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Record W4301499369 · doi:10.56026/imu.1.1.a21

Abstract from the International Medical Education Conference 2007 (OS)

2007· article· en· W4301499369 on OpenAlexaff
Rajendra Latha, Sethuraman Kumar, Mala Maung, Zoraini Wati Abas, Azman Abdullah, Rahimah Abdul Kadir, Allan Pau, Ray Croucher, Jennifer Perera, Khin Win, Nagarajah Lee, Lionel Wi J E S U R I Y A, Joachim Perera

Bibliographic record

VenueInternational e-Journal of Science Medicine & Education · 2007
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicinePolitical scienceMedical education

Abstract

fetched live from OpenAlex

Introduction: Students approach their learning in at least two qualitatively different ways.In the surface approach, students see tasks as being imposed, for which they develop coping strategies focused on reproduction of essentials and memorizing information for assessment rather than for understanding.Surface learning is the tacit acceptance of information and memorization as isolated and unlinked facts.It leads to superficial retention of material for examinations and does not promote understanding or long-term retention of knowledge and information ( Evans et al., 2003).In the deep approach, students seek to understand ideas to allow them to relate and integrate knowledge from other parts of their study and thereby develop conceptual frameworks from which they can derive solutions to novel problems.Deep learning involves the critical analysis of new ideas, linking them to already known concepts and principles, and leads to understanding and long-term retention of concepts so that they can be used for problem solving in unfamiliar contexts.Deep learning promotes understanding and application for life (Gordon et al., 2002).Objectives: To assess the learning styles of medical students using the Biggs questionnaire.To assess the preferred teaching methods adopted by medical students in AIMST. Materials and Methods:Study design was cross-sectional study of medical students and dental students in AIMST.Setting: AIMST Medical school, Sungai Petani, Kedah.Participants: A total of 463 students (417 Medical students and 110 Dental students) participated in the study.Main outcome measures: Learning approach (surface and deep learning style), preferred study habits, academic achievement.M e t h o d s : A 20-item in Biggs's Revised Study Process Questionnaire (R-SPQ-2F) was employed to measure the students' learning methods/approaches (Kember et al., 2004).The questionnaire was also used to examine the preferred method of teaching (Kember et al., 2001).The students were asked to choose whether they preferred PBL or Lecture.Next they were asked to choose whether they preferred learning through simulation teaching in clinical skill lab or clinical bedside teaching in the hospital.The reasons why they liked or disliked a preferred method of teaching were elicited.The SPM and STPM grades of the students were also collected to be used as an indicator of achievement.Statistical Analysis: Descriptive analysis of the data was done using SPSS 13.0.Karl's Pearson Correlation was used to look for a relation between academic achievement and type of learner.Also it was used to look a correlation between method of teaching (Lectures, PBL, Simulator and Clinical Bedside Teaching) and type of learner (Superficial and deep approach).Results: 52.7% of dental and medical students liked lectures.47.1% liked the PBL sessions while 0.2% liked both equally.56.4% liked clinical bed side teaching, 41.7% liked simulator teaching in clinical skill lab while 1.9% liked both equally.Karl's Pearson Correlation revealed a significant positive correlation between high academic achievement and deep approach learners and a positive correlation between low academic achievement and surface learners.Karl's Pearson Correlation revealed a significant correlation between deep approach learning and PBL; surface approach learning and lectures; deep approach learning and clinical bed side teaching; Simulator teaching showed a negative correlation with deep learners and no correlation with superficial learners.The main reasons for students liking lecture method was that all topics were covered and for liking PBL was that it was interesting and more participation was possible as smaller groups were involved.The clinical bed side teaching was preferred as patients were real; those who preferred simulators said that practicing in a dummy was easier.Discussion: As the coverage of topics important for the exams were more extensive, a majority of the students preferred lectures to PBL.But the deep approach learners liked the PBL sessions as they were able to gain more knowledge through self directed learning as they faced new problems.Thus PBL suited students who have self discipline to take active responsibility for their own learning.Similarly clinical bed side teaching was preferred as it gave them real life experience with the patients.Some students agreed that simulator and bed side teaching were complementary.Deep approach learners were convinced that PBL and clinical bed side teaching helped them in building up communication skills, better participation, more involvement, interpersonal relationship and problem solving capacity. Conclusions:Deep approach learners supported problembased learning (PBL) and clinical bed side teaching as an effective method of learning and superficial learners supported lectures.The findings suggest that students with deep learning motives and approaches reap the most benefit from PBL and clinical bed side teaching.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.339
Threshold uncertainty score0.942

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.000
Scholarly communication0.0050.001
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.3390.107

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 head0.025
GPT teacher head0.413
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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2007
Admission routes1
Has abstractyes

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