Running down this side idea as part of my research dreading today, in conjunction with my data analysis.
Specifically — and this is something I’ve “felt” about research but haven’t articulated until I saw this in another paper’s discussion — leadership doesn’t take root in a vaccum. In the case of socially constructed leadership theory, such as the Relational Leadership Theory that I’ve been reading (in particular, the social constructionist stream), leadership is happening in the “spaces between” individuals as much as it’s a direct influence.
But in most RLT discussions, things still end up boiling down to either the leadership actions people take (making strategic decisions, choosing to interact with an unhappy colleague to resolve a difficulty, asking questions, listening) or in their attempts to communicate (to exert leadership influence over others, for example). Talking (writing) or doing, that seems to be it, right? Does all “leadership theory” boil down to this?
I’m currently rereading a paper on RSCL (Relational Social Constructionist Leadership) Theory by Endres and Weibler (2017) which will likely provide an RLT model I’ll reference in my data analysis. I will probably write about that more some other time.
Endres, S. & Weibler, J. 2017. Towards a Three-Component Model of Relational Social Constructionist Leadership: A Systematic Review and Critical Interpretive Synthesis.International Journal of Management Reviews, Vol. 00, 1–23 (2016). DOI: 10.1111/ijmr.12095
For example, I like the way they integrate the social constructionist perspective into RLT: “Relational social constructionism is inextricably linked to the notion of ongoing interaction among individuals who are involved in intersubjective processes of interpreting and constructing social realities” (p. 225). We socially construct our understanding of our contexts, our teams or departments, the way we relate to “leaders” or “influencers” or people who seem to just be “followers.” We socially construct our responses to limitations — budgets, resources, social mores, unspoken expectations — as well.
There’s a reality out there, sure, but in the world of work, there’s also a reality inside my head which interacts with other people’s head-realities in ways that forces mine to adapt.
What caught my eye today in their discussion was this notion (emphasis mine) that structures are also integral to how leadership develops:
Huxham and Vangen (2000) revealed the informal, emergent nature of leadership in interorganizational collaborations, and developed the notion of leadership as not only being carried out by individuals, but also through the structures and processes. Similarly, Weibler and Rohn-Endres (2010) described the empirical manifestation of leadership in interorganizational networks as an emergent phenomenon appearing as highly developed learning conversation and occurring through the interplay of structures, individuals and the collective.
Endres & Weibler 2017, p 226
Weibler wrote a paper with Rohn-Endres in 2010 which I can’t get my hands on (grrr) that notes in its abstract, “More importantly, our findings support the idea that individual network leadership would not emerge without embeddedness in certain high-quality collective processes of relating and dialogue.”
OK, so dialogue takes us back to talking, which is already well-entrenched in leadership studies. But how about “embededdness in …high-quality collective processes”? That sounds like a structure to me — a folkway, a set of expectations in play, something that people built (or maybe allowed to happen) and now it exists outside the humans themselves as a supporting player.
I’m not pretending this is a new idea — there’s oodles of research on “leadership structures,” team leadership, what contextual factors enable or hinder shared leadership to develop, how context affects relationships among people at work.
Process leadership theories in general take a look at more than traditional, hierarchical views of leadership theory might consider, such as how people are engaged in a web of influence and relationship, or an ongoing rather than “moment in time” perspective of what leadership looks like.
I used the Connected Papers tool (it’s so cool! Try it out!) to track down other papers which have referenced Weibler and Rohn-Endres’s 2010 paper.
None that I found in my quick review seemed to dig specifically into context-specific, structural factors which might influence how leadership emerges and develops in groups. Not saying it’s not there, I just didn’t put my hands on anything quickly (though I did skim some excellent articles on shared leadership research).
Researchers have investigated a number of individual factors typically present in one’s working environment, so I might just need to go looking specifically for organizational structures which enhance leadership emergence.
But not today. Today I’m back to data analysis and writing chapter 4. 🙂
Allen, S. (2019). Exploring Quaker Organising to Consider the Possibilities for Relational Leadership. Quaker Studies, 24(2), 249–270. https://doi.org/10.3828/quaker.2019.24.2.5
Sometimes I read an article and think to myself, “Well, that’s it. Roll it up, walk away for today. You’re not going to write anything better than this.”
It’s both discouraging and encouraging at the same time. Yes, I could write crisp, clear sentences that communicate exactly what I’m trying to say, sentences which perfectly encapsulate complex concepts synthesized from multiple scholarly sources, yet condensed in a way that brings greater meaning out of the whole.
Yeah, sure. I’m sure there are days when I might could possibly write sentences like that. Just….not most days. lol
Allen’s article on relational leadership as an aspect of Quaker egalitarian practice caught my eye the other day because of its theme (RLT, one of my research interests). I sat down this morning to “bag and tag” it into my Zotero collection, taking time to read the article through. (If you head to the link above, click to “Full-text” to read the full thing online, or you can download the PDF. I appreciate open-source journals.)
Allen’s article is a master class in clear writing. His literature / thematic review of RLT within Leadership theory artfully and succinctly summarizes decades of research. Here’s one paragraph from the review where he carefully explains one element that distinguishes RLT from standard theories of leadership:
The concept of relational leadership suggests that leadership influence is momentary among evolving relations. As suggested by Wood and Dibben, from a process perspective ‘leadership does not congeal into human subjects, but is always an achievement that is momentary within an ever-evolving field of relations’ (2015: 39). They go on to argue that ‘leadership [is] not given, but [is] always in the process of becoming, on the way in or out’ (Wood and Dibben 2015: 39)—it is an ‘event in the making’ (41). What this means is that because the interactions between people, places, words and actions happen in dynamic interplay (e.g. we physically move around, events change our views and offer us new information, words gain new meanings and associations based on our experiences) so the spaces for leading emerge through and among these flowing relations. Consequently, the possibilities for influence and being influenced are formed and reformed by the changing constellations of our social and physical relations.
Allen, 2019, p. 253
Likewise, his study design is straightforward and clean; his description of abductive data collection and thematic data analysis offer a short yet sufficient explanation of his research methods.
The article continued to impress me as I read his summary of his interview data and the themes he identified. Tying each emerging theme back to the literature review, Allen demonstrates a qualitative investigation of proposed theory, enriching the literature by investigating RLT within the Quaker context where members pride themselves on rejecting power hierarchies.
His analysis sections tie back into the theory, adding useful content to the overall leadership discussion. For example, although Quakers pride themselves on non-hierarchical structures, clearly all of his participants use mental categories of “leadership” — perhaps from an even less reflective stance than if they participated in a community where leadership influence was regularly examined — to help them understand their interactions with certain peers. Allen found that the Quaker members he interviewed were able to hold complex dynamics in mind, but he also questions whether RLT is too mired in capitalist, hierarchical perspectives to offer enough tools to analyze a non-hierarchical society.
I appreciate Allen’s non-fussy approach to “doing qualitative research” – a simple thematic analysis which he then aligns to three core concepts in his literature review. Yes, this is “basic” good research writing. It’s also extremely difficult to do well, and I think Allen’s article represents a great model for anyone looking.
Tip of the hat to Stephen Allen. He brought his A-game to this study.
Learning from Allen’s example
Rather than packing it in for today and going home (though tempting, I’ll be honest), I’m going to jot down some takeaways for my own research.
First, while I can’t go back in time and change my doctoral research choices, I commend Allen and his faculty advisors (assuming he did this at University of Sheffield, probably for a masters or doctoral thesis) for locating his research within a community that naturally offers a unique case to explore for the theory he’s studying. I imagine Allen is a Quaker (or at least raised in the community); he was able to combine a situation he understood with a theory to help him understand it better. I do not feel as confident in my own study context, but I know the “solution” is to persevere and bring as much light to the situation as I can from the interviews I’m conducting.
Second, speaking more specifically to the article itself, I admire Allen’s concise and eminently readable literature and theory review section covering leadership theories. I can return to my own chapter 2 (literature review) discussions to summarize points for greater clarity. (And I should remember that this is likely Allen’s re-write of his actual research, condensing his main points to make them clearer. Dissertation literature reviews are often tasked with being extensive rather than being succinct.)
Third, my chapters will improve (*knocks on wood*) once they are complete and I have a chance to revise them specifically for clarity. I’ve considered hiring an editor to help me with this stage because two brains are better than one, and a good editor is worth double their weight in gold to polish ideas until they are easily understood.
Finally, as I contemplate my adjustment of research methods toward case study and away from formal grounded theory, I found Allen’s thematic analysis to be readable and accessible. I may return to his article as an inspiring example when in the trenches of my own interview analysis and coding.
To its discredit but not its fault, APA (5th edition) was the fourth documentation system I had to learn during my academic career, and as such, I’ve always held a bit of resentment in my heart toward APA style.
The in-text parenthetical citations are cludgy and interrupt the reader’s flow through the body text. My first graduate degree followed Turabian style, and I learned to glory in a well-written footnote which provided commentary or analsys beyond a mere citation note. The ability to hold a conversation with yourself via footnotes is utterly lost for APA readers, and it is truly their loss.
I’ve made my peace, for the most part, with the APA references page. I understand why they move the year near the head of the reference: I can get behind advancing the chronology of sources. Using sentence case for article titles sets my teeth on edge though … but at least APA 7th has simplified most references down to their bare bones.
Today, I’m here to rant about the 7th edition decision to strip out nearly all authors’ names in the text. Anything past two, and you’re instructed to use et al.
I realize that some fields (especially in sciences and medicine) stack in names like it’s the thank-you list for a Kickstarter. I also know that the position of the names varies within disciplines; in some, the second name is the advisor or sponsor; in other fields, like some sciences, the final name in the list indicates the person who sponsored or funded the research.
But my point is, in fields where author lists don’t tend to be longer than 3-5 names, stripping everything down to “et al” also removes an important narrative thread from the reader’s view. I cannot trace one scholar’s thread through the chronology.
Any given scholar’s name will change position in the author list depending on their role for the research. One person might be lead in one project and third name in another, but a central cast of characters orbiting the topic remains the same even as peripheral authors jump in or drop off.
For example, Mary Uhl-Bien is a well-recognized name in leadership and organizational theory research. In my dissertation chapters, were I allowed to show you more than 2 names at a time, you too could trace Uhl-Bien’s journey through the entity approach to LMX theory, which some term “relational leadership” and writing alongside Russ Marion on Complexity Leadership Theory, into her work with Sonia Ospina to define a constructionist approach to Relational Leadership Theory that represents a good movement in the field away from positivist research.
But you won’t see that in my dissertation chapters, because APA 7th won’t actually let me show you.
This may be the nerdiest post I’ve ever written. :)’
Just give me back my damn footnotes…. *grumbles* *thinks about switching to history*
In a search to understand how ideas emerge and spread, researchers have used complexity theory to analyze individual departments or working groups, whole organizations, and “ecologies” of multiple organizations, institutions, and agents of change who collectively drive innovation forward (Dougherty & Dunne, 2011). These three realms can be labeled as micro, memo, and macro levels of organizational emergence.
Viewed as an analogy for the loosely connected departments within a university, might an ecology approach be relevant to the study of instructional innovation in higher education institutions?
Doughterty and Dunne (2011) write of larger innovation ecologies which encompass whole institutions, businesses, and institutions trying to solve a complex problem together:
Product and service innovation involves the creation, combination, and recombination of knowledge to meet needs in new and better ways and to create new uses. However, in these ecologies, knowledge and other resources are dispersed across many different entities, so multiple organizations must actively participate in innovation. Innovations are generated not by single firms but by the entire ecology. Moreover, innovations in these domains are complex. These new products comprise many parts with unknown and unpredictable interactions (Simon 1996, Anderson 1999), the products emerge from the innovation process only after many years (Van de Ven et al. 1999), and each project may require enormous investments without any assurance that viable new products will result.
The challenge of organizing ecologies of complex innovation concerns how to foster the necessary collaborations among so many diverse organizations over such long time periods under such ambiguity.
Doughterty & Dunne 2011, p. 1214
Comparing HEIs to sectors of innovation
Upon examination of Doughterty and Dunne’s discussion above, a number of descriptors appear to match the situation found in large universities:
“Products and services” translate to courses offered and their design, the curriculum for a major and how students experience it, student interactions with faculty and academic support personnel;
Innovation of instructional practices demands what the authors prescribe for all innovation: “the creation, combination, and recombination of knowledge to meet needs in new and better ways”;
Resources and knowledge are dispersed widely across multiple departments such as faculty development offices, professional development trainings (external and internal), educational researchers embedded in a department (DBRs), and the knowledge faculty possess from their teaching experiences.
Doughterty and Donne say “innovations in these domains are complex” — perhaps the complexity in academia is less surprising than in an industry trying to develop a fuel cell vehicle or address climate change, but the barriers to easy collaboration certainly exist within departments: siloed members, the solitary work of teaching, too busy to collaborate, and struggles for power over resources, prestige, and the attention of administrative managers.
Like an industrial sector, academic departments must choose to invest money in the “R&D” needed to identify and implement a new curricular strategy or overhaul a major, with no guarantee that methods working at other universities would be successful when translated to a new context.
Why apply innovation research to higher ed?
My research involves looking at the Knowledge Life Cycle (cf. McElroy & Firestone) in STEM departments at the university level. As I’ve interviewed faculty — whether they work at R1 powerhouses or small liberal arts / teaching colleges — many of the barriers to innovation within their curriculum and courses remain the same across institutions: Too much to do, too little time, no institutional incentive to innovate around teaching, the “publish or perish” culture; lack of job security or a need to wrangle departmental politics; unsupportive managers and administrators.
Most faculty got a Ph.D. because they love research; teaching is only a part of the gig for them. Yet teaching must be central to the work of a successful university, or it fails in its mission of educating those who come through the doors to earn degrees. This tension lies at the heart of the university enterprise.
HEIs have rested on an outdated model of course delivery for decades, but a number of external pressures are forcing them to grow or vanish amidst fierce competition for tuition dollars. Public funding of HEIs has not regained pre-Great Recession levels. University education in general took a hit when so many Millennials graduated into the Recession and were left saddled with debt in low-paying jobs. New models like Southern New Hampshire or Western Governors University are snapping up students who need job training and a credential and do not want to put up with the typical schedule required by university instruction.
Students from less privileged backgrounds challenge established ways of teaching which may no longer work once STEM faculty are asked to train thousands more graduates to fill America’s urgent need for skilled STEM workers. And research into best practices for STEM instruction is continuing to mound up evidence that students learn better when they are active participants, not passive recipients of education.
What to organize when trying to innovate
The authors summarize research under three practices related to organizing activities to “continuously create, develop, and launch new products or services (p. 1216).
First they point out the terms problem setting and problem solving: “problem setting involves defining the things of the situation and framing the context for addressing the problem, whereas problem solving involves experimenting with alternate solutions” (p. 1216). The three major categories represent social practices; these are actions and interactions among the people involved. I see clear connections here to practices needed for emergent leadership (cf. complexity leadership theory, relational leadership theory) as well as understanding an organization’s Knowledge Life Cycle (see McElroy 2003 – The New Knowledge Management). The three key practices are
Orchestrating knowledge capabilities to support multiple innovation projects
Ongoing strategizing to frame and direct new products and services over time
Developing public policy for public safety and public welfare so that safe yet productive experimentation continues.
Orchestrating knowledge capabilities: improve the knowledge life cycle
It’s not enough to collect knowledge; organizations must transform to develop “long-term capabilities” to support many different types of innovation projects which are interconnected (p.1216). Industry examples of innovations might include miniaturization, flexible materials, agile production, supply chain management. Firms must move away from “managing” this knowledge to become a firm continually shaping its capacity to see the connections between new ideas and generate more new ideas.
How? Doughterty and Dunne offer many examples (p. 1216-18):
foster many connections within a firm (among departments);
hire T-shaped individuals (deep knowledge in their skill area + broad knowledge of many areas);
enhance areas of “deviation” – push people into “boundary” situations where they must recombine ideas to generate new solutions for cross-disciplinary problems;
enhance knowledge transfer in a firm by working on product development as a central activity (me: kind of like how creative people don’t wait for ideas to happen to them; they set up structures and processes to be generating and capturing ideas all the time);
setting industry standards which often force innovation;
create R&D consortia to solve the barriers to innovation common to the industry;
Applications to higher education institutions:
This first section in the article addresses the knowledge life cycle of a department or institution. Some faculty probably reject the idea that innovating for better teaching and learning needs to be central to their jobs; they are overworked in nearly every case, and faculty are asked to balance teaching, researching, and service requirements that easily chew up more than a full work week. Given my observations, I believe many faculty departments are under-hired.
Academic leaders who want to see innovations emerge among faculty need to give them time and space to do the work of innovation, and leaders need to restructure faculty incentives to ensure that professors are genuinely rewarded to spending time thinking together about how they teach. Moving “product development” activities to the center of faculty life is a key application: removing some busywork or administrative duties to make room for revamping curriculum, generating ideas, working alongside educational researchers, and testing ideas in the classroom then reporting to peers on the outcomes.
Some parallels to R&D consortia exist in higher ed — there are communities of practice organized around the Scholarship of Teaching and Learning (SoTL), with nationwide conferences such as the one sponsored by the Lily Foundation. But faculty usually attend these events as individuals. There is not, as far as I can tell, a cross-institutional effort to buckle down and solve problems of instructional innovation at scale. The “industry standards” set by accrediting bodies are not robust enough to force change, though some disciplinary bodies are working to address instruction (e.g.: the Vision and Change document from biological sciences).
Ongoing strategizing for product innovation
A strategy that defines how the ecology will use innovation to accomplish goals is necessary to enable the ongoing orchestration of knowledge capabilities for innovation.
A strategy defines the goals of the enterprise, the value the enterprise will create for customers, and the plan of action to achieve those goals over time, including redirecting action in response to changes in the environment.
Research shows that until firms’ strategies included innovation, they did not innovate well because short-term issues deflect attention away from longer-term development. The strategy motivates innovators to keep going despite difficulties by giving them a sense of how they can contribute to the enterprise and defines priorities among projects.
p. 1218
To do this, Dougherty and Dunne suggest that firms master the three activities of emergence and apply them to strategy:
foster many connections: bundle working groups together to tackle rising market challenges;
amplify deviations: focus on process over content; managers cannot control innovation; they can only “establish and modify the direction and the boundaries within which improvised, self-organized solutions can evolve” (p. 1218-19);
recombine and stabilize: commit to innovation over the long-term; purposefully probe for new applications and “remix” ideas; foster strategic alliances across institutions to take advantage of scale for R&D.
A long-term commitment is crucial, because innovation takes time. New, powerful ideas do not translate quickly into applications or new business models. The authors also point out the pitfalls of strategic alliances which tend to last only for a short time, and larger firms can quickly grab innovations and rush them to market. They suggest that public policies might be a solution, leading them to the third section.
Applications to higher ed:
As I summarized the section above, I considered how HEIs are not built to innovate teaching techniques despite being institutions of education. The entire incentive stream in higher education – at least in the United States – supports getting grants, developing now concepts within one’s discipline, and involving students in those processes (as appropriate) as part of student learning. Faculty who balance their research and teaching by including students are usually doing so at an individual level, usually in contrast to the natural incentives of their jobs.
A push to innovate around the teaching itself will require commitment over the long haul by upper management in higher education, from the president’s office down to the academic deans and department heads. Such a commitment might be possible if institutions banded together in strategic alliances to amplify their brainpower and resources in a world where HEIs are being asked to fund themselves through grants and tuition dollars. And higher ed leaders must become convinced 1) that traditional teaching methods are less effective and 2) actually educating students well is the central activity of a university and therefore 3) worth a realignment of resources. Most faculty will not support any realignment that reduces available research funding, partly because we’ve built a system that makes their jobs dependent on their publication.
I’m honestly depressed by this section as I write it. Nothing stops HEIs from taking the steps outlined above….except literally everything about the higher ed sector as it currently exists in the United States.
Public policy for public welfare and safety
This section may be in some ways both the most relevant and least relevant within the article for HEIs — like any long-term innovation project, higher education falls prey to competition, withholding of information to gain power or position in the market, and the risk of large and well-funded institutions overwhelming smaller ones and running off with innovation insights to make money first. Dougherty and Dunne note that public policy could be crafted in traditional industries to promote sharing and innovation while perhaps lowering the risk for those firms who choose to engage. This advice carries over to the educational sector.
Further, much of the discussion around the rise of the neoliberal university, intended to run “like a business” yet somehow expected to prioritize expensive, custom-fit activities like educating students despite reduced public funding, identifies the competitive landscape and capitalist-lite environment as part of the problem. If higher education were returned to a status of a public good, meant to be funded and maintained by public investment and tax dollars for the benefit of all citizens (and subsidized tuition), wouldn’t the wheels of innovation in both instructional technique and disciplinary research move more quickly? I would hope so.
The authors suggest that government agencies and large public infrastructure institutions can sponsor social networks, public policy to cover the “rules of the game” (p. 1220), and help spark boundary-crossing and other activities that encourage ideas to deviate (so innovations can emerge).
I found this insight to be particularly helpful as HEIs face increased regulation from accreditors and agencies of the government, yet – as with industry settings – very little energy is given to fostering collaboration.
“Public policy is not adequately integrated in organization science. … Organization economics tends to emphasize regulations that constrain firms rather than shape collective action. …. One reason is that the complex politics of regulation involve very large, powerful industries and organizations. Regulatory regimes may also be limited because our theories do not explain the need for enabling dynamics of emergence or how to do so.”
Dougherty & Dunne, 2011, p. 1220
My Takeaways for Higher Education innovation-in-teaching
Higher education institutions are complex adaptive systems, and attempts to change them top-down will always fail if leaders ignore complexity science. True innovation is emergent and divergent and cannot be “managed” — but it can and should be encouraged.
Instructional innovations will emerge in boundary-crossing environments, where faculty from different departments and institutions cross paths with an opportunity to work together and share ideas. While these environments do exist at some conferences and in Faculty Learning Communities, very few “innovation ecologies” exist in higher ed which draw whole departments or institutions together across institutional lines to foster real change in teaching practices.
Large-scale change across the HEI sector is desperately needed before educators will be able to reach learners of all abilities who are in college to gain access to the STEM job market. The US cannot afford to throw away “average” students who are capable of learning and entering productive jobs; many STEM departments are still structured to serve only the “high-performing” academic learners.
Outside groups wishing to push HEI change should attempt to shape public policy (invest in STEM education as a public good; fund collaboration rather than competition) and work with regulatory bodies like accrediting agencies to incorporate instructional change via teaching standards (not necessarily “student learning outcomes”).
Dougherty, D., & Dunne, D. D. (2011). Organizing Ecologies of Complex Innovation. Organization Science, 22(5), 1214–1223. https://doi.org/10.1287/orsc.1100.0605
An excerpt from my research writing — I don’t think this passage will make it into my actual dissertation. The primary source for this discussion is a 2019 revision of the book Talking about Leaving, a study of university students (in the U.S.) who left STEM or who considered abandoning their major but stuck it out:
Seymour, E., & Hunter, A.-B. (Eds.). (2019). Talking about Leaving Revisited: Persistence, Relocation, and Loss in Undergraduate STEM Education. Springer International Publishing. https://doi.org/10.1007/978-3-030-25304-2
Seymour and Hunter (2019, eds.) revisited their 1997 book Talking about Leaving to update the research after two decades of work to address weaknesses in STEM university education. Why do STEM majors fail to persist and graduate? Surveying the data, Seymour, Hunter, and Weston sound alarms about STEM teaching and learning in chapter one, “Why we are still talking about leaving” (pp. 1-53). Although factors unrelated to the classroom also have significant effects on students’ decisions whether to remain in a difficult STEM major or shift to a different program of study, the authors uncovered a number of disturbing trends tied directly to the classroom environment. “Weed-out” courses, passive teaching approaches, and overwhelming workloads drive students away, and these losses disproportionately harm women and students of color. Citing a 2016 study, the authors offer this example of the problem:
To increase the retention of able women, Ellis, Fosdick, and Rasmussen (2016) propose a strategy that focuses on the impact of a single course, Calculus 1, on STEM losses. Because of its gateway role in all or most STEM majors, they argue that, were women to proceed to Calculus 2 at the same rate as men, the number of women entering the STEM workforce would increase by 75% and thereby bring an additional 20% of new graduates into the STEM workforce. What prevents this is not, they argue, lack of ability or effort. Rather, it is the loss of incoming confidence that teaching and assessment methods designed to weed out students engender among women, especially women of color. The challenge to the professorate that the study authors present is fundamentally the same as that made by the researchers in the early 1990s; namely, that it is unacceptable to discard high proportions of students who enter with the interest and ability to undertake an undergraduate science education (as cited in Seymour & Hunter, 2019, page 4, emphasis mine).
Further, because these losses tend to disproportionately affect non-white and male students as well as first-generation students (those whose parents did not attain a college degree), a significant number of those who leave a STEM major — especially early in their college career — drop out of college entirely (p. 4). The authors note, “The more serious consequences of STEM switching evident in the TAL [Talking about Leaving] data, however, may be wastage of talent, compromise or distortion of career aspirations, time and money wasted, debts increased, [as well as] lost confidence, pride, and a sense of a direction — all of which also affect switchers’ families and communities (p. 4).
What prevents [student success] is not lack of ability or effort. Rather, it is the loss of incoming confidence that teaching and assessment methods designed to weed out students engender among women, especially women of color.
Despite twenty years of efforts to change teaching practices in the sciences and related fields, students in STEM as well as those who abandon their majors find professors’ teaching practices to be significantly poor. Curriculum issues, course material organization, and other content issues emerged in both sets of data as a significant problem for students who elected to abandon their majors (p. 93-94).
Using both the 1997 and 2019 data, the authors emphasize several key conclusions related to STEM education at the undergraduate level: More than a third of students leaving STEM majors cited faculty pedagogy as a significant contributing factor, and even those students who remained in their majors tended to criticize faculty instructional techniques (p. 8, 90). Additionally, over 90% of students who change majors (in 2019) cite poor pedagogy as a major cause, and among students who persisted to graduation in a STEM major, over 70% of students complained about the instructional practices in their STEM courses (74% in 1997, 72% in 2019) (p. 8, 90).
Further, a comparison between the 1997 and 2019 data shows that students are facing a rising tide of external problems (such as financial pressures) on top of weaker overall preparation for difficult college coursework coming out of high school (in 1997, 40% of students cited this; in 2019, 64%). Students also cited “difficulties in seeking and getting appropriate timely help” in both sets of data (p. 90-91).
It is notable that poor teaching hurts STEM programs even more when students were able to experience engaging, intellectually-stimulating teaching in other fields. This “push-pull” tension led some students to change majors once they found more interesting material elsewhere (p. 8), and by 2019 had become a major factor in switching majors due to losing interest in their initial field (p. 90).
[P]oor teaching and the dullness of classes made it hard (even for students with a strong liking for science and mathematics) not to feel drawn towards disciplines where they experienced the excitement of intellectual exploration and debate. Unfortunately, students who most often reported that they were “bored out of the sciences” by the teaching in foundational courses were high-performing, multi-talented students who moved to non-STEM majors with greater intellectual appeal. … [T]his is one of the contradictory effects of “weed-out class” teaching methods that we encountered (p. 11).
To be clear, a full quarter of students leaving STEM in the 2019 TALR study are high-performing students with a GPA of 3.5 or better (p. 95). Experiencing low grades perhaps for the first time in their careers, these students already have a variety of possible career paths open to them and choose to abandon a STEM major rather than endure what they see as a competitive, hopeless, brutal environment — especially for women and students of color (p. 329-30).
High-performing students may choose to abandon a STEM major rather than endure what they see as a competitive, hopeless, brutal environment — especially for women and students of color.
Further, students (even those who remained in their STEM majors) could tell the difference between faculty who loved teaching (whom students tended to associate with non-STEM majors) and STEM faculty whose warmth and personal interest in research or lab situations seemed to evaporate when they were in the classroom (p. 11-12). Ironically, this effect could be compounded for students who had enjoyed a highly engaged high school science classroom.
The distancing behavior of STEM instructors in foundational courses had particular consequences for students who had learned their high school science and math in interactive settings that included both peer–peer and teacher–student dialogue. Learning to learn in supportive relationships left students especially vulnerable to culture shock in early STEM courses. Inability to evoke a supportive interaction from instructors prompted many students to doubt their ability and interest and undermined their confidence. These effects were particularly marked among women and first-generation students, including students of color and students from small high schools (p. 12).
Earning low-grades erodes the confidence of students and can incite them to find an easier major. Although competition was not a commonly cited factor in 1997, by 2019 over 80% of students who changed majors cited as a reason “competitive, unsupportive STEM culture makes it hard to belong” (p. 90).
Lori's Notes 