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Record W2475613644

The 3 x 2 Achievement Goal Model in Predicting Online Student Test Anxiety and Help-Seeking.

2016· article· en· W2475613644 on OpenAlexvenueno aff
Yan Yang, Jeff Taylor, Li Cao

Bibliographic record

VenueInternational journal of e-learning & distance education · 2016
Typearticle
Languageen
FieldPsychology
TopicEducation, Achievement, and Giftedness
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyCompetence (human resources)HumanitiesSocial psychologyTest anxietyAnxietyNeed for achievement
DOInot available

Abstract

fetched live from OpenAlex

This study investigates the utility of the new 3 × 2 achievement goal model in predicting online student test anxiety and help-seeking. Achievement goals refer to students’ general aims for participating in learning and the standard by which they judge their achievement (Pintrich, 2000). According to Elliot and his colleagues (2011), there are six types of achievement goals based on three dimensions of competence (self-, task-, and other-) and two dimensions of valence (approach and avoidance). The sample included 209 students enrolled in distance education classes who volunteered for the study. Separate hierarchical regression was employed to examine the predictive power of achievement goals in online student test anxiety and help-seeking beyond self-efficacy and demographic differences. Achievement goals predicted online help-seeking differently from traditional classes. Students who endorsed other-avoidance or self-approach goals reported more help-seeking, while those with other-approach or self-avoidance goals reported less help-seeking. The study results and practical implications for online instruction and course design are discussed. Cette étude examine l'utilité du nouveau modèle 3 × 2 des buts d’accomplissement à prédire l'anxiété au test et la demande d'aide de l’étudiant en ligne. Les buts d’accomplissement visent les buts généraux des étudiants pour participer à l'apprentissage et la norme par laquelle ils jugent leur accomplissement (Pintrich, 2000). Selon Elliot et ses collègues (2011), il existe six types de buts d’accomplissement en s’appuyant sur trois dimensions de la compétence (soi-, tâche-, et autrui-) et deux dimensions de valence (approche et évitement). L'échantillon comprenait 209 étudiants inscrits dans des classes d'enseignement à distance qui se sont portés volontaires pour l'étude. La régression hiérarchique distincte a été utilisée pour examiner le pouvoir prédictif des buts d’accomplissement de l’étudiant en ligne pour l'anxiété au test et la demande d'aide au-delà de l'auto-efficacité et des différences démographiques. Les buts d’accomplissement ont prédit la demande d'aide en ligne différemment de celle des classes traditionnelles. Les étudiants qui ont approuvé les buts autrui-évitement ou soi-approche ont signalé plus de demande d'aide, tandis que ceux avec les buts autrui-approche ou soi-évitement ont signalé moins de demande d'aide. Les résultats de l'étude et les implications pratiques pour l'enseignement en ligne et la conception de cours sont discutés.

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)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.349
Teacher spread0.335 · 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 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

Citations18
Published2016
Admission routes1
Has abstractyes

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