By Per Nagbøl, junior researcher during fall 2016 and spring 2017.
Studying Literature at Danish Universities and University Colleges as a Member of Ethos-lab
I have decided to deliver this semester’s output as a Junior Researcher in an essay format. I made this decision because my engagement with Ethos during this semester has been an experimental learning process. This essay will therefore not be a scientific argument, but rather a reflection about my research this semester in relation to the Ethos-lab. A research that has not only been bound to my topic of inquiry, but also closely related to the social environment of Ethos-lab. Hence, this essay will acknowledge my research, social engagements and collaborations that have supported me in the course of my research.
Being a Part of the Ethos-laboratory
Despite my Bachelor’s Degree in Informatics and Educational Science, I am new to the field of research conducted by the use of digital methods and the study of literature. My previous research has been ethnographic. This means that the digital approach is a quite new methodology to me. In this regard, I have found Ethos-lab to be an excellent place to seek new knowledge. This is because the open and experimental environment of the Ethos-lab supports engagement with new topics. This semester, Ethos-lab has been a place where I have tested what was a new field for me with new approaches. During this project, I benefitted a lot from conversations, meetings and feedback sessions with other junior researchers, researchers, Heads of lab and other members of the Ethos community. The possibility of seeking advice from people with shared interests and extensive knowledge has been very valuable for me in this learning process, both when struggling with technical problems and when in need of new perspectives. The insights gained from above-stated dialogues and feedback sessions are embedded in this project. I have not succeeded in embedding all feedback and advice given to me during the project in this report, but I have learned a lot from them. The theoretical perspectives have provided me with a lot of food for thought and expanded the perspective from which I have experimented with the dataset. I have furthermore also tried to incorporate the feedback regarding visualizations, which has shifted my focus of experimentation from bipartite to monopartite networks.
Collaboration with Academic Books
During this semester, I had a very productive collaboration with Academic Books, which—through the help of Christina Jørgensen—has given me insight, information and access to both digital and analog materials necessary to carry out this project. In relation to this project, Christina supported me in accessing the data in the right format and with the right structure. I have decided due to the amount of digital material to refrain from using the analog material. With the help of Christina, I gained a very beneficial insight into how the classification of material at Academic Books is made and the limitations of said material. This insight is part of the foundation on which I have explored the data.
Studying Academic Literature in an Institutional Context
This projects stems from the in many ways naive idea about that visualised curricular literature could support students in their choice of education. The idea was to open up the curriculum in different programs, so students could choose a program that supported not them only in what they strove to become, but also gave them the possibility to choose the program that correlated with their own theoretical, ontological and epistemological beliefs. This idea of transparency was addressing co-students concerns across different universities, because they found the curricular literature misleading in relation to what they expected from their courses or study programs. Christina provided me with well-structured Excel sheets containing information about courses and literature from Academic Books’ databases. The data included literature from the following institutions: University of Copenhagen(KU), Copenhagen Business School(CBS), Roskilde University(RUC), IT University of Copenhagen, University College Capital (UCC), and University College Sjælland(UCSJ), Police Academy, Metropol, and Studieskolen. The data was delivered so that the content of each bookstore was placed in separate Excel sheets. University of Copenhagen has bookstores at the following campuses: Søndre, Nørre, City, Jura and Frederiksberg. There is then one bookstore each at RUC and CBS. ITU and the university colleges have their course material sold in above-listed bookstores. This means that the University of Copenhagen is split into five sub-categories. I have decided to let these subcategories remain (in my dataset) due to the size of the university. It also seems that the campuses are designed so that they host fields that are connected to each other. This could be an interesting topic to investigate in the future. Each of the delivered files contained the course material that Academic Books provided each semester from spring 2014 to autumn 2016. This amount of data is too large for the scope of this project, so I decided to include all universities and university colleges but limit the scope to autumn semester 2016. This narrows data down to around 4500 columns and 17 rows of information. The Information still had to be cleaned despite its very nice format. This is because that it seems that every bookstore includes a different amount of information about the courses. Some list assigned teachers and staff and some do not. Some list all staff in one column and others in in multiple columns. Some include a lot of additional info and some do not. Working with this dataset has been a balance between trying to paint a bigger picture while still validating the details. For this semester’s work, it has been an incomprehensible task. This is due to the size of the dataset and how the IT-system of Academic Books reacts to changes in courses and programs. The design of the IT-system has been influential on my research possibilities regarding the studies. Christina has again been very helpful in explaining these issues. One of the issues was that I was not capable of getting insight into what the compendiums contained with the data that I was delivered. Academic Books told me that if I wanted to, I could come visit their stores and look inside the compendiums. This was a generous offer that I declined for two main reasons. First, because it would be too time consuming, but I said that it might be relevant for future research. It would furthermore also be possible to access information there which is stored in analog format. This information goes back further than spring 2014, which is the limit for digital information. Secondly, I needed to narrow down my scope. In addition, I think that even with access to the compendiums in a digital format, it would still have been too large a task for one semester. Another issue related to the system is how the system handles changes. If one, for example, were to change the course number. This will not only change the course number for the current year but also for the years before. This means, in practice, that some literature is listed incorrectly for previous years. For example, if one assigns a course number to philosophy, this change will not occur only from this point forward. It will also change past listings. This means that if this code has previously assigned to French, it will now seem like French literature has been a part of the Philosophy curriculum in previous years. This also means that a course there has taken place at the IT-University of Copenhagen, for example, now appears in the system as a course that took place at the University of Copenhagen. The titles of the respective staffs are also incorrect, which means that some people that are not professors have been given a professor title and vice versa. I therefore decided to mostly explore the autumn semester 2016 and not focus so much on the staff. Which leaves the earlier-mentioned ca. 4500 columns and 17 rows of information. This data is only representative of material ordered through Academic Books. It is very likely that some courses buy their books elsewhere or distribute them to their students in a digital format. In the process of cleaning data, I acknowledged that it is easy it is to find errors regarding one’s own courses as it is hard to find in other programs. The errors in one’s own program makes you assume that there will also be errors in other programs. In continued work with this project, one of the tasks could therefore be a method to find, investigate, and correct these errors. Since the data only covers the autumn semester, courses in the spring semester were not present. This is important to remember because one might look at one of the visualizations and wonder why a particular book on education is absent when similar educations are using it. It is important to remember that similar educations can be constructed differently and some educations work with winter start and some with summer start.
Visualizing the Data
Data can be analyzed and visualized in many ways. I primarily experimented with the tools Tableau, table2net, and Gephi to categorize/classify, analyze and visualize the data. One way to visualize how books are brought into the university is to look at how they are represented in different study programs. The following visualizations show the books expected to sell the most at a given university. The sub-boxes inside the boxes are the book titles. The size of these sub-boxes is defined by the amount of books ordered by the library: The more books, the bigger the box. The sub-boxes within the sub-boxes are made to show which programs and courses use the books. The colors represent the ISBN number in case a book title has more than one ISBN number. The ISBN numbers are also shown below the name of the book in each box. In the case of ITU, the book Strategic Management – Awareness and Change was the book expected to sell the most copies, while the book The Design of Everyday Things was used across more programs and courses at ITU. I have decided to limit myself to only the most represented educational institutions in the dataset.
This visualization shows the five most popular books at ITU based on copies that Academic Books expected to sell.
This visualization shows the five or six most popular books at Metropol based on copies that Academic Books expected to sell. The books titled Ergonomi have the same title but different ISBN numbers.
This visualization shows the five most popular books at Copenhagen University (Nørre Campus) based on copies that Academic Books expected to sell.
This visualization shows the five most popular books at Copenhagen University (unknown bookstore location) based on copies that Academic Books expected to sell.
This visualization shows the five or six most popular books at Copenhagen Business School based on copies that Academic Books expected to sell. The books titled Rentesregning have the same title but different ISBN numbers.
This visualization shows the five, six or seven most popular books at Copenhagen University (City Campus) based on copies that Academic Books expected to sell. The books titled Macroeconomics and Videnskabsteori have the same title but different ISBN numbers.
This visualization shows the five most popular books at Copenhagen University (Frederiksberg Campus) based on copies that Academic Books expected to sell.
This visualization shows the most popular books at Copenhagen University (Jura) based on copies that Academic Books expected to sell.
This visualization shows the five most popular books at Copenhagen University (Søndre) based on copies that Academic Books expected to sell.
This visualization shows the five most popular books at Roskilde University based on copies that Academic Books expected to sell.
This visualization shows the five most popular books at UCC Nordsjælland based on copies that Academic Books expected to sell.
This visualization shows the five most popular books at UCSJ based on copies that Academic Books expected to sell.
The following visualization provide an overview of the nine most popular books across the whole dataset. The popularity is based on the expected amount of sold copies.
The visualization below is an expanded edition of the above visualization showing where the literature is used.
Another approach that I experimented with regarding books and educational institutions is how books link institutions together. The above visualization underlines which books are expected to be sold the most by an institution. These two graphs below attempt to investigate how study programs and educational institutions are related based on their shared books.
The first visualization shows the relation between books and study programs. The network graph is bipartite, which means that two types of nodes are connected through edges. The green nodes represent books and the red nodes represent study programs. The edges between them are made when a book is part of a study program. In this graph, it is possible for a study program to be connected to many books and it is also possible for a book to be connected to many study programs.
The two sub-visualizations below are zoomed-in views of the above visualization.
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These two sub-visualizations make the nodes readable and thereby more interpretative. The aim of this essay is not to reach a scientific conclusion, since my time in Ethos-lab mostly has been concerned with engaging in a learning process, but I will suggest that the reader pick a node and just follow its edges and see how quickly they end up in a whole other field of study.
My last visualization is a monopartite network graph showing the relationship between books and educational institutions. The books are represented by the nodes while the educational institutions are represented by the edges. The edges are created when two books are used by the same educational institution. It is possible for the edges to have more than one relation in this graph. The books that are only used at one educational institution are clustered together, while books used by many educational institutions are placed between the clusters. The meaning of the edges’ colors are shown in the table below. The grey color is given to educational institutions which are less represented in the dataset and to edges where two or more educational institutions have the same “right” to the node. This last scenario occurs when two or more books are represented at the same institution. A thought example: Book A is both used at RUC and CBS, while Book B is also represented at both RUC and CBS, which makes the edge a combination between RUC and CBS.
Relflection on this Essay
This essay is a description of my work done in the Ethos-lab in the autumn semester 2016, which was, for me, intended for learning about a new field and about gaining experience in working with methods that I am new to. The work should therefore be more understood as window into my process rather than an actual end product. I will therefore suggest that the reader does not rely too much on the results. This is also because the process of cleaning could have been more comprehensive, but I had to neglect this a bit in order to find time to work with the tools. With that said, I think studying literature in relation to education provides an interresting view on educational institutions, literature and study programs. It would be possible with the current dataset—after some extensive cleaning—to analyze and visualize many interesting relationships. An example of this is that the staff connected to the courses are spread accross many different columns in the dataset.