Design and evaluation of multimodal learning resources in blended teaching of college English
Vol 9, Issue 7, 2024, Article identifier:
VIEWS - 236 (Abstract) 53 (PDF)
Abstract
Higher Education (HE) is transforming towards embracing global pedagogical standards, particularly emphasizing student-centered learning models. In conjunction with these progressive initiatives, the incorporation of the Internet is aimed at enhancing course flexibility for both university instructors and students. Blended Learning (BL), a synthesis of online and face-to-face instruction, emerges as a methodology capable of leveraging the advantages inherent in both traditional classroom learning and online learning environments. The research being examined discovers the HE sector's application of globally recognized educational ethics and student-focused teaching. Online access has better-quality course flexibility. This type of education, which participates in classroom teaching with Online Education (OE), is being verified for communication skills. The research uses Moodle and a predictable informative setting to deal with several modes of education. The 49 HE pupils shared in a pre and post-test. Combining teaching improves communication skills, increasing the relationship between students and educators for a more practical education practice.
Keywords
Full Text:
PDFReferences
1. Syakur, A., Fanani, Z., & Ahmadi, R. (2020). The Effectiveness of Reading English Learning Process Based on Blended Learning through" Absyak" Website Media in Higher Education. Budapest International Research and Critics in Linguistics and Education (BirLE) Journal, 3(2), 763-772.
2. Rianto, A. (2020). Blended Learning Application in Higher Education: EFL Learners' Perceptions, Problems, and Suggestions. Indonesian Journal of English Language Teaching and Applied Linguistics, 5(1), 55-68.
3. Anthony, B., Kamaludin, A., Romli, A., Raffei, A. F. M., Phon, D. N. A. E., Abdullah, A., & Ming, G. L. (2022). Blended learning adoption and implementation in higher education: A theoretical and systematic review. Technology, Knowledge and Learning, 1-48.
4. Holiver, N., Tetiana, K., & Bondar, I. (2020). Blended learning for sustainable education: Moodle-based English for specific purposes teaching at Kryvyi Rih National University.
5. Islam, M. K., Sarker, M. F. H., & Islam, M. S. (2022). Promoting student-centered blended learning in higher education: A model. E-Learning and Digital Media, 19(1), 36-54.
6. Syakur, A. (2020). The effectiveness of English learning media through Google Classroom in Higher Education. Britain International of Linguistics Arts and Education (BIoLAE) Journal, 2(1), 475-483.
7. Sriwichai, C. (2020). Students' Readiness and Problems in Learning English through Blended Learning Environment. Asian Journal of Education and Training, 6(1), 23-34.
8. Rahman, A. M. A., Azmi, M. N. L., & Hassan, I. (2020). Improvement of English writing skills through blended learning among university students in Malaysia. Universal Journal of Educational Research, 8(12A), 7694-7701.
9. Alamri, H. A., Watson, S., & Watson, W. (2021). Learning technology models that support personalization within blended learning environments in higher education. TechTrends, 65, 62-78.
10. Yaroslavova, E. N., Kolegova, I. A., & Stavtseva, I. V. (2020). Flipped classroom blended learning model for the development of students’ foreign language communicative competence. Перспективы науки и образования, (1), 399-412.
11. B. Acharjya and S. Das, ‘Adoption of E-Learning During the COVID-19 Pandemic: The Moderating Role of Age and Gender’, International Journal of Web-Based Learning and Teaching Technologies, vol. 17, no. 2, 2022.
12. R. Setiawan et al., ‘IoT Based Virtual E-Learning System for Sustainable Development of Smart Cities’, Journal of Grid Computing, vol. 20, no. 3, 2022.
13. L. Zhang, S. Sengan, and P. Manivannan, ‘The Capture and Evaluation System of Student Actions in Physical Education Classroom Based on Deep Learning’, Journal of Interconnection Networks, vol. 22, 2022.
14. M. K. Thota, F. H. Shajin, and P. Rajesh, ‘Survey on software defect prediction techniques’, International Journal of Applied Science and Engineering, vol. 17, no. 4, pp. 331–344, 2020.
15. Naik, S. C. Satapathy, and A. Abraham, ‘Modified Social Group Optimization—a meta-heuristic algorithm to solve short-term hydrothermal scheduling’, Applied Soft Computing Journal, vol. 95, 2020.
16. Ruwali, A. J. S. Kumar, K. B. Prakash, G. Sivavaraprasad, and D. V. Ratnam, ‘Implementation of Hybrid Deep Learning Model (LSTM-CNN) for Ionospheric TEC Forecasting Using GPS Data’, IEEE Geoscience and Remote Sensing Letters, vol. 18, no. 6, pp. 1004–1008, 2021.
17. S. Deshmukh, K. Thirupathi Rao, and M. Shabaz, ‘Collaborative Learning Based Straggler Prevention in Large-Scale Distributed Computing Framework’, Security and Communication Networks, vol. 2021, 2021.
18. E. Rajesh Kumar, K. V. S. N. Rama Rao, S. R. Nayak, and R. Chandra, ‘Suicidal ideation prediction in Twitter data using machine learning techniques’, Journal of Interdisciplinary Mathematics, vol. 23, no. 1, pp. 117–125, 2020.
19. S. Kumar, A. Jain, A. Kumar Agarwal, S. Rani, and A. Ghimire, ‘Object-Based Image Retrieval Using the U-Net-Based Neural Network’, Computational Intelligence and Neuroscience, vol. 2021, 2021.
20. S. Sengan, P. Vidya Sagar, R. Ramesh, O. I. Khalaf, and R. Dhanapal, ‘The optimization of reconfigured real-time datasets for improving classification performance of machine learning algorithms’, Mathematics in Engineering, Science, and Aerospace, vol. 12, no. 1, pp. 43–54, 2021.
21. A. Alnuaim et al., ‘Human-Computer Interaction for Recognizing Speech Emotions Using Multilayer Perceptron Classifier,’ Journal of Healthcare Engineering, vol. 2022, 2022.
22. D. Bhavana, K. Kishore Kumar, M. B. Chandra, P. V. Sai Krishna Bhargav, D. Joy Sanjana, and G. Mohan Gopi, ‘Hand sign recognition using CNN,’ International Journal of Performability Engineering, vol. 17, no. 3, pp. 314–321, 2021.
23. S. Routray, P. P. Malla, S. K. Sharma, S. K. Panda, and G. Palai, ‘A new image denoising framework using bilateral filtering based non-subsampled shearlet transform’, Optik, vol. 216, 2020.
24. S. Kailasam, S. D. M. Achanta, P. Rama Koteswara Rao, R. Vatambeti, and S. Kayam, ‘An IoT-based agriculture maintenance using pervasive computing with machine learning technique’, International Journal of Intelligent Computing and Cybernetics, vol. 15, no. 2, pp. 184–197, 2022.
25. V. N. Mandhala, D. Bhattacharyya, B. Vamsi, and N. Thirupathi Rao, ‘Object detection using machine learning for visually impaired people’, International Journal of Current Research and Review, vol. 12, no. 20, pp. 157–167, 2020.
26. D. Balamurugan, S. S. Aravinth, P. C. S. Reddy, A. Rupani, and A. Manikandan, ‘Multiview Objects Recognition Using Deep Learning-Based Wrap-CNN with Voting Scheme’, Neural Processing Letters, vol. 54, no. 3, pp. 1495–1521, 2022.
27. M. Mohammed, R. Kolapalli, N. Golla, and S. S. Maturi, ‘Prediction of rainfall using machine learning techniques’, International Journal of Scientific and Technology Research, vol. 9, no. 1, pp. 3236–3240, 2020.
28. S. C. Dharmadhikari, V. Gampala, C. M. Rao, S. Khasim, S. Jain, and R. Bhaskaran, ‘A smart grid incorporated with ML and IoT for a secure management system’, Microprocessors and Microsystems, vol. 83, 2021.
29. A. Naik and S. C. Satapathy, ‘A comparative study of social group optimization with a few recent optimization algorithms’, Complex and Intelligent Systems, vol. 7, no. 1, pp. 249–295, 2021.
30. S. P. Jaiprakash, M. B. Desai, C. S. Prakash, V. H. Mistry, and K. L. Radadiya, ‘Low dimensional DCT and DWT feature-based model for detection of image splicing and copy-move forgery’, Multimedia Tools and Applications, vol. 79, no. 39–40, pp. 29977–30005, 2020.
31. K. Mannepalli, P. N. Sastry, and M. Suman, ‘Emotion recognition in speech signals using optimization based multi-SVNN classifier’, Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 2, pp. 384–397, 2022.
32. S. Rajasoundaran et al., ‘Machine learning based deep job exploration and secure transactions in virtual private cloud systems’, Computers and Security, vol. 109, 2021.
33. N. V. Kimmatkar and B. Vijaya Babu, ‘Novel approach for emotion detection and stabilizing mental state by using machine learning techniques’, Computers, vol. 10, no. 3, 2021.
34. J. R. K. K. Dabbakuti, A. Jacob, V. R. Veeravalli, and R. K. Kallakunta, ‘Implementation of IoT analytics ionospheric forecasting system based on machine learning and ThingSpeak’, IET Radar, Sonar and Navigation, vol. 14, no. 2, pp. 341–347, 2020.
35. S. Sekar et al., ‘Autonomous Transaction Model for E-Commerce Management Using Blockchain Technology’, International Journal of Information Technology and Web Engineering, vol. 17, no. 1, 2022.
36. S. D. M. Achanta, T. Karthikeyan, and R. V. Kanna, ‘Wearable sensor-based acoustic gait analysis using phase transition-based optimization algorithm on IoT’, International Journal of Speech Technology, 2021.
37. T. Rajesh Kumar, G. R. Suresh, S. Kanaga Subaraja, and C. Karthikeyan, ‘Taylor-AMS features and deep convolutional neural network for converting nonaudible murmur to normal speech’, Computational Intelligence, vol. 36, no. 3, pp. 940–963, 2020.
38. S. P. Praveen, T. B. Murali Krishna, C. H. Anuradha, S. R. Mandalapu, P. Sarala, and S. Sindhura, ‘A robust framework for handling health care information based on machine learning and big data engineering techniques’, International Journal of Healthcare Management, 2022.
39. K. N. Reddy and P. Bojja, ‘A new hybrid optimization method combining moth–flame optimization and teaching–learning-based optimization algorithms for visual tracking’, Soft Computing, vol. 24, no. 24, pp. 18321–18347, 2020.
40. S. R. Kadiri, P. Gangamohan, S. V. Gangashetty, P. Alku, and B. Yegnanarayana, ‘Excitation Features of Speech for Emotion Recognition Using Neutral Speech as Reference’, Circuits, Systems, and Signal Processing, vol. 39, no. 9, pp. 4459–4481, 2020.
41. A. Banchhor and N. Srinivasu, ‘FCNB: Fuzzy Correlative Naive Bayes Classifier with MapReduce Framework for Big Data Classification’, Journal of Intelligent Systems, vol. 29, no. 1, pp. 994–1006, 2020.
42. G. S. Lalotra, V. Kumar, A. Bhatt, T. Chen, and M. Mahmud, ‘iReTADS: An Intelligent Real-Time Anomaly Detection System for Cloud Communications Using Temporal Data Summarization and Neural Network’, Security and Communication Networks, vol. 2022, 2022.
43. Venkateswarlu, V. V. Shenoi, and P. Tumuluru, ‘CAViaR-WS-based HAN: conditional autoregressive value at risk-water sailfish-based hierarchical attention network for emotion classification in COVID-19 text review data’, Social Network Analysis and Mining, vol. 12, no. 1, 2022.
44. S. R. Nayak, S. Sivakumar, A. K. Bhoi, G.-S. Chae, and P. K. Mallick, ‘Mixed-mode database miner classifier: Parallel computation of graphical processing unit mining’, International Journal of Electrical Engineering Education, 2021.
45. S. A. Devi and S. S. Kumar, ‘A hybrid document features extraction with clustering based classification framework on large document sets’, International Journal of Advanced Computer Science and Applications, vol. 11, no. 7, pp. 364–374, 2020.
46. S. Rani, D. Ghai, and S. Kumar, ‘Reconstruction of Simple and Complex Three Dimensional Images Using Pattern Recognition Algorithm’, Journal of Information Technology Management, vol. 14, pp. 235–247, 2022.
47. M. V. V. P. Kantipudi, S. Kumar, and A. K. Jha, ‘Scene text recognition based on bidirectional LSTM and deep neural network’, Computational Intelligence and Neuroscience, vol. 2021, 2021.
48. S. Depuru, A. Nandam, P. A. Ramesh, M. Saktivel, K. Amala, and Sivanantham, ‘Human Emotion Recognition System Using Deep Learning Technique’, Journal of Pharmaceutical Negative Results, vol. 13, no. 4, pp. 1031–1035, 2022.
49. J. Saha, C. Chowdhury, D. Ghosh, and S. Bandyopadhyay, ‘A detailed human activity transition recognition framework for grossly labelled data from smartphone accelerometer’, Multimedia Tools and Applications, vol. 80, no. 7, pp. 9895–9916, 2021.
50. A. A. Shaik, V. D. P. Mareedu, and V. V. K. Polurie, ‘Learning multi-view deep features from skeletal sign language videos for recognition’, Turkish Journal of Electrical Engineering and Computer Sciences, vol. 29, no. 2, pp. 1061–1076, 2021.
51. K. R. Prasad, B. E. Reddy, and M. Mohammed, ‘An effective assessment of cluster tendency through sampling based multi-viewpoints visual method’, Journal of Ambient Intelligence and Humanized Computing, 2021.
52. H. A. El-Wahed Khalifa, P. Kumar, and F. Smarandache, ‘On optimizing neutrosophic complex programming using lexicographic order’, Neutrosophic Sets and Systems, vol. 32, pp. 330–343, 2020.
53. S. Immareddy and A. Sundaramoorthy, ‘A survey paper on design and implementation of multipliers for digital system applications’, Artificial Intelligence Review, vol. 55, no. 6, pp. 4575–4603, 2022.
54. K. P. Somase and S. S. Imambi, ‘Develop and implement unsupervised learning through hybrid FFPA clustering in large-scale datasets’, Soft Computing, vol. 25, no. 1, pp. 277–290, 2021.
55. H. K. Yadla and P. V. R. D. P. Rao, ‘Machine learning based text classifier centred on TF-IDF vectoriser’, International Journal of Scientific and Technology Research, vol. 9, no. 3, pp. 583–586, 2020.
56. J. Yadav, M. Misra, N. P. Rana, and K. Singh, ‘Exploring the synergy between nano-influencers and sports community: behaviour mapping through machine learning’, Information Technology and People, vol. 35, no. 7, pp. 1829–1854, 2022.
57. B. K. Durga and V. Rajesh, ‘A ResNet deep learning-based facial recognition design for future multimedia applications’, Computers and Electrical Engineering, vol. 104, 2022.
58. S. Rani, K. Lakhwani, and S. Kumar, ‘Three-dimensional objects recognition & pattern recognition technique; related challenges: A review’, Multimedia Tools and Applications, vol. 81, no. 12, pp. 17303–17346, 2022.
59. M. Suneetha, P. M.V.D., and K. P.V.V., ‘Multi-view motion modelled deep attention networks (M2DA-Net) for video-based sign language recognition’, Journal of Visual Communication and Image Representation, vol. 78, 2021.
60. P. V. V. S. Srinivas and P. Mishra, ‘An Improvised Facial Emotion Recognition System using the Optimized Convolutional Neural Network Model with Dropout’, International Journal of Advanced Computer Science and Applications, vol. 12, no. 7, pp. 381–390, 2021.
DOI: https://doi.org/10.59429/esp.v9i7.2085
(236 Abstract Views, 53 PDF Downloads)
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Jiarui Zhang, Lei Ma
This work is licensed under a Creative Commons Attribution 4.0 International License.