Managing Cognitive Complexity of Academic Writing Tasks in High Stakes Exams via Mentor Text Modeling: A Case of Iranian EFL Learners
Notice bibliographique
Résumé
Cognitive complexity is traditionally used for describing human cognition along a simplicity-complexity axis in tests like TOFEL IBT and GRE where text creation and rhetorical organization are quintessentially important. Accordingly, this study sought to investigate the impact of mentor text modelling on cognitive complexity of academic writing tasks in terms of students’ responses to the test inputs. For this purpose, from the population of applicants applying for various high stake exams at Jihahde Daneshgahi, Isfahan University, three intact classes were selected based on a convenient sampling method. The students, both male and female, were graduates from various majors in applied sciences whose age range was between 24 and 29 and they had all passed the preparatory classes required for attending academic writing courses. Each targeted class with twenty-five applicants was concurrently programmed for three writing tasks with various cognitive complexity levels: Independent, integrated, and analytical. The classes, a total of 75 EFL learners, were randomly assigned to three equal groups labeled as product based (PBG), process based (PRBG), and mentor text modeling (MTMG) respectively. Employing a posttest only quasi-experimental design, learners in the three groups received their instruction on advanced writing during a sixteen session course. The learners in each group were taught based on the selected writing approaches. At the end of the treatment, the learners' writing performance was assessed on test tasks within the pre-specified time and word limits by utilizing a relevant posttest. Data analysis reflected that mentor text modeling enjoyed a potentially higher pedagogical efficacy since the learners in the MTM experimental sample performed better in terms of both accuracy and fluency compared with the groups receiving their writing instruction through either product or process based approaches. Notably, the findings revealed that mentor text modeling is a functionally dependable resource for managing writing tasks cognitive complexity and neutralizing the trade-off effect between accuracy and fluency by offering insightful pedagogical hints to EFL teachers, test takers, and writing material developers who have always had a hard time calibrating writing accuracy and fluency in high stake exams.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».