The Critic as Designer: How Metacognition Makes Transdisciplinarity Possible
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
For students to solve complex problems from heterogeneous domains, they need practice engaging in transdisciplinary teamwork. According to Tan, Nesbit, Ellis, and Ostafichuk (2018), “Metacognition also plays a significant role in transdisciplinary interactions by enabling individuals to monitor, reflect on, and adapt learning processes in a multidimensional context”. Simply put, metacognition is the process of a learner self-monitoring their own thoughts and viewpoints. In our context of transdisciplinary design education, improving metacognition is relevant as students learn problem-framing and human-centered approaches while engaging in continuous collaboration and critique with students and faculty from other disciplines. The practice of critique is central to design processes across disciplines, though it is enacted in very different ways. The Critical Response Process, created by Liz Lerman, provides feedback in the form of inquiry from Responders that first try to understand the Creator’s perspective and thought process. The Creator is encouraged to reflect on their work from a new perspective. Finally, the Responders give their opinion on the project with the Creator’s full permission. Our study explores how Lerman’s Critical Response Process paired with guided self-reflection facilitates the elements of metacognition (setting goals, self-monitoring, controlling, and evaluating) in a transdisciplinary design course based in an engineering department. Our goal in this course is for students to engage in Critical Response Process feedback with peers to promote transdisciplinary learning. We address these research questions: In what ways is metacognition helpful to students as they learn to value different ways of knowing? Do learner/artist-centered critique techniques, like Lerman’s Critical Response Process, encourage participants to withhold immediate judgment and to consider various perspectives? How does students’ thinking about transdisciplinary teams and their own learning change after engaging in the Critical Response Process? Our methods include analyzing student assignments completed across the span of one semester. We use thematic coding informed by literature on metacognition and the learning outcome goals for the course. The student assignments used are Free Writes, where the students take five minutes to respond to suggested prompts; written Critical Response Process peer critiques sent to other groups; and a Reflection on their own peer critique after hearing from others. Anticipated results include insights into how students respond to giving and receiving critiques, and how this engagement influences their use of the elements of metacognition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it