Predictive assessment of learners through initial interactions with encoding techniques in deep learning
Abstract
Recent academic research has prominently focused on predicting student achievements in online learning. However, ongoing challenges persist for teachers and researchers, primarily revolving around the selection of relevant features. To address this issue, the study aims to predict the correlation between the use of a Virtual Learning Environment (VLE) and students’ performance based solely on their initial interactions with the platform during the initial segment of the course module within the annual study sequence of the academic year 2014–2015. The assessment employed a specialized data model that integrates the chronological order of a learner’s interactions with the educational materials provided on the online learning platform throughout the sequence employing data from the Open University Learning Analytics Dataset (OULAD), which included 32,593 undergraduates in all courses. Innovative methods, such as the VLE, and Long Short-Term Memory (LSTM) neural network rooted in deep learning, were applied to ensure the prediction of student achievement. Preliminary findings demonstrated a 60% accuracy of the model using only learner interaction data within the first third of the course duration and employing various variable encoding techniques. The successful integration of the esteemed OULAD, combined with the implementation of an LSTM neural network architecture in our data model, proved to be a highly effective strategy. This approach yielded valuable perceptions facilitating the identification of crucial data points essential for predicting student success and contributing to informed decision-making in formulating strategies to assess and support student performance effectively.
Keywords
Full Text:
PDFReferences
1. Müller FM, Souza MV. The role of Knowledge Media in Network Education. International Journal for Innovation Education and Research. 2020, 8(7): 76-93. doi: 10.31686/ijier.vol8.iss7.2457
2. Goyal S. E-Learning: Future of Education. Journal of Education and Learning (EduLearn). 2012, 6(4): 239. doi: 10.11591/edulearn.v6i4.168
3. Romero C, Ventura S. Educational data mining and learning analytics: An updated survey. WIREs Data Mining and Knowledge Discovery. 2020, 10(3). doi: 10.1002/widm.1355
4. Slimani K, Bourekkadi S, Messoussi R, et al. Sharing emotions in the distance education experience: attitudes and motivation of university students. International Conference on Intelligent Systems and Computer Vision (ISCV); IEEE. 2020. pp. 1-10.
5. Bekele R, Menzel W. A bayesian approach to predict performance of a student (bapps): A case with ethiopian students. Algorithms. 2005; 22(23): 24.
6. Mubarak AA, Cao H, Zhang W, et al. Visual analytics of video‐clickstream data and prediction of learners’ performance using deep learning models in MOOCs’ courses. Computer Applications in Engineering Education. 2020, 29(4): 710-732. doi: 10.1002/cae.22328
7. Khadija S, Ruichek Y, Messoussi R. Compound facial emotional expression recognition using cnn deep features. Engineering Letters. 2022; 30(4): 1402-1416.
8. AhmetBerk U. Effects of Mobile Learning in Blended Learning Environments. Journal of Information and Communication Technologies. 2019; 1(1): 1–14.
9. Torres Martín C, Acal C, El Homrani M, et al. Impact on the Virtual Learning Environment Due to COVID-19. Sustainability. 2021, 13(2): 582. doi: 10.3390/su13020582
10. Schneckenberg D, Ehlers U, Adelsberger H. Web 2.0 and competence‐oriented design of learning—Potentials and implications for higher education. British Journal of Educational Technology. 2011, 42(5): 747-762. doi: 10.1111/j.1467-8535.2010.01092.x
11. Annansingh F. Mind the gap: Cognitive active learning in virtual learning environment perception of instructors and students. Education and Information Technologies. 2019, 24(6): 3669-3688. doi: 10.1007/s10639-019-09949-5
12. Galán JG, Pérez CL, López JÁM, et al. VLE Environments and MOOC Courses. Innovation and ICT in Education. Published online September 1, 2022: 77-91. doi: 10.1201/9781003338567-9
13. Jokiaho A, May B, Specht M, et al. Barriers to using E Learning in an Advanced Way. International Journal of Advanced Corporate Learning (iJAC). 2018, 11(1): 17. doi: 10.3991/ijac.v11i1.9235
14. Miranda L, Alves P, Morais C. Assessment of virtual learning environments by higher education teachers and students. ECEL 2013-Proceedings for the 12th European Conference on eLearning: ECEL 2013. Academic Conferences Limited. 2013. p. 311.
15. Sclater N, Peasgood A, Mullan J. Learning analytics in higher education. A review of UK and international practice. 2016; 8(2017): 176.
16. Kuzilek J, Zdrahal Z, Fuglik V. Student success prediction using student exam behaviour. Future Generation Computer Systems. 2021, 125: 661-671. doi: 10.1016/j.future.2021.07.009
17. Alves P, Miranda L, Morais C. The Influence of Virtual Learning Environments in Students’ Performance. Universal Journal of Educational Research. 2017, 5(3): 517-527. doi: 10.13189/ujer.2017.050325
18. Das CR, Das S, Panda S. Groundwater quality monitoring by correlation, regression and hierarchical clustering analyses using WQI and PAST tools. Groundwater for Sustainable Development. 2022, 16: 100708. doi: 10.1016/j.gsd.2021.100708
19. Mondal A, Mukherjee J. An Approach to Predict a Student’s Academic Performance using Recurrent Neural Network (RNN). International Journal of Computer Applications. 2018, 181(6): 1-5. doi: 10.5120/ijca2018917352
20. Alamri R, Alharbi B. Explainable Student Performance Prediction Models: A Systematic Review. IEEE Access. 2021, 9: 33132-33143. doi: 10.1109/access.2021.3061368
21. Okubo F, Yamashita T, Shimada A, et al. A Neural Network Approach for Students’ Performance Prediction. In the Proceedings of the Seventh International Learning Analytics & Knowledge Conference. 2017; 598-599.
22. Hood N, Littlejohn A, Milligan C. Context counts: How learners’ contexts influence learning in a MOOC. Computers & Education. 2015, 91: 83-91. doi: 10.1016/j.compedu.2015.10.019
23. Al-azazi FA, Ghurab M. ANN-LSTM: A deep learning model for early student performance prediction in MOOC. Heliyon. 2023, 9(4): e15382. doi: 10.1016/j.heliyon.2023.e15382
24. Kim BH, Vizitei E, Ganapathi V. GritNet: Student Performance Prediction with Deep Learning. arXiv preprint arXiv.2018; 1804.07405:1-5.
25. Imran M, Latif S, Mehmood D, et al. Student Academic Performance Prediction using Supervised Learning Techniques. International Journal of Emerging Technologies in Learning (iJET). 2019, 14(14): 92. doi: 10.3991/ijet.v14i14.10310
26. Sekeroglu B, Dimililer K, Tuncal K. Student Performance Prediction and Classification Using Machine Learning Algorithms. In: Proceedings of the 8th International Conference on Educational and Information Technology; New York, NY, USA; 2 March 2019. pp. 7–11.
27. Al-azazi FA, Ghurab M. ANN-LSTM: A deep learning model for early student performance prediction in MOOC. Heliyon, 2023, 9(4). doi: 10.1016/j.heliyon.2023.e15382
28. Rather AM. Deep Learning and Autoregressive Approach for Prediction of Time Series Data. Journal of Autonomous Intelligence. 2021, 3(2): 1. doi: 10.32629/jai.v3i2.207
29. Aljaloud AS, Uliyan DM, Alkhalil A, et al. A Deep Learning Model to Predict Student Learning Outcomes in LMS Using CNN and LSTM. IEEE Access. 2022, 10: 85255-85265. doi: 10.1109/access.2022.3196784
30. Qu S, Li K, Wu B, et al. Predicting Student Achievement Based on Temporal Learning Behavior in MOOCs. Applied Sciences. 2019, 9(24): 5539. doi: 10.3390/app9245539
31. Liu Q, Huang Z, Yin Y, et al. EKT: Exercise-Aware Knowledge Tracing for Student Performance Prediction. IEEE Transactions on Knowledge and Data Engineering. 2021, 33(1): 100-115. doi: 10.1109/tkde.2019.2924374
32. Arsad PM, Buniyamin B, Manan JLA. A Neural Network Students’ Performance Prediction Model (NNSPPM). IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA); IEEE. 2013; 1-5.
33. Cerda P, Varoquaux G, Kégl B. Similarity encoding for learning with dirty categorical variables. Machine Learning. 2018, 107(8-10): 1477-1494. doi: 10.1007/s10994-018-5724-2
34. Hancock JT, Khoshgoftaar TM. Survey on categorical data for neural networks. Journal of Big Data. 2020, 7(1). doi: 10.1186/s40537-020-00305-w
35. Yang H, Kong J, Hu H, et al. A Review of Remote Sensing for Water Quality Retrieval: Progress and Challenges. Remote Sensing. 2022, 14(8): 1770. doi: 10.3390/rs14081770
36. Jiao Q, Zhang S. A Brief Survey of Word Embedding and Its Recent Development. 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). Published online March 12, 2021. doi: 10.1109/iaeac50856.2021.9390956
37. Raunak V, Gupta V, Metze F. Effective Dimensionality Reduction for Word Embeddings. Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019). Published online 2019. doi: 10.18653/v1/w19-4328
38. Peters M, Neumann M, Zettlemoyer L, et al. Dissecting Contextual Word Embeddings: Architecture and Representation. arXiv 2018; arXiv:1808.08949:1-3.
39. Zhao J, Mudgal S, Liang Y. Generalizing Word Embeddings Using Bag of Subwords. arXiv 2018; arXiv:1809.04259:3-5.
40. Sabbeh SF, Fasihuddin HA. A Comparative Analysis of Word Embedding and Deep Learning for Arabic Sentiment Classification. Electronics. 2023, 12(6): 1425. doi: 10.3390/electronics12061425
41. Yu Y, Si X, Hu C, et al. A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures. Neural Computation. 2019, 31(7): 1235-1270. doi: 10.1162/neco_a_01199
42. Kuzilek J, Hlosta M, Zdrahal Z. Open University Learning Analytics dataset. Scientific Data. 2017, 4(1). doi: 10.1038/sdata.2017.171
43. Wang X, Yu X, Guo L, et al. Student Performance Prediction with Short-Term Sequential Campus Behaviors. Information. 2020, 11(4): 201. doi: 10.3390/info11040201
44. IOFFE, S, SZEGEDY. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In International conference on machine learning. 2015; pp. 448-456.
45. Kusumawardani SS, Alfarozi SAI. Transformer Encoder Model for Sequential Prediction of Student Performance Based on Their Log Activities. IEEE Access. 2023, 11: 18960-18971. doi: 10.1109/access.2023.3246122
46. Al-Zawqari A, Peumans D, Vandersteen G. A flexible feature selection approach for predicting students’ academic performance in online courses. Computers and Education: Artificial Intelligence. 2022, 3: 100103. doi: 10.1016/j.caeai.2022.100103
47. Sehaba K. Learner Performance Prediction Indicators Based on Machine Learning. In Proceedings of the 12th International Conference on Computer Supported Education (CSEDU 2020). 2020, 1: 47-57.
DOI: https://doi.org/10.32629/jai.v7i4.1443
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Mariame Ouahi, Samira Khoulji, Mohammed Laarbi Kerkeb
License URL: https://creativecommons.org/licenses/by-nc/4.0/