نوع مقاله : مقاله علمی - پژوهشی

نویسندگان

گروه مهندسی صنایع، دانشکده مهندسی، دانشگاه سیستان و بلوچستان، زاهدان، ایران

10.22047/ijee.2022.265728.1812

چکیده

 با توجه به نقش دانشگاه‌ها در آموزش مهندسی، بررسی وضعیت نظام آموزشی و نقاط قوت و ضعف آن به منظور بهبود فرآیند آموزش مهندسی ضرورت دارد. در این تحقیق عوامل موثر بر افزایش سنوات تحصیلی دانشجویان رشته‌های مهندسی و وضعیت دانشجویانی که در سنوات مجاز، تحصیل خودرا به اتمام نمی‌رسانند، بررسی شده‌است. در ابتدا شاخص‌های تاثیرگذار بر افزایش سنوات تحصیلی دانشجویان مهندسی شناسایی و با استفاده از فن AHP اولویت‌بندی شد. نتایج رتبه‌بندی نشان‌داد معدل دروس پایه، معدل دروس اصلی، معدل دروس عمومی، تعداد نیمسال‌های مشروطی، معدل دروس اختیاری و تعداد واحد افتاده از نظر خبرگان بیشترین تاثیر را بر افزایش سنوات تحصیلی دانشجویان مهندسی دارند. سپس به ارائه الگویی برای پیش‌بینی افزایش سنوات تحصیلی با توجه به وضعیت تحصیلی دانشجویان رشته‌های مهندسی با استفاده از شبکه عصبی‌مصنوعی پرداخته‌شد. براساس نتایج شبکه عصبی عوامل تعداد واحد‌های افتاده، معدل دروس اصلی، معدل دروس پایه، تعداد نیمسال‌های مشروطی، مدت تاهل و میانگین معدل دروس ریاضی و فیزیک دبیرستان بیشترین اثرگذاری را بر افزایش سنوات تحصیلی دارند. در نهایت با مقایسه نتایج حاصل از روش AHP و شبکه عصبی، عامل‌های معدل دروس پایه و اصلی، تعداد نیمسال‌های مشروطی و تعداد واحدهای افتاده در هر دو روش عوامل با تاثیرگذاری بیشتر شناخته شدند که در‌حین تحصیل دانشجویان رشته‌های مهندسی باید توجه بیشتری به آنها شود.

کلیدواژه‌ها

عنوان مقاله [English]

Determining the effective factors in engineering education and predicting the increase of academic years with multi-criteria decision making and data mining approach (Artificial Neural Network)

نویسندگان [English]

  • Mohammad Reza Shahraki
  • fatemeh haghani

Industrial Engineering Department, Engineering Faculty, sisatan and Baluchestan University, Zahedan, Iran

چکیده [English]

Considering the role of universities in engineering education, it is necessary to study the status of the educational system and its strengths and weaknesses in order to improve the process of engineering education. In this research, the factors affecting the increase of academic years of engineering students and the status of students who do not complete their studies in the authorized years have been investigated. At first, the effective indicators on increasing the academic years of engineering students were identified and prioritized using the AHP technique. The ranking results show the average of basic courses, the average of main courses, the average of general courses, the number of conditional semesters, the average of optional courses and the number of units dropped have the most impact on increasing the academic years of engineering students. Then, a model was provided to predict the increase in academic years according to the educational status of engineering students using an artificial neural network. According to the results of the neural network, the factors of the number of units dropped, the average of the main courses, the average of the basic courses, the number of conditional terms, the duration of marriage and the average of the high school math and physics courses have the greatest impact on increasing academic years. Finally, by comparing the results of AHP method and neural network, the average factors of basic and main courses, the number of conditional terms and the number of units dropped in both methods were identified as the most effective factors that should be paid more attention while studying engineering students. 

کلیدواژه‌ها [English]

  • Engineering education
  • predicting
  • academic years
  • AHP
  • artificial neural network
  • university
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