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Record W2762075928 · doi:10.5539/jel.v7n1p96

A Theoretical Perspective on the Case Study Method

2017· article· en· W2762075928 on OpenAlexvenueno aff
Zafer Çakmak, İsmail Hakan AKGÜN

Bibliographic record

VenueJournal of Education and Learning · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsTeaching methodBoredomProcess (computing)Variety (cybernetics)Context (archaeology)Mathematics educationPerspective (graphical)Computer scienceAppealPsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

Ensuring that students reach the determined goals of the courses at the desired level is one of the primary goals of teaching. In order to achieve this purpose, educators use a variety of teaching strategies and methods, and teaching materials appropriate to the content and the subject of the courses in the teaching process. As a matter of fact, it is known that the methods and materials that appeal to different sense organs of the students influence the learning process positively. In addition, the use of various teaching methods, techniques and materials in the teaching process both attract students’ attention and save the lesson from boredom, which affects the learning positively. In the process of teaching students sometimes have problems using their theoretical knowledge in real life situations. In this context, various teaching methods are also used for the practicalization of theoretical knowledge in the teaching processes. The case study method is one of the effective methods to achieve this purpose, because the case study method is a teaching method that enables students to acquire the knowledge and skills to deal with the problem they are working on and to produce information-based solutions in real life situations similar to the situations they are working with. In this study, the literature related to the case study method was examined and the application of the method, its history, application types, points to be considered in the application process, advantages of using the method in the teaching process and its limitations and teaching techniques to be used with the method are explained.

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)0.023
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.977
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.005
Science and technology studies0.0040.023
Scholarly communication0.0100.008
Open science0.0040.004
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0120.002

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 head0.050
GPT teacher head0.472
Teacher spread0.422 · 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.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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

Citations33
Published2017
Admission routes1
Has abstractyes

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