Discourse of Objectivity
Discourse of objectivity
In a recent discussion with some colleagues, I found myself making the argument that knowledge is subjective and the stamp of who does the knowing is imprinted on the knowledge. I struggled to come up with examples in the context of math and physics that were as clean and convincing as the ones from the sphere of biology and ecology. My ignorance, my bad. I should read/think more. The discussion left me feeling a bit emotional, and frustrated, at a time when the emotional state was already raw with the anxieties around the US elections. I also had a recent conversation with a very dear friend and colleague, Jessica Watkins, who encouraged me to think more about what I am feeling about the election rather than the election itself. She reminded me of the sense of powerlessness we can sometimes feel, and of trying to recognize when the object of our feelings might be dominated by us, versus the outside world. And as I stewed trying to think about ways to justify why knowledge in math is subject to subjectivities and positionalities of the mathematicians, I was reminded of my friend’s gentle suggestion.
Why was I emotional and frustrated? Especially when I haven’t been particularly invested in proving if the ontology of math/physics knowledge is objective or not. It is something I find intellectually curious, sure. But I am not sure if that is the most important question for me, personally, to engage with. And I have had lots of arguments with myself and others trying to argue how what we now recognize and teach as laws of motion are objective or not. But I am also unconvinced that trying to argue against the supposed “objective” nature of F=ma is the route towards liberatory STEM Education. I put “objective” in quotes since there can be so many meanings of that word, and I am, for now, leaving that unspecified.
Exploring those emotions, I feel, that I emote strongly about this because the discourse of objectivity has stymied me at many fronts. And I am starting to think that the problem of the discourse of objectivity is different (but linked, of course) with the question of whether or not the ontology of knowledge is objective. But the latter was clouding my thinking, when much of my emotions and arguments are linked to the discourse of objectivity. I have struggled internally and externally (and continue to do so) with these discourses of objectivity in the course of my journeys in education research.
Early in my forays in education research, I gravitated towards qualitative research. In our group, there was a practice of collaborative analysis in group meetings. We would argue back and forth amongst different interpretations of some bit of audio or video or lines of transcript. This was partly because the group I joined as a postdoc specialized in qualitative research. But I also found myself drawn to the aesthetic of qualitative research. While the interpretive nature of the work going on in education research was intriguing, it felt really different from the kind of work that I was used to — working with equations on paper, writing code to model physical systems, running simulations to generate patterns from which to make arguments about how the physical system worked, or make predictions that could be tested in an experiment. But over time I came to see how to construct defensible arguments (why do we need to create “defensible” arguments always? Who and what are we defending against?) in qualitative education research that, to me, seemed as robust (from it’s own epistemological position), as the physics research I was used to. But to do that meant, for me to reject the tight coupling of robust argumentation to statistical reproducibility.
This tension around statistical reproducibility as defining the value of knowledge produced, I soon came to learn, has been long present in physics education research (PER). One of my mentors told me his version of how statistical reproducibility was beneficial in efforts to establish PER within physics departments. He was one of the physicists who in 1980s started doing physics education research in earnest. One of the first external challenges they had to contend with was that the claims of education research are really subjective. This contention was not simply an epistemological contention. The epistemological argument was marshaled to de-legitimize the work of education research within physics departments, to put it in it’s place, to challenge it’s status as research itself. Such delegitimization has impact on allocation of material resources within departments. One of the things, he said, that made a difference was hard data. As concept-testing tools were developed alongside pedagogies that rejected lecture-dominated models of teaching, class sizes in introductory physics courses were also growing. These allowed for large-N statistical tests to be performed, comparing students’ performance between different pedagogies. My mentor noted, that when faced with the skeptic physicist, he would share one of the concept-testing tools, and convince them to it in their classroom. And that when they saw how the tools reproduced the national level data in their own classroom, they were more convinced that physics education researchers were doing something legitimate.
I feel, however, that both the situation of PER in physics and the success of numeric reproducibility has accentuated the qualitative/quantitative divide in PER. And also, in engineering education research, as I would find out later. (These rifts, divides, tensions aren’t static though, and I have seen the landscape change even over the last 15 years.) In paper after paper, my co-authors and I had to defend our qualitative work in physics and engineering education research to argue that we can make scholarship-worthy claims based on a single interview, a small set of students, segments of conversations, etc. We had to defend against offensive wielding of phrases/words like “just interpretation,” “anecdote,” “isn’t data,” etc. And this time, these were wielded by physics and engineering education researchers. So much so, that in one paper, we literally had to put in a section to defend “N=1Case Studies.”  Of course, we also found companions on this journey (see for example ). Amy Slaton, Alice Pawley, Donna Riley, and a few others in engineering education have repeatedly drawn attention to the idea that this obsession with statistical reproducible knowledge as knowledge (and insisting on the objectivity of that) serves to maintain our classrooms and our pedagogies as White, cis, hetero, patriarchal.
The discourse of objectivity denies the positionality of the speaker/doer as relevant, hides how one is interpreting even statistical data, creates illusions of rationality, and stymies the consideration of emotions, values, and ideologies in conversations.
For example, the graduate chair of a department could interpret the crisis of mental health amongst graduate students as further evidence that those students did not belong in department (cause those who belong should not experience the discipline as hard in the first place, right?). These can lead to further tightening of department’s admission policies on seemingly “objective” measures such as GRE scores, GPA, etc. Decision-makers use the “objectiveness” of these measures to justify their decisions, even as the selection processes result in Whiteness. The perception of these measures as objective serves to protect the decisions and the decision-makers from any accusations of prejudice. Amy Slaton has talked about how meritocracy and technocracy entangle to make our universities less diverse, and thus maintain them as anti-democratic spaces . And O’ Connor, Peck, Cafarella, & McWilliams  show how, if we look closely, seemingly “objective” measures such as grades are reliant on various subjective assumptions and decisions of the instructors and teaching assistants. But once made, the numbers acquire an objective sense, then get tied into further decision-making for gatekeeping who does and doesn’t get to do engineering.
The discourse of objectivity trusts in numbers and existing definitions of boundaries. Thus, citation numbers and h-indices can be marshaled for appointment and tenure decisions. These and existing notions of what counts as doing physics education, or engineering education, etc can make it seem harder to diversify conference panels and plenary speaker slots. The discourse of objectivity, thus, in certain cases, functions to the maintain our disciplines as white supremacist spaces.
 Danielak, B. A., Gupta, A., & Elby, A. (2014). Marginalized identities of sense‐makers: Reframing engineering student retention. Journal of Engineering Education, 103(1), 8–44.
 Slaton, A. E., & Pawley, A. L. (2018). The power and politics of engineering education research design: Saving the ‘Small N’. Engineering Studies, 10(2–3), 133–157.
 Slaton, A. E. (2015). Meritocracy, technocracy, democracy: Understandings of racial and gender equity in American engineering education. In International perspectives on engineering education (pp. 171–189). Springer, Cham.
 O’Connor, K., & Peck, F. A., & Cafarella, J., & McWilliams, J. J. (2016, June), Working in the Weeds: How do Instructors Sort Engineering Students from Non-Engineering Students in a First-Year Pre-Calculus Course? Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.27054