Bibliographic record
Abstract
Elizabeth MarquisAt first glance, the articles included in this issue of CJSoTL-RCACEA seem to have little in common.Ranging from an examination of the impact of 'catchy' vs. 'conventional' course titles on student interest (Flaherty, McAdams, & LeBlanc) to an analysis of the ways in which conflicts between student and instructor epistemologies influence student transitions to higher education (Crooks), the work contained in the pages that follow in many ways reflects the broad diversity of SoTL, and might even be said to push at its boundaries.Nevertheless, I'm also struck by how the articles, as a set, encourage us to consider both the tools and approaches we deploy in teaching, learning, and SoTL, and the extent to which those tools (broadly defined) function as we think they do.In her oft-cited introduction to Opening Lines, Pat Hutchings (2000) notes that one of the primary questions for many scholars of teaching and learning is whether a particular pedagogical approach or intervention is working-that is, having the desired effect on student learning.This interest in 'what works' is surely reflected in the articles in this issue, as authors consider important questions about effects and outcomes related to inquiry-and research-based pedagogies (Symons, Colgoni, & Harvey; Woolf), reflective writing (Boutet, Vandette, & Valiquette-Tessier) and the course dossier methodology (Khanam & Kalman).At the same time, the scholarship represented here also encourages us to consider whether these pedagogical approaches work equally for everyone, pointing out, for instance, the potential for particular tools or strategies to generate disparities in performance related to gender (Normandeau, Iyengar, & Newling) or personality (Lakhal, Frenette, & Sevigny).In this respect, the issue also asks us to think seriously about the extent to which the tools we use for assessment-both in teaching and in research-actually assess the things they're intended to assess.This issue is brought further to the fore by articles considering the potential impact of item ordering on multiple choice tests (Carnegie), and the role of survey question construction in shaping understanding of how students perceive the benefits of an undergraduate degree (Cole & Martini).These are essential questions that contribute to the ongoing growth and vitality of SoTL as a field.In noting the broad (though by no means uniform) emphasis on issues pertaining to 'what works' in this issue, I also feel compelled to underline the potential significance of the other types of questions Hutchings (2000) describes.For instance, alongside meaningful and critical explorations of the tools and strategies of teaching and learning, Hutchings underscores the potential to ask 'what is' questions that seek to better understand what is happening in particular learning situations, and theory-building questions that aim to develop conceptual frameworks for SoTL.Likewise, in a recent article in Teaching & Learning Inquiry, Bloch-Schulman and colleagues (2016) encourage SoTL scholars to ask a wider range of questions that might allow SoTL to contribute not only to enhancing learning in particular classrooms, but also to transforming higher education as a whole.While CJSoTL-RCACEA will always welcome thought-provoking, rigorous explorations of 'what works' questions (such as the many contained in this issue), we also affirm our commitment to publishing research that cuts across this spectrum of inquiry.Indeed, several articles in this issue take up some of these other types of questions either in tandem with their focus on 'what works' or as a central point of scholarly focus.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.090 | 0.054 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".