Improving the Introduction to Computational Social Science

This year, the computational social science course had four different components;

  • lectures
  • mandatory reading assignments
  • learning diary
  • research plans

I asked students to evaluate each section separately. My first learning as that the course evaluation should always be integrated into the classroom activities as the response rate was rather low (n=6, < 50%).

More examples to the lectures?

While students stated the lectures presented an overview of computational social science, the application of computational methods was less present in students’ view. While the qualitative notes did not converge, I assume following statement captures the main challenges:

Maybe some basic things could be covered in further detail. For example how computational social science is actually done in practice

On the other hand, one might have focused more on example studies applying computational social science, but I’m not sure if that is what we should do in an introductory course. We have other courses which take a more hands-on approach to research while this course intentionally aimed to give an overview of the research tools available through computation.

However, then the justification to have such classes is to give social scientists a social science background and contextualization of the research problems.  Therefore, a proposal made by another student:

Maybe the case studies presented in the beginning of the class could be referred to more during the lecture. Applying the CSS approaches was quite difficult so that could have helped with the problem.

has some merit to it. I will reconsider how to bring more context to these classes via the introductory examples.

Communicate the purpose of pre-readings better

A student noted that

The lectures could have perhaps drawn a bit more on the pre-readings. Although they did at times, I think that most of the readings were just assumed as a background and not explicitly commented on or discussed.

and this seemed to be an overall consensus. The pre-reading was useful and give (a bit) more social science background to the things. Thus, I feel this indicates a poor communication.For me, the aim of the background reading materials was to shift some of the very basics on each of the topics for students themselves so we could deepen a few topics during the lectures. I aimed to construct each lecture so that they had three major themes which were supposed to go way beyond the reading materials. However, while making this choice,

For me, the aim of the background reading materials was to shift some of the very basics on each of the topics for students themselves so we could deepen a few topics during the lectures. I aimed to construct each lecture so that they had three major themes which were supposed to go way beyond the reading materials. However, while making this choice, I didn’t want to cover basic terms or ideas within the classrooms (e.g., “what are ties and nodes?” or “how is a simulation build”) to use the time in more interactive manner. I aim to communicate this better in the future.

More guidance to write learning diaries

I added this exercise to this year’s class to increase social interaction between the students and to force the students to consider what the lecture actually was about and what it means to them. Sometimes, it actually seemed to work rather well, creating questions and discussion among the students. I also used them to feed my next lecture, naturally, to cover emergent important topics.

One student observed that

I realized that commenting on others’ diaries was unnatural when it is compulsory. If I have something to comment, I would. But, trying to find a argument to raise felt unnatural so that I could not do it unfortunately.

This is indeed a challenge and might be solved with more guidance to ensure every learning diary would have been somewhat open for comments. Furthermore, as suggested by another student, more proper guidance in written format (I did cover some aspects of these in the first and second lecture) might have helped to structure the process further.

More instruction for the research plan

The final part of the course was to write the research plan from a topic of interest. My idea of the exercise was to force students to take the rather abstract concepts discussed during the lectures and adapt those to the subject area one is an expert of, to consider the opportunities of the “new paradigm.” Thus, the aim was that during this phase, students would have contextualised computational social science. Based on the numerical evaluations, this failed rather badly (I haven’t yet read these myself, so I don’t know what to say). Looking these, I assume there are two main reasons for this

  • A research plan is an unknown format to students and thus, may not convey the purpose of doing this. We did have an example available, but again more instructions might help further.
  • The course didn’t provide enough details about a research and students may have required more details about existing research to help them imagine future research. It might also be that students assumed they should write much lower level research plan I originally intended.

This said, only three student students used the opportunity to get comments on drafted research plans, so even the support structures did not work the way I intended here. It might relate to other courses and/or activities students had during this time but should be remembered next year too.


Planning next Spring

We’ve just had an organization meeting for next Spring Computational Social Science teaching. We’ll organize, as planned, two classes

Simulation models

This course will cover the basics of system simulations, microsimulations and agent-based model simulations for social science. We will create such simulations in the class and discuss how to interpreted and apply those in practice.

The course is led by Matti Nelimarkka and it’s organized during 3rd period, Fridays 8-10.

Network analysis

This course will focus on applying network analysis in empirical research. This means basic descriptive statistics and metrics of networks and the visualization of a network as well as more computational approaches to study social networks (e.g., community detection and random walks).

The course is co-lead by Arho Toikka and Matti Nelimarkka and it’s organised during 4th period, Fridays 8-10.

The more concrete information of these courses will come before the start of Spring semester, including some syllabus. See the course pages for simulation and network analysis for further detail. Both classes will assume good familiarity of R or Python as they focus on hands-on skills and will include interactive workshopping as a teaching method.

Thinking about summer education

We might be organising two courses during the summer, Introduction to programming for social scientists and Data Extraction.

Rethinking data extraction class

One of the experiences in Spring 2016 class Data extraction was that the scope was too wide. We’re currently checking if we could meaningfully separate it to three separate classes

  1. collecting, cleaning and preprocessing data for social science research
  2. computational data analysis on textual data
  3. computational data analysis on numerical data

If you have particular hopes about new classes that we should organise, now is your time to influence it!

Complex Systems course organized for first time

The Complex Systems course was organized for the first time this spring as a part of the Computational Social Sciences program in the University of Helsinki. There were 20 students and four guest lecturers. The course offered an intensive introduction to the study of complex phenomena in social systems for graduate and doctoral students and professionals alike.

The course consisted of quest lectures, workshops and a poster session. The topics of the quest lectures varied from philosophical underpinnings of simulation methods to conceptual and empirical understanding and examples of the study of complex social systems.

The focus was on concepts relevant to the social sciences, such as emergence, self-organization, feedback, resilience, patterns of interaction, (social) mechanisms, autocatalysis, complex networks, collective behavior, simulation methods, risks, big data, among others.

During the course students carried out group projects related to themes covered in the program. They conducted group projects, which outcomes were presented in a collective poster session:

We hope that the course was as rewarding to the students and guest lecturers as it was for us! We hope that the students could adopt new relevant tools, ideas, and insights to their research and work.