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STUDY APPROACHES OF POSTGRADUATE STUDENTS IN ODL SYSTEM: A LONGITUDINAL SURVEY

2024· article· en· W4403568471 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Social Sciences Development · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsMedical educationMathematics educationPsychologyComputer scienceMedicine

Abstract

fetched live from OpenAlex

The study aimed to analyze the study approach of the postgraduate students across various semesters of studies. Longitudinal survey design was adopted to conduct this study. The participants of study were enrolled in Education degree program in university (Pakistan). There were two cohorts of students who participated in this study with 12 students in cohort-1 and 10 students in cohort-2. Approaches and Study Skills Inventory for Students (ASSIST- short version with 52 items) was used to collect data from students at three different times i.e., first time at the start of the second (coursework) semester, second time at end of second semester (development of research proposal stage) and third time during the dissertation stage. Similarly, the data were analyzed using descriptive and inferential statistical techniques. The study results reported that the students used deep and strategic approach to study more than surface approach to study however, the percentage of using the surface approach was also quite high. It was also found that there was no gender wise difference in the surface, deep and strategic approaches of both cohorts of the research study. It is recommended to provide the students with guidance and facilitation for shifting their study approach from surface to deep approach.

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.

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)0.020
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.079
Threshold uncertainty score0.707

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.245
GPT teacher head0.416
Teacher spread0.171 · 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