Abstract
Education is the basic requirement for living a good life with valuable prestige and self-assurance. Several approaches for educational learning are being adapted by various institutions to enhance the learning quality. The schools, training institutes and colleges follow similar criteria to evaluate students’ knowledge by conducting exams and tests after completion of course work. To estimate the quality of learning and teaching, educational evaluation is vital. In this paper authors considered a dataset on knowledge status of several students which includes educational objectives and educational features as the duration and time interval devoted to study, the total repetitive number of study sessions conducted, the difficulty level, and the type of questions being asked. The dataset is used for analysis of its features to understand the relationship between different instances to compute the knowledge status of different students and to use machine learning algorithms for analysis and classification of dataset.
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References
Altabrawee, H., Ali, O.A.J., Ajmi, S.Q.: Predicting students’ performance using machine learning techniques. J. Univ. Babylon Pure Appl. Sci. 27, 194–205 (2019). https://doi.org/10.29196/jubpas.v27i1.2108
Naidu, V.R., Singh, B., Farei, K.A., Suqri, N.A.: Machine learning for flipped teaching in higher education—a reflection. In: Al-Masri, A.N., Al-Assaf, Y. (eds.) Sustainable Development and Social Responsibility—Volume 2. ASTI, pp. 129–132. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-32902-0_16
Kahraman, H.T., Sagiroglu, S., Colak, I.: Development of adaptive and intelligent web-based educational systems. In: 2010 4th International Conference on Application of Information and Communication Technologies (2010). https://doi.org/10.1109/icaict.2010.5612054
Virvou, M., Alepis, E., Tsihrintzis, G.A., Jain, L.C.: Machine learning paradigms. In: Virvou, M., Alepis, E., Tsihrintzis, G.A., Jain, L.C. (eds.) Machine Learning Paradigms. ISRL, vol. 158, pp. 1–5. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-13743-4_1
Lau, E.T., Sun, L., Yang, Q.: Modelling, prediction and classification of student academic performance using artificial neural networks. SN Appl. Sci. 1(9), 1–10 (2019). https://doi.org/10.1007/s42452-019-0884-7
Sajda, P., Gerson, A., Muller, K.-R., Blankertz, B., Parra, L.: A data analysis competition to evaluate machine learning algorithms for use in brain-computer interfaces. IEEE Trans. Neural Syst. Rehabil. Eng. 11, 184–185 (2003). https://doi.org/10.1109/tnsre.2003.814453
Fatima, M., Pasha, M.: Survey of machine learning algorithms for disease diagnostic. J. Intell. Learn. Syst. Appl. 09, 1–16 (2017). https://doi.org/10.4236/jilsa.2017.91001
Asri, H., Mousannif, H., Moatassime, H.A., Noel, T.: Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Comput. Sci. 83, 1064–1069 (2016). https://doi.org/10.1016/j.procs.2016.04.224
Zhang, M.-L.: A k-nearest neighbor based multi-instance multi-label learning algorithm. In: 2010 22nd IEEE International Conference on Tools with Artificial Intelligence (2010). https://doi.org/10.1109/ictai.2010.102
Patra, A., Singh, D.: A survey report on text classification with different term weighing methods and comparison between classification algorithms. Int. J. Comput. Appl. 75(7), 14–18 (2013)
Mohammad Suhaimi, N., Abdul-Rahman, S., Mutalib, S., Abdul Hamid, N.H., Hamid, A.: Review on predicting students’ graduation time using machine learning algorithms. Int. J. Mod. Educ. Comput. Sci. 11, 1–13 (2019). https://doi.org/10.5815/ijmecs.2019.07.01
Sulmont, E., Patitsas, E., Cooperstock, J.R.: Can you teach me to machine learn?. In: Proceedings of the 50th ACM Technical Symposium on Computer Science Education (2019). https://doi.org/10.1145/3287324.3287392
Suykens, J., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9, 293–300 (1999). https://doi.org/10.1023/A:1018628609742
Vishwanathan, S.V.M., Narasimha Murty, M.: SSVM: a simple SVM algorithm. In: Proceedings of the 2002 International Joint Conference on Neural Networks, IJCNN 2002 (Cat. No. 02CH37290) (2020). https://doi.org/10.1109/ijcnn.2002.1007516
Bijalwan, V., Kumar, V., Kumari, P., Pascual, J.: KNN based machine learning approach for text and document mining. Int. J. Database Theory Appl. 7, 61–70 (2014). https://doi.org/10.14257/ijdta.2014.7.1.06
Ooi, H.-L., Ng, S.-C., Lim, E.: ANO detection with k-nearest neighbor using Minkowski distance. Int. J. Signal Process. Syst. 1, 208–211 (2013). https://doi.org/10.12720/ijsps.1.2.208-211
Nugrahaeni, R.A., Mutijarsa, K.: Comparative analysis of machine learning KNN, SVM, and random forests algorithm for facial expression classification. In: 2016 International Seminar on Application for Technology of Information and Communication (ISemantic) (2016). https://doi.org/10.1109/isemantic.2016.7873831
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Shyla, Bhatnagar, V. (2022). Probabilistic Evaluation of Distinct Machine Learning Algorithms. In: Sugumaran, V., Upadhyay, D., Sharma, S. (eds) Advancements in Interdisciplinary Research. AIR 2022. Communications in Computer and Information Science, vol 1738. Springer, Cham. https://doi.org/10.1007/978-3-031-23724-9_27
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