The trend towards adjunct, or “contingent”, faculty has been steady and strong: 70% or more of faculty in the US are now part-time and non-tenured. The pro-adjunct rationale is typically a financial argument: contingent faculty have a lower cost to the university. Typically, adjuncts don’t receive benefits like health insurance. They don’t have long-term contracts, and so their employment can be throttled based on demand. They don’t have union representation or support from a faculty senate, which means that they won’t lobby for financial protections. The financial benefit to the institution, however, seems dwarfed by the problems imposed by part-time, short-term educators. Adjunct professors publicly bemoan the lack of respect and support they receive from their departments: “I’ve heard stories about professors with PhD’s sleeping in homeless shelters, of adjuncts teaching in rooms filled with cockroaches, of professors without health insurance, unable to afford heat in their homes, living on food stamps.” [source]

The picture of the adjunct, then, is one that is promises financial reward for the university at the expense of both emotional and educational stability within the program. Adjuncts typically lack perspective into the broader curriculum in which they teach, because they aren’t part of longitudinal planning committees. They aren’t told the larger program goals in which they are to operate, and they describe feelings of isolation. During our recent research study, one adjunct lecturer juxtaposed two experiences, both of which indicate a broken model: “Some places, they really do treat you like… I don’t want to say disposable labor, but you really are not respected. You are really not taken into account. They just give you a class and say ‘Teach this’… They gave me the syllabus, the assignments, and so the first semester I kind of just went through it. Then I’ve had other schools that just give you a course title and it’s like, ‘Figure out how to teach this class’ – and I’m like, ‘What am I supposed to cover? I hope I’m doing this right.’”

Perhaps most problematic, adjunct professors aren’t aware of what pre-requisite skills or competencies they can expect from their students, and so they are forced to make sweeping assumptions that often prove to be wrong. My own adjunct teaching experience affirms this feeling; I was given a graduate class to teach at University of Texas at Austin. Based on the course sequencing published on the website, I assumed a base level of skill in fundamental aspects of design, and built a course around these assumptions. My assumptions proved to be wrong, and I spent a great deal of the course teaching undergraduate fundamentals rather than advanced graduate topics. I had no familiarity with the degree curricula, the demands placed on the students, the culture of learning, or the style of grading that students were familiar with, and I can only imagine that what was strange and disappointing to me was downright confusing for the students in my class.

I compare this to my experience teaching full time at Savannah College of Art and Design. During my time there, I shared a faculty office with other professors, where we could exchange knowledge of our students and our course designs in a causal manner. This type of knowledge sharing encouraged a more comprehensive educational plan across courses; we could confidentially assume that a skill or method had been taught in an earlier class, and confirming the assumption was as simple as asking across the office.

At the heart of this adjunct problem is a “who learned what” problem, one that’s similar to the “who knows what” (or “transactive memory“) problem in large organizations. In a large company, one of the hardest problems is keeping everyone “aligned” as time marches on. If I have a meeting with someone else, we’re theoretically in sync during that meeting. The minute the meeting is over, we’re out of sync, and the longer we go without speaking to one another, the more disjoint our views of reality become.

The curriculum suffers from the same problem. As an adjunct professor, I’m aware of what students have learned in my own class, but I have no idea of what’s been taught to them in other classes; I don’t have visibility into the narrative arc that is the educational journey. If I teach one class in a one-year graduate program, this means I know only 5 or 6% of the entire curriculum that the student will experience. I may never sync up with the other professors in the department in a formal (or even social) manner, and so I am unable to broad perspective of how my class fits into a larger educational story.


The trend towards adjunct professors isn’t going away, and there’s an obvious opportunity to help these teachers by providing them with a broad understanding of curriculum content as well as insight into the students in their classes. Here’s three ways we could help.

  1. Given that all course content and course sequencing information is digital, and increasingly, all outcome and assessment data is digital, it appears a trivial task to provide a dashboard report for adjunct faculty prior to the course beginning. This report could show them where their course lives in the broader context of the degree plan, and could articulate a list of the skills and competencies they can assume from the general student body of their course, based on the accreditation outcomes from pre-requisite classes.
  2. A slightly more advanced undertaking might be to offer an adjunct professor a way to view the students in their class, and drill down into the specific skills these students have acquired along the way. A nutrition adjunct might see an aggregate view of their incoming class, and could easily identify that a majority of the students received poor grades on food safety topics in a previous class. This type of aggregate view, informed by data, could then be considered in the context of the course itself, and the curriculum could be adjusted as necessary.
  3. An even more complex future might algorithmically examine the competencies that are to be taught, and offer statistically-significant insight into the success of teaching those competencies to the particular class of students enrolled in the course. Based on collaborative filtering and “big data” analysis, adjunct faculty can gain a better view of how their particular class of students may perform with the intended course work. For example, if I’m going to be teaching an Introduction to Chemistry class as an adjunct and I don’t know anything about my incoming class of students, I can leverage the historic data I have of how all other sections of the course performed in the past. I can automatically examine historical performance data about the new batch of students, and identify topics that are going to be difficult for a majority of the class. I can then change my course plan to spend more time on these tricky ideas and topics.  We’re already seeing this type of data being used by students to identify statistical anomalies in grade distributions. This data would be better served by helping adjunct professors gain context for their teaching.

I’m generally nervous of big data, because I see so many ways to misuse it. But the cards are stacked against adjunct professors, and they can use all the help they can get. Providing them insight into their students can help them be better teachers, and while big data won’t offer adjunct professors health insurance or a sense of community, it can be used to give them useful context about the larger educational landscape in which they teach.

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