Pedagogical Content Knowledge Classification using CNN with Bi-LSTM

Pedagogical Content Knowledge Classification using CNN with Bi-LSTM

© 2022 by IJETT Journal
Volume-70 Issue-8
Year of Publication : 2022
Authors : Murthy V. S. N. Tatavarthy, V. Naga Lakshmi
DOI : 10.14445/22315381/IJETT-V70I8P228

How to Cite?

Murthy V. S. N. Tatavarthy, V. Naga Lakshmi, "Pedagogical Content Knowledge Classification using CNN with Bi-LSTM," International Journal of Engineering Trends and Technology, vol. 70, no. 8, pp. 264-271, 2022. Crossref,

Pedagogical Content Knowledge (PCK) is the establishment of academics that provides an interesting idea of teaching. PCK is an idea of the belief that teaching is considerably needed more than providing the knowledge of subject contents to students. It is also the knowledge teachers acquire over time and by the experience to explain particular contents in specific ways to students' understanding. The growth of the internet and larger digital technology has come up with various difficulties. The larger amount of data on the internet is probably unorganized and unstructured, making it difficult to utilize and manipulate the data process. Deep learning, as well as machine learning mechanisms for classifying the texts, is the importance of PCK. In the present research, the pedagogical contents are classified using Condensed Nearest Neighbor (CNN) with Bi-LSTM. In general, the CNN classifier is a simple process and constructs the subsets of example that classifies the original data correctly. The Bi-LSTM enhances LSTM, which improves the model's performance in the sequence classification process. The proposed CNN with Bi-LSTM has achieved an accuracy of 78.79compared to the existing KNN accuracy of 77.5% for the classification of pedagogical content in experiment analysis.

Bi-directional long short-term memory, Condensed nearest neighbor, Digital technology, Internet, Pedagogical content knowledge.

[1] S. N. Liao, D. Zingaro, K. Thai, C. Alvarado, W. G. Griswold, and L. Porter, “A Robust Machine Learning Technique to Predict Low-Performing Students,” Acm Trans. Comput. Educ, vol. 19, no.3, pp. 18., 2019.
[2] R. Salas-Rueda, “Construction and Evaluation of a Web Application for the Educational Process on normal Distribution Considering the Science of Data and Machine Learning,” Research In Learning Technology, vol.27 , pp.2085, 2019.
[3] A. Wahlen, C. Kuhn, O. Zlatkin-Troitschanskaia, C. Gold, T. Zesch, and A. Horbach, “Automated Scoring of Teachers' Pedagogical Content Knowledge-A Comparison Between Human and Machine Scoring,” Front. Educ, vol. 5 , pp.149, 2020.
[4] S. K. Fahrurozi, C. W. Budiyanto, and R. Roemintoyo, “Technological Pedagogical and Content Knowledge (Tpack) for Overcoming Teacher Problems In Vocational Education and Challenges in the 21st Century,” Journal of Mechanical Engineering and Vocational Education, vol. 2, no.1, pp. 33–40, 2019.
[5] H. Cui, S. Tancock, and N. Dahnoun, “E-Learning tool to Enhance Technological Pedagogical Content Knowledge,” In 8 th Mediterranean Conference on Embedded Computing (Meco), pp. 1–4, 2019.
[6] J. Geng, C. S. Chai, M. S. Y. Jong, and E. T. H. Luk, Understanding the Pedagogical Potential of Interactive Spherical Video-Based Virtual Reality From the Teachers’ Perspective Through the Ace Framework, Interactive Learning Environments , vol.29, no.4, pp.618–633, 2021.
[7] L. Huang, “Integrating Machine Learning to Undergraduate Engineering Curricula Through Project-Based Learning,” In Ieee Frontiers In Education Conference (Fie), pp.1–4, 2019.
[8] K. Sethi, V. Jaiswal, and M. D. Ansari, “ Machine Learning Based Support System for Students to Select Stream (Subject),” Recent Advances In Computer Science and Communications, vol.13, no.3, pp. 336–344, 2020.
[9] M. Hussain, W. Zhu, W. Zhang, S. M. R. Abidi, and S. Ali, “Using Machine Learning to Predict Student Difficulties From Learning Session Data,” Artificial Intelligence Review, vol. 52, no.1, pp. 381–407, 2019.
[10] S. Saarinen, S. Krishnamurthi, K. Fisler, and P. T. Wilson, “Harnessing the Wisdom of the Classes: Classsourcing and Machine Learning for Assessment Instrument Generation,” In Proc. 50th Acm Technical Symposium on Computer Science Education (Sigcse’19), pp. 606–612, 2019.
[11] H. Mushtaq, I. Siddique, B. H. Malik, M. Ahmed, U. M. Butt, R. M. T. Ghafoor, H. Zubair, and U. Farooq, “Educational Data Classification Framework for Community Pedagogical Content Management Using Data Mining,” Int. J. Adv. Comput. Sci. Appl, vol.10, no.1, pp.329–338, 2019.
[12] Y. Madani, H. Ezzikouri, M. Erritali, and B. Hssina, “Finding Optimal Pedagogical Content In An Adaptive E-Learning Platform Using a New Recommendation Approach and Reinforcement Learning,” J. Ambient Intell. Hum. Comput, vol. 11, no.10, pp.3921–3936, 2020.
[13] X. Zhai, K. C. Haudek, M. A. Stuhlsatz, and C. Wilson, “Evaluation of Construct-Irrelevant Variance Yielded By Machine and Human Scoring of A Science Teacher Pck Constructed Response Assessment,” Studies In Educational Evaluation , vol.67, pp.100916, 2020.
[14] S. B. Khoza, and A. T. Biyela, “Decolonising Technological Pedagogical Content Knowledge of First Year Mathematics Students,” Education and Information Technologies, vol. 25, no.4, pp. 2665–2679, 2020.
[15] V. Apuk, and K. P. Nuçi, “Classification of Pedagogical Content Using Conventional Machine Learning and Deep Learning Model,” Menonet Journal: Works In Progress In Embedded Computing (Wipiec), vol.7, no.1, pp. 1–7, 2021.
[16] Aboelmagd, Amna Nagaty, Ebtsam S. Mahrous, and Sabah Saleh Hassan, “Effect of Educational Interventional Program for Preschool Children on their Knowledge and Practice Regarding Sexual Harassment," Ssrg International Journal of Nursing and Health Science , vol.5, no. 2 , pp.12-19, 2019.
[17] Z. Kastrati, A. Kurti, and A. S. Imran, “Wet: Word Embedding-topic Distribution Vectors for Mooc Video Lectures Dataset,: Data Brief 28 , pp. 105090, 2020.
[18] "Boosting credibility of a Recommender System using Deep Learning Techniques - An Empirical Study" International Journal of Engineering Trends and Technology 69.10(2021):235-243.
[19] Ayush Jain, Prathamesh Patil, Ganesh Masud, Prof. Sunantha Krishnan, Prof. Vijaya Bharathi Jagan, "Detection of Sarcasm through Tone Analysis on video and Audio files: A Comparative Study On Ai Models Performance" SSRG International Journal of Computer Science and Engineering 8.12 (2021): 1-5. Crossref,
[20] Dr.Ahmadi Begum, "Innovative and Effective Teaching Methods for Engineering Students," International Journal of Humanities and Social Science, vol. 6, no. 1, pp. 1-3, 2019. Crossref,