توسعه مدلی برای تخمین و دسته‌بندی عملکرد آموزشی دانشجویان کارشناسی با استفاده از ترکیب شبکه‌های عصبی چندلایه (مطالعه موردی: دانشگاه قم)

نویسندگان

1 گروه مهندسی صنایع؛ دانشکده فنی و مهندسی؛ دانشگاه قم؛ قم، ایران

2 گروه مهندسی صنایع، دانشکده فنی و مهندسی، دانشگاه قم، قم، ایران

3 دانشکده مهندسی صنایع، دانشگاه علم و صنعت، تهران، ایران

چکیده

رشد و پیشرفت دانشجویان به‌عنوان سرمایه‌های آینده کشور همواره مورد توجه و اهمیت نظام آموزش و پرورش بوده است. چه بسیار دانشجویانی که خلاقیت و استعدادشان شکوفا شده و در مقابل، سالیانه با تعداد کثیری از دانشجویان مواجه هستیم که استعدادهایشان تلف شده و از موفقیت دور مانده‌اند. امروزه، با افزایش دانشجویان در مقاطع مختلف دانشگاهی و تنوع رشته‌های تحصیلی، لزوم هدایت صحیح دانشجویان بیشتر از پیش احساس می‌شود. بدین‌منظور، داده‌کاوی آموزشی در سال‌های اخیر مورد توجه ویژه مسئولان نظام آموزش و پرورش قرار گرفته است. تاکنون روش‌ها و تکنیک‌های دسته‌بندی متنوعی در حوزه داده‌کاوی و یادگیری ماشین به‌منظور دسته‌بندی و پیش‌بینی عملکرد دانشجویان به‌کار رفته است. اما این دسته‌بندهای منفرد برای پیش‌بینی عملکرد در فرایند آموزش، دارای محدودیت‌هایی از قبیل پیچیدگی و عدم ثبات است. برای مقابله با این مشکل، دسته‌بندهای جمعی به‌عنوان روش‌های نوین و کارآمد مطرح می‌شوند. سیستم‌های دسته‌بندی جمعی نتایج چندین دسته‌بندی منفرد را ترکیب می‌کنند و مدلی با عملکرد بهتر ارائه می‌دهند. در این پژوهش یک دسته‌بند جمعی جدید با استفاده از شبکه‌های عصبی چندلایه و خوشه‌بندی SOM به‌منظور تخمین و دسته‌بندی معدل دانشجویان دوره کارشناسی ارائه‌ شده است. همچنین، از روش ترکیبی میانگین‌گیری و رأی اکثریت برای ترکیب نتایج دسته‌بندهای منفرد استفاده‌ شده است. نتایج ارزیابی بر روی داده‌های واقعی دانشگاه نشان می‌دهد که مدل پیشنهادی ارائه‌شده در این پژوهش دقت و کارایی بیشتری نسبت به روش‌های دسته‌بندی منفرد مشهور و پرکاربرد دارد. همچنین، مدل پیشنهادی در مقایسه با روش‌های جمعی معروف، عملکرد بهتری در دسته‌بندی معدل دانشجویان داشته است.

کلیدواژه‌ها


عنوان مقاله [English]

Development of a model for estimating and classifying the educational performance of undergraduate students using a combination of multilayer neural networks (Case study: Qom University)

نویسندگان [English]

  • Hadiyeh Mahdavi 1
  • Jalal Rezaei Noor 2
  • Mohammad Amini 3
چکیده [English]

Students’ growth and development have always been considered important by the education system as they are the future assets of their country. Although many students can flourish their talents and creativity, we are faced with a large number of students each year whose talents are squandered leading them to a position far from success. Today, with the more students entering in different academic levels and the variety of study disciplines, the necessity to properly guide students is felt more than ever. For this purpose, educational data mining has received special attention from the educational system’s officials in recent years. So far, various classification methods and techniques in data mining and machine learning have been used to predict student performance. However, these individual classifiers have limitations such as complexity and instability for predicting performance in the education process. To tackle this problem, ensemble classification has been proposed as a new and efficient method. Ensemble classification systems combine the results of several individual classifiers to provide a model with better performance. In this paper, a new Ensemble classification system is presented using multilayer neural networks and SOM clustering in order to estimate and classify the grade point average of undergraduate students. In addition, we used averaging and majority voting as combination methods for aggregating the results of individual classifiers. Evaluation results on real university data show that our proposed ensemble system provides better accuracy and performance compared to prevalent individual classification methods. Also, the proposed ensemble system obviously outperforms other popular ensemble methods in classifying students’ GPA.

کلیدواژه‌ها [English]

  • Clustering
  • Educational Data Mining
  • Ensemble Classification
  • Neural Networks
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