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Record W3192686216 · doi:10.5430/jnep.v11n12p38

Self-directed learning with a modified team-based strategy: A quasi-experimental study

2021· article· en· W3192686216 on OpenAlexvenueno aff
Huei‐Lih Hwang, Chin‐Tang Tu, Tian‐Yuan Kuo

Bibliographic record

VenueJournal of Nursing Education and Practice · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicE-Learning and COVID-19
Canadian institutionsnot available
Fundersnot available
KeywordsAutodidacticismTest (biology)Flexibility (engineering)Team-based learningMedical educationPsychologyQualitative propertyComputer scienceMedicine

Abstract

fetched live from OpenAlex

Background and objective: Physical assessment skills are not effectively put into practice for nursing students, requiring an improvement in pre-registration programs and planning more tailored training courses for them. More flexibility in teaching methods can thus contribute to self-directed learning. The purpose of this study is to identify the effects of team-based learning when combined with an online teaching platform on self-directed learning compared to inquiry-based learning for junior college nursing students.Methods: In this quasi-experimental study, 103 students completely participated in the Self-directed Learning Instrument test before and after the course. Collected data were analysed using independent t-test and ANCOVA with the statistical package SPSS 21.0 for Windows. Qualitative focus group interviews were conducted after the survey with 14 participants.Results: Compared to the control group, the adjusted mean post-test score for self-directed learning ability was significantly higher in the intervention group. Students also reported that they were quite engaged in completing assignments and team learning activities in classroom, specifically for the group test.Conclusions: The modified team-based learning strategy was useful at engaging students to improve self-directed learning and to satisfy them. Educators are encouraged to integrate online response system technology into their classroom activities.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.634

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.078
GPT teacher head0.451
Teacher spread0.373 · 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 designQualitative
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
Published2021
Admission routes1
Has abstractyes

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