بررسی عوامل موثر بر موفقیت دانش‌آموزان پایه دهم با استفاده از روش‌های داده‌کاوی: مطالعه موردی آموزشگاه‌های شهر کاشمر

علی‌پور فرگی, اعظم; سعادت فر, حمید

doi:10.22052/scj.2025.255049.1257

بررسی عوامل موثر بر موفقیت دانش‌آموزان پایه دهم با استفاده از روش‌های داده‌کاوی: مطالعه موردی آموزشگاه‌های شهر کاشمر

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مهندسی کامپیوتر، دانشکده مهندسی برق و کامپیوتر، دانشگاه بیرجند، بیرجند، ایران.

10.22052/scj.2025.255049.1257

چکیده

موفقیت دانش‌آموزان در هر پایه تحصیلی یکی از اهداف مهم و قابل توجه در وزارت آموزش و پرورش است. در این میان، انجام برنامه‌ریزی مناسب برای موفقیت تحصیلی دانش‌آموزان پایه دهم، آن هم به دلیل انتخاب رشته تحصیلی در این پایه، از اهمیت بیشتری برخوردار است. همین‌طور تصمیم‌گیری صحیح، مستلزم گردآوری اطلاعات جامعی از تمام ابعاد زندگی یک دانش‌آموز است. از اینرو ابزار چرخ تعادل زندگی در شش بخش جسمی، مالی، فکری، عاطفی، اجتماعی و معنوی می‌تواند راهکار مناسبی برای این هدف باشد. در این مقاله ابتدا مجموعه داده‌ای با کمک اطلاعات چرخ تعادل زندگی، جمعیت‌شناختی و پیشینه آموزشی دانش‌آموزان پایه دهم آموزشگاه‌های شهر کاشمر از طریق پرسش‌نامه ایجاد می‌شود. در ادامه با استفاده از روش‌‌های بسته‌بندی و فیلتر، ویژگی‌های موثر انتخاب و الگوریتم‌های شبکه عصبی پرسپترون چندلایه، درخت تصمیم J48، جنگل تصادفی ، طبقه‌بند بیز ساده، طبقه‌بندی SVM و KNN با هدف پیش‌بینی موفقیت دانش‌آموزان در پایه دهم مورد آموزش قرار می‌گیرند. مقایسه نتایج نشان می‌دهد که الگوریتم بسته‌بندی توانسته ویژگی‌های تاثیرگذارتری را نسبت به سایر الگوریتم‌ها انتخاب کند به طوری که ویژگی‌های پول‌توجیبی، میزان مطالعه، جنسیت، رشته تحصیلی، تحصیلات پدر، تحصیلات مادر و بعد روحی بیشترین تاثیر را دارند. همین‌طور الگوریتم پرسپترون چندلایه با دقت 85.62 درصد و الگوریتم بیز ساده با دقت 86.25 درصد، به ترتیب قبل و بعد از فرآیند انتخاب ویژگی بهترین عملکرد را از خود نشان داده‌اند.

کلیدواژه‌ها

موضوعات

داده کاوی

عنوان مقاله [English]

Investigating the factors affecting the success of 10th grade students using data mining methods: a case study of educational institutions in Kashmar

نویسندگان [English]

Azam Alipour Fargi
Hamid Saadatfar

Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran.

چکیده [English]

The success of students at every educational level is one of the significant goals of the Ministry of Education. In the meantime, proper planning for the academic success of tenth-grade students is more valuable because of the choice of study field in this grade. Likewise, making a correct decision requires gathering comprehensive information from all aspects of a student's life. Therefore, the life balance wheel tool in six dimensions -physical, financial, intellectual, emotional, social, and spiritual- can be a suitable solution. In this article, first, a dataset is created using the life balance wheel, along with demographic and educational background information of 10th-grade students from educational institutions in Kashmar, collected through a questionnaire. Then, well-known Wrapper and Filter methods are employed for selection of affective features and data mining algorithms including Multilayer Perceptron, J48 decision tree, Random Forest, Naive Bayes, SVM, and KNN are used to predict the success of students in 10th grade. The results show that the Wrapper algorithm can select more impact features than others; specifically, the features of pocket money, study time, gender, field of study, father’s education, mother’s education, and spiritual dimension have the most impact. Also, the Multilayer Perceptron algorithm with 85.62% accuracy and the Naïve Bayes algorithm with 86.25% accuracy demonstrated the best performance before and after the feature selection process, respectively.

کلیدواژه‌ها [English]

Data mining
Feature selection
GPA prediction
Life balance wheel
Academic success

مراجع

[1] S. Hussain and M. Q. Khan, “Student-Performulator: Predicting Students’ Academic Performance at Secondary and Intermediate Level Using Machine Learning,” Ann. Data Sci., vol. 10, no. 3, pp. 637-655, 2023, doi: 10.1007/s40745-021-00341-0.
[2] E. C. Abana, “A Decision Tree Approach for Predicting Student Grades in Research Project Using Weka,” Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 7, pp. 314-319, 2019, doi: 10.14569/IJACSA.2019.0100739.
[3] O. Khalaf Beigi, S. A. Bashiri Mosavi, and S. Gharloghi, “Applying Character-Level Neural Network-Based Sentiment Analysis Model on Persian Comments of the Social Media-Online Store Platforms,” Soft Comput. J., vol. 11, no. 2, pp. 118-133, 2023, doi: 10.22052/scj.2023.248311.1094 [In Persian].
[4] M. Eftekharian and A. Nodehi, “Breast Cancer Diagnosis and Classification Improvement Based on Deep Learning and Image Processing,” Soft Comput. J., vol. 12, no. 1, pp. 22-26, 2023, doi: 10.22052/scj.2023.246416.1067.
[5] J. D. Dresser, K. M. Whitfield, L. J. Kremer, and K. J. Wilby, “Exploring How Postmillennial Pharmacy Students Balance Life Priorities and Avoid Situations Known to Deplete Resilience,” Amer. J. Pharm. Educ., vol. 85, no. 4, p. 8410, 2021, doi: 10.5688/ajpe8369.
[6] E. Alyahyan and D. Duştegor, “Predicting Academic Success in Higher Education: Literature Review and Best Practices,” Int. J. Educ. Technol. High. Educ., vol. 17, p. 3, 2020, doi: 10.1186/s41239-020-0177-7.
[7] S. Alturki and N. Alturki, “Using Educational Data Mining to Predict Students’ Academic Performance for Applying Early Interventions,” J. Inf. Technol. Educ. Innov. Pract., vol. 20, pp. 121-137, 2021, doi: 10.28945/4835.
[8] B. Al Breiki, N. Zaki, and E. A. Mohamed, “Using Educational Data Mining Techniques to Predict Student Performance,” in Proc. Int. Conf. Elect. Comput. Technol. Appl. (ICECTA), 2019, pp. 1-5, doi: 10.1109/ICECTA48151.2019.8959676.
[9] A. E. Tatar and D. Dustegor, “Prediction of Academic Performance at Undergraduate Graduation: Course Grades or Grade Point Average?,” Appl. Sci., vol. 10, no. 14, p. 4967, 2020, doi: 10.3390/app10144967.
[10] M. A. Al-Barrak and M. Al-Razgan, “Predicting Students Final GPA Using Decision Trees: A Case Study,” Int. J. Inf. Educ. Technol., vol. 6, no. 7, pp. 528-533, 2016, doi: 10.7763/IJIET.2016.V6.745.
[11] H. Turabieh, “Hybrid Machine Learning Classifiers to Predict Student Performance,” in Proc. 2nd Int. Conf. New Trends Comput. Sci. (ICTCS), 2019, pp. 1-6, doi: 10.1109/ICTCS.2019.8923093.
[12] C. Jalota and R. Agrawal, “Analysis of Educational Data Mining Using Classification,” in Proc. Int. Conf. Mach. Learn. Big Data Cloud Parallel Comput. (COMITCon), 2019, pp. 243-247, doi: 10.1109/COMITCon.2019.8862214.
[13] N. Alangari and R. Alturki, “Predicting Students Final GPA Using 15 Classification Algorithms,” Romanian J. Inf. Sci. Technol., vol. 23, no. 3, pp. 238-249, 2020.
[14] A. Siddique et al., “Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers,” Appl. Sci., vol. 11, no. 24, p. 11845, 2021, doi: 10.3390/app112411845.
[15] M. Kumar, C. Sharma, S. Sharma, N. Nidhi, and N. Islam, “Analysis of Feature Selection and Data Mining Techniques to Predict Student Academic Performance,” in Proc. Int. Conf. Decis. Aid Sci. Appl. (DASA), 2022, pp. 1013-1017, doi: 10.1109/DASA54658.2022.9765236.
[16] D. Hooshyar, M. Pedaste, and Y. Yang, “Mining Educational Data to Predict Students’ Performance Through Procrastination Behavior,” Entropy, vol. 22, no. 1, p. 12, 2019, doi: 10.3390/e22010012.
[17] M. H. B. Roslan and C. J. Chen, “Predicting Students’ Performance in English and Mathematics Using Data Mining Techniques,” Educ. Inf. Technol., vol. 28, no. 2, pp. 1427-1453, 2023, doi: 10.1007/s10639-022-11259-2.
[18] A. Alhassan, B. Zafar, and A. Mueen, “Predict Students’ Academic Performance Based on Their Assessment Grades and Online Activity Data,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 4, pp. 231-239, 2020, doi: 10.14569/IJACSA.2020.0110425.
[19] C.-C. Kiu, “Data Mining Analysis on Student’s Academic Performance Through Exploration of Student’s Background and Social Activities,” in Proc. 4th Int. Conf. Adv. Comput. Commun. Autom. (ICACCA), 2018, pp. 1-5, doi: 10.1109/ICACCAF.2018.8776809.
[20] B. K. Francis and S. S. Babu, “Predicting Academic Performance of Students Using a Hybrid Data Mining Approach,” J. Med. Syst., vol. 43, no. 6, p. 162, 2019, doi: 10.1007/s10916-019-1295-4.
[21] D. T. Ha, P. T. T. Loan, C. N. Giap, and N. T. L. Huong, “An Empirical Study for Student Academic Performance Prediction Using Machine Learning Techniques,” Int. J. Comput. Sci. Inf. Secur., vol. 18, no. 3, pp. 75-82, 2020.
[22] W. F. W. Yaacob, S. A. M. Nasir, and N. M. Sobri, “Supervised Data Mining Approach for Predicting Student Performance,” Indones. J. Electr. Eng. Comput. Sci., vol. 16, no. 3, pp. 1584-1592, 2019, doi: 10.11591/ijeecs.v16.i3.pp1584-1592.
[23] F. J. Kaunang and R. Rotikan, “Students’ Academic Performance Prediction Using Data Mining,” in Proc. 3rd Int. Conf. Informatics Comput. (ICIC), 2018, pp. 1-5, doi: 10.1109/IAC.2018.8780547.
[24] T. Ahajjam et al., “Predicting Students’ Final Performance Using Artificial Neural Networks,” Big Data Min. Anal., vol. 5, no. 4, pp. 294-301, 2022, doi: 10.26599/BDMA.2021.9020030.
[25] M. Najafi, M. Afzali, and M. Moradi, “Use Data Mining to Identify Factors Affecting Students’ Academic Failure,” Intell. Multimedia Process. Commun. Syst. (IMPCS), vol. 2, no. 1, pp. 23-33, 2021, dor: 20.1001.1.27832570.1399.1.2.4.0 [In Persian].
[26] S. Aydogdu, “Predicting Student Final Performance Using Artificial Neural Networks in Online Learning Environments,” Educ. Inf. Technol., vol. 25, no. 3, pp. 1913-1927, 2020, doi: 10.1007/s10639-019-10053-x.
[27] H. Zeineddine, U. Braendle, and A. Farah, “Enhancing Prediction of Student Success: Automated Machine Learning Approach,” Comput. Electr. Eng., vol. 89, p. 106903, 2021, doi: 10.1016/j.compeleceng.2020.106903.
[28] Z. Shao, H. Sun, X. Wang, and Z. Sun, “An Optimized Mining Algorithm for Analyzing Students’ Learning Degree Based on Dynamic Data,” IEEE Access, vol. 8, pp. 113543-113556, 2020, doi: 10.1109/ACCESS.2020.3001749.
[29] Z. M. Taras and V. I. Mesyura, “Application of the Wheel of Life Balance to Time Management Software,” in Proc. Int. Conf. Develop. Appl. Syst. (DAS), 2022, pp. 178-185, doi: 10.1109/DAS54948.2022.9786194.
[30] G. M. Robertson, Life Balance Assessment and Action Planning Guide. New York, NY, USA: Robertson Consulting, 2002.
[31] F. Zare Mehrjardi, M. Yazdian-Dehkordi, and A. Latif, “Evaluating Classical Machine Learning and Deep-Learning Methods in Sentiment Analysis of Persian Telegram Message,” Soft Comput. J., vol. 11, no. 1, pp. 88-105, 2022, doi: 10.22052/SCJ.2022.246281.1058 [In Persian].
[32] H. Al-Shehri et al., “Student Performance Prediction Using Support Vector Machine and K-Nearest Neighbor,” in Proc. IEEE 30th Can. Conf. Elect. Comput. Eng. (CCECE), 2017, pp. 1-4, doi: 10.1109/CCECE.2017.7946847.
[33] M. Yagci, “Educational Data Mining: Prediction of Students’ Academic Performance Using Machine Learning Algorithms,” Smart Learn. Environ., vol. 9, no. 1, p. 11, 2022, doi: 10.1186/s40561-022-00192-z.
[34] I. Sandoval-Palis, D. Naranjo, R. Gilar-Corbi, and T. Pozo-Rico, “Neural Network Model for Predicting Student Failure in the Academic Leveling Course of Escuela Politecnica Nacional,” Front. Psychol., vol. 11, p. 515531, 2020, doi: 10.3389/fpsyg.2020.515531.