TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/20.500.14365/4

Browse

Filters

2015 - 201912020 - 20221

Settings

Subject: search.filters.subject.Curriculum

Search Results

Now showing 1 - 2 of 2
  • Article
    Citation - WoS: 2
    Citation - Scopus: 1
    A De Novo Tool To Measure the Preclinical Learning Climate of Medical Faculties in Turkey
    (Edam, 2015) Yilmaz, Nilufer Demiral; Velipasaoglu, Serpil; Sahin, Hatice; Basusta, Bilge Uzun; Midik, Ozlem; Coskun, Ozlem; Budakoglu, Isil Irem; İfakat Tengiz, Funda; Midike, Ozlem; Basustad, Bilge Uzun; Velipasaoglub, Serpil; Sahinc, Hatice; Demirel, Nilufer Yilmaza; Budakoglug, Isıl Irem
    Although several scales are used to measure general and clinical learning climates, there are no scales that assess the preclinical learning climate. Therefore, the purpose of this study was to develop an effective measurement tool in order to assess the preclinical learning climate. In this cross-sectional study, data were collected from 3,540 preclinical medical students of six medical faculties in Turkey. The methodology included the following activities: generate an item pool, receive expert opinions, perform a pretest to purify the instrument, and conduct factor and reliability analyses. According to the factor analysis, eight factors were determined and their contribution to the variance was 50.39%. In addition, the item factor loadings ranged from .31 to .91, Cronbach's alpha coefficients for the subscales ranged from .72 to .77, and the item-total correlation coefficients for the subscales ranged from .44 to .76. All the items significantly discriminated between the low- and high-performing students (t = 99.57; p = .01). The scale included 52 items with the following subscales: management, teaching, teaching staff, institutional commitment, emotions, inter-student relationships, physical environment, and motivation. The analysis of this newly developed Preclinical Learning Climate Scale (PLCS) indicated that its psychometric properties are appropriate and this scale can be employed to evaluate medical education programs.
  • Article
    Citation - WoS: 18
    Citation - Scopus: 34
    Modelling AI in Architectural Education
    (Gazi Univ, 2022-12-01) Başarır, Lale
    This work displays an outlook on major questions concerning the integration of Artificial Intelligence (AI) in Architectural education. Gradually, part of the domain knowledge and hard skills become either irrelevant or insufficient by the time the students graduate. This paper suggests that integrating AI in the architectural design curriculum is beneficial for raising designers’ awareness of all areas of architectural design, in the form of input, process, and output. The study views consecutive learning experiences in a continuum and explores the potentials of integrating AI applications and techniques in architectural education, and how architectural design practice may benefit from it. Consequently, it provides insights into how architectural design education may transform itself considering the future impact of AI on the Architecture Engineering Construction (AEC) industry.