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Fostering formative assessment: teachers' perception, practice and challenges of implementation in four Sudanese medical schools, a mixed-method study.

Elaf Abdulla AlmahalAbrar Abdalfattah Ahmed OsmanMohamed Elnajid TahirHamdan Zaki HamdanArwa Yahya GaddalOmer Tagelsir Abdall AlkhidirHosam Eldeen Elsadig Gasmalla
Published in: BMC medical education (2023)
Formative assessment (assessment for learning) enhances learning (especially deep learning) by using feedback as a central tool. However, implementing it properly faces many challenges. We aimed to describe the perception of medical teachers towards FA, their practice, challenges of implementing FA and present applicable solutions. A mixed-method, explanatory approach study was applied by administering a validated questionnaire to 190 medical teachers in four medical schools in Sudan. The obtained results were further studied using the Delphi method. Quantitative analysis revealed that medical teachers perceived their grasping of the concept of FAs and their ability to differentiate formative from summative assessments as very well (83.7%) and (77.4%), respectively. However, in contradiction to the former results, it was noteworthy that (41%) of them mistakenly perceived FA as an approach conducted for purposes of grading and certification. The qualitative study defined the challenges into two main themes: lack of understanding of formative assessment and lack of resources. Medical teachers' development and resource allocation were the main recommendations. We conclude that there is misunderstanding and malpractice in implementing formative assessment attributed to the lack of understanding of FA as well as the lack of resources. We as well present suggested solutions derived from the perception of the medical teachers in the study and evolved around three approaches: faculty development, managing the curriculum by allocating time and resources for FA, and advocacy among stakeholders.
Keyphrases
  • healthcare
  • quality improvement
  • primary care
  • deep learning
  • depressive symptoms
  • social support
  • mental health
  • convolutional neural network
  • medical students
  • artificial intelligence