نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی دکتری رشته مدیریت فناوری اطلاعات، دانشکده مدیریت و اقتصاد، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
2 استاد، گروه مدیریت فناوری اطلاعات، دانشکده مدیریت و اقتصاد، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران نویسنده مسئول : r.radfar@srbiau.ac.ir
3 دانشیار، گروه مدیریت فناوری اطلاعات، دانشکده مدیریت و اقتصاد، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
چکیده
هدف این تحقیق بررسی عوامل موثر در پیش بینی عملکرد تحصیلی دانشجویان مقطع کارشناسی در طبقه بندی چهار کلاسه می باشد. برای دستیابی به این هدف، مطالعه از روش داده کاوی کریسپ پیروی می کند. مجموعه داده ها از سیستم آموزشی ناد برای مقطع کارشناسی در دانشگاه شاهد برای ورودی سال های 1390 تا 1400 استخراج شده است. تعداد 1468 رکورد در داده کاوی استفاده شده است. ابتدا شاخص‌های مؤثر بر عملکرد تحصیلی دانشجویان استخراج شد. مدلسازی با استفاده از ابزار رپیدماینر9.9 انجام شد. برای بهبود عملکرد طبقه‌بندی و دقت پیش‌بینی رضایت‌بخش ، از ترکیبی از تجزیه و تحلیل مؤلفه اصلی همراه با الگوریتم های یادگیری ماشین و تکنیکهای انتخاب ویژگی و الگوریتم‌های بهینه‌سازی استفاده می‌کنیم. عملکرد مدل‌های پیش‌بینی با استفاده از اعتبارسنجی متقاطع 10 برابری تأیید شده است. نتایج نشان داد که الگوریتم درخت تصمیم بهترین الگوریتم در پیش‌بینی عملکرد دانشجویان با دقت 84.71 درصد است. این الگوریتم به درستی فارغ التحصیلی 77.88 درصد از دانشجویان عالی و 85.26 درصد از دانشجویان خوب و 84.69 درصد از دانشجویان متوسط و 85.96 درصد از دانشجویان ضعیف را بر اساس معدل نهایی پیش بینی کرد.متغیر معدل دیپلم بیشترین تأثیر را در پیش‌بینی عملکرد دانشجویان دارد.
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
Predicting students' performance using machine learning algorithms and educational data mining (a case study of Shahed University)
نویسندگان [English]
- Mozhdeh Salari 1
- Reza Radfar 2
- Mahdi Faghihi 3
1 Ph.D. Student of Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
2 Professor of Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran Corresponding Author: r.radfar@srbiau.ac.ir
3 Associate Professor of Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
چکیده [English]
Abstract
The purpose of this research is to investigate the effective factors in predicting the academic performance of undergraduate students in the classification of four classes. To achieve this goal, the study follows the CRISP data mining method. The data set was extracted from the NAD educational system for the bachelor's degree in Shahed University for the entry of the years 2011 to 2021. 1468 records were used in data mining. First, the effective features on students' academic performance were extracted. Modeling was done using Rapidminer9.9 tool. To improve classification performance and satisfactory prediction accuracy, we use a combination of principal component analysis combined with machine learning algorithms and feature selection techniques and optimization algorithms. The performance of the prediction models is verified using 10-fold cross-validation. The results showed that the decision tree algorithm is the best algorithm in predicting students' performance with an accuracy of 84.71%. This algorithm correctly predicted the graduation of 77.88% of excellent students, 85.26% of good students, 84.69% of medium students, and 85.96% of weak students based on the final GPA.
Introduction
The main problem in this research is to identify the factors that are effective in predicting the academic performance of undergraduate students in Shahed University. Choosing the best machine learning algorithm in predicting academic performance among different modeling methods based on validation and evaluation of models is another issue in the present research. The purpose of this research is to investigate the effective factors in predicting the academic performance of undergraduate students in Shahed University using educational data mining based on classification models.
Research questions
The main question in this research is what factors affect the prediction of undergraduate students' performance and improving their performance?
Sub questions
1- Which modeling algorithms have better results in predicting student performance?
2- What methods have been used to predict students' performance?
3- What is the validity of the developed model for Shahed University students?
2- Research background
1-2- Theoretical foundations
Educational data mining
The processing of educational data improves the prediction of student behavior and new approaches to educational policies (Capuano & Toti, 2019) (Viberg et al., 2018)
Academic performance
Academic performance of students means the extent to which they achieve educational goals (Banik & Kumar, 2019).
2-2- review of past studies
The highlighted cells in Table 1, based on past research, show the classification algorithms that have the most accuracy and effectiveness in predicting students' performance in the relevant research. The decision tree algorithm has been used the most in previous researches. The NB algorithm has been the most used in research after the decision tree. RF and ANN algorithms are next in use. After that, SVM and KNN algorithms have been used in research
Table 1. The results of research literature based on the use of classification algorithms
Data mining algorithm
DT
RF
NB
KNN
SVM
ANN
Line RL
LR
Accuracy
(Batool et al., 2023)
*
*
(Marjan et al., 2023)
*
*
*
*
*
*
(Abdelmagid & Qahmash, 2023)
*
*
*
*
(Manoharan et al., 2023)
*
*
*
*
*
(Alghamdi & Rahman, 2023)
*
*
*
99.34%
(Alboaneen et al., 2022)
*
*
*
*
*
(Yağcı, 2022)
*
*
*
*
*
70-75%
(Dabhade et al., 2021)
*
*
*
83.44%
(Najafi & etal,2021)
*
95%
(Soltani & etal,2021)
*
*
*
(Cruz-Jesus et al., 2020)
*
*
*
*
50-81%
(Sokkhey & Okazaki, 2020)
*
*
*
*
(Rebai et al., 2020)
*
*
(Jayaprakash et al., 2020)
*
*
*
(Zulfiker et al., 2020)
*
*
*
(Musso et al., 2020)
*
(Waheed et al., 2020)
*
85%
(Salal & Abdullaev, 2019)
*
*
*
*
*
(Turabieh, 2019)
*
*
*
*
(Xu et al., 2019)
*
*
*
(ghodoosi & etal,2019)
*
*
(fadavi & etal,2019)
*
95.84%
(Ajibade et al., 2019)
*
*
*
*
91.5%
(Ahmad & Shahzadi, 2018)
*
85%
(Hasani & Bazrafshan, 2018)
*
*
(Hussain et al., 2018)
*
*
*
*
(Umer et al., 2017)
*
*
*
*
*
(Khasanah, 2017)
*
*
(Asif et al., 2017)
*
(Hoffait & Schyns, 2017)
*
*
*
92.34%
(khosravi &etal,2017)
*
*
(Mueen et al., 2016)
*
*
*
86%
(Amrieh et al., 2015)
*
*
*
(Yehuala, 2015)
*
*
92.34%
(zahedi & etal,2015)
*
*
*
(Punlumjeak & Rachburee, 2015)
*
(Osmanbegović et al., 2014)
*
*
71%
(Shamloo & et al.,2014)
*
(Asadi & et al.,2013)
*
(Kabakchieva, 2013)
*
*
*
60-75%
(Oskouei & Askari, 2014)
*
*
*
*
96%
(Nghe et al., 2007)
*
*
present research
*
*
*
*
*
*
94.17%
3- Method
This study follows the popular training data mining method CRISP. The data collection of Nad educational system for bachelor's degree in non-medical fields of Shahed University has been extracted from 2011 to 2021. We used the Label Encoder technique to encode the features. In this research, C4.5 and ID3 decision tree classification algorithms, random forest, Naïve Bayes, k-nearest neighbor and artificial neural network and gradient enhanced tree were used to analyze and classify students and predict the final GPA. Modeling was done using RapidMiner 9.9. To improve the classification performance and solve the misclassification problem, we use a combination of principal component analysis and feature selection techniques and optimization algorithms. In this research, prediction accuracy was evaluated using 10-fold cross-validation method for all algorithms. Also, different algorithms were compared using the analytical descriptive method and based on evaluation criteria, and the best prediction model was introduced in this research.
4-Data analysis
4-1 Introduction
The best model is the model that has the best values for the selected performance measurement criteria(Lever et al., 2016). Figure 1 is a graph that compares the accuracy of the algorithms used in this research.
Figure 1. Comparative chart of the accuracy of the algorithms
According to Table 2, the DTC4.5 algorithm is able to predict the class of 1235 objects out of 1458, which gives it an accuracy value of 84.71%.
Table 2. Confusion matrix of DT C4.5-GI&OSE research model
precision
Students with poor performance
Students with average performance
Students with good performance
Students with excellent performance
78.64%
0
0
22
81
Prediction 1
78.67%
9
49
295
22
Prediction 2
86.46%
50
498
27
1
Prediction 3
89.36%
361
41
2
0
Prediction 4
85.95%
84.69%
85.26%
77.88%
Recall
4-2 important features
The prioritization of predictive variables based on their weight is as follows:
Diploma GPA: 0.262
Semester 1 GPA: 0.201
Semester 2 GPA: 0.197
Number of honors semesters: 0.122
Conditional number: 0.114
Year of entry: 0.104
4-3 The results of the implementation of the student performance prediction model
The results of the prediction model are shown in Table 3:
Table 3. The results of the DT C4.5-GI&OSE model implementation
5- Discussion
In the main method of research, namely DT C4.5-GI&OSE, in the classification mode of four classes, it is observed that the average of the diploma has the greatest effect on the process of predicting student performance. In response to the sub-question of a research, the best algorithm in the four-class mode is Decision Tree C4.5-GI&OSE with a prediction accuracy of 84.71. This model showed 84.17% accuracy, 83.42% sensitivity and 0.780 kappa. DT C4.5-GI&OSE technique correctly predicted the graduation of 77.88% of excellent students, 85.26% of good students, 84.69% of average students, and 85.96% of poor students.
6-Conclusion
The obtained results show that there is a relationship between students' social and academic characteristics and their academic performance. DT C4.5-GI&OSE algorithm was the best algorithm for predicting the final GPA scores of students at the end of studies with a prediction accuracy of 84.71%. In this model, the average grade point average of the diploma has the greatest effect on the prediction process. Using machine learning models as a decision support tool improves the academic level of students and reduces the number of potential unsuccessful and dropout students. This study was carried out at the undergraduate level, which can be used in future research for the master's and doctoral level.
Keywords: student performance prediction, data mining, machine learning, modeling, improving the quality of education
کلیدواژه‌ها [English]
- student performance prediction
- data mining
- machine learning
- modeling
- improving the quality of education
نجفی محمود.، افضلی، مهدی.، مرادی، محمود. (1400). کاربرد داده‌کاوی آموزشی جهت شناسایی عوامل مؤثر بر افت تحصیلی دانش آموزان. فصلنامه سیستم‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند.
سلطانی، ستاره.، جاودانی گندمانی، تقی. (1400). مقایسه تحلیلی عملکرد الگوریتم‌های داده‌کاوی در پیش‌بینی پیشرفت تحصیلی دانشجویان. دومین کنفرانس ملی آخرین دستاوردهای مهندسی داده و دانش و محاسبات نرم
رئیسی وانانی، سینا.، رئیسی وانانی، ایمان.، تقوی فرد، محمدتقی. (1399). مدلی برای بخش بندی یادگیرندگان و بهبود عملکرد آموزشی با استفاده از الگوریتم‌های داده‌کاوی. نشریه علمی مطالعات مدیریت کسب‌وکار هوشمند، سال نهم، شماره 33 -38- 5
فدوی رودسری، آزاده.، صالحی، کیوان.، خدایی، ابراهیم.، مقدم زاده، علی.، جوادیپور، محمّد. (1398). مدل شبکه بیزی عوامل مرتبط با افت تحصیلی دانشجویان دانشگاه تهران، مجله علوم روان‌شناختی، ۱۸ (۷۶): ۴۲۹-۴۱۷
خسروی، هادی .، شفیعی، ریحانه.(1396). پیش بینی عملکرد دانش آموزان با استفاده از داده کاوی، دانشکده مهندسی کامپیوتر.
شاملو، رسول.، امید، منوچهر.، امین فر، فائزه. (1393). بررسی پیش‌بینی رفتار آموزشی دانشجویان با رویکرد داده‌کاوی در مؤسسات آموزش عالی (مطالعه موردی دانشگاه آزاد واحد بویین‌زهرا). گروه صنایع، دانشگاه آزاد اسلامی، واحد قزوین، دانشکده صنایع و مکانیک.
ایرجی، اعظم.، مینایی، بهروز.، شکورنیاز ونوس. (1392). به‌کارگیری فن‌آوری داده‌کاوی به‌منظور آسیب‌شناسی افت تحصیلی هنرجویان هنرستانی و استخراج نمایه‌ساز توصیفی در ارائه تمایز دانش آموزان ضعیف و ممتاز تهران، دانشکده مهندسی کامپیوتر دانشگاه علم و صنعت ایران.
یقینی مسعود.، اکبری، امین.، شریفی، سید محمدمهدی. (1387). پیش‌بینی وضعیت تحصیلی دانشجویان با استفاده از تکنیکهای داده‌کاوی، دومین کنفرانس داده‌کاوی ایران، تهران.
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استناد به این مقاله: سالاری، مژده.، رادفر، رضا.، فقیهی، مهدی. (1403). پیش‌بینی عملکرد دانشجویان با استفاده از الگوریتم‌های یادگیری ماشین و داده‌کاوی آموزشی (مطالعه موردی دانشگاه شاهد)، مطالعات مدیریت کسب وکار هوشمند، 12(47)، 315-366. DOI: 10.22054/ims.2023.75523.2375
Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..
نجفی محمود.، افضلی، مهدی.، مرادی، محمود. (1400). کاربرد داده‌کاوی آموزشی جهت شناسایی عوامل مؤثر بر افت تحصیلی دانش آموزان. فصلنامه سیستم‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند.
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استناد به این مقاله: سالاری، مژده.، رادفر، رضا.، فقیهی، مهدی. (1403). پیش‌بینی عملکرد دانشجویان با استفاده از الگوریتم‌های یادگیری ماشین و داده‌کاوی آموزشی (مطالعه موردی دانشگاه شاهد)، مطالعات مدیریت کسب وکار هوشمند، 12(47)، 315-366. DOI: 10.22054/ims.2023.75523.2375
Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..
نجفی محمود.، افضلی، مهدی.، مرادی، محمود. (1400). کاربرد داده‌کاوی آموزشی جهت شناسایی عوامل مؤثر بر افت تحصیلی دانش آموزان. فصلنامه سیستم‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند.
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استناد به این مقاله: سالاری، مژده.، رادفر، رضا.، فقیهی، مهدی. (1403). پیش‌بینی عملکرد دانشجویان با استفاده از الگوریتم‌های یادگیری ماشین و داده‌کاوی آموزشی (مطالعه موردی دانشگاه شاهد)، مطالعات مدیریت کسب وکار هوشمند، 12(47)، 315-366. DOI: 10.22054/ims.2023.75523.2375
Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..
نجفی محمود.، افضلی، مهدی.، مرادی، محمود. (1400). کاربرد داده‌کاوی آموزشی جهت شناسایی عوامل مؤثر بر افت تحصیلی دانش آموزان. فصلنامه سیستم‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند.
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استناد به این مقاله: سالاری، مژده.، رادفر، رضا.، فقیهی، مهدی. (1403). پیش‌بینی عملکرد دانشجویان با استفاده از الگوریتم‌های یادگیری ماشین و داده‌کاوی آموزشی (مطالعه موردی دانشگاه شاهد)، مطالعات مدیریت کسب وکار هوشمند، 12(47)، 315-366. DOI: 10.22054/ims.2023.75523.2375
Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..
نجفی محمود.، افضلی، مهدی.، مرادی، محمود. (1400). کاربرد داده‌کاوی آموزشی جهت شناسایی عوامل مؤثر بر افت تحصیلی دانش آموزان. فصلنامه سیستم‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند.
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استناد به این مقاله: سالاری، مژده.، رادفر، رضا.، فقیهی، مهدی. (1403). پیش‌بینی عملکرد دانشجویان با استفاده از الگوریتم‌های یادگیری ماشین و داده‌کاوی آموزشی (مطالعه موردی دانشگاه شاهد)، مطالعات مدیریت کسب وکار هوشمند، 12(47)، 315-366. DOI: 10.22054/ims.2023.75523.2375
Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..
نجفی محمود.، افضلی، مهدی.، مرادی، محمود. (1400). کاربرد داده‌کاوی آموزشی جهت شناسایی عوامل مؤثر بر افت تحصیلی دانش آموزان. فصلنامه سیستم‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند.
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استناد به این مقاله: سالاری، مژده.، رادفر، رضا.، فقیهی، مهدی. (1403). پیش‌بینی عملکرد دانشجویان با استفاده از الگوریتم‌های یادگیری ماشین و داده‌کاوی آموزشی (مطالعه موردی دانشگاه شاهد)، مطالعات مدیریت کسب وکار هوشمند، 12(47)، 315-366. DOI: 10.22054/ims.2023.75523.2375
Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..
نجفی محمود.، افضلی، مهدی.، مرادی، محمود. (1400). کاربرد داده‌کاوی آموزشی جهت شناسایی عوامل مؤثر بر افت تحصیلی دانش آموزان. فصلنامه سیستم‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند.
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استناد به این مقاله: سالاری، مژده.، رادفر، رضا.، فقیهی، مهدی. (1403). پیش‌بینی عملکرد دانشجویان با استفاده از الگوریتم‌های یادگیری ماشین و داده‌کاوی آموزشی (مطالعه موردی دانشگاه شاهد)، مطالعات مدیریت کسب وکار هوشمند، 12(47)، 315-366. DOI: 10.22054/ims.2023.75523.2375
Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..
نجفی محمود.، افضلی، مهدی.، مرادی، محمود. (1400). کاربرد داده‌کاوی آموزشی جهت شناسایی عوامل مؤثر بر افت تحصیلی دانش آموزان. فصلنامه سیستم‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند.
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استناد به این مقاله: سالاری، مژده.، رادفر، رضا.، فقیهی، مهدی. (1403). پیش‌بینی عملکرد دانشجویان با استفاده از الگوریتم‌های یادگیری ماشین و داده‌کاوی آموزشی (مطالعه موردی دانشگاه شاهد)، مطالعات مدیریت کسب وکار هوشمند، 12(47)، 315-366. DOI: 10.22054/ims.2023.75523.2375
Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..
نجفی محمود.، افضلی، مهدی.، مرادی، محمود. (1400). کاربرد داده‌کاوی آموزشی جهت شناسایی عوامل مؤثر بر افت تحصیلی دانش آموزان. فصلنامه سیستم‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند.
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استناد به این مقاله: سالاری، مژده.، رادفر، رضا.، فقیهی، مهدی. (1403). پیش‌بینی عملکرد دانشجویان با استفاده از الگوریتم‌های یادگیری ماشین و داده‌کاوی آموزشی (مطالعه موردی دانشگاه شاهد)، مطالعات مدیریت کسب وکار هوشمند، 12(47)، 315-366. DOI: 10.22054/ims.2023.75523.2375
Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..
نجفی محمود.، افضلی، مهدی.، مرادی، محمود. (1400). کاربرد داده‌کاوی آموزشی جهت شناسایی عوامل مؤثر بر افت تحصیلی دانش آموزان. فصلنامه سیستم‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند.
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استناد به این مقاله: سالاری، مژده.، رادفر، رضا.، فقیهی، مهدی. (1403). پیش‌بینی عملکرد دانشجویان با استفاده از الگوریتم‌های یادگیری ماشین و داده‌کاوی آموزشی (مطالعه موردی دانشگاه شاهد)، مطالعات مدیریت کسب وکار هوشمند، 12(47)، 315-366. DOI: 10.22054/ims.2023.75523.2375
Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..
نجفی محمود.، افضلی، مهدی.، مرادی، محمود. (1400). کاربرد داده‌کاوی آموزشی جهت شناسایی عوامل مؤثر بر افت تحصیلی دانش آموزان. فصلنامه سیستم‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند.
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استناد به این مقاله: سالاری، مژده.، رادفر، رضا.، فقیهی، مهدی. (1403). پیش‌بینی عملکرد دانشجویان با استفاده از الگوریتم‌های یادگیری ماشین و داده‌کاوی آموزشی (مطالعه موردی دانشگاه شاهد)، مطالعات مدیریت کسب وکار هوشمند، 12(47)، 315-366. DOI: 10.22054/ims.2023.75523.2375
Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..
نجفی محمود.، افضلی، مهدی.، مرادی، محمود. (1400). کاربرد داده‌کاوی آموزشی جهت شناسایی عوامل مؤثر بر افت تحصیلی دانش آموزان. فصلنامه سیستم‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند.
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استناد به این مقاله: سالاری، مژده.، رادفر، رضا.، فقیهی، مهدی. (1403). پیش‌بینی عملکرد دانشجویان با استفاده از الگوریتم‌های یادگیری ماشین و داده‌کاوی آموزشی (مطالعه موردی دانشگاه شاهد)، مطالعات مدیریت کسب وکار هوشمند، 12(47)، 315-366. DOI: 10.22054/ims.2023.75523.2375
Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..
نجفی محمود.، افضلی، مهدی.، مرادی، محمود. (1400). کاربرد داده‌کاوی آموزشی جهت شناسایی عوامل مؤثر بر افت تحصیلی دانش آموزان. فصلنامه سیستم‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند.
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استناد به این مقاله: سالاری، مژده.، رادفر، رضا.، فقیهی، مهدی. (1403). پیش‌بینی عملکرد دانشجویان با استفاده از الگوریتم‌های یادگیری ماشین و داده‌کاوی آموزشی (مطالعه موردی دانشگاه شاهد)، مطالعات مدیریت کسب وکار هوشمند، 12(47)، 315-366. DOI: 10.22054/ims.2023.75523.2375
Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..
نجفی محمود.، افضلی، مهدی.، مرادی، محمود. (1400). کاربرد داده‌کاوی آموزشی جهت شناسایی عوامل مؤثر بر افت تحصیلی دانش آموزان. فصلنامه سیستم‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند.
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استناد به این مقاله: سالاری، مژده.، رادفر، رضا.، فقیهی، مهدی. (1403). پیش‌بینی عملکرد دانشجویان با استفاده از الگوریتم‌های یادگیری ماشین و داده‌کاوی آموزشی (مطالعه موردی دانشگاه شاهد)، مطالعات مدیریت کسب وکار هوشمند، 12(47)، 315-366. DOI: 10.22054/ims.2023.75523.2375
Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..
نجفی محمود.، افضلی، مهدی.، مرادی، محمود. (1400). کاربرد داده‌کاوی آموزشی جهت شناسایی عوامل مؤثر بر افت تحصیلی دانش آموزان. فصلنامه سیستم‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند.
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استناد به این مقاله: سالاری، مژده.، رادفر، رضا.، فقیهی، مهدی. (1403). پیش‌بینی عملکرد دانشجویان با استفاده از الگوریتم‌های یادگیری ماشین و داده‌کاوی آموزشی (مطالعه موردی دانشگاه شاهد)، مطالعات مدیریت کسب وکار هوشمند، 12(47)، 315-366. DOI: 10.22054/ims.2023.75523.2375
Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..
نجفی محمود.، افضلی، مهدی.، مرادی، محمود. (1400). کاربرد داده‌کاوی آموزشی جهت شناسایی عوامل مؤثر بر افت تحصیلی دانش آموزان. فصلنامه سیستم‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند.
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استناد به این مقاله: سالاری، مژده.، رادفر، رضا.، فقیهی، مهدی. (1403). پیش‌بینی عملکرد دانشجویان با استفاده از الگوریتم‌های یادگیری ماشین و داده‌کاوی آموزشی (مطالعه موردی دانشگاه شاهد)، مطالعات مدیریت کسب وکار هوشمند، 12(47)، 315-366. DOI: 10.22054/ims.2023.75523.2375
Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..
نجفی محمود.، افضلی، مهدی.، مرادی، محمود. (1400). کاربرد داده‌کاوی آموزشی جهت شناسایی عوامل مؤثر بر افت تحصیلی دانش آموزان. فصلنامه سیستم‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند.
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استناد به این مقاله: سالاری، مژده.، رادفر، رضا.، فقیهی، مهدی. (1403). پیش‌بینی عملکرد دانشجویان با استفاده از الگوریتم‌های یادگیری ماشین و داده‌کاوی آموزشی (مطالعه موردی دانشگاه شاهد)، مطالعات مدیریت کسب وکار هوشمند، 12(47)، 315-366. DOI: 10.22054/ims.2023.75523.2375
Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..
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استناد به این مقاله: سالاری، مژده.، رادفر، رضا.، فقیهی، مهدی. (1403). پیش‌بینی عملکرد دانشجویان با استفاده از الگوریتم‌های یادگیری ماشین و داده‌کاوی آموزشی (مطالعه موردی دانشگاه شاهد)، مطالعات مدیریت کسب وکار هوشمند، 12(47)، 315-366. DOI: 10.22054/ims.2023.75523.2375
Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..
نجفی محمود.، افضلی، مهدی.، مرادی، محمود. (1400). کاربرد داده‌کاوی آموزشی جهت شناسایی عوامل مؤثر بر افت تحصیلی دانش آموزان. فصلنامه سیستم‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند.
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استناد به این مقاله: سالاری، مژده.، رادفر، رضا.، فقیهی، مهدی. (1403). پیش‌بینی عملکرد دانشجویان با استفاده از الگوریتم‌های یادگیری ماشین و داده‌کاوی آموزشی (مطالعه موردی دانشگاه شاهد)، مطالعات مدیریت کسب وکار هوشمند، 12(47)، 315-366. DOI: 10.22054/ims.2023.75523.2375
Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..
نجفی محمود.، افضلی، مهدی.، مرادی، محمود. (1400). کاربرد داده‌کاوی آموزشی جهت شناسایی عوامل مؤثر بر افت تحصیلی دانش آموزان. فصلنامه سیستم‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند.
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استناد به این مقاله: سالاری، مژده.، رادفر، رضا.، فقیهی، مهدی. (1403). پیش‌بینی عملکرد دانشجویان با استفاده از الگوریتم‌های یادگیری ماشین و داده‌کاوی آموزشی (مطالعه موردی دانشگاه شاهد)، مطالعات مدیریت کسب وکار هوشمند، 12(47)، 315-366. DOI: 10.22054/ims.2023.75523.2375
Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..
نجفی محمود.، افضلی، مهدی.، مرادی، محمود. (1400). کاربرد داده‌کاوی آموزشی جهت شناسایی عوامل مؤثر بر افت تحصیلی دانش آموزان. فصلنامه سیستم‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند.
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استناد به این مقاله: سالاری، مژده.، رادفر، رضا.، فقیهی، مهدی. (1403). پیش‌بینی عملکرد دانشجویان با استفاده از الگوریتم‌های یادگیری ماشین و داده‌کاوی آموزشی (مطالعه موردی دانشگاه شاهد)، مطالعات مدیریت کسب وکار هوشمند، 12(47)، 315-366. DOI: 10.22054/ims.2023.75523.2375
Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..
نجفی محمود.، افضلی، مهدی.، مرادی، محمود. (1400). کاربرد داده‌کاوی آموزشی جهت شناسایی عوامل مؤثر بر افت تحصیلی دانش آموزان. فصلنامه سیستم‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند.
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استناد به این مقاله: سالاری، مژده.، رادفر، رضا.، فقیهی، مهدی. (1403). پیش‌بینی عملکرد دانشجویان با استفاده از الگوریتم‌های یادگیری ماشین و داده‌کاوی آموزشی (مطالعه موردی دانشگاه شاهد)، مطالعات مدیریت کسب وکار هوشمند، 12(47)، 315-366. DOI: 10.22054/ims.2023.75523.2375
Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..
نجفی محمود.، افضلی، مهدی.، مرادی، محمود. (1400). کاربرد داده‌کاوی آموزشی جهت شناسایی عوامل مؤثر بر افت تحصیلی دانش آموزان. فصلنامه سیستم‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند.
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استناد به این مقاله: سالاری، مژده.، رادفر، رضا.، فقیهی، مهدی. (1403). پیش‌بینی عملکرد دانشجویان با استفاده از الگوریتم‌های یادگیری ماشین و داده‌کاوی آموزشی (مطالعه موردی دانشگاه شاهد)، مطالعات مدیریت کسب وکار هوشمند، 12(47)، 315-366. DOI: 10.22054/ims.2023.75523.2375
Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..
نجفی محمود.، افضلی، مهدی.، مرادی، محمود. (1400). کاربرد داده‌کاوی آموزشی جهت شناسایی عوامل مؤثر بر افت تحصیلی دانش آموزان. فصلنامه سیستم‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند.
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استناد به این مقاله: سالاری، مژده.، رادفر، رضا.، فقیهی، مهدی. (1403). پیش‌بینی عملکرد دانشجویان با استفاده از الگوریتم‌های یادگیری ماشین و داده‌کاوی آموزشی (مطالعه موردی دانشگاه شاهد)، مطالعات مدیریت کسب وکار هوشمند، 12(47)، 315-366. DOI: 10.22054/ims.2023.75523.2375
Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..
نجفی محمود.، افضلی، مهدی.، مرادی، محمود. (1400). کاربرد داده‌کاوی آموزشی جهت شناسایی عوامل مؤثر بر افت تحصیلی دانش آموزان. فصلنامه سیستم‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند.
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استناد به این مقاله: سالاری، مژده.، رادفر، رضا.، فقیهی، مهدی. (1403). پیش‌بینی عملکرد دانشجویان با استفاده از الگوریتم‌های یادگیری ماشین و داده‌کاوی آموزشی (مطالعه موردی دانشگاه شاهد)، مطالعات مدیریت کسب وکار هوشمند، 12(47)، 315-366. DOI: 10.22054/ims.2023.75523.2375
Journal of Business Intelligence Management Studies is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License..
نجفی محمود.، افضلی، مهدی.، مرادی، محمود. (1400). کاربرد داده‌کاوی آموزشی جهت شناسایی عوامل مؤثر بر افت تحصیلی دانش آموزان. فصلنامه سیستم‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند.
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استناد به این مقاله: سالاری، مژده.، رادفر، رضا.، فقیهی، مهدی. (1403). پیش‌بینی عملکرد دانشجویان با استفاده از الگوریتم‌های یادگیری ماشین و داده‌کاوی آموزشی (مطالعه موردی دانشگاه شاهد)، مطالعات مدیریت کسب وکار هوشمند، 12(47)، 315-366. DOI: 10.22054/ims.2023.75523.2375
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نجفی محمود.، افضلی، مهدی.، مرادی، محمود. (1400). کاربرد داده‌کاوی آموزشی جهت شناسایی عوامل مؤثر بر افت تحصیلی دانش آموزان. فصلنامه سیستم‌های پردازشی و ارتباطی چندرسانه‌ای هوشمند.
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