Advanced Deep Learning Model for Predicting the Academic Performances of Students in Educational Institutions
Source abstract
Abstract
Educational institutions are increasingly focused on supporting students who may be facing academic challenges, aiming to enhance their educational outcomes through targeted interventions. Within this framework, leveraging advanced deep learning techniques to develop recommendation systems becomes essential. These systems are designed to identify students at risk of underperforming by analyzing patterns in their historical academic data, thereby facilitating personalized support strategies. This research introduces an innovative deep learning model tailored for pinpointing students in need of academic assistance. Utilizing a Gated Recurrent Neural Network (GRU) architecture, the model is rich with features such as a dense layer, max-pooling layer, and the ADAM optimization method used to optimize performance. The effectiveness of this model was tested using a comprehensive dataset containing 15,165 records of student assessments collected across several academic institutions. A comparative analysis with existing educational recommendation models, like Recurrent Neural Network (RNN), AdaBoost, and Artificial Immune Recognition System v2, highlights the superior accuracy of the proposed GRU model, which achieved an impressive overall accuracy of 99.70%. This breakthrough underscores the model’s potential in aiding educational institutions to proactively support students, thereby mitigating the risks of underachievement and dropout.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Big Data and Business Intelligence · Online Learning and Analytics · Advanced Data Processing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computing, Gachon University, Seongnam 13120, Republic of Korea
Department of Data Science and Artificial Intelligence, Al-Ahliyya Amman University, Amman 19111, Jordan
Ubion, Seoul 08378, Republic of Korea
Funding
Basic Science Research Program through the National Research Foundation of Korea (NRF) · Ministry of Science and ICT
References (42)
Show source-provided references
1. Survey of papers for Data Mining with Neural Networksto Predict the Student’s Academic Achievements
2. Beikzadeh, M.R., and Delavari, N. (2005, January 7–9). A New Analysis Model for Data Mining Processes in Higher Educational Systems. Proceedings of the 6th Information Technology Based Higher Education and Training, Istanbul, Turkey.
3. Learning Analytics and Educational Data Mining: An Overview of Recent Techniques
4. Big Data Application and its Impact on Education10.3991/ijet.v15i17.14459 ↗
5. Predicting Student Success Using Big Data and Machine Learning Algorithms10.3991/ijet.v17i12.30259 ↗
6. Data-intensive applications, challenges, techniques, and technologies: A survey on Big Data10.1016/j.ins.2014.01.015 ↗
7. Learners’ Autonomous Learning Behavior in Distance Reading Based on Big Data10.3991/ijet.v17i09.31373 ↗
8. Soft Voting Machine Learning Classification Model to Predict and Expose Liver Disorder for Human Patients
9. Attention-based Long Short Term Memory Model for DNA Damage Prediction in Mammalian Cells
10. Neural Network Prediction Model to Explore Complex Nonlinear Behavior in Dynamic Biological Network10.3991/ijim.v16i12.30467 ↗
11. Krish, K. (2013). Data Warehousing in the Age of Big Data, Elsevier.
12. Kastranis, A. (2019). Artificial Intelligence for People and Business, O’ Reily Media Inc.
13. Siemens, G., and Baker, R.S. (May, January 29). Learning analytics and educational data mining: Towards communication and collaboration. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, Vancouver, BC, Canada.
14. Aher, S.B. (2011, January 23–24). Data Mining in Educational System using WEKA. Proceedings of the International Conference on Emerging Technology Trends (ICETT), Nagercoil, India.
15. Mining Association Rule in Classified Data for Course Recommender System in E-Learning
16. Felix, I.M., Ambrosio, A.P., Neves, P.S., Siqueira, J., and Brancher, J.D. (2017, January 21–23). Moodle Predicta: A Data Mining Tool for Student Follow Up. Proceedings of the International Conference on Computer Supported Education, Porto, Portugal.
17. (2024, January 25). International Educational Data Mining Society. Available online: www.educationaldatamining.org.
18. The relationship between students’ approaches to learning and the assessment of learning outcomes10.1007/BF03173560 ↗
19. Evaluation of the Performance of K-Nearest Neighbor Algorithm in Determining Student Learning Style
20. A Review of Machine Learning in Education
21. Educational Data Mining and Analysis of Students’ Academic Performance Using WEKA
22. López, M. (2012, January 19–21). Classification via clustering for predicting final marks based on student participation in forums. Proceedings of the 5th International Conference on Educational Data Mining, Chania, Greece.
23. E-Learning personalization based on hybrid recommen recommendation strategy and learning style identification10.1016/j.compedu.2010.11.001 ↗
24. Data Mining Model for Higher Education System
25. Mapping Student’s Performance Based on the Data Mining Approach (A Case Study)
26. Bovo, A. (2013, January 23–25). Clustering moodles data as a tool for profiling students. Proceedings of the International Conference on E-Learning and E-Technologies in Education (ICEEE), Lodz, Poland.10.1109/ICeLeTE.2013.6644359 ↗
27. Using cluster analysis for data mining in educational technology research10.1007/s11423-012-9235-8 ↗
28. Impact of absenteeism on academic performance under compulsory attendance policies in first to fifth year university students10.5209/rced.70917 ↗
29. Chavez, H., Chavez-Arias, B., Contreras-Rosas, S., Alvarez-Rodríguez, J.M., and Raymundo, C. (2023). Artificial neural network model to predict student performa. Front. Educ., 8.10.3389/feduc.2023.1106679 ↗
30. A Proposed Hybrid CNN-RNN Architecture for Student Performance Prediction
31. Hybrid learning clonal selection algorithm10.1016/j.ins.2014.10.056 ↗
32. Saidi, M., Chikh, A., and Settouti, N. (2011, January 13–15). Automatic Identification of Diabetes Diseases using an Artificial Immune Recognition System2 (AIRS2) with a Fuzzy K-Nearest Neighbor. Proceedings of the Conférence Internationale sur l’Informatique et ses Applications, Saida, Algeria.10.1007/s10916-011-9748-4 ↗
33. Students’ Performance Prediction Using Deep Neural Network
34. Dey, R., and Salem, F.M. (2017, January 6–9). Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks. Proceedings of the 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), Boston, MA, USA.10.1109/MWSCAS.2017.8053243 ↗
35. Cho, K., Van Merriënboer, B., Bahdanau, D., and Bengio, Y. (2014). On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. arXiv.10.3115/v1/W14-4012 ↗
36. Light Gated Recurrent Units for Speech Recognition10.1109/TETCI.2017.2762739 ↗
37. Pomerat, J., Segev, A., and Datta, R. (2019, January 9–12). On Neural Network Activation Functions and Optimizers in Relation to Polynomial Regression. Proceedings of the IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA.10.1109/BigData47090.2019.9005674 ↗
38. Zhang, Z. (2018, January 4–6). Improved Adam Optimizer for Deep Neural Networks. Proceedings of the 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), Banff, AB, Canada.10.1109/IWQoS.2018.8624183 ↗
39. (2023, December 20). K. Document. Available online: https://keras.io/search.html?query=maxpooling.
40. (2024, January 15). Dense Layer. Keras. Available online: https://keras.io/api/layers/core_layers/dense/.
41. Prediction Model on Student Performance based on Internal Assessment using Deep Learning
42. Classification assessment methods10.1016/j.aci.2018.08.003 ↗