Teaching IS

Work in progress.

View project on GitHub

Introduction of and by Kai Eckert

I am professor in information science at Stuttgart Media University. My background is computer science and my research currently mainly focuses on NLP and applied Artificial Intelligence, usually to extract information from text to improve access to textual resources and/or gain further knowledge from these information. A current project is JudaicaLink.org where we create a domain-specific knowledge graph from structured and unstructured data sources to help in entity recognition and contextualization of texts.

The idea for this journey, however, has more to do with my teaching obligations. Stuttgart Media University has a long tradition as a library school, going back to the 19th century. IT played only a minor part until recently, with some very basic introduction in the first semester and some optional courses at the end which, due to the lack of prior knowledge, often had to be rather theoretical, with limited hands-on experience and without involving programming.

When I joined the university in 2015, it was part of my job description to change this. In the last 4 years, we recreated the whole curriculum, with a much higher focus on IT. To pave the ground for lectures and projects (we do a lot of project-based learning), we created three introductory lectures: Programming, Web and Data. They are all very broad but designed to provide the basis of what we see as the main building blocks of todays technology.

I would like to exchange experiences and ideas about developing modern information science curricula, particularly at the intersection of information science, data science and computer science.

Here are some further details about our developments:

Basic Lectures

  1. Programming: Programming is not software development, but aims at teaching students to solve problems using a computer and to aquire the competence to find answers to questions (and fixes for bugs) by themselves.

  2. Web: Web broadly introduces the internet and the Web from a technological point of view, with HTTP, HTML and CSS as the main components. We combine the practical exercises with theoretical input which is mostly driven by the interests of the students and can involve topics ranging from SQL injection techniques over the Darknet to current legislation like GDPR or copyright law.

  3. Data: Data is not about databases which have traditionally been introduced, but starts from working with text files, then CSV, then JSON, XML, then document-oriented databases and finally (briefly) SQL. The focus is on comparing all techniques and to gather hands-on experience how to work with data and also different data formats.

Lectures and projects using this basis include for example: Data- and Textanalytics, Web-Programming and Software-Development. From the beginning we use Python and Jupyter Notebooks as main technological framework.

Challenges

When we introduced the new curriculum, we actually faced a big problem which I would summarize (maybe somewhat boldly):

Students interested in libraries are afraid of this much IT and students interestes in IT don’t think that libraries are interesting.

We had a serious drop in applications and a lot of complaints from students who told us that IT has nothing to do with libraries (which is of course wrong, but what some of them think).

Current program

We reacted to this by offering two paths in our curriculum. The whole course is now called Information Science. No L-Word, formerly it was called Library and Information Management.

One path is called Data- and Information Management (DIM) (again, no L-Word here, and all IT topics), the other Library-, Culture-, and Education Management (LCEM) (here we expect people to go who think IT has nothing to do with libraries).

The LCEM Path still has Programming and Web as mandatory lectures in the first two semesters, together with the DIM path (not an ideal acronym in english, we know…), but nothing else. And in programming, we will basically set two different expectations for our students, where we want the DIM students to really learn programming and the LCEM students to at least understand the basic concepts and stop thinking of a computer as a strange black box.

Thoughts and questions

All in all, I think what we actually face here is a bigger problem: the image of libraries in the minds of teenagers is horribly outdated and many (of course not all) students start studying our program for wrong reasons and with wrong expectations. And the ones that libraries would actually need don’t think that working in a library is attractive.

Content-wise, the main questions currently boil down to these:

  • how much computer science is needed in information science?
  • What can be sacrificed from computer science (math?), what from information science?
  • How does data science relate to information science?

I would like to discuss this and related topics with as many as I can, e.g.:

  • librarians to discuss what a library looks like in the future and who is needed to run it, also understand better what the difference is between European or German libraries and American libraries which seem to have a better image in the mind of teenagers.

  • current practitioners in general, ideally with information science background, to discuss what competencies are actually needed right now and in the foreseeable future.

  • lecturers who want to exchange ideas and experiences with current curricula.