مدیریت اطلاعات

تحلیل آینده‌نگر عملکرد تحصیلی دانش‌آموزان بر مبنای ابعاد شخصیت با یادگیری ماشین خودکار (Auto ML)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استاد، گروه مدیریت فناوری، دانشکده مدیریت صنعتی و فناوری، دانشکدگان مدیریت، دانشگاه تهران، تهران، ایران

2 دانشیار، گروه مدیریت فناوری اطلاعات، دانشکده مدیریت صنعتی، دانشگاه علامه طباطبائی، تهران، ایران

3 کارشناس ارشد، گروه مدیریت فناوری اطلاعات، دانشکده مدیریت صنعتی و فناوری، دانشکدگان مدیریت، دانشگاه تهران، تهران، ایران

10.22034/aimj.2023.194441

چکیده

پیش‌بینی عملکرد دانش‌آموزان برای والدین و معلمان آن‌ها از اهمیت بسزایی برخوردار است. در سال‌های اخیر، برای ارزیابی داده‌های آموزشی و عوامل تأثیرگذار بر عملکرد تحصیلی، از تحلیل یادگیری استفاده شده است. عوامل متعددی از جمله ویژگی‌های فردی، خانوادگی، اجتماعی و محیطی بر این موضوع تأثیرگذار هستند. یکی از مهم‌ترین عوامل فردی، شخصیت است که تأثیر این عامل را بر عملکرد تحصیلی بررسی خواهیم کرد. برای این منظور، داده‌های پنج دوره از دانش‌آموزان مدرسه‌ای در منطقه 3 تهران را تحلیل می‌کنیم. پژوهش‌های انجام‌شده در این حوزه بیشتر به هم‌بستگی ابعاد مختلف پرداخته‌اند، اما هدف اصلی این پژوهش، پیش‌بینی عملکرد دانش‌آموزان به‌منظور شناسایی دانش‌آموزان در معرض افت تحصیلی است که با علم خانواده به این موضوع برای بهبود آنان اقدام کنند. متدولوژی استفاده‌شدهCRISP-DM  است. برای تحلیل، از تکنیک داده‌کاوی یادگیری ماشین خودکار (Auto ML) استفاده شده است. براساس معیار Accuracy، طبقه‌بندی در مقایسه با روش شبکه عصبی مصنوعی، با دقت بیشتری انجام شده است. طبق یافته‌های این پژوهش، بعد وظیفه‌شناسی در پیش‌بینی عملکرد دانش‌آموزان، بیشترین تأثیر و بعد روان‌رنجوری کمترین میزان تأثیر را داشته است.

کلیدواژه‌ها

عنوان مقاله [English]

Prospective Analysis of Students' Academic Performance based on Personality Dimensions with Automatic Machine Learning (Auto ML)

نویسندگان [English]

  • Babak Sohrabi 1
  • Iman Raeesi Vanani 2
  • Ali Norouzi 3

1 Prof., Department of IT, Faculty of Industrial Management and Technology, College of Management, University of Tehran, Tehran, Iran

2 Associate Prof., Department of IT, Faculty of Industrial Management, Allameh Tabataba'i University, Tehran, Iran

3 MSc., Department of IT, Faculty of Industrial Management and Technology, College of Management, University of Tehran, Tehran, Iran

چکیده [English]

Predicting students’ performance is important for their parents and their teachers. In recent years, learning analytics has been used to evaluate educational data and factors affecting academic performance. Numerous factors, including individual, family, social and environmental characteristics affect this issue. One of the most important individual factors is personality, which we will examine the impact of this factor on academic performance. For this purpose, we analyze the data of a school students in District One of Tehran. Researches in this field has focused on the correlation of different dimensions, but the main purpose of this study is predicting student performance in order to identify students at risk of academic failure, which with the knowledge of the family to improve They take action. The methodology used is CRISP-DM. The Auto ML data mining technique was used for analysis. According to the Accuracy criterion, the classification is more accurate than the artificial neural network method. According to the findings of this study, the conscientious dimension had the greatest effect and the neurotic dimension had the least effect on predicting students' performance.

کلیدواژه‌ها [English]

  • Learning Analytics
  • Personality
  • Academic Performance
  • Academic failure
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مدیریت اطلاعات
دوره 8، شماره 2 - شماره پیاپی 15
اسفند 1401
صفحه 131-149
  • تاریخ دریافت: 07 دی 1401
  • تاریخ بازنگری: 03 اسفند 1401
  • تاریخ پذیرش: 24 بهمن 1402