Welcome to the course – Data Management (2025)

In this course you will get a thorough introduction to the value and importance of Data Management and how to do it. Data management is becoming more and more of a requirement by directives, institutions, and funders. The aim is to help you with your own data management by providing background, practice, and tools.

Our focus will be on 2 main topics and how they connect.

• The FAIR-principles for research data
• The Data Management Plan (template)

The course will introduce the FAIR principles, why they are important, and how to implement them in your research. We will go through the topics of the typical data management plan, you will work on your own as part of the homework, and we will discuss each other’s experiences.

The course is split in two workshops. 1 full day and ½ a day two weeks apart.

Before the 1^st day of teaching, 4 hours of homework is expected to get an introduction to the topics and materials. Before the 2^nd day, you are expected to do 15 hours of homework drafting a DMP and a dataset-exercise. At sign-up, you will be enrolled in both workshops, and attendance at both are required to pass.  

Enrolment, details and information: 

Welcome to AI for the People

Description: The notion of Artificial Intelligence (AI) dates back approx. 70 years as a research field and even longer if one considers fiction writers. A number of different definitions of AI has been suggested over the years, but none seem to capture what AI is. This might be due to the fact that AI is about computer algorithms that behave intelligently. And since the capabilities of computer algorithms improve over time, no static definition is possible.

One aspect of AI is the ability to learn or adapt dynamically. This concept has inspired numerous Sci-fi books and movies with the underlying theme of man vs AI (often manifested in a robot). From this follows naturally ethical and regulatory considerations. But until recently, such considerations (see for example the three Robotic laws defined by the sci-fi writer I. Asimov) have been speculative since current AI algorithms (and their manifestation in mechanical devices) have performed poorly and hence never left university labs around the world. Recently, however, fast hardware and massive amount of data have allowed revisiting one particular AI algorithm invented in the 80s, namely Artificial Neural Networks (ANN), and increasing the size of the networks used in these models. This was exemplified via image processing for recognizing hand-written digits and resulted in amazing results. Inspired by this success ANN (now known as Deep Learning (DL)) was quickly picked up by other research fields where similar successes have been witnessed.

DL algorithms can now outperform humans on a number of tasks. Moreover, they can, to a certain degree, learn new tasks. An important point in this regard is that the algorithm is so complex that it is next to impossible to understand its inner workings. So, we seem to be facing a reality where AI, in a not too distant future, will be used to make decisions (simply because it is of better than humans). This raises a number of ethical and regulative questions such as, for instance, 1) how we ensure that AI systems are not discriminating against certain groups in the population, 2) how do we ensure transparency about the decisions made by AI systems, and relatedly 3) could and should individuals be given a substantial right to an explanation of decisions made by such systems and a substantial right not to be subjected to automated decision-making (GDPR). Since many of the currently developed AI systems operate on the basis of large amounts of data, the development and use of such systems also reinvigorate the ethical issues related to ‘Big data’. Finally, there are problems related to the efficacy and safety of AI systems. This raises questions not only of how appropriate monitoring of the development of these systems can be secured, but also and more importantly about the appropriate domains for use.

These questions and related questions are the core focus of the PhD course on ‘AI for the people’. The aim is to raise an awareness in the participants. To this end the course will be a combination of lectures, debates and an assignment, and includes the following topics:

• Introduction to AI
• Ethical issues in the development and use of AI
• Industry perspective on AI

Prerequisites: None - besides an open mind and interest in AI and how it is affecting society and individuals.

Enrolment, details and information: 

https://phd.moodle.aau.dk/course/view.php?id=2485 - October

Welcome to University Teaching in Social Science and Humanities

The course objective is to develop participants’ foundational capacity toteach in a university setting. Specifically, the course aims to develop PhD students’ teaching competencies equivalent to level 1 of the Danish framework for advancing university pedagogy (https://www.iaspbl.aau.dk/projects/danish-framework-for-advancing-university-pedagogy).

Requirements:

The basis of the course is the practice of the participants. Thus, it is an absolute requirement that participants hold teaching obligations the semester they follow the course.

Timeframe:

The course is spread out over more or less an entire semester to allow participants to experiment with their own teaching and receive feedback in the course.

The course opens with a full day (9-16). Followed by four half days (9-12.30) a month between each day

This opening day is placed before the beginning of the semester (Ultimo January/ Ultimo August). The day provides a basic introduction to higher education pedagogy. Furthermore, participants are divided into small groups or professional learning communities (PLC’s) in which the participants will work the rest of the course. PLC’s will meet and discuss teaching experiences at every meeting.

Overall Learning Objectives:

• The overall learning objectives link to the Danish framework of advancing university pedagogy and ensure that participants under supervision are able to:
• Plan and carry out individual teaching and supervision activities
• Organize and carry out assessment
• Evaluate their teaching and teaching activities

Further, the course ensures that participants can:

• Create a constructive learning-oriented teaching environment
• Participate in collaboration on teaching with peers
• Identify resources for developing teaching quality

Teaching methods:

The course combines short lectures with student centred activities such as training exercises, group discussions, peer observation and feedback. The methods adopted will be discussed as exemplars of teaching strategies.

Description of teaching portfolio:

Participants are required to create a teaching portfolio. They will receive instructions in creating a portfolio and they will be required to give each other feedback on their respective portfolio. The portfolio can be in writing. However, participants’ will be encouraged to experiment with form and content. Furthermore, the course will discuss the requirements of a well-functioning portfolio in future job applications. 

Programme day one:

Structure:

Program Opening day

• 9.00-10.00: Welcome, introduction to the program
• 10.00-11.00: University teaching at a PBL university
• 11.00-12.00: Group supervision
• 12.00: Lunch
• 13-14: Lecturing
• 14:00: First PLC meeting
• 15.30: wrapping up

Program PLC meetings:

Each PLC meeting is divided into two sections. In the first 1½ hours a theme is discussed. Typically, one of the conveners will open with a short presentation to be discussed by the group afterwards. The group is also required to read texts in relation to the theme of the day sent out before the PLC meeting. These texts will likewise be included in the conversation. Theme of the day is decided by the group on the prior meeting. Example of themes could be Lecturing, group examination, groups supervision and group conflict and more. The other half of the meeting takes place in the PLC’s in which participants discuss their own teaching experiences. These experiences are used as basis for collective reflections on teaching and learning. Finally, each day ends with a lunch (12-12.30). In this way the idea is that the course will not only provide participants with theoretical and practical knowledge about university teaching but also help them establish a social network across departments.

Peer observations and supervision:  

Between each PLC meeting participants are to engage in peer supervision, which involves observation of each other’s teaching followed by peer supervision. Participants will receive training in teaching observations and peer supervision prior to the first round of observations.

We are of course aware that not everybody will have teaching obligations from one month to the next but hopefully at least one will have some obligations between each meeting. Furthermore, other arrangements can be made. One time the group will be invited to observe one of the conveners teach and it is also possible to arrange observations of other (more or less) experienced colleagues.

• 16.00: See you next time

Nikolaj Stegeager, Culture & Learning, SHARE-PBL

• Maria Hvid Stenalt, Culture & Learning, SHARE-PBL
• Jes Lynning Harfeld, Culture & Learning, RECAST
• Nikolaj Stegeager, Culture & Learning, SHARE-PBL

19/8:   9-15.00
26/9:   9-12.30
21/10: 9-12.30
18/11: 9-12.30
12/12: 9-12.30

AAU Innovate, Thomas Manns Vej 25

Important information concerning PhD courses: 

There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.

We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course. 

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at phdcourses@adm.aau.dk  When contacting us please state the course title and course period. Thank you.