Maintaining Effort and Interest despite Challenges during the COVID-19 Pandemic: A Process Tracing Approach to a Teacher’s Grit during an Online L2 Course
Bibliographic record
Abstract
Grit—the ability to maintain effort and interest for long-term goals—is argued to be an important individual factor for achievement, especially in the face of obstacles. However, little research has examined the possible fluctuations of effort and interest and how challenges may trigger the changes of effort and interest. In this study, we measured a teacher’s grit at the beginning of an online course during the COVID-19 pandemic, and we focused on the changes in a teacher’s effort and interest throughout the course. In this case study we unpacked the explanations of possible changes in grit via process tracing. Despite the fact that the teacher scored high on the grit scale, we found that the sudden shift from in-person to online teaching had put much pressure and demand on the teacher. The new teaching challenge influenced the teacher’s self-evaluation of their teaching performance and students’ engagement, which led to changes in effort and interest. Therefore, we argue that one’s average grit (e.g., measured by grit scale) cannot be the representation of their ability to maintain interest and effort on different occasions due to the influence of different situational causes or pressure. Specifically, during the course, the teacher’s effort and interest underwent changes on four occasions, characterized by four distinct dynamic patterns in terms of the interaction of high and low interest and effort. The four emerging patterns of L2 teacher effort and interest indicate that the construct of grit could be explained in terms of four dynamic clusters or archetypes. This study provides implications for understanding the complex dynamic nature of grit, which can be further explored through cluster analytic approaches in future studies.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".