MétaCan
Menu
Back to cohort
Record W4321351905 · doi:10.5430/jct.v12n1p183

Effectiveness of Online Learning with the Web-based TPACK Scaffolding for Enhancement TPACK Ability of Pre-service Chemistry Teachers

2023· article· en· W4321351905 on OpenAlexvenueno aff
Mardhiyah Ayu Astari, Mohammad Masykuri, Endang Susilowati, Sri Yamtinah

Bibliographic record

VenueJournal of Curriculum and Teaching · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationTest (biology)Rasch modelChemistryComputer scienceMultimediaPsychology

Abstract

fetched live from OpenAlex

This study investigates the effectiveness of web-based TPACK scaffolding to enhance the TPACK ability of pre-service chemistry teachers through online learning. Participants in this study were 74 pre-service chemistry teachers in Chemistry Education program who took a chemistry learning planning course. This study used the quantitative research method approach. The experimental study with pre-and post-test design examined the more significant increase in TPACK ability between the experimental and control classes. The research instrument consisted of 20 multiple-choice questions containing the TPACK components. Analysis of pre-and post-test data used the stacking and racking method in the Rasch model. The stacking analysis result indicated that the pre-service chemistry teachers' ability increased from pre-test to post-test. The racking analysis result indicated that the pre-service chemistry teachers could answer TPACK items easier in the post-test conditions after being given intervention. The various types of scaffolding available in web-based TPACK online learning effectively support pre-service chemistry teacher TPACK ability enhancement. Online learning with web-based TPACK scaffolding is advisable to develop pre-service chemistry teachers' TPACK to prepare them better to use various types of technology in classroom learning.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.333
Teacher spread0.319 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Curriculum and TeachingSame topicTechnology-Enhanced Education StudiesFrench-language works237,207