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Record W2780728325 · doi:10.5539/ies.v11n1p11

Toward Better Goal Clarity in Instruction: How Focus On Content, Social Exchange and Active Learning Supports Teachers in Improving Dialogic Teaching Practices

2017· article· en· W2780728325 on OpenAlexvenueno aff
Martina Alles, Tina Seidel, Alexander Gröschner

Bibliographic record

VenueInternational Education Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsnot available
FundersDeutsche Forschungsgemeinschaft
KeywordsCLARITYDialogicPsychologyFocus groupPedagogyMathematics educationTeaching methodIntervention (counseling)Sociology

Abstract

fetched live from OpenAlex

Goal clarity is an essential element of classroom dialogue and a component of effective instruction. Until now, teachers have been struggling to implement goal clarity in the classroom dialogue. In the present study, we investigated the classroom practice of teachers in a video-based intervention called the Dialogic Video Cycle (DVC) and compared it to the classroom practice of teachers in a traditional control group. We conducted video analysis (N = 20 lessons) of teaching practices at the beginning (pre-test) and at the end of the school year (post-test). Furthermore, we performed video analysis of intervention group teacher discussions during DVC meetings (N = 6 meetings). Comparative analysis between groups revealed changes in teaching practices towards better goal clarity for DVC teachers in comparison to the traditional control group. In-depth analysis of teacher discussions during DVC meetings showed that teachers continuously focused on goal clarity as the content of teacher professional development (TPD). They shared learning experiences and were actively involved in TPD learning activities. The study illustrates how components of effective TPD programs (content focus, social and active learning) translated into redefining and changing the teaching practice.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.347
GPT teacher head0.479
Teacher spread0.132 · 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 teacher head, not a consensus.

Study designObservational
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

Citations25
Published2017
Admission routes1
Has abstractyes

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