ارائه رویکردهای مبتنی بر یادگیری ماشین سنتی و رگرسیونی روی پیش بینی عملکرد دانش آموزان مؤسسات عالی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیارگروه برنامه ریزی درسی، واحد مرند؛ دانشگاه آزاد اسلامی؛مرند؛ایران.

2 دانشجو دکتری تخصصی گزوه برنامه ریزی درسی دانشگاه آزاد اسلامی واحد مرند

10.22034/naes.2024.451241.1385

چکیده

مقدمه: پیش‌بینی عملکرد دانش‌آموزان به یک خواسته مبرم در اکثر نهادها و مؤسسات آموزشی و آموزشی عالی تبدیل شدهاست. این مسئله برای کمک به دانشآموزان در معرض خطر و اطمینان از حفظ آنها، ارائه منابع و تجربیات عالی یادگیری و بهبود رتبه و شهرت مؤسسات ضروری است. با این حال، دستیابی به آن برای مؤسسات استارت‌آپی که سوابق کوچکی برای تجزیه و تحلیل دارند، ممکن است دشوار باشد. هدف از پژوهش حاضر ارائه رویکردهای مبتنی بر یادگیری ماشین سنتی و رگرسیونی روی پیش بینی عملکرد دانش آموزان بود.
روش: پژوهش حاضر از نوع پژوهش‌های کیفی بوده و از لحاظ هدف کاربردی و از لحاظ روش از نوع پژوهش‌های تحلیلی آزمایشی بود. در این پژوهش از روش‌های رگرسیون خطی، درخت تصمیم، جنگل تصادفی و ماشین بردار پشتیبانی استفاده شد. در این بخش پس از معرفی محیط پیاده‌سازی، پارامترهای شبیه‌سازی معرفی شد. در ادامه نیز با معرفی معیارهای ارزیابی کارایی روش پیشنهادی بر اساس معیارهای ارزیابی موصوف بررسی و یافته‌ها با دیگر روش‌های مشابه مقایسه شد. که برای این مقایسات از رویکرد یادگیری عمیق مبتنی بر شبکه کانولوشنی عمیق و دیگر رویکردهای یادگیری عمیق استفاده می‌شود. در این تحقیق همچنین از مجموعه داده‌های مدرسه پسرانه دکتر هشت رودی که جزو 10 مؤسسه برتر در تهران می‌باشد استفاده شد.
نتیجه‌گیری: نتایج اصلی این مطالعه کارایی جنگل تصادفی را در آموزش داده‌های کوچک و در تولید نرخ آزمون دقیق نشانمیدهد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Presenting approaches based on traditional machine learning and regression on predicting the performance of students of higher institutions

نویسندگان [English]

  • Shahram Ranjdoust 1
  • Zinat Khezridenkhe 2
1 Associate Professor Department of curriculum planinng,Marand branch,Islamic Azad University,Marand,Iran.
2 doctoral student of crriculum planning department of islamic azad university marand branch
چکیده [English]

Abstract
Introduction: Predicting student performance has become an urgent demand in most educational and higher education institutions. This is essential to help at-risk students and ensure their retention, provide excellent learning resources and experiences, and improve the ranking and reputation of institutions. However, this may be difficult to achieve for start-up organizations with small records to analyze. The purpose of the current research was to provide approaches based on traditional machine learning and regression on predicting students' performance.
Method: The current research was of the qualitative research type and applied in terms of purpose and experimental analytical research in terms of method. Linear regression, decision tree, random forest and support vector machine methods were used in this research. In this section, after introducing the implementation environment, simulation parameters were introduced. In the following, by introducing the efficiency evaluation criteria of the proposed method, based on the described evaluation criteria, the findings were compared with other similar methods. For these comparisons, deep learning approach based on deep convolutional network and other deep learning approaches are used. In this research, the data collection of Dr. Hasht Roudi boys' school, which is among the top 10 institutions in Tehran, was used. The data of this institution are publicly available and can be downloaded through the GitHub site. Further investigation has been done on these data. The figure below shows the frequency of features in the dataset. that these features are considered as and on models.

کلیدواژه‌ها [English]

  • Keywords: prediction of student performance
  • regression system
  • machine learning
  • higher institutions
[1] Nieto Y, Gacía-Díaz V. Montenegro CC. González C, Crespo RG. "Usage of machine learning for strategic decision making at higher educational institutions, " IEEE Access, 2019.
[2] Alsalman YS, Halemah NKA, AlNagi ES, Salameh W. "Using and Decision Tree Artificial Neural Network to Predict Students Academic Performance, " in 2019 10th International Conference on Information and Communication Systems (ICICS), 2019; 104-109: IEEE.
[3] Helal S. "Predicting academic performance by considering student heterogeneity, " Knowledge-Based Systems, 2018;  161: 134-146.
[4] Daud A, Aljohani NR, Abbasi RA, Lytras MD, Abbas F, Alowibdi JS. "Predicting student performance using advanced learning analytics, " in Proceedings of the 26th international conference on world wide web companion, 2017; 415-421: International World Wide Web Conferences Steering Committee.
[5] Ibrahim Z, Rusli D. "Predicting students’ academic performance: comparing artificial neural network and linear regression, " in 21st Annual SAS Malaysia Forum, 5th September, 2007.
[6] LIU Ty, Xiu L. "Finding out reasons for low completion in MOOC environment: an explicable approach using hybrid data mining methods," DEStech Transactions on Social Science, Education and Human Science, no. meit, 2017.
[7] Wang W, Yu H, Miao C. "Deep model for dropout prediction in MOOCs, " in Proceedings of the 2nd International Conference on Crowd Science and Engineering, 2017; 26-32: ACM.
[8] Al-Shabandar R, Hussain A, Laws A, Keight R, Lunn J, Radi N. "Machine learning approaches to predict learning outcomes in Massive open online courses, " in 2017 International Joint Conference on Neural Networks (IJCNN), 2017; 713-720: IEEE.
[9] Al-Shabandar R, Hussain A, Laws A, Keight R, Lunn J. "Towards the differentiation of initial and final retention in massive open online courses, " in International Conference on Intelligent Computing, 2017; 26-36: Springer.
[10] Whitehill J, Mohan K, Seaton D, Rosen Y, Tingley D. "Delving deeper into MOOC student dropout prediction, " arXiv preprint arXiv:1702.06404, 2017.
[11]   Nagrecha S, Dillon JZ, Chawla NV. "MOOC dropout prediction: lessons learned from making pipelines interpretable, " in Proceedings of the 26th International Conference on World Wide Web Companion, 2017; 351-359: International World Wide Web Conferences Steering Committee.
[12] Xing W, Chen X, Stein J, Marcinkowski M. "Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization, " Computers in human behavior, 2016; 58: 119-129.
[13] Robinson C, Yeomans M, Reich J, Hulleman C, Gehlbach H. "Forecasting student achievement in MOOCs with natural language processing, " in Proceedings of the sixth international conference on learning analytics & knowledge, 2016; 383-387: ACM.
[14] Liang J, Li C, Zheng L. "Machine learning application in MOOCs: Dropout prediction, " in 2016 11th International Conference on Computer Science & Education (ICCSE), 2016; 52-57: IEEE.
[15] Crossley S, Paquette L, Dascalu M, McNamara DS, Baker RS, "Combining click-stream data with NLP tools to better understand MOOC completion, " in Proceedings of the sixth international conference on learning analytics & knowledge, 2016; 6-14: ACM.
[16] Whitehill J, Williams J, Lopez G, Coleman C, Reich J. "Beyond prediction: First steps toward automatic intervention in MOOC student stopout, " Available at SSRN 2611750, 2015.
[17] Boyer S,  Veeramachaneni K. "Transfer learning for predictive models in massive open online courses, " in International conference on artificial intelligence in education, 2015; 54-63: Springer.
[18] Chaplot DS, Rhim E, Kim J. "Predicting Student Attrition in MOOCs using Sentiment Analysis and Neural Networks, " in AIED Workshops, 2015; (53): 54-57.
[19]   Coleman CA, Seaton DT, Chuang I. "Probabilistic use cases: Discovering behavioral patterns for predicting certification, " in Proceedings of the Second (2015) ACM Conference on Learning@ Scale, 2015; 141-148: ACM.
[20] Kizilcec RF, Halawa S. "Attrition and achievement gaps in online learning, " in Proceedings of the second (2015) ACM conference on learning@ scale, 2015; 57-66: ACM.
[21] Fei M, Yeung DY. "Temporal models for predicting student dropout in massive open online courses, " in 2015 IEEE International Conference on Data Mining Workshop (ICDMW), 2015; 256-263: IEEE.
[22] Kloft M, Stiehler F, Zheng Z, Pinkwart N. "Predicting MOOC dropout over weeks using machine learning methods, " in Proceedings of the EMNLP 2014 workshop on analysis of large scale social interaction in MOOCs, 2014, pp. 60-65.
[23]   Murphy KP. Machine learning: a probabilistic perspective. MIT press, 2012.