende får i lektion 9 en introduktion til den klasse af digitale filtre, der kaldes IIR (infinite impulse response).
De er i sidste lektion blevet introduceret til FIR-filtre (finite impulse response). Vi kommer ind på, hvordan de to filterklasser adskiller sig fra hinanden, og om hvad man kan med IIR-filtre, som man ikke kan med FIR.
Desuden kommer vi ind på sammenhængen mellem differensligning og overføringsfunktion og ser på, hvordan et IIR-filter udføres, skridt for skridt.
Endelig vil vi se på poler og nulpunkters virkning på overføringsfunktion og stabilitet.
De studerende udarbejder et miniprojekt vedr. brug af digitale filtre på en konkret problemstilling.Intended Learning Outcomes:
- Redegøre for Z-transformationens anvendelse til beskrivelse af lineære digitale signaler og systemer.
- Anvende simple digitale filtre på typiske signaler.
- Redegøre for principperne bag FIR og IIR filtre.
- Håndtere digitale signaler i MATLAB herunder at programmere FIR og IIR filterstrukturer.
- Klassificere faglige problemer og redegøre for mulige løsninger.
- Argumentere for og implementere en valgt løsning på et fagligt problem.
- Arbejde selvstændigt og tage ansvar for egen læring og faglig fokusering.
- Kommunikere tekniske resultater til teknisk ligestillede.
-
Teaching geologists programming
Description:
Context
The learning design described here is to be used in the 10 ECTS course Geoelektromagnetism and Numerical Methods, which is an obligatory 2nd year course at the Department of Geoscience, Aarhus University. About 20 students attend each year, constituting a mix of geology and geophysics students. Specifically, the learning design described here is intended for the part of the course covering Numerical Methods (8 weeks, ~5 ETCS). This part of the course includes theory and hands-on exercises on numerical methods, and consists of a combination of lectures given by an instructor (2 x 2h per week) and theoretical exercises supervised by a teaching assistant (TØ; 3h per week). The final assessment is an oral exam where the list of questions is given beforehand.Learning design
In order to develop self-efficacy and promote deeper learning and peer-interaction, I explore the possibility of adding new out-of-class activities, as a complement to existing lectures and TØ. These activities will mainly be implemented as online e-tivities, and I will follow the 5-stage model by Salmon (2011), in order facilitate a successful online learning experience for the students. In the first week of the course, the online e-tivities will focus on stage 1 and 2: 1) familiarize the students with the digital learning tools (discussion boards in Blackboard, making a tablet cast), and 2) promote interaction between students, whereas all following weeks will have e-tivities on stage 3-5: 3) information exchange, 4) knowledge construction, and 5) development.
Intended Learning Outcomes:
- Calculate algebraic expressions numerically, including interpolation, differentiation and integration, as well as solve equations.
- Produce curve and surface plots of mathematical functions and observed data in regular grids, read and write data on disk files, and fit analytical expressions to such data.
- Write, debug and apply elementary Matlab code in connection with the above-mentioned learning goals.
- Solve simple differential equations numerically.
- Combine and relate these electromagnetic and numerical methods for solving geoscientific problems.
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