The Impact of Differentiated Instruction in a Teacher Education Setting: Successes and Challenges
Bibliographic record
Abstract
This study examined the impact of using a differentiated instructional approach to teaching second year students pursuing an undergraduate course in curriculum studies at a tertiary institution. These prospective teachers varied in terms of their interests, experiences, personal circumstances, and learning preferences. Four hundred and thirty-four students in two education campuses took the course over a period of one semester. Half of the student body experienced differentiated instruction while the other half was exposed to the whole- class instructional approach. At the end of the course, an assessment was made to determine the extent to which differentiated instruction had a positive impact on students’ general understanding of the course. Findings of the study revealed that students at both campuses responded favourably to the differentiated instructional approach, with 90% of participants reporting higher levels of intellectual growth and interest in the subject. Assessment of student learning revealed that the majority of students in the differentiated classrooms demonstrated sound understanding of major concepts taught in the curriculum studies course. Almost all of the students (99%) expressed willingness to experiment with differentiated instruction in subsequent practicum sessions during their tenure at the university, and 88% indicated a desire to use a differentiated instructional approach in their classrooms upon graduation.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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 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".