نوع مقاله : مقاله پژوهشی

نویسندگان

1 عضو هیات علمی، گروه ایمنی شناسی، دانشکده پزشکی، دانشگاه علوم پزشکی جندی شاپور اهواز، اهواز، ایران

2 عضو هیأت علمی، گروه علوم تربیتی، دانشکده علوم تربیتی و روانشناسی، دانشگاه شهید چمران اهواز، اهواز، ایران.

3 کارشناس ارشد آموزش پزشکی، دانشگاه علوم پزشکی شیراز، شیراز ، ایران.

4 عضو هیأت علمی، گروه انگل شناسی، دانشکده پزشکی، دانشگاه علوم پزشکی جندی شاپور اهواز، اهواز، ایران.

چکیده

افت تحصیلی یکی از مشکلات آموزش عالی است که هر سال منجر به هدر رفتن میزان زیادی از امکانات، منابع واستعدادهای بالقوه  انسانی می­شود و تأثیرات غیرقابل جبرانی را در ابعاد فردی، سازمانی و اجتماعی بر جای می­گذارد. پیش بینی افت تحصیلی می­تواند گامی مفید جهت پیشگیری از وقوع و آسیب­های ناشی از آن باشد. این مطالعه با هدف شناسایی و پیش بینی عوامل مؤثر بر افت تحصیلی دانشجویان دانشگاه علوم پزشکی جندی شاپور اهواز انجام شده است. پژوهش حاضر یک پژوهش همبستگی از نوع پیش بین است که با استفاده از روش تحلیل رگرسیون لوجستیک و با هدف پیش بینی عضویت گروهی (دانشجویان مشروط) و دستیابی به یک معادله لوجیت طراحی شده است. نمونه مورد بررسی 82 دانشجوی مشروط و 82 دانشجوی غیر مشروط با ویژگی­های همسان با گروه مشروط بودند که به عنوان گروه شاهد وارد مطالعه شدند. آزمون رگرسیون لجستیک به روش گام به گام نشان داد که میزان اثر متغیرهای معدل دیپلم (646/۰)، سلامت معنوی (.065)، خستگی تحصیلی (156/۰)، سبک یادگیری واگرا (048/0- )، فشارزاهای شرایط تحصیلی (079/0) و بی علاقگی تحصیلی (178/۰-) می باشد و روی همرفته این 6 متغیر 2/76 درصد عضویت در گروه مشروطین در دانشجویان را پیش بینی می­کنند. یک مدل رگرسیون لجستیک برای پیش بینی افت تحصیلی و شناسایی دانشجویان در معرض خطر شکست تحصیلی یک ابزار کارآمد برای تصمیم­گیری مناسب در دانشگاه­هاست. با شناسایی عوامل مؤثر در افت تحصیلی دانشجویان می­توان از بروز افت تحصیلی پیشگیری کرد.

کلیدواژه‌ها

عنوان مقاله [English]

Identification of the factors predicting students' academic failure in Ahvaz Jundishapur University of medical sciences in 2019

نویسندگان [English]

  • Abdolhussein Shakurnia 1
  • Hussein Elhampour 2
  • Maryam Aslami 3
  • Mahdi Tavalla 4

1 Faculty member, Department of Immunology. Faculty of medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.

2 Faculty member, Department of Education, Faculty of Education and Psychology, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

3 M.A. in medical education, Shiraz University of Medical Sciences, Shiraz, Iran.

4 Faculty member, Department of Parasitology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.

چکیده [English]

Academic failure is one of the problems of higher education, which leads to the loss of a large amount of facilities, resources and potential human talents every year, and leaves irreparable effects on individual, organizational and social dimensions. Predicting academic failure can be a useful step in preventing its negative consequences and the resulting damage. The aim of this study was to identify and predict the factors affecting the academic failure of the students at Ahvaz Jundishapur University of Medical Sciences. The present study is a predictive correlational study that was designed using Logistics regression analysis method with the aim of predicting group membership (students on academic probation) and achieving a Logit equation. The study sample consisted of 82 students on academic probation and 82 otherwise-matched students as a control group. Step-by-step logistics regression test showed that the effect of diploma grade point average (0.646), spiritual health (.065), academic fatigue (0.156), divergent learning style (-0.048), study condition stressors (0.079) and academic disinterest (-0.178) on academic failure, and in total, these 6 variables predict 76.2% of being put on academic probation.  A logistic regression model for predicting academic failure and identifying students at risk of academic failure is an effective tool for making informed decisions in universities. By identifying the factors influencing students' academic failure, it is possible to prevent academic failure in these students.
 

کلیدواژه‌ها [English]

  • Academic Failure
  • Medical Sciences Students
  • Logistic Regression
  • Medical education
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