MétaCan
Menu
Back to cohort

Efficacy of Learning Modules to Enhance Study Skills

2018· article· en· W2803907227 on OpenAlexaff
Kenneth M. Cramer, Craig M. Ross, Lisa Plant, Rebecca Pschibul

Bibliographic record

VenueInternational Journal of Technology and Inclusive Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsComputer scienceMathematics educationPsychology

Abstract

fetched live from OpenAlex

A key roadblock in students' success in higher education involves a lack of preparedness. That is, students may be ill-equipped for demands inherent in the pursuit of an academic degree (such as managing one's time, taking class notes, and preparing for examinations). To address these missing resources, educators have turned to offering students learning modules as face-to-face or online educational supplements. Two studies were conducted to investigate both the usefulness and effectiveness of learning modules for students in an introductory psychology course. Specifically, we compared students' midterm and examination scores by those who received two modular skill sets (both examination-taking strategies and time management) before or after the course midterm. Students' relative levels of each of perceived motivation, interest, and effectiveness of the modules were measured at the conclusion of the course. Results showed a significant association between module receipt and improved performance on the midterm and final examination, regardless of when the modules were presented (that is, either before or after the midterm). Additionally, students who completed the modules indicated that they enjoyed them, scoring significantly higher on their final examination. Based on these results, we encourage instructors and educational developers to design and offer learning modules to students (in first-year courses in particular) to enhance student success across their college or university experience.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.665
Threshold uncertainty score0.279

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.007
GPT teacher head0.395
Teacher spread0.388 · 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 designOther design
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

Citations4
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Technology and Inclusive EducationSame topicEducation and Critical Thinking DevelopmentFrench-language works237,207