توقع لأداء الطلاب المقبولين الجدد في امتحان المستوى باستخدام التنقيب في البيانات

المؤلفون

  • يوسف صالح أبو زر | Yousef Saleh Abuzir Al-Quds Open University, Palestine
  • إسلام يونس عمرو | Islam Younis Amro Al-Quds Open University
  • بسام ترك | Bassam Tork Al-Quds Open University
  • ماهر عيسى | Maher Issa Al-Quds Open University

DOI:

https://doi.org/10.33977/2106-000-004-003

الكلمات المفتاحية:

تنقيب في البيانات، الانحدار اللوجستي، الشبكات العصبية، علامة امتحان المستوى،K-means ، EM خوارزمية ، خريطة كوهن ذاتية التنظيم (KSOM) والتكتل.

الملخص

مهتمون بإيجاد العوامل الرئيسية التي تؤثر على هذه العلامة او النتيجة. من أجل التحليل والتنبؤ بما سيحدث خلال المراحل المختلفة لعملية التسجيل في الجامعة، يمكن استخدام نماذج التنقيب عن البيانات والتي  ستساعد الجامعة في تحديد التدخلات والتدابير واتخاذ الإجراءات اللازمة وفقًا لذلك في الوقت المناسب. لإجراء التحليلات والتنبؤات ، استخدمنا أداة Waikato’s Knowledge Analysis Environment (WEKA)  وخوارزميات مثل K-Means ، والانحدار اللوجستي ، وخريطة Kohonen ذاتية التنظيم (KSOM) و EM  لتحديد العوامل الأكثر تأثيرًا على تنبؤ علامة للطالب في اختبار المستوى. أظهرت نتائج هذا البحث أن EM تظهر اداء جيد لتحديد العوامل الرئيسية التي تؤثر على العلامة النهائية للطالب في اختبار المستوى. تعد خوارزميات الثلاثة الأخرى المستخدمة الانحدار اللوجستي،  K-Means، KSOM نموذجًا تنبؤيًا لعلامة الطالب في امتحان المستوى.

السيرة الشخصية للمؤلف

يوسف صالح أبو زر | Yousef Saleh Abuzir، Al-Quds Open University, Palestine

أستاذ مشارك

المراجع

Abuzir Y., Abuzir M. and Abuzir A., Using Artificial Neural Networks (ANN) To Detect the Diabetes, accepted in COMMUNICATION & COGNITION (C&C) Journal, V53, N2-1 (2020). Ghent, Belgium.

Abuzir Y., Abuzir S., Data Mining Techniques for Prediction of Concrete Compressive Strength (CCS), Palestinian Journal of Technology and Applied Sciences (PJTAS), No 3 (2020).

Abuzir Y. and Baraka A.M. , Financial Stock Market Forecast Using Data Mining in Palestine, Palestinian Journal of Technology and Applied Sciences, No 2 (2019).

Abuzir Y., Predict the Main Factors that Affect the Vegetable Production in Palestine Using WEKA Data Mining Tool, Palestinian Journal of Technology and Applied Sciences, pp 58-71, No 1 (2018).

Abeer B. A., Ibrahim S. E., Data Maning: A Prediction for Student’s Performance Using Classification Method, 2014.

Mohammed M. Abu Tair, A. M. E, Mining Educational Data to Improve Students’ Performance: A Case Study, International Journal of Information 2 (2), 2012.

Brijesh K. B., Saurabh P., Mining Educational Data to Analyze Students’ Performance, 2011.

Sonali A., Pandey G. N., and Tiwari M. D., Data Mining in Education: Data Classification and Decision Tree Approach, 2012.

El-Halees A., Mining Students Data to Analyze Learning Behavior: A Case Study, 2009.

Wati, M., Indrawan, W., Widians, J.A., & Puspitasari, N. (2017). Data mining for predicting students’ learning result. 2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT), 1-4.

Shahiri A. M. , Husain W. , and Rashid N. A. , “A review on predicting student’s performance using data mining techniques,” Procedia Computer Science, Vol. 72, pp. 414–422, 2015.

Algarni A., “Data Mining in Education” International Journal of Advanced Computer Science and Applications, Vol. 7, Issue. 6, pp. 456-461, 2016.

Dahiya V., “A Survey on Educational Data Mining”, International Journal of Research in Humanities, Arts and Literature, Vol. 6, Issue.5, pp. 23-30, 2018.

Purani K.D. and Chaudhary M.B.(2019), Educational Data Mining: A Survey of Analyzing Student Academic Performance Methods, International Journal of Computer Sciences and Engineering, Survey Paper Vol.-7, Issue-2, Feb 2019.

AlHakami, H., Alsubait, T., & Al-Jarallah, A.S. (2020). Data Mining for Student Advising. International Journal of Advanced Computer Science and Applications, 11.

Dweib M., Abuzir Y. (2018), Optimization of the Neural Networks Parameters, Palestinian Journal of Technology and Applied Sciences, pp 34 -47, No 1 (2018).

Brown M., Data mining techniques, IBM DeveloperWork, December 2012, online https://www.ibm.com/developerworks/library/ba-data-mining-techniques/

Villanueva A., Moreno L.G. & Salinas M.J.(2018), Data mining techniques applied in educational environments: Literature review, Digital Education Review - Number 33, June 2018.

Bayer, J., Bydzovská, H., & Géryk, G. (2012). Predicting dropout from social behaviour of students. International Educational Data Mining Societ, (Dm), 103–109.

Thomas, J. (2015). Predicting College Students Dropout using EDM Techniques, 123(5), 26–34.

Yukselturk, E., & Education, C. (2014). Predicting Dropout Student□: An Application of Data Mining Methods In An Online Education Program, 17(1).

https://doi.org/10.2478/eurodl-2014-0008

Yahya A., A., Osman, A., & Abdu Alattab, A. (2013). Educational Data Mining□: A Case Study of Teacher’s Classroom Questions. IEEE 13th International Conference On, (February), 92–97.

He, W. (2013). Examining Students ‘ Online Interaction in a Live Video Streaming Environment Using Data Mining and Text Mining Computers in Human Behavior. Computers in Human Behavior, (February), 90–102. https://doi.org/10.1016/j.chb.2012.07.020

Rabbany, R., Elatia, S., Takaffoli, M., & Zaïane, O. R. (2014). Collaborative Learning of Students in Online Discussion Forums: A Social Network Analysis Perspective. EDM 2014, 1–25.

Dutt, A., Aghabozrgi, S., Akmal, M., Ismail, B., & Mahroeian, H. (2015). Clustering Algorithms Applied in Educational Data Mining, 5(2), 112–116. https://doi.org/10.7763/IJIEE.2015.V5.513

Badr, A., Din, E., & Elaraby, I. S. (2014). Data Mining□: A prediction for Student’s Performance Using Classification Method. World Journal of Computer Application and Technology, 2(2), 43– 47. https://doi.org/10.13189/wjcat.2014.020203

Hu, Y. H., Lo, C. L., & Shih, S. P. (2014). Developing early warning systems to predict students’ online learning performance. Computers in Human Behavior, 469–478.

Shahiri, A. M., & Husain, W. (2015). A Review on Predicting Student’s Performance using Data Mining Techniques. Procedia Computer Science, 72, 414–422. https://doi.org/10.1016/j.procs.2015.12.157

Hung, J., Hsu, Y., & Rice, K. (2012). Integrating Data Mining in Program Evaluation of K-12 Online Education, 15, 27–41.

Chalaris, M., Gritzalis, S., Maragoudakis, M., & Sgouropoulou, C. (2014). Improving Quality of Educational Processes Providing New Knowledge using Data Mining Techniques. Procedia - Social and Behavioral Sciences, 147, 390–397. https://doi.org/10.1016/j.sbspro.2014.07.117

Belsis, P., Chalaris, I., Chalaris, M., & Skourlas, C. (2014). The Analysis of the Length of Studies in Higher Education based on Clustering and the Extraction of Association Rules. Procedia - Social and Behavioral Sciences, 147, 567–575. https://doi.org/10.1016/j.sbspro.2014.07.159

Mayilvaganan, M., & Kalpanadevi, D. (2015). Cognitive Skill Analysis for Students through Problem Solving Based on Data Mining Techniques. Procedia - Procedia Computer Science, 47, 62–75. https://doi.org/10.1016/j.procs.2015.03.184

Mugla, H. G. (2014). Modeling Student Performance in Higher Education Using Data Mining Modeling Student Performance in Higher Education Using Data Mining, (February 2016). https://doi.org/10.1007/978-3-319-02738-8

Campagni, R., Merlini, D., Sprugnoli, R., & Verri, M. C. (2015). Data mining models for student careers. Expert Systems with Applications, 42, 5508–5521.

Barracosa, J., & Antunes, C. (2011). Anticipating Teachers’ Performance. KDD 2011 Workshop: Knowledge Discovery in Educational Data, 77–82.

López, M. I., Luna, J. M., Romero, C., & Ventura, S. (2012). Classification via clustering for predicting final marks based on student participation in forums. International Educational Data Mining Society, 148–151.

Şen, B., Uçar, E., & Delen, D. (2012). Predicting and analyzing secondary education placement-test scores□: A data mining approach. Expert Systems with Applications, 39, 9468–9476. https://doi.org/10.1016/j.eswa.2012.02.112

Kaur, P., Singh, M., & Singh, G. (2015). Classification and prediction based data mining algorithms to predict slow learners in education sector. Procedia - Procedia Computer Science, 57, 500– 508. https://doi.org/10.1016/j.procs.2015.07.372

Trivedi, S., Pardos, Z. A., Sárközy, G. N., & Heffernan, N. T. (2016). Spectral Clustering in Educational Data Mining. EDM 2011,

التنزيلات

منشور

2021-04-05

كيفية الاقتباس

Yousef Saleh Abuzir ي. ص. أ. ز. |, Islam Younis Amro إ. ي. ع. |, Bassam Tork ب. ت. |, & Maher Issa م. ع. |. (2021). توقع لأداء الطلاب المقبولين الجدد في امتحان المستوى باستخدام التنقيب في البيانات. المجلة الفلسطينية للتكنولوجيا والعلوم التطبيقية, (4). https://doi.org/10.33977/2106-000-004-003

إصدار

عدد 4 (2021)

القسم

الأبحاث

الرخصة

  1. The editorial board confirms its commitment to the intellectual property rights
  2. Researchers also have to commit to the intellectual property rights.
  3. The research copyrights and publication are owned by the Journal once the researcher is notified about the approval of the paper. The scientific materials published or approved for publishing in the Journal should not be republished unless a written acknowledgment is obtained by the Deanship of Scientific Research.
  4. Research papers should not be published or republished unless a written acknowledgement is obtained from the Deanship of Scientific Research.
  5. The researcher has the right to accredit the research to himself, and to place his name on all the copies, editions and volumes published.
  6. The author has the right to request the accreditation of the published papers to himself. 

الأعمال الأكثر قراءة لنفس المؤلف/المؤلفين

عذراً: هذه الإضافة تتطلب تمكين إضافة إحصائيات/تقارير واحدة على الأقل حتى تتمكن من العمل. إن كانت إضافات الإحصائيات لديك تقدم أكثر من مقياس واحد، فعليك أيضاً اختيار مقياس رئيسي منها عند صفحة إعدادات الموقع و/أو عند صفحات الإدارة الخاصة برئيس تحرير المجلة.