the relationship between erciyes, selçuk, and akdeniz medical school third-year students’ learning approaches and their non-attendance attitude and tendencies

the relationship between erciyes, selçuk, and akdeniz medical school third-year students’ learning approaches and their non-attendance attitude and tendencies

;Zeynep Baykan;Yeşim Şenol;Ayşen Melek Aytuğ Koşan;Melis Naçar
chemical engineering science 2017 Vol. 16 pp. -
218
baykan2017journalthe

Abstract

Background: Non-attendance is an undesirable student behavior. Although some studies about the factors for non-attendance behavior have been carried out at medical schools, the learning approach of a student has not been studied and it can also be a factor for non-attendance. We aimed to assess the relationship between learning approaches and non-attendance attitude and tendency.
Methods: This is a correlational study. 644 students registered in three medical schools were enrolled. Data were collected during May 2015. “The Revised Two Factor Learning Approach Scale”, “Non-attendance
Attitude Scale” and “Non-attendance Tendency Scale” were used as data collection tools.
Results: Out of 478 studied students, 10.3% mentioned that they never missed theoretical classes and 71.3% mentioned that they never missed practical classes. Sleeplessness was the most common reason for
non-attendance. 45.6% of all students thought that non-attendance affected student success. The students’ mean score for deep learning was 29.5±6.1 and for superficial learning was 30.5±5.6. The mean score
for non-attendance attitude scale was 54.4±12.8 and from non-attendance tendency scale was 90.5±19.6. Conclusion: Learning approach is an effective factor for attendance. As deep learning approach is adopted, tendency for non-attendance decreases and the attitude becomes positive.
Keywords: LEARNING, STUDENTS, MEDICAL, ATTENDANCE, ATTITUDE

Citation

ID: 188594
Ref Key: baykan2017journalthe
Use this key to autocite in SciMatic or Thesis Manager

References

Blockchain Verification

Account:
NFT Contract Address:
0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
188594
Unique Identifier:
10.22037/jme.v16i2.13954
Network:
Scimatic Chain (ID: 481)
Loading...
Blockchain Readiness Checklist
Authors
Abstract
Journal Name
Year
Title
5/5
Creates 1,000,000 NFT tokens for this article
Token Features:
  • ERC-1155 Standard NFT
  • 1 Million Supply per Article
  • Transferable via MetaMask
  • Permanent Blockchain Record
Blockchain QR Code
Scan with Saymatik Web3.0 Wallet

Saymatik Web3.0 Wallet