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Record W2893797090 · doi:10.25316/ir-1140

Innovative teaching strategies that meet the needs of all students

2018· dissertation· en· W2893797090 on OpenAlexaboutno aff
Lakhwinder Kaur Mann

Bibliographic record

VenueVIURRSpace (Vancouver Island University) · 2018
Typedissertation
Languageen
FieldComputer Science
TopicEducational Innovations and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationEngineering managementEngineeringComputer scienceMedical educationPsychologyMedicine

Abstract

fetched live from OpenAlex

This project work was prepared to explore innovative teaching strategies for rural Indian primary schools with an intention to prepare a grade one to five handbook for teachers. The design was based on the British Columbia (BC) curriculum model developed in Canada on the basis of innovative teaching strategies. Most of the rural Indian primary school use the traditional teaching strategies; these out-dated strategies are ones that I experienced both as a student and teacher. These teaching strategies did not help the teachers engage students in the classroom. My experiences and a strong concern about today‘s students in rural India were the driving forces behind this research project. I hope this handbook will be helpful in providing a new path for the rural Indian primary school teachers. I believe this research project will result in (a) providing innovative teaching strategies to rural Indian school teachers and (b) more engaged students in rural Indian classrooms.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0070.003
Open science0.0020.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.003

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.

Opus teacher head0.017
GPT teacher head0.269
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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