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Record W2313396906 · doi:10.5539/jel.v5n2p250

Teachers’ Motivating Methods to Support Thai Ninth Grade Students’ Levels of Motivation and Learning in Mathematics Classrooms

2016· article· en· W2313396906 on OpenAlexvenueno aff
Sansanee Nenthien, Jyrki Loima

Bibliographic record

VenueJournal of Education and Learning · 2016
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsnot available
FundersChulalongkorn University
KeywordsPsychologyMathematics educationNinthClass (philosophy)Self-determination theoryIntrinsic motivationInterpersonal communicationMotivation to learnAutonomyVariety (cybernetics)Goal theoryLearning stylesTeaching methodPedagogySocial psychologyMathematics

Abstract

fetched live from OpenAlex

<p>The aims of this qualitative research were to investigate the level of motivation and learning of ninth grade students in mathematics classrooms in Thailand and to reveal how the teachers supported students’ levels of motivation and learning. The participants were 333 students and 12 teachers in 12 mathematics classrooms from four regions of Thailand. The results showed, first, that the students’ levels of learning ranged from low to moderate-high while the levels of motivation were from moderate to high. In addition, most students had intrinsic motivation; however, some students still lacked motivation and were only motivated by external sources. Second, teachers enhanced students’ learning by encouraging them to learn as a whole class the most by using lectures and asking questions. The other top methods were allowing time for self-paced learning and answering, and relying on internal sources of motivation using positive interpersonal and group activities. Most teachers employed both autonomy-supportive and controlling motivating styles in classroom. Even with a variety of motivating methods, the students’ learning did not seem to be supported adequately, especially for students who showed a low level of learning.</p>

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.068
GPT teacher head0.416
Teacher spread0.348 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

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