Every year around this time, I set aside a few hours in my work schedule to prepare a seminar on data analysis for students writing a master’s thesis in the Language Acquisition and Language Processing Lab. Almost all theses involve data collection in the lab, in kindergartens or in schools. Very often these data come from simple surveys, questionnaires or standardized tests. But occasionally students design and conduct their own experiments using more advanced measures, such as eye movements or brain potentials. For all intents and purposes, these are ‘miniature’ research projects, with their own peculiar challenges and rewards. The training we offer to our students is tailored to the requirements of their respective theses. Like other forms of individualized tutoring, it falls under the rubric of ‘student supervision’.
Still, my seminars do involve quite a bit of teaching in classical forms. One has to cover the basics of probability and statistics to explain students how to arrange the data, how to choose and apply appropriate tests, and how to interpret their results. Interaction with the students in these ‘foundational’ seminars has taught me that they need more training in this area, and that they are well prepared to receive it. They understand how data are relevant to their future professional and personal lives. They want to learn to think correctly and critically about other people’s data. Possibly, they want to be able to generate and use their own data sets, too. Surveys I have conducted yearly as a lecturer in 3000-level courses, as well as the growing number of attendees at my data analysis seminar each spring, have convinced me that the students’ demand for training in quantitative methods is real, and does not just come from the small sample of self-selected students who write a thesis in our lab. Are we doing enough to meet this demand?
There are currently some courses within the Faculty of Humanities that try to give students basic training in research methods, data analysis and even statistics. Yet, these courses are accessible only to a fraction of the students due to ‘curricular barriers’ and to the high levels of specificity of the course contents. Courses of that sort can be found in the Phonetics (FON1134 and FON3309) and History (HIST1505) programmes. Our own Department of Language and Literature will introduce a new Research Methods course in the fall. There are signs that Departments are responding to a demand for training in quantitative methods. In addition, the expertise to provide such training is in place. But more is needed. NTNU should offer basic training in quantitative methods to all humanities students, in the form of a course on the essential concepts, applications and interpretations of data analysis. The book ‘Scientific Methods for the Humanities’ by Willie van Peer, Frank Hakemulder and Sonia Zyngier is a good example of the kind of material that the course could cover.
My guess is that such course would be very popular among students. Most importantly, it would give them useful tools for their academic present and for their professional future. Much research in the humanities, in the past and present, has made and makes extensive use of data. Historians consult demographic, economic or other statistics to reconstruct and interpret the past. Linguists use corpora, observational or experimental data to validate their theories. Philosophers are paying more attention to empirical data, in particular ethicists and philosophers of language. All this is happening at NTNU (and elsewhere) right now, but it is not adequately reflected in what we teach students. Too often, we keep cutting-edge research methods away from them. Exposing only a few students to quantitative research methods as part of supervision for their thesis is too little, too late.
Most of our students will be employed outside the academia. And everyone outside the academia works with data. Students with a humanities degree must engage intelligently and critically with doctors, engineers, scientists, economists, politicians. To do that, they need to understand the data that these professionals generate and operate with. If a student with an English degree becomes a high school teacher, she would want to track progress of her students over terms or years: being able to work with data helps there, too. Data are increasingly used in journalism, and some of our students do choose that professional path. Others embark on careers as public servants and politicians: understanding whatever data come their way is mandatory to make informed decisions. The humanities rest on several pillars which should remain at the center of a liberal education: critical thinking, logical and rhetorical analyses of discourse, a discussion of the presuppositions of scientific inquiry, emphasis on the historical dimensions of human activity and knowledge, close attention to language as a medium of such activities. All this is still relevant, and perhaps as relevant as ever. But all these skills require an object to which they can be applied. Traditional and new objects of humanistic inquiry are now often presented in the form of data sets.
There is another reason why I believe we should offer humanities students training in data analysis methods, which is as important as the utilitarian value of possessing such skills. This reason has to do with the comparative advantages and disadvantages between students from different faculties. If we consult NSD’s ‘Database for statistikk om høgre utdanning’ (DBH) and we track the numbers of students per faculty, enrolled at NTNU in the fall over the past 14 years (2002-2015), we note several important trends. The number of students at NTNU has grown from 19310 in 2002 to 23898 in 2015. The number of students in the Humanities has remained very stable. On average, 3660 students are enrolled in the fall term each year. Students enrolled in science and technology subjects are more numerous each year. For example, Engineering has nearly doubled its students over this period, and the number of Medicine students has nearly tripled.
The issue here is not raw numbers, but proportions. As the total number of students at NTNU increases, so does the proportion of students enrolled in STEM subjects, while the share of humanities students decreases. What is decreasing as a result is also the proportion of students who do not receive training in quantitative methods at NTNU: sociologists, political scientists, geographers, psychologists, economists, educationalists graduating at our university increasingly do. There is a comparative disadvantage that arises from not being conversant with data analysis. For humanities students, the comparative disadvantage is growing every year. It is therefore a matter of fairness to offer everyone training in quantitative methods.
Perhaps the ‘core humanities’ skills listed above that our students acquire are their comparative advantage, and nobody else’s. I believe they are. But I also believe that does not compensate for a lack of quantitative training. To put it more positively, the comparative advantage of ‘core humanities’ skills plus quantitative training could potentially be huge. That is because, as I have argued above, those skills need an object: data are today one kind of object that demands sustained critical thinking and interpretation. Rens Bod, in his ‘A New History of the Humanities’, has suggested that ‘seeking patterns’ in empirical data (languages, texts, the past) has defined some of the most prominent forms of humanistic inquiry. These are not opposed to the methods characteristic of natural science. Quantitative training would both constrain and enhance the ‘pattern seeking’ abilities that our students may need in their professional lives.
Humanities students at NTNU have much to gain from learning to collect, analyze and interpret empirical data. We should provide more than what is currently made available to students in most curricula. Formal training in quantitative methods should not be confined to master’s thesis supervision or to highly specialized subjects that only few students take. It should most definitely not become a new bachelor or master program in which we train ‘humanistic data experts’. On the contrary, it should be a course accessible to all humanities students, not too late in their studies, and possibly with a few program-specific ‘variants’ along the lines of the ‘Exphil’. Also because of its mission to provide ‘humanistic perspectives’ necessary to understand science and technology in society, the Faculty of Humanities at NTNU may be uniquely positioned to offer this kind of training.