Bibliographic record
Abstract
Abstract Two conceptual models of Task Complexity, Cognition Hypothesis ( Robinson, 2001 , 2007 ) and Limited Attentional Capacity ( Skehan, 1998 , 2003 , 2018 ) have been proposed and widely debated in the task-based language teaching (TBLT) literature. However, little empirical evidence exists to suggest either of the models is based on teacher input or being used by teachers for classroom use. Drawing on pre-service teacher analysis of task difficulty, the study aimed to develop an in-depth understanding of task features they consider when evaluating task difficulty. Participants, 127 pre-service teachers at the end of their one-year MA TESOL program in Ontario, Canada, evaluated two sets of sample tasks, ranking them according to their degree of difficulty and identifying the features that contributed to this difficulty. 727 pieces of raw data, extracted from the task difficulty analysis, were categorized. Five main categories of task difficulty were identified, namely (1) linguistic demand, (2) cognitive operational demand, (3) design features, (4) informational demand, and (5) communicative demand. Learner related factors, external to task design, were also suggested as issues related to difficulty. We propose a set of task difficulty features that can be used in replication and validation studies to help with the development of a teacher evidence-based model of task difficulty.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.006 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".