Posts that are engineering edu-ish
Whoa, I’m actually enjoying this writing thing. This section is still rough — you’ll see at least one “NEED A REFERENCE HERE!” note — but I’m starting to go from the (more) comfortable place of “hey, narratives — they’re a thing!” into the weirder territory of “HEY EVERYONE LET’S ALL EXPLORE POSTSTRUCTRUALISM!” which is where my heart really lies (in terms of this project, anyway).
In the preceding section, we explored cognitive apprenticeships as one possible way to see faculty-as-learners. Despite many advantages, this perspective had one significant drawback: because it was originally designed for young learners, its assumptions of agency are limited, as is its ability to draw on the prior experiences of learners.
Another way to see faculty-as-learners is to view them as narrators. Glesne speaks of the writer/narrator role as threefold: (1) artist, (2) translator/interpreter, and (3) transformer. (Glesne, 2011, p. 219) The role of narrator is thus a high-agency role, with narrators deciding how to create (as artists) a story-telling moment and how to translate it for their audience in order to elicit the desired reaction/transformation. Agency is heightened even more when the narratives are about one’s own past. Autobiographical narrators paint themselves as characters in their own stories, drawing from their prior experiences and using their agency in the present to articulate their agency in the past. This “intentional state entailment” is a key feature of narratives; without characters with agency who make choices, we cannot have narratives at all. (Bruner, 1991, p. 7)
Narratives thus give us both a method and methodology for understanding faculty as learners that address the shortfalls of agency and prior-experience we found in cognitive apprenticeship theory. We will discuss narrative as method — the concrete step-by-step process of carrying out a project — later in this document. This section will explore narrative as methodology, examining the philosophical perspectives underlying and shaping the method.
In terms of methodology, narrative work usually falls within the intepretivist paradigm, where the purpose of research is to seek understanding (as opposed to creating predictions or causing emancipation). (Glesne, 2011, p. 7) Bruner’s landmark 1991 paper, “The Narrative Construction of Reality,” removes the boundaries between the mental process of thought and the discourse of its expression as a narrative. The narrative is not sitting precomposed in some idealized platonic space, waiting to be spoken or written by an unthinking scribe. Rather, narrative “operates as an instrument of mind in the construction of reality,” (p. 6) and as such, cannot simply be chopped into parts for neat analysis because of its “part-whole textual interdependence.”
Looking at the world through a narrative paradigm also requires that we examine our epistemological assumptions about the nature of “knowledge” and “truth.” We distinugish between the “constructions generated by logical and scientific procedures that can be weeded out by falsification” and the “version of reality whose acceptability is governed by convention and ‘narrative necessity’ rather than by empirical verification and logical requiredness” (Bruner, 1991, p. 4-5). The first is called “forensic truth,” the second one “narrative truth.” As a knowledge-seeking methodology, narrative does not seek forensic truth; rather, it seeks verisimilitude, the possibility or resemblance of forensic truth and the “truth of personal recollection and memory.” (PRECISE REF NEEDED, South African Truth & Reconciliation report).
Coming from this intensely personal perspective, narratives become boundary objects and mediated dialogues between an author (speaker) and a reader (listener), with both parties taking a highly active role in the process. In Bruner’s words, narratives have “hermeneutic composability,” meaning that they are things through which people express and extract meaning, but there is no single absolute meaning that can simply be dissected. Two people reading the same book can come out with very different insights. The interpretation being made depends on the background knowledge and intention of both author and reader, as well as what the author and reader know about each other. (p. 7-11) However, the information dissemination model of learning does not account for this hermeneutical interaction.
This hermeneutic composability leads to an expanded perspective on the part of both author and reader. Bruner writes about how narratives have context sensitivity and negotiability. By seeing that we and others may have different contexts, we are able to accept these differences. We recognize that we can immerse ourselves, like anthropologists, into someone else’s process for constructing meaning. (Bruner, 1991, p. 16-18) Belenky describes the process from the perspective of a constructivist, where participants engage in “…becoming and staying aware of the workings of their minds… [seeking] to stretch the outer boundaries of their consciousness — by making the unconscious conscious, by consulting and listening to the self, by voicing the unsaid, by listening to others and staying alert to all the currents and undercurrents of life about them, by imagining themselves inside the new poem or person or idea that they want to come to know and understand.”(Belenky, 1997, p. 141)
Narrative hermeneutics do not just expand perspectives within the author-to-reader connection. In fact, Bruner specifically depicts narratives as communal. Human societies pull multiple narratives into a larger assemblage of many narratives — a “narrative accrual” — that we share with others of our culture. (Bruner, 1991, p. 18-20) The history of a country, the “foundational” papers of an academic discipline, and the dinner-table stories that “everyone in the family knows” are all examples of narrative accruals. These narrative accruals are important enough that we legislate that children to learn national and world history in school, require graduate students to focus their first few years on reading a common core of “foundational” works, and make sure that prospective sons- or daughters-in-law learn certain family stories and traditions when they come to visit. Communities of practice and their narrative accruals therefore co-construct each other.
Learning the stories of one’s community — becoming a fluent reader of this narrative accrual — is a key part of enculturating into a community of practice. Without a common language, we are unable to communicate. Think about two kids on the playground bonding over a favorite TV show: “Did you see the episode where Superman did…?” “Yeah, but my favorite episode is where he…” Alternatively, think about the way researchers refer to common theories to get their ideas across; by invoking Wenger & Lave’s communities of practice theory in the preceding section, and Bruner’s narrative analysis work here, I draw my research into a web of ideas others have already thought and written about. Practitioners tell each other stories about their work all the time; telling the “right” kinds of stories about the “right” kinds of things (for instance, in a literature review) is a mark of belonging in its own right. “For newcomers,” say Lave and Wenger, “the purpose is not to learn from talk as a substitute for legitimate peripheral participation; it is to learn to talk as a key to legitimate peripheral participation.” (1991, p. 109)
However, it is not enough to become a passive reader of this narrative accrual; a full community member must contribute to the joint process of creating the collective story pool. “Our individual autobiographies… depend on being placed within a continuity provided by a constructed and shared social history in which we locate our Selves and individual continuities. It is a sense of belonging to this canonical past that permits us to form our own narratives of deviation while maintaining complicity with the canon.” (Bruner, 1991, p. 20) As we place and shape our own narratives within the narrative accrual of our community, we place and shape ourselves within and in relationship to our community. The transition from passive reader to active writer can be difficult. “She must learn again to speak,” says a poem by Marge Piercy, evoking the learning process of individual-yet-communal sensemaking that engaging in narration fosters. “starting with I / starting with We” — and yet it is only in this mutual engagement with the stories of “I” and “We” that sensemaking can begin its work of bridging and transforming.
Just as communities of practice overlap and bridge across each other, so do narrative accruals. A Japanese child my age may have shared my weekly viewing of the Pokemon TV show, but she may have watched the Japanese show Sailor Moon immediately afterwards, whereas I turned off the TV and read American novelists like Mark Twain. These overlaps can and do often interact to cause interesting shifts and merges in the “libraries” of individuals, and eventually in the narrative accruals of a broader culture itself. For instance, two teenagers may talk about their favorite band, then one may introduce the other to a new musician: “If you like Sara Bareilles, you should listen to Vienna Teng.” Eventually, if enough people in their social group come to enjoy Vienna Teng, that musician’s albums enter their narrative accrual and become a source of lyrics to be quoted, songs to be sung on road trips, and so forth. Similarly, a researcher may start writing from what she thinks will be common ground with her readership (“you’re probably already familiar with some cognitive apprenticeship literature and the idea of narrative analysis…”) and then branch out into what’s likely to be less familiar territory (“now let me introduce a fellow named Roland Barthes, and we’ll explore how his ideas tie in.”)
With that in mind, we now turn to a fellow named Roland Barthes.
(Still) writing my dissertation proposal. Apparently, I write non-linearly (no surprise to anyone who knows me), which means ideas coalesce in an ever-expanding fuzzy cloud for a long, long time — and then burst into a thunderstorm at the end. Right now I’m thunder-storming the section that talks about 3 different ways of thinking about faculty-as-learners: cognitive apprentices, narrators, and agents in a poststructural universe. Here’s the apprenticeship part, now that I think it’s world-readable.
Oh. Side note. Those who have heard me talk about cognitive apprenticeship in the past will notice the technique list is longer — in addition to modeling, coaching, scaffolding, and fading (originally called “exploration,” btw) there were also originally “articulation” and “reflection.” (I didn’t realize this until I dug back into the old literature after my last talk and went “WAIT, THERE’S MORE?”) So yeah, my next Hacker School talk is getting revised a bit, because it turns out those two are useful too.
One way of thinking about faculty-as-learners is to view them as participants in a cognitive apprenticeship (Collins et al, 1987). The cognitive apprenticeship framework was developed alongside theories of situated cognition and communities of practice (CoP) in the late 1980s and early 1990s. If situated learning says that all knowledge is contextual (Brown et al, 1989), and communities of practice are the domain-specific groups of practitioners with whom we share a fellowship (Wenger, 1999), then cognitive apprenticeships are how newcomers learn a contextually-situated intellectual craft by working with and observing others in a CoP. Faculty, especially novice faculty or those new to a particular school, can be thought of as apprentices who learn from watching more-seasoned faculty navigate the practice of teaching engineering.
The creators of cognitive apprenticeship theory were education researchers inspired by anthropological observations of traditional craft apprenticeships such as building furniture or delivering babies. They noticed that apprentices developed their skills in a meaningful context where their novice efforts clearly contributed to the “real practice” of their craft and the building of their skill in that craft (Brown et al, 1989). Reading or talking about designing engineering curricula is not the same thing as actually designing or teaching it, one possible reason the engineering education research-to-practice transfer problem is more difficult than the information dissemination perspective might make it sound. Apprenticeship-style learning is effective at modifying practice because it is modified practice, an deliberate experience of situated learning within a CoP.
Situated learning and CoPs are not new ideas in engineering education. In “Situated Engineering Learning: Bridging Engineering Education Research and the Learning Sciences,” Johri and Olds (2011) provide numerous examples of how situated learning is already embedded in engineering education by virtue of its emphasis on tangible, real-world, hands-on project work. CoP theory is even more widely used in engineering education, including usage specifically geared towards faculty development. For example, the NSF-funded Rigorous Research in Engineering Education (RREE) workshops were based on a CoP model (Streveler, Smith, & Miller, 2005). Engineering education conferences feature workshops and special sessions with titles such as “Feminist engineering education: building a community of practice” (Pawley et al, 2009) and “Communities in practice in engineering education: what are we learning?” (Adams et al, 2005). While not limited to engineering or technology faculty, the Faculty Learning Community (FLC) movement within faculty development has been described as a specific type of CoP (Cox, 2004). Finally, the American Society of Engineering Education (ASEE) is developing a NSF-funded virtual CoP model for faculty, citing familiar-sounding frustrations with the “inherent limitations” of the “develop-disseminate” model in which researchers develop new materials “and then try to convince others to use them… without any follow-up activity,” (Pimmel et al, 2013, p. 2).
Cognitive apprenticeships, then, provide an alternative perspective. How could the apprenticeship-style learning from traditional crafts be transferred to the development of cognitive skills such as teaching and curriculum development? One large divide needs to be bridged: the difference between cognitive and traditional apprenticeship is that cognitive apprenticeship focuses on cognitive and metacognitive, not physical, skills. Apprenticeships, argued Collins and his co-authors, exposed the process of creation to apprentices; a young man would see his teacher sanding a cabinet, a young woman would watch her teacher wrap a newborn child. It’s somewhat harder to “see” what goes on inside a faculty member’s head when (for instance) they are reconceptualizing what it means to teach design across the disciplines. Since cognitive activity is not visible by default, teachers of intellectual subjects would need to practice “making thinking visible,” or “the externalization of processes that are usually carried out internally… to bring these tacit processes into the open.” (Collins et al, 1991, p. 6) In effect, Collins and his coauthors were saying that facilitating cognitive apprenticeships is all about making one’s metacognition visible to learners in one’s CoP. They articulated several techniques for doing so:
- Modeling, where another practitioner performs the task in front of learners so they can see how it is done. Faculty-as-learners have extensive experience with this technique: namely, they have sat as a student in someone else’s classroom for years, and their experiences as a student have a significant impact on how they teach — although not as much as their experiences as teachers (Oleson and Hora, 2012). As faculty, they can engage in classroom observations to watch other faculty teach. In an ideal modeling situation, the practitioner who is modeling also thinks out loud, slows down, and explains intermediate steps: why was a particular decision made in designing this curriculum? What was the practitioner paying attention to while teaching?
- Coaching, where another practitioner watches the learner perform the task and gives them hints from the sidelines as they attempt it. Peer reviews of teaching, where classroom observations by a fellow faculty or staff member are followed by a feedback session, are one way some faculty already engage in this technique.
- Scaffolding, where another practitioner helps the learner perform the task, for example by starting it halfway and then letting the learner finish it once the task is at an easier point. Team-teaching with a more experienced faculty member is an example of this.
- Fading (or “Exploration”), where learners are encouraged to go out and tackle the process on their own with less and less help from the preceding practices. This is similar to the transition from teaching assistant to independent instructor.
Two other techniques were described by Collins et al in the original list, but have dropped out of usage over time:
- Articulation, where a practitioner asks learners to think out loud – an inversion of the “modeling” technique that helps learners become teachers to each other.
- Reflection, where learners compare their process to an expert’s process or their internal model of a “good” process.
Regardless of the specific techniques employed, the ultimate goal of cognitive apprenticeship is the development of metacognition and self-monitoring in students – or as Donald Schoen would put it, the ability of reflection-in-action (Schoen, 1983). Developing this ability enables practitioners to serve as “teachers” to one another; faculty can help other faculty develop without being faculty development specialists or even “expert” practitioners of teaching or curriculum design themselves. The only requirement is that the practitioner is self-aware enough to articulate their own performance, including their mistakes. For this reason, I have used the word “practitioner” rather than “teacher” in describing the cognitive apprenticeship techniques, unlike Collins et al, who were writing for an audience of K-12 teachers and education researchers.
It is here that we find one of the major limitations of the cognitive apprenticeship framework in thinking about faculty-as-learners: it was developed in a context where the learners were children, whereas engineering and technology faculty are adults. Agency is thus implicitly assumed to originate with the “teacher,” eventually flowing outward to the learner. This is evident not just in phrases such as “give students the opportunity” (Collins et al, 1987, p. 18) and “create a culture… for students” (p. 7), but in the techniques themselves — “modeling” is a predominantly passive act on the part of the learner, and “scaffolding” and “fading” are simply the deliberate and gradual transfer of agency to the learner. The end goal is for the student to have agency, to allow “the role of ‘expert’ and ‘student’ to be transformed,”(Collins et al, 1991, p. 17) — but it does not start out that way. However, Jane Vella’s 1997 book points out that adult learners must be seen as subjects rather than objects in learning from the very beginning (p. 129-148); they are already used to acting as independent agents, and bring a rich storehouse of past experiences to the table. In the next section, we will explore another viewpoint that takes these past experiences of agency into account.
This particular brainramble is edited from an IRC log with Seb Benthall, an old buddy of mine from the hacker world who’s now pursuing his own PhD in Informatics at UC Berkeley. Once again, half-baked thoughts that are scrambling rapidly as I make sense of this by throwing it into what hopefully will be my final(ish) prelim document.
I’m doing a poststructural narrative analysis dissertation on engineering/technology faculty storytelling. It’s poststructural because it destabilizes the centrality of the author (or in this case, the main researcher) and focuses on the sensemaking of the readers (in this case, primarily the narrators), and doesn’t expect their perspectives to converge or triangulate on an absolute truth or meaning.
The narrative analysis part means I work with stories. Specifically, I look at narrators retelling the same story over and over again in about 6 dialogic narrative episodes per narrator. I’m using Bruner’s work to say, “Hey stories are a thing, we can’t dismantle them, we need to treat them as stories.”
I’m also pulling in Bruner’s notions of how communities interact with their story corpus. As individuals, we make sense of our own narratives by shaping them with, against, within, <insert preposition here> a community’s story corpus (which is itself malleable). This means that there’s this whole community socializing, community-of-practice entry, apprenticeship-style dynamic to stories. I’m using narrative analysis to start poking at the edge of it. You can think of communities as narratively constructed, and their story corpus as socially constructed — they mutually co-construct each other.
Because of this sensemaking process, Bruner talks about all texts being hermeneutic. An author works to put meaning in; it’s an active process. A reader also works to draw meaning out; interpretation happens on both ends and is shaped by the context surrounding the interpretation. This thought — “all texts are hermeneutic” — comes from narrative analysis, but has a parallel in poststructuralism: “all texts are writerly.” The idea of “writerly” texts comes from Barthes, who contrasted them with “readerly” texts; the two terms refer to what role the text assigns its reader. A “readerly” text treats the reader as a passive consumer. In constrast, a “writerly” text demands the reader become actively engaged in wrestling with and coauthoring its material, bringing their own contexts and interpretations into play.
Another way of thinking about it that you’ll get, Seb, because we’ve worked together in the open source space: the hacker mindset is effectively to see the world in a writerly way. The writerly hacker.
You can see readerly/writerly as an epistemological view, in some sense. Readerly epistemology is the sort of epistemology that we think of for “stereotypical” school — what’s the role of the student? Brain-as-empty-bucket. What’s the role of the teacher? Garden hose that fills the bucket. Now, next step: if you don’t see your community’s stories as writerly — and your own story in that field as writerly — your future in that discipline looks like… it’s not much of a future.
(“How is this different from Constructivism?” Seb asked. “Or is it like a translating of that into poststructuralist language?” I had to think about this for a bit.) Constructivism is all about hands-on learning, and that hands-on learning is in order to support meaning-making. Whereas I’m concerned with meaning-making, whether or not the activities are hands-on. It’s a different angle.
Instead of focusing on students, I’ve decided I wanted to work with professors. Because the thing is, we keep telling professors “hey, you should teach your students with this hackerly/writerly epistemology,” but the professors themselves have to experience it first. If you look at a lot of the faculty development resources for STEM profs, they’re all like, “hey, we’re going to lecture you on why you shouldn’t lecture your students.” Which is to me sort of hypocritical and incredibly ironic. But because we’re used to efficiently converging on an answer in the STEM fields — converging on an answer and then transmitting it — we accept that and we think, “oh, okay, lecture on not-lecturing, cool, gotcha.”
That convergence is the sort of thing I challenge with a poststructuralist stance. Taking that stance means I’m refusing to converge on a single “we have the answer of the truth, we’re done done done!” point, and pointing out that the refusal to converge is a big part of what… I’m trying to say. For lack of a better way to say it.
I want to get professors out of their stuck narratives by having them tell their stories about designing technical curricula over and over and over and over. And every time they do a storytelling session — on the same story — the prompt isn’t from me. The prompt is either “here’s some verbatim phrases you said last time, let’s analyze your own story by retelling the story” — or those same sorts of phrases, but from another professor’s story. Maybe the prof down the hall you hardly speak to, or a prof from the same dept at a different uni who teaches the same class there. The narrators seem to think this is pretty cool. So far, anyway. They say things like — paraphrasing here — “zomg, did I say XYZ? that I meant ABC instead.” Or “Wait, actually, I really like how Dr. Foobar phrased it better than the way I said this before.”
I can pull this off in a single session because I use realtime transcription, so halfway through the “interview session” I stop the storytelling and flip the laptop screen around and say “here’s what you said, let’s read it and talk about it.” It’s an integrated member check. Also, part of the IRB is that they agree to release their transcripts with their name on it under an open license (CC-BY-SA right now), so the member check is also an editing session to make sure they’re ok with whatever is in there going public. It’s “Oh gosh take out what I said about my dean” time, just in case, but nobody’s really used that capability yet.
So we end up with: (1) an open dataset of (2) repeatedly told narratives that (3) intertwine and reference/edit/critique each other.
Seb pointed out that I’m “studying up,” meaning that I’m studying people of a higher status, which makes for an easier IRB proposal. He made me realize how important the deliberate lack of convergence was, and that I ought to point that out more. (“Hypothetically,” I said, “you could continue the process forever; there’s no ‘perfect’ version of the story, we keep rewriting our stories of the past based on what we think in the present anyway.”)
Seb said that I was designing a tool (the “interview” method — or to use Ileana’s term, the “dialogical narrative episode”) for exposing the messy hermenutical psychological underbelly of the engineering curriculum; emancipatory-reflexive work, but aimed at the people teaching the technical material. Since we’re both hackers ourselves, we like the idea that this may help create more writerly engineers (“All the engineers will then get trained as hackers,” Seb said, “because you’ve planted a poststructuralist idea into the minds of the STEM faculty.” “I’m not telling them it’s poststructuralist,” I countered. “I’m just saying hey, isn’t this interesting?”)
This is a (cleaned-for-grammar) transcript of a tape-recorded conversation with my classmate, Ileana Cortes Santiago, whom I begged to “sit and listen to me blab about my dissertation, and then help me figure out what the heck I’m saying.” This process was incredibly helpful, so I’m collecting my notes here so that I can remix useful sentences and paragraphs into my prelim… soon.
DISCLAIMER: These thoughts are very, very half-baked. As I continue to review, edit, and publish notes from similar conversations, you’ll see me editing these thoughts, rearranging them, throwing them out… (for instance, I no longer say I’m “developing a format for faculty development.” In fact, my research question has fundamentally shifted its wording at this point.) This is the sausage-making process, exposed.
Ok, thanks, Ileana. The recording’s on. So… here we go. My dissertation centers around developing and demonstrating a format for faculty development. This format will be created and tested specifically with and for engineering and technology faculty, and in the context of transforming their teaching and curriculum development practices, but is likely to generalize beyond these populations and contexts.
The traditional model of faculty development is an information dissemination model. We have to get information out to people. The problem is they’re not reading the right books or listening to the right talks. If more people read our work, our work will impact them.
The problem with that view is that it doesn’t solve the problem of research-to-practice transfer. That problem doesn’t exist solely because the information’s not available. Faculty do have access to papers and books; they simply prioritize other things. They don’t want to read these papers, and they’re not changing their practice when they do read them. We have literature on how engineering faculty change their practice, and the main finding is that hey, it’s not because they go hunt for papers on teaching and then read them. In order to have a transformation, we need something more than “let’s just get the information out to them.”
Think of the way we talk about the epistemology of teaching and learning for our undergraduate students. One common framing was articulated by Baxter-Magolda and King; they described how a lot of students enter college thinking of students as passive recipients of information and teachers as knowledge-containers who transfer that knowledge to students in a copy-paste sort of model. Engineering educators critique that model as being limiting to the student. Instead, we advocate an epistemology where we as teachers are not giving information, we’re helping them develop their identities as adults in our discipline. We need to move these students towards more agency — more self-authorship, more of a transformative space where they can see themselves as not just developing professionals, but professionals who develop themselves.
I find it ironic that when we look at the epistemology of faculty development in engineering education. When faculty members are in a student role, like when they attend a faculty workshop, how do we treat them? Exactly the same way we tell those faculty not to treat their 18-year-old students. So part of what I’m saying is: people, this is silly. If we want teachers to teach their students with a certain epistemological paradigm, we should teach them that way in their own professional development. We should take a self-authorship view of faculty development and be concerned with their identity formation as teachers.
You can’t just give someone a teaching identity. You can’t recite a bunch of facts and have them memorize them and then think, poof, magially they’ll have a teaching identity. A lot of that teaching identity development and expression comes out in other ways. The particular way I’m exploring it in my dissertation is through storytelling. When people tell stories about their teaching, they themselves are characters in their stories. By tracing the way they portray themselves in their stories, you can get at what their teaching identity is, and how it’s forming and transforming over time. These stories give you — and the faculty — a concrete thing to actually reflect on. For example, you could say “Oh, did you notice you portrayed yourself as an outsider?” (Or a pioneer, or a misunderstood rebel, or etc.) Now there’s something concrete — their words, recorded in a place where they can see and manipulate them — and they can go “Oh, gosh, you’re right, I did say that. Huh.”
Engineering and technology educators have pretty well developed identities as researchers, because we spend lots of time in grad school developing our research identity, topics, expertise. But when it comes to teaching identity, then — whoops, sorry, we haven’t really done that. There’s not that corresponding space for development. If you look at the lived experiences of faculty day to day, there’s no space for storytelling about their teaching practices. They might tell some stories to the students in their classes about content knowledge, either in stories of practice (“Let me tell you about the time we were designing this robot…”) or as explanations or analogies (“This physics concept is a lot like when you’re riding a bicycle…”).
However, teachers don’t have the space to tell stories about being teachers, only the stories about them doing engineering stuff. When students come up crying after the midterm, or when you’re trying to design a homework assignment that assesses certain things — those stories and that identity development, there’s no space in a faculty emmbers life for that. When would you do that? It sometimes comes up in grad school socializers when we’re drinking with our friends and complaining about all the grading TAs have to do. It sometimes comes up when professors meet up with their buddies at conference dinners and complain about their dean. But how often does that happen? Once a term? Every other year? And do they ever get repeated, or are they mostly one-offs? These interactions are usually so transient that it’s very difficult to learn from them.
The people that you could argue faculty need share these stories with the most — their colleagues that they see and work with every day — there’s no space for that at all. In faculty meetings, you don’t have time for teaching storytelling. It’s all stuff like, “Oh, we need someone to teach these courses next semester.” It’s very procedural and agenda-driven. This means we end up with people going and teaching in their little silos. Teachers don’t visit each other’s classes. There’s no ongoing required professional development like in K12 where teachers get together, get exposed to new ideas, and talk about those ideas and about what’s happening in their specific classrooms. Then we complain, “Hey, you’ve taught this class the same way for the past 30 years.” Of course you have. That’s what you’re rewarded for doing; it’s efficiency. Why do we not have engineeirng professors reflecting on their teaching? Well, we sort of penalize them for doing that.
Let me talk a little about the specific faculty I think this model would work with. I’ll start by talking about who I am not designing for. First, there are professors who don’t care about reflecting on their teaching process. They might not even like teaching. That’s fine. I’m not interested in spending energy here; I’m not trying to persuade these professors they should be interested.
Ironically, I’m also not designing for the opposite side of the spectrum. There are some faculty members who are super self-aware. They pratically have a 2nd PhD in education. They have very well developed and articulated teaching identities. We know these faculty are found both in small teaching colleges and large research universities and everywhere in between. We think they’re awesome. And quite frankly, they don’t need my help. So this is not for them either.
However, there’s this group of faculty in the middle. I think there’s a lot of them. These are the faculty who think about their teaching, but they know they don’t think about it enough, or in the way they’d like to. It’s not that they think it’s not important; it’s that they’re not quite able to do this critical reflection and identity articulation to the degree they want — at least not on their own. Maybe it’s never been modeled for them. They probably don’t have time set aside for it. There’s probably no support for it. But it is in their zone of proximal development (ZPD). That means that if someone helps them through that process, they’ll be able to do it.
And that process — and scaffolding them through that process — is what the faculty development model presented in dissertation does. It’s a model for faculty development that takes these faculty who have reflection in their ZPD, and gets them to actually do it.
So what does that model look like? It’s very simple. It’s repeated storytelling. For example, let’s say we have Dr. Jones. I’ll say, “Ok, Dr. Jones, let’s schedule 6 sessions over the next school year of 60-90 minutes each.” This is a grand total of 6-9 hours of total commitment over the course of 9-12 months. Is that a lot of time, or is that a very small amount of time? It’s both. Asking a faculty member for 6 meetings of any sort is a lot of time, but it’s far less than they’d give to a single-credit independent study student, for instance. It’s extremely doable. But my favorite point of comparison is to think about the lived experiences of faculty and how many minutes per year they usually spend in this talking-about-teaching storytelling space at all. Maybe it’s 5 minutes a week. If that’s the case, getting to an hour of storytelling time would take 12 weeks. 6 hours is 72 weeks. 9 hours is 108 weeks. So during this process, we’ve gone through the equivalent of approximately 2 years of storytelling in a normal faculty life in just a few deliberate meetings. This is massive, proportionally, compared to their usual lived experience.
Each session is a two-part process. The first part is the first half-hour. Let’s say you’re the faculty member. You’d tell me a story from when you designed a class, some sort of teaching story. And remember there’s that technique I used in class where it got transcribed in realtime and showed up on my laptop screen, we do that for the interview also. So after the first half hour, you basically have the story all written up. And then together we read through that story you just told, and now you’re reading your own words which you’ve just said, and you’re seeing patterns and you’re seeing things in your own words and that becomes the second half, the second half is recorded and transcribed discussion of us talking about the story you just told. So it’s almost like you’re starting to analyze your own words.
Well, actually, you are analyzing it already. The boundary between data collection and analysis gets blurry. It’s a reflective process, it starts already, so it ends up in some interesting things and the first one is sometimes faculty members will realize for the first time patterns in their own behavior. So they go, oh my gosh, right, I do talk about myself as an outsider, is that something I want to do? And also sometimes they go a lot deeper into something they said before. When I said the word “literacy” I didn’t, now I realize you could interpret that phrase differently. What I mean by “literacy” is actually XYZ, let me give you an example. And they go off into another story, so you get this — you get past their canned narratives because sometimes people go through this spiel that you can tell it’s something they’ve said thousands of times before. And this really gets you past that.
The next session — I don’t have a set list of questions. It’s open-ended. Your prompt is reading excerpts either from a story you’ve told me in the past, or from someone else’s story. So maybe it’s your colleagues in the office next door, but you’ve never heard their side of it. Or maybe it’s a professor who teaches the same sort of thing but at another university. Is there something that inspires your your own story? So they’re co-analyzing each other’s experiences. Their narratives start overlapping and intertwining and making meaning of each other. You end up with the side effect of this big corpus of interlinked narratives of faculty, and anyone else can use them for later studies also, but that’s sort of a side note.
My research question asks how faculty make sense of their teaching identities through public storytelling, and all 6 narrators for this run are talking specifically about a curriculum revision focused on integrating “design thinking” into all levels of a 4-year undergraduate program. We’ll end up with 36 stories — 6 stories — or 2 stories — or 1 story, depending on how you look at it. On one level, since each narrator tells the story 6 times, we have 36 stories. But each narrator is looping around the same narrative space every time, so you could also say there are 6 stories — one per person — and 6 revisions of each story. The narrators are also colleagues — 3 come from one school, 3 come from another, and each group of 3 from a school is talking about the same curriculum revision, so you could say there are 2 stories, one per school. Or you could say there’s one big story of this study as a whole.
I should note, off to the side, that we’re not looking for convergence on “the true story” through these various revisions/rounds of storytelling, as a constructivist approach might assume. Since this is a poststructural study, we take all the stories to be some version of truth even as we question what truth is — it might be contradictory, messy, and so on — and they don’t converge on any sort of “done” state; they are part of an ongoing process of constant growth, change, and rearticulation. We could continue the storytelling process indefinitely.
I skipped over the part where I compare this model of faculty development to other models of faculty development, with a bunch of radial graphs and such, because we need to go to class soon and there isn’t time.
In the theoretical space, the way I’m talking about this is a mix between Jerome Bruner and Roland Barthes. Bruner is the inventor of narrative analysis; his famour paper is “Narrative Construction of Reality.” That says things like stories need to be analyzed as stories and can’t be chopped up, you lose something vital. There’s something to them greater than the sum of their individual parts. All texts are hermeneutic. We put meaning into these texts, and other people take their own meaning out of those texts. It’s not a direct brain-to-brain transmission. Its a very active process on both sides. You and I could work through the same book and get very different things from it. The information dissemination model doesn’t account for that, it’s limited in that way.
Bruner also talks about the context of putting things into these artifacts and taking things out of these artifacts, there’s that stated dialogue through artifact that comes with the narration. He talks about community belonging as it relates to collections of narratives. How communities develop these connections of stories, so me and every other American kid my age would have things like Power Rangers or Pokemon and they are these stories that are part of our vocabulary. A Japanese kid my age would have an overlapping but slightly different set. My parents share part of my culture but grew up in a different time, so they have an overlapping but different set entirely. One of the ways we have this community belonging thing is sharing the same sets of narratives. We can refer to them, reference our norms and values and practices through this collection of stories. How our individual lives are personal narratives, these are also narratives in this connection. We make sense of that through finding ways to place them in that collection in relation to all the other stories in that collection. So I can see my childhood and say “it’s like when the Pink Power Ranger did this,” and another kid my age would say “oh, yeah, yeah.”
As part of human engagement, as part of it is learning to speak the spoken language, then there’s the disciplinary language, but there are also the stories. At an even broader level you share the same stories. That’s why at the grad student level they have us read these classic papers, books. Because these are foundational knowledge. That means you’ll have that common reference point. I can say “this is like Friere’s Pedagogy of the Oppressed,” and both of us know exactly what that means. You can make references to the same story, in this case Fiere’s story.
And this is where Barthes comes in. Barthes is a poststructuralist writer and talks about readerly versus writerly texts. Those words refer to the role of the reader. Writerly texts are very hermeneutically rich text, where as you’re reading you’re sort of co-constructing that meaning with the author, very active. A lot of self-help workbooks will have things like “can you think of an example in your life when…” or — it’s very much a book that’s only complete when I put my own words into it. Or like a workbook as opposed to a textbook. A readerly text is a text that takes that epistemological view of you are a passive recipient of meaning that the author has put into it. And the meaning the author puts into it is the meaning of the book. And the thing Barthes says is that all texts are actually writerly. Bruner’s version is saying all texts are hermeneutic. So all texts are writerly, all texts can have these very active hermeneutics…. but it’s just some texts pretend to be readerly. They make you forget you have that agency and they make you forget that you can question them and have that hermeneutics of suspicion and that you are also an author as a reader of the text.
If you are only exposed to readerly texts, you never get into coauthoring that collection of narratives. You never become an author in your community of practice. You never really take your own narrative and place it in the context of the rest of them and help modify that central corpus. It’s more like, “Oh, these classic authors are great, but they’re magical and I can never aspire to these heights.” As opposed to going “Oh, I can do that too, right now!”
Faculty development currently consists of throwing a lot of readerly texts at people. I want to broaden it to include the idea of making teachers conscious of how they are already writing the narratives of their teaching identities, and helping them recognize and actualize their agency to analyze and edit and shape not only their own stories, but each other’s stories. When we do this, we grow the shared corpus of language that engineering and technology faculty can use to talk about their teaching — a stronger language for their community of practice.
Ileana — a literacy education scholar — pointed out that she sees my methodology as poststructural because the narrator and I are purposefully deconstructing and reconstructing a personal story in the moment whenever we engage. She pointed out that I needed to be descriptive of my own story and positionality, because my history and perspective and story naturally gets enmeshed with that of the narrators. I groaned and said I probably could write a couple paragraphs on that, yes, yes, you’re right.
She agreed that what I was doing wasn’t “interviewing,” and suggested an alternate noun phrase for my data sources: “dialogical narrative episodes.” (“Episode of narrative construction” or “episode of narrative experience” are possible alternate noun phrases; she suggested staying away from “storytelling” for reasons I’m still puzzling out.) “Dialogical” was a new word for me: it means “going from one person to another.” (Roughly. I’ll need to look up a better definition.) This term allows me to incorporate the editing process into my data source, whereas “interview” would need to be distinct from the grounded indigenous coding.
Stephen recently asked for advice on how I’d teach programming using Python to fellow academics. Specifically, an English major and a Materials Science (Matsci) researcher — smart people deeply into their own disciplines… who happen to not have had programming experience. Here’s what I said; if you have more comments/advice for Stephen, leave them in the comments.
Showing them the fundamentals in a common way, then diverging into their disciplinary interests, sounds about right. If they’re both completely new to programming, you’ll have a little while before they really branch off.
If you want a common starter text, Think Python may be good for this specific case. Just skip the sections related to graphics (Turtle at the start, Tkinter at the end). I also enjoy (and under different circumstances, would recommend) Learn Python The Hard Way and Dive Into Python, but the former is more geared towards web development and the latter is for people who have programmed in another language before.
For your English major friend, one of the exercises gets you into Markov analysis/generation of texts. This should be a fun place to play with poetry. The Markov Bible is a hilarious example of the sorts of things that can be done, and people have written entire books on text processing with Python.
For your Matsci friend, who you said was interested in data analysis, Allen’s next book starts playing with things like census data — that exercise should start being doable around the same time as the Markov analysis, because they’re both fundamentally about “read from a file, do math, spit out to a file.” They’ll probably want to jump off into SciPy at some point to make plots and crunch more complex data.
For a fun change of pace and/or an intro session and/or while people are installing software on their machines, you can try CodingBat exercises. The Boston Python Workshop’s exercises for Friday and Saturday are a tiny manageable collection. This also gets them into the habit of test-driven development (which is also a good approach to curriculum design, although I need a new name for it because Test Driven Learning is already taken). CodingBat problems are very basic, so this only applies for the first few sessions before it’ll get too easy for them.
I would structure your learning sessions primarily as pair programming time. They’ll learn from each other’s approaches, debug/unstick each other naturally, and learn how to cleanly structure and communicate about code. If you have them pair-program their way through the book, you can spend a chunk of your time writing your own dissertation while being on-call, as in passive pair programming.
Whatever you do, teach them fundamentals of software engineering as you go along — commenting, testing, and version control specifically. Software Carpentry has good resources for this sort of thing. With the same rationale, take the first session and have them make and use github accounts while working through their starter exercises for the sake of everybody’s sanity. This gets them in the habit of working in public, which is important if you ever want to…
…introduce them to a broader community of programmers, which you should do as quickly as possible. Whether that’s “hey folks, join this Python mailing list” or “let’s go to a local Python meetup and get you asking questions — I’ll go to your first one with you and model this introducing-self and question-asking thing in programming-land” or whatever you have around in your neck of the woods, it’s basically the act of teaching them to learn from people other than you. And then… they’re off, and they don’t need you any more to keep learning and doing what they want to do. It’s a great job, making ourselves obsolete.
Hope this helps, and good luck.
Stacey asked me for a refresher on Test Driven Learning for Hacker School, so here we go.
Test Driven Learning is a software engineer’s articulation of Wiggin & McTighe’s Understanding by Design framework after being strongly influenced by Ruth Streveler’s ”Curriculum, Assessment, and Pedagogy” course at Purdue.
Many software engineers are familiar with the process of Test Driven Development (TDD).
- Decide on the goal.
- Write the test (“how will you know if it’s working, exactly?”)
- Make the code pass the test.
Test Driven Learning (TDL) simply says “it’s the same thing… for your brainnnnn!”
- Decide on the goal (“learning objective”).
- Design the assessment (“how will you know if you’ve learned it, exactly?”)
- Go through the experiences/etc. you need to pass your assessment.
That’s it. Really.
Step 2 is the part most people flub. With software tests, you have a compiler/interpreter forcing you to be precise. With learning assessments, you don’t — but you need exactly the same level of precision and external execution. If you asked a group of external people (with appropriate expertise) whether you’d passed the assessment you set for yourself, there should be no disagreement. If there’s disagreement, your assessment needs a redesign.
A good assessment is a goal that helps you stretch and reach it; sometimes it encourages you to do more. But sometimes it also gives you permission to stop doing stuff – you’ve written the code, you’ve delivered the talk, they met the criteria you set — and now you’re done. You can absolutely set a new goal up and keep on learning. However, you’re no longer allowed to say you Haven’t Learned X, because you’ve just proven that you have.
Here are some rough-draft quality TDL assessments you might start with, and a bit of how you might improve them.
I will learn Python. (What does that even mean? How will you know you’ve learned it?) I will complete and pass any 50 CodingBat exercises in Python. (But I could do that by solving 50 really easy problems.) Only 10 of those 50 problems can be warm-ups, and at least 20 of them must be Medium difficulty or greater. (Does it matter if you get help with the problems?) Nope, I can get as much help as I want from anyone, as long as I could explain the final solution to another programmer.
I will get better at testing. (What do you mean by “testing”?) I write a lot of code, but I’ve never written tests for any of it. I hear the nose framework is nice. (What do you mean by “better”?) Well, I’ve never written a test at all, so even going from 0 to 1 would be an improvement. I could use nose to write tests for 3 different pieces of working code I’ve already written. (Do these need to be big or exhaustive tests?) Nope, I’m just trying to learn what writing tests is like, not get full test coverage on my code… at least not yet. Even if I write a 3-line test that checks out one minor function, it counts as one of the 3 tests. (What does it mean for a test to be “done”?) When someone else can check out and successfully run my code and my test suite on their computer without needing to modify either bit of code, it’s done.
I will understand how databases work. (By “understand,” do you mean the mathematical theory behind their design? Or how to actually implement and use one?) Oh geez, the latter. I don’t care about the math so long as I know how to interface with a database. Any sort of database. (So you need to make a demo.) Yeah, but that’s not enough; I can blindly type in code from a tutorial, but that doesn’t mean I’d be able to field questions on it. (What could you do about that?) I will give a presentation to fellow Hacker Schoolers demonstrating a small database interaction in code I have written. That’s an easy binary to check; either I’ve given the presentation or I haven’t.
Thoughts, questions, ideas? Got your own example TDL assessment (at any stage of revision), or ways to improve the ones above? Holler in the comments.
Here’s the next iteration of my dissertation proposal, a big step up from the first version. By “big step up,” I mean that it’s written in complete sentences (I think!) instead of bullet points, actually references specific pieces of literature, and (maybe?) even describes the project in a way that’s understandable to someone who’s not on my committee.
That having been said: not done yet. Good grief. The process of getting through my prelim is a grueling one that I am glad for, because it’s exposing so many structural weaknesses in my practice as a scholar. These are highly related to what Ignatius might call my “disordered attachments” as a person — that which leads me away from being the best Mel I could be. I did not expect my prelim to be a spiritual exercise, but there are some definite connections. For instance, as Robin said, “you tend to hide out when you are really struggling.” Yup! Escapism! Because the best way not to disappoint the world is to hide from it until you can prove your worthiness All By Yourself, right?
Right. So I told her that, look, I don’t want to squeak through this exam with a paper produced by a crappy process (of running away, and slipping deadlines, and late-night work sprints with terrible ergonomics) even if the paper itself might squeeze itself over the bar. (I’m not convinced this paper does that in the first place.) I want to pass with a good process, a way of working professionally with my committee, a way of working in a steady and satisfying manner on scholarly writing — none of which I have right now. (I’m so sorry, committee.) It won’t be a perfect process, but I want it to at least be one I’d be ok using (and improving on) for the remainder of my dissertation — a baseline habit I can build on for my career as a professor. That good process should produce a good paper and a happy, humanely rested and non-anxious Mel.
So, what things need to improve for the next version? Freewriting below.
On a mechanical level: the typesetting is wonky; I want to fix that by finishing my implementation of a workflow that will automate its beautiful creation. For this round, I experimented with github, tex, and Scrivener for writing workflow, which works much better than writing in one giant LibreOffice document. However, I didn’t fully finish creating that workflow, so this version’s typesetting/references were ultimately done via the method of ”argh I don’t have time to figure out these formatting configurations so I’m going to copy-paste into LibreOffice and edit inconsistently in WYSIWYG.”
Side bonus for finishing that workflow (which should not take me more than a day to finish up): a better implementation of “release early, release often” that automatically pushes my daily edits to github. I’m trying to get away from my habit of hiding my scholarly work, which is really misaligned for someone who gives talks and teaches workshops on “release early, release often” and the importance of transparency in research.
Also on a mechanical level: my references section is undisciplined. Zotero continues to be a Very Good Idea despite its suboptimal search functionality, and I’d like to get in the habit of inserting and cleaning up every reference I put in a paper. I almost always do this, but sometimes I use the Magic Import Tool and live with whatever capitalization, punctuation, missing fields, etc. it populates until it’s Time To Ship A Paper, when I need to go back and fix what I didn’t fix before. But mm, citation/notes management. Such a good thing to be comfy with, and I am proud that I am. Freewriting, reverse-outlining, and shuffling around index cards for outlining are also a comfortable part of my workflow now, which is awesome.
On more than a mechanical level, I get a little vague. I need to not look at this document for a little while before I can answer that question. Actually, I need outside feedback. So I’ll rest and let that happen for a bit. But I suspect I could benefit from another round of reverse-outlining, pruning, re-outlining, and fleshing-out to structure my ideas into a more logical flow of support. And I suspect I could use the literature better as support structures, especially for the sections on existing faculty development scholarship and change. Currently those sections are “hey, here’s a paper/book on this, and now I wave my hands around!”
I do want to say that I am really proud of myself for making work lower priority this semester. More important than work: consistently nurturing my physical (exercising, sleeping, eating well, stretching and bodywork), social (taking dedicated time to really be present with friends and family), and spiritual (prayer, Mass) selves. When those things are in balance, I do better work, and I learn more from the work I do (and the mistakes I make while doing it). My intellectual side is healthier when it’s not overemphasized.
If I ever have graduate students myself, and they are reading this post in the future, I just want to say: working on this draft involved a lot of angst, frustration, late nights, and more than a few tears, and I’m not done yet. I don’t know what I’m doing, and I’ve been avoiding the people who could help me with it because these are my professors! and I love and respect them tremendously and don’t want to waste their time! And I want to show them (and myself) that I am Really Good At Stuff and Worthy of Existence by producing perfect work via a perfect process, and boy is this output and process totally not worthy of anyone’s time, and… well, it’s hard to capture the thought patterns in words, but if you picture me slouched in bed at 5am desperately swigging water from a massive gallon jug as a nervous tic while alternating between writing Yet Another Passive Voice Sentence and going “aaah aah it’s not done yet aaah,” you might get a bit of the jumbled picture I’ve been feeling like. I’m writing from an agonizing sense of knowing what it’s like to be midway through this apprenticeship, fumbling in a fog, repeatedly dropping my sense of direction and self-confidence only to have it dusted off and handed to me again by some very kind colleague or mentor in the middle of a panicked hour.
Someday I will be on the other side of that dynamic. Right now, I’m on this side. And right now, here’s what I’ve got.
Mel’s Prelim v.1
Aha! This is the best version so far of my criteria for Radically Transparent Research (website really needs rewriting; it’s currently a mess of writing from my first year of grad school), which is basically “a methodology for producing Free Scholarship.” I know, I know, there all tons of Open Research movements and projects out there; I’m trying to write an examination right now and will loop back and check in with them again after I pass it, ok?
- The work is public and freely accessible.
- The artifacts (data, analysis, etc.) used to create the work is also public and freely accessible so that it can be studied and peer-reviewed by communities of practitioners.
- The work and its artifacts can be freely modified and distributed so others in these communities can benefit from and build atop it.
“Chua’s 3 criteria for RTR” (or whatever less-silly name I can tack on that later) comes directly from a Free Culture + Academia mashup. From academic-land (specifically, the scholarship of teaching and learning), we have Lee Shulman’s 3 criteria for scholarship (paraphrased):
- It is public
- It is peer-reviewed by the practitioner’s community
- It can be used by that community as a stepping-stone towards future work
From Free Culture, Richard Stallman’s 4 criteria for software Freedom:
- The freedom to run the program, for any purpose.
- The freedom to study how the program works, and change it so it does your computing as you wish. Access to the source code is a precondition for this.
- The freedom to redistribute copies so you can help your neighbor.
- The freedom to distribute copies of your modified versions to others. By doing this you can give the whole community a chance to benefit from your changes. Access to the source code is a precondition for this.
See the connections between Shulman and Stallman? I don’t imagine this is the final or best statement ever (and look forward to seeing what future version comes up), but it sounds pretty good to me right now.
It strikes me that I use the word “depressing” far too often in conjunction with readings from “Class, Race, & Gender” class. I’d like to use words like “action-inspiring” (or some other trumpet-blasty, call-to-action sound effect) instead, but… there’s so much to notice, problematize, and fix, and I… am tired. So here is thinking and writing, which is an action in and of itself.
Of all the readings on intersectionality last week, the original data grabbed me most: the Nelson Diversity Surveys are now-famous statistics of faculty diversity in “top 50″ USA STEM departments. Wikipedia’s summary is pretty good. Nelson tracked down complete statistics for every. single. faculty member. in each of those departments. Every. Single. One. I have an overwhelming respect for how much tenacity that must have taken: universities don’t publish these statistics (probably because they sound bad), so Nelson had to write and call and hound and hound and hound department chairs with superhuman persistence.
Click on any dataset. Look at the “Native” column. Empty, almost always. Hispanic? Black? Asian? So many singletons. So many lonely individuals: not only are you the only Hispanic woman in your department, but (as Alice said in class) now you can look around and see that you’re the only Hispanic woman with tenure at a top research university in your entire discipline. You are a unicorn!*
Compare the 2002 and 2007 versions of the same dataset. See any changes? In minority groups, can you pick out individuals — ah, there was a Black male assistant professor of Computer Science at University of Such-and-So in 2002, and not in 2007; he probably did not get tenure, and if we look at the old university directory we can probably find his name…
As an Asian-American, I also wondered: how many of the Asians in the “Asian” column were American-born? I stroll the hallways of my own R1 and see the office doors of Chinese professors strung out down the hallway, but they feel like people who are Not Like Me — not that international hires are fundamentally bad, but not all Asians are alike and we can’t ”support diversity” by importing people; their genes may be similar to mine, but their culture isn’t. So in a department full of Chinese-born Chinese, I still feel very, very much alone.
Nelson Diversity Surveys” Donna J. Nelson, Diversity in Science Association: Norman, OK, 2004; http://chem.ou.edu/~djn/diversity/top50.html
*The Unicorn Law, coined by Emma Jane Hogbin, states that “If you are a woman in Open Source, you will eventually give a talk about being a woman in Open Source.” I personally think the Law extends to STEM in general.
Once upon a time, I promised I’d work on my dissertation in as ridiculously transparent a manner as possible. And so I am a bit sheepish it’s taken me this long to post one of the Big Stages towards a dissertation, a Preliminary Proposal (aka “prelim”). It’s where you say what you will do for your dissertation, a sort of contract that you make with your committee: “if I do X, then that means I’m done.” Of course, this means your committee needs to approve your prelim first. Which means you need to write it first, which means you produce all sorts of crazy halfway-scribbles on the way to that, which means…
…this document is not my prelim. Rather, it’s the first version of a document towards a prelim that I sent to my committee… oh, about a month ago. (“Here’s what I propose proposing!”) Research involves plenty of fumbling around, half-baked ideas, placeholders, dead ends, and (in my case) cartoons. This document has — unashamedly — gigantic bugs. Some I knew about, and some I missed entirely (but my committee caught).
Now, I don’t need feedback on this version of the document — I already have the next iteration which takes my committee’s first-round feedback into account (mostly). But if you’re practicing being a researcher and want to compare your comments with “the expert answers,” they’re below the document.
Mel’s Prelim v.0 by Mel Chua
- Everyone: This is a really good starting point, and you’re headed in the right direction. All you have to do is flesh it out; the stuff that needs to go into the document is already in your head. (Me: *starts breathing again*)
- Matt Jadud: If you cut research question (RQ) #2, your dissertation project’s scope dramatically improves. (Me: You’re right!)
- Robin Adams: You’re proposing a faculty development initiative. What’s state-of-the-art in faculty development these days, and where does your project fit into it? (Me: Good question. I’ll get on that.)
- Everyone: You’re saying that you’re doing both narrative analysis and grounded theory. We call bullshit on the grounded theory. You’re really only doing narrative analysis; it’s just extremely structured and transparent narrative analysis. (Me: …oh. So, wait… just because the theory emerges from the data, it’s not grounded theory? Ohhhhhh. Yes. You’re right.)
- Ruth Streveler: What’s storytelling? What’s the relationship between storytelling, story-hearing, and personal identity… and why do we care about that in the context of faculty development? (Me: I have to explain that? I can’t just take it for granted? Darn!)
- Robin Adams and Alice Pawley: What is the difference between storytelling and narrative analysis? Why narratives? Why public performance? Why are these things important? (Me: I… but they’re… mnergh, fine, I will justify all my design decisions. Mumblegrumble. But I know you’re right.)
- Robin Adams: What’s your epistemology and your ontology? (Me: Epistemology is that knowledge is social and negotiated, ontology is that being is performed identity, and… oh, right, okay, then narrative as a methodology makes perfect sense. Yes. Thank you. I should write that down.)
- Everyone: What is your positionality with relation to your subjects? How does your poststructural viewpoint inspire you? (Me: Um… Transparency? Calling participants “storytellers” rather than “subjects”? Data display and final writeup format? Rejection of metanarratives? Yeah, let me write this down.)
- Alice Pawley: Your examples of data and first-pass analysis are excellent. Now show me how that first-pass analysis starts answering your research question. (Me: Oh! Sure! No problem!)
Someday, when I have graduate students of my own, I will point them here when it comes time for their prelim, and then we will all laugh at younger-me together, all earnest and confused and wrapped in a pink sweater right before my dance rehearsal. Future Doctor Mel: remember how intimidating and unfamiliar this dissertation process once was, and be compassionate. Maybe with chocolate. Grad Student Mel appreciates good chocolate.