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Research Article

ACME: automated classification model for E-learning feedback

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Received 21 Jun 2022, Accepted 01 Apr 2023, Published online: 27 Apr 2023

References

  • Aggarwal, C. C. (2018). Neural Networks and Deep Learning (1, pp. 1–497). Springer Cham. https://doi.org/10.1007/978-3-319-94463-0
  • Altrabsheh, N., Cocea, M., & Fallahkhair, S. (2014a). Learning sentiment from students’ feedback for real-time interventions in classrooms. Lecture Notes in Computer Science LNAI, 8779(1), 40–49. https://doi.org/10.1007/978-3-319-11298-5_5
  • Altrabsheh, N., Cocea, M., & Fallahkhair, S. (2014b). Sentiment analysis: Towards a tool for analysing real-time students feedback. Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, 419–423. https://doi.org/10.1109/ICTAI.2014.70
  • Assegaf, B. (2017). Student academic performance prediction on problem based learning using support vector machine and K-nearest neighbor. Journal of Telematics and Informatics, 5(1), 22–28. https://doi.org/10.12928/jti.v5i1.22-28
  • Bayhaqy, A., Sfenrianto, S., Nainggolan, K., & Kaburuan, E. R. (2018). Sentiment analysis about E-commerce from tweets using decision tree, K-nearest neighbor, and Naïve Bayes. International Conference on Orange Technologies, ICOT, October. https://doi.org/10.1109/ICOT.2018.8705796
  • Bhatt, C., Kumar, I., Vijayakumar, V., Singh, K. U., & Kumar, A. (2021). The state of the art of deep learning models in medical science and their challenges. Multimedia Systems, 27(4), 599–613. https://doi.org/10.1007/s00530-020-00694-1
  • Bower, M. (2019). Technology-mediated learning theory. British Journal of Educational Technology, 50(3), 1035–1048. https://doi.org/10.1111/bjet.12771
  • Burman, I., & Som, S. (2019). Predicting students academic performance using support vector machine. Proceedings – Amity International Conference on Artificial Intelligence, AICAI, 756–759. https://doi.org/10.1109/AICAI.2019.8701260
  • Chakrabarty, A., Chaturvedi, A., & Garain, U. (2019). CNN-based context sensitive lemmatization. ACM International Conference Proceeding Series, 334–337. https://doi.org/10.1145/3297001.3297054
  • Chanaa, A., Chanaa, A., & Faddouli, N. E. (2021). E-learning text sentiment classification using hierarchical attention network. International Journal of Emerging Technologies in Learning (IJET), 16(13), 157–167. https://doi.org/10.3991/ijet.v16i13.22579
  • Chaudhary, A., Kolhe, S., & Kamal, R. (2016). An improved random forest classifier for multi-class classification. Information Processing in Agriculture, 3(4), 215–222. https://doi.org/10.1016/j.inpa.2016.08.002
  • Chen, R., Guo, S., Wang, X., & Zhang, T. (2019). Fusion of multi-RSMOTE with fuzzy integral to classify bug reports with an imbalanced distribution. IEEE Transactions on Fuzzy Systems, 27(12), 2406–2420. https://doi.org/10.1109/TFUZZ.2019.2899809
  • Chen, S., Webb, G. I., Liu, L., & Ma, X. (2020). A novel selective Naïve Bayes algorithm. Knowledge-Based Systems, 192(1), Article 105361. https://doi.org/10.1016/j.knosys.2019.105361
  • Chen, X., Vorvoreanu, M., & Madhavan, K. P. C. (2014). Mining social media data for understanding students’ learning experiences. IEEE Transactions on Learning Technologies, 7(3), 246–259. https://doi.org/10.1109/TLT.2013.2296520
  • Cinquin, P. A., Guitton, P., & Sauzéon, H. (2019). Online e-learning and cognitive disabilities: A systematic review. Computers & Education, 130(1), 152–167. https://doi.org/10.1016/j.compedu.2018.12.004
  • Deka, B., Huang, Z., Franzen, C., Hibschman, J., Afergan, D., Li, Y., Nichols, J., & Kumar, R. (2017). Rico: A mobile app dataset for building data-driven design applications. Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology. https://doi.org/10.1145/3126594
  • Denisko, D., & Hoffman, M. M. (2018). Classification and interaction in random forests. Proceedings of the National Academy of Sciences of the United States of America, 115(8), 1690–1692. https://doi.org/10.1073/pnas.1800256115
  • Garcia, C. I., Grasso, F., Luchetta, A., Piccirilli, M. C., Paolucci, L., & Talluri, G. (2020). A comparison of power quality disturbance detection and classification methods using CNN, LSTM and CNN-LSTM. Applied Sciences, 10(19), 6755. https://doi.org/10.3390/app10196755
  • George, G., & Lal, A. M. (2019). Review of ontology-based recommender systems in e-learning. Computers & Education, 142(1), Article 103642. https://doi.org/10.1016/j.compedu.2019.103642
  • Gianey, H. K., & Choudhary, R. (2018). Comprehensive review on supervised machine learning algorithms. Proceedings – International Conference on Machine Learning and Data Science, MLDS, January, 38–43. https://doi.org/10.1109/MLDS.2017.11
  • Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., & Chen, T. (2018). Recent advances in convolutional neural networks. Pattern Recognition, 77(1), 354–377. https://doi.org/10.1016/j.patcog.2017.10.013
  • Hayashida, Y., Uetsuji, T., Ebara, Y., & Koyamada, K. (2017). Category classification of text data with machine learning technique for visualizing flow of conversation in counseling. Proceedings – NICOGRAPH International, NICOInt 2017, 37–40. https://doi.org/10.1109/NICOINT.2017.35
  • Hertel, L., Collado, J., Sadowski, P., Ott, J., & Baldi, P. (2020). Sherpa: Robust hyperparameter optimization for machine learning. SoftwareX, 12(1), Article 100591. https://doi.org/10.1016/j.softx.2020.100591
  • Hindle, A., Alipour, A., & Stroulia, E. (2016). A contextual approach towards more accurate duplicate bug report detection and ranking. Empirical Software Engineering, 21(2), 368–410. https://doi.org/10.1007/s10664-015-9387-3
  • Hussein, D. M. E. D. M. (2018). A survey on sentiment analysis challenges. Journal of King Saud University - Engineering Sciences, 30(4), 330–338. https://doi.org/10.1016/j.jksues.2016.04.002
  • Hussein Abdulzhraa Al-Sagheer, R., Liu, H., A Alsagheer, R. H., H Alharan, A. F., & A Al-Haboobi, A. S. (2017). Popular decision tree algorithms of data mining techniques: A review. International Journal of Computer Science and Mobile Computing, 6(6), 133–142.
  • Jet, A., & O, H. J. (2017). Supervised machine learning algorithms: Classification and comparison. International Journal of Computer Trends and Technology, 48(1). https://doi.org/10.14445/22312803/IJCTT-V48P126
  • Jiang, H., Nazar, N., Zhang, J., Zhang, T., & Ren, Z. (2017). PRST : A PageRank-based summarization technique. International Journal of Software Engineering and Knowledge Engineering, 27(6), 869–896. https://doi.org/10.1142/S0218194017500322
  • Kartal, H., Oztekin, A., Gunasekaran, A., & Cebi, F. (2016). An integrated decision analytic framework of machine learning with multi-criteria decision making for multi-attribute inventory classification. Computers and Industrial Engineering, 101, 599–613. https://doi.org/10.1016/j.cie.2016.06.004
  • Pirjatullah, Kartini, D., Nugrahadi, D. T., Muliadi, & Farmadi, A. (2021). Hyperparameter tuning using GridsearchCV on the comparison of the activation function of the ELM method to the classification of pneumonia in toddlers. Proceedings – 4th International Conference on Computer and Informatics Engineering: IT-Based Digital Industrial Innovation for the Welfare of Society, IC2IE, 390–395. https://doi.org/10.1109/IC2IE53219.2021.9649207
  • Katerina, G.-P., & Tyo, J. (2018). Identification of security related bug reports via text mining using supervised and unsupervised classification. IEEE International Conference on Software Quality, Reliability and Security (QRS), 344–355. https://doi.org/10.1109/QRS.2018.00047
  • Katragadda, S., Ravi, V., Kumar, P., & Lakshmi, G. J. (2020). Performance analysis on student feedback using machine learning algorithms. 6th International Conference on Advanced Computing and Communication Systems, ICACCS, 1161–1163. https://doi.org/10.1109/ICACCS48705.2020.9074334
  • Kechaou, Z., Ben Ammar, M., & Alimi, A. M. (2011). Improving e-learning with sentiment analysis of users’ opinions. IEEE Global Engineering Education Conference, EDUCON, 1032–1038. https://doi.org/10.1109/EDUCON.2011.5773275
  • Kietzmann, J., & Pitt, L. F. (2020). Artificial intelligence and machine learning: What managers need to know. Business Horizons, 63(2), 131–133. https://doi.org/10.1016/j.bushor.2019.11.005
  • Lakshmanaprabu, S. K., Shankar, K., Ilayaraja, M., Nasir, A. W., Vijayakumar, V., & Chilamkurti, N. (2019). Random forest for big data classification in the internet of things using optimal features. International Journal of Machine Learning and Cybernetics, 10(10), 2609–2618. https://doi.org/10.1007/s13042-018-00916-z
  • Liang, X. W., Jiang, A. P., Li, T., Xue, Y. Y., & Wang, G. T. (2020). LR-SMOTE – An improved unbalanced data set oversampling based on K-means and SVM. Knowledge-Based Systems, 196(1), 105845. https://doi.org/10.1016/j.knosys.2020.105845
  • Mahesh, B. (2018). Machine learning algorithms – A review. International Journal of Science and Research. https://doi.org/10.21275/ART20203995
  • Mathew, J., Luo, M., Pang, C. K., & Chan, H. L. (2015). Kernel-based SMOTE for SVM classification of imbalanced datasets. IECON – 41st Annual Conference of the IEEE Industrial Electronics Society, 1127–1132. https://doi.org/10.1109/IECON.7392251
  • Messaoud, M. B., Jenhani, I., Jemaa, N. B., & Mkaouer, M. W. (2019). A multi-label active learning approach for mobile app user review classification. Lecture Notes in Computer Science, 11775(1), 805–816. https://doi.org/10.1007/978-3-030-29551-6_71
  • Mir, A., & Nasiri, J. (2019). LightTwinSVM: A simple and fast implementation of standard twin support vector machine classifier. Journal of Open Source Software, 4(35), 1252. https://doi.org/10.21105/joss.01252
  • Mubarak, A. A., Cao, H., & Zhang, W. (2020). Prediction of students’ early dropout based on their interaction logs in online learning environment. Interactive Learning Environments, 30(8), 1414–1433. https://doi.org/10.1080/10494820.2020.1727529
  • Oloruntoba, S. A. (2017). Student academic performance prediction using support vector machine. IJESRT International Journal of Engineering Sciences & Research Technology, 6(12), 588–598.
  • Onan, A. (2020). Mining opinions from instructor evaluation reviews: A deep learning approach. Computer Applications in Engineering Education, 28(1), 117–138. https://doi.org/10.1002/cae.22179
  • Ortigosa, A., Martín, J. M., & Carro, R. M. (2014). Sentiment analysis in Facebook and its application to e-learning. Computers in Human Behavior, 31(1), 527–541. https://doi.org/10.1016/j.chb.2013.05.024
  • Patricia Aguilera-Hermida, A. (2020). College students’ use and acceptance of emergency online learning due to COVID-19. International Journal of Educational Research Open, 1(1), Article 100011. https://doi.org/10.1016/j.ijedro.2020.100011
  • Petkovic, D., Sosnick-Pérez, M., Okada, K., Todtenhoefer, R., Huang, S., Miglani, N., & Vigil, A. (2016). Using the random forest classifier to assess and predict student learning of Software Engineering Teamwork. Proceedings – Frontiers in Education Conference, FIE. https://doi.org/10.1109/FIE.2016.7757406
  • Rahman, S. S. M. M., Biplob, K. B. M. B., Rahman, M. H., Sarker, K., & Islam, T. (2020). An investigation and evaluation of N-Gram, TF-IDF and ensemble methods in sentiment classification. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 325(1), 391–402. https://doi.org/10.1007/978-3-030-52856-0_31
  • Rajesh, P., & Suseendran, G. (2020). Prediction of N-Gram language models using sentiment analysis on E-learning reviews. Proceedings of International Conference on Intelligent Engineering and Management, ICIEM, 510–514. https://doi.org/10.1109/ICIEM48762.2020.9160260
  • Ranjan, G. S. K., Kumar Verma, A., & Radhika, S. (2019). K-Nearest neighbors and grid search CV based real time fault monitoring system for industries. IEEE 5th International Conference for Convergence in Technology, I2CT. https://doi.org/10.1109/I2CT45611.2019.9033691
  • Ray, S. (2019). A quick review of machine learning algorithms. Proceedings of the International Conference on Machine Learning, Big Data, Cloud and Parallel Computing: Trends, Prespectives and Prospects, COMITCon, 35–39. https://doi.org/10.1109/COMITCON.2019.8862451
  • Roy, N. K. S., & Rossi, B. (2017). Cost-sensitive strategies for data imbalance in bug severity classification: Experimental results. 43rd Euromicro Conference on Software Engineering and Advanced Applications (SEAA), 426–429. https://doi.org/10.1109/SEAA.2017.71
  • Rusli, A., Suryadibrata, A., Nusantara, S. B., & Young, J. C. (2020). A comparison of traditional machine learning approaches for supervised feedback classification in Bahasa Indonesia. IJNMT (International Journal of New Media Technology), 7(1), 28–32. https://doi.org/10.31937/ijnmt.v1i1.1485
  • Sarica, S., & Luo, J. (2021). Stopwords in technical language processing. PLOS ONE, 16(8), e0254937. https://doi.org/10.1371/journal.pone.0254937
  • Schonlau, M., & Zou, R. Y. (2020). The random forest algorithm for statistical learning. The Stata Journal: Promoting Communications on Statistics and Stata, 20(1), 3–29. https://doi.org/10.1177/1536867X20909688
  • Schratz, P., Muenchow, J., Iturritxa, E., Richter, J., & Brenning, A. (2019). Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data. Ecological Modelling, 406(1), 109–120. https://doi.org/10.1016/j.ecolmodel.2019.06.002
  • Selvapandian, D., Thamba Meshach, W., Babu, K. S. S., Dhanapal, R., & Immanuel, J. D. (2020). An efficient sentiment analysis on feedback assessment from student to provide better education. Proceedings of the 4th International Conference on IoT in Social, Mobile, Analytics and Cloud, ISMAC, 1293–1300. https://doi.org/10.1109/I-SMAC49090.2020.9243594
  • Sen, P. C., Hajra, M., & Ghosh, M. (2020). Supervised classification algorithms in machine learning: A survey and review. Advances in Intelligent Systems and Computing, 937(1), 99–111. https://doi.org/10.1007/978-981-13-7403-6_11
  • Silva, D. A. L., Giusti, G., Rampasso, I. S., Junior, A. C. F., Marins, M. A. S., & Anholon, R. (2021). The environmental impacts of face-to-face and remote university classes during the COVID-19 pandemic. Sustainable Production and Consumption, 27, 1975–1988. https://doi.org/10.1016/j.spc.2021.05.002
  • Sindhu, I., Muhammad Daudpota, S., Badar, K., Bakhtyar, M., Baber, J., & Nurunnabi, M. (2019). Aspect-based opinion mining on student’s feedback for faculty teaching performance evaluation. IEEE Access, 7(1), 108729–108741. https://doi.org/10.1109/ACCESS.2019.2928872
  • Smagulova, K., & James, A. P. (2019). A survey on LSTM memristive neural network architectures and applications. The European Physical Journal Special Topics, 228(10), 2313–2324. https://doi.org/10.1140/epjst/e2019-900046-x
  • Solangi, Y. A., Solangi, Z. A., Aarain, S., Abro, A., Mallah, G. A., & Shah, A. (2019). Review on Natural Language Processing (NLP) and its toolkits for opinion mining and sentiment analysis. IEEE 5th International Conference on Engineering Technologies and Applied Sciences, ICETAS. https://doi.org/10.1109/ICETAS.2018.8629198
  • Somvanshi, M., Chavan, P., Tambade, S., & Shinde, S. V. (2017). A review of machine learning techniques using decision tree and support vector machine. Proceedings – 2nd International Conference on Computing, Communication, Control and Automation, ICCUBEA. https://doi.org/10.1109/ICCUBEA.2016.7860040
  • Subbiah, U., Ramachandran, M., & Mahmood, Z. (2019). Software engineering approach to bug prediction models using machine learning as a service (MLaaS). ICSOFT – Proceedings of the 13th International Conference on Software Technologies, 879–887. https://doi.org/10.5220/0006926308790887
  • Tasdelen, A., & Sen, B. (2021). A hybrid CNN-LSTM model for pre-miRNA classification. Scientific Reports, 11(1), 1–9. https://doi.org/10.1038/s41598-021-93656-0
  • Winkler, J. P., Grönberg, J., & Vogelsang, A. (2019). Optimizing for recall in automatic requirements classification: An empirical study. Proceedings of the IEEE International Conference on Requirements Engineering, 40–50. https://doi.org/10.1109/RE.2019.00016
  • Yeshambel, T., Mothe, J., & Assabie, Y. (2021). Construction of morpheme-based amharic stopword list for information retrieval system. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 384(1), 484–498. https://doi.org/10.1007/978-3-030-80621-7_35
  • Yogish, D., Manjunath, T. N., & Hegadi, R. S. (2019). Review on natural language processing trends and techniques using NLTK. Communications in Computer and Information Science, 1037(1), 589–606. https://doi.org/10.1007/978-981-13-9187-3_53
  • Zhang, Hongpo, Huang, Lulu, Wu, Chase Q, & Li, Zhanbo. (2020). An effective convolutional neural network based on SMOTE and Gaussian mixture model for intrusion detection in imbalanced dataset. Computer Networks, 177(1), 107315. https://doi.org/10.1016/j.comnet.2020.107315
  • Zhang, S., Cheng, D., Deng, Z., Zong, M., & Deng, X. (2018). A novel kNN algorithm with data-driven k parameter computation. Pattern Recognition Letters, 109(1), 44–54. https://doi.org/10.1016/j.patrec.2017.09.036
  • Zhou, Y., Zhao, J., & Zhang, J. (2020). Prediction of learners’ dropout in E-learning based on the unusual behaviors. Interactive Learning Environments. https://doi.org/10.1080/10494820.2020.1857788

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