Tech Quest Language Learning
Main Article Content
Abstract
Tech Quest Language Learning is an innovative web-based platform designed to facilitate language learning through interactive quizzes tailored for engineering students and professionals. The platform offers a diverse range of quizzes covering various engineering subjects, providing users with an engaging and effective way to test their knowledge and skills. Through a user-friendly interface, participants can navigate seamlessly between quizzes, receive instant feedback on their performance, and track their scores over time. Additionally, the platform incorporates user authentication mechanisms, ensuring secure access to personalized learning experiences. With its emphasis on interactivity, accessibility, and user engagement, "Tech Quest Language Learning" aims to enhance language proficiency and academic success in the engineering domain.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
How to Cite
References
Interactive Learning Environments (2021): Patel and Kim's 2021 study, "Engaging Learning: The Impact of Quizzes and Simulations," focused on the role of interactive elements on platforms like Coursera.
Chen, N. S., Wei, C. W., & Chen, H. J. (2008). Mining e-Learning domain concept map from academic articles. Computers & Education, 50(3), 1009-1021. https://doi.org/10.1016/j.compedu.2006.10.001
Interdisciplinary Insights: A Comprehensive Review of Technologies in Language Learning Platforms (2021): Authors: Smith, A., Patel, M., Kim, S., et al. This comprehensive literature review aims to provide an interdisciplinary examination of the integration of diverse technologies in contemporary language learning platforms.
Smith, J., Brown, A., & Lee, C. (2021). "Revolutionizing Language Learning Platforms."
Smith, A., et al. (2021). "Interdisciplinary Insights: A Comprehensive Review of Technologies in Language Learning Platforms."
Kurose, J. F., & Ross, K. W. (2017). "Computer Networking: A Top-Down Approach."
Mensah, J. K., Abandoh-Sam, J. A., Amankwah, H., & Tchouchu, E. (2024). The Relevance of Development and Deployment of Software Defined Networking Solutions for a University Network. In Indian Journal of Data Communication and Networking (Vol. 4, Issue 2, pp. 5–12). https://doi.org/10.54105/ijdcn.c5034.04020224
Goyal, Ms. P., & Deora, Dr. S. S. (2022). Reliability of Trust Management Systems in Cloud Computing. In Indian Journal of Cryptography and Network Security (Vol. 2, Issue 1, pp. 1–5). https://doi.org/10.54105/ijcns.c1417.051322
Saraswathi, Dr. M., Akhila, J., & Sireesha, K. (2023). Predictive Insights: using Machine Learning to Determine Your Future Salary. In International Journal of Soft Computing and Engineering (Vol. 13, Issue 2, pp. 1–6). https://doi.org/10.35940/ijsce.b3605.0513223
Chavan, Mr. P. M., Abhang, Prof. S. P., & Shinde, Prof. U. B. (2019). DevOps Intelligent Networking with Automated Deployment in Linux. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 9, Issue 2, pp. 5750–5752). https://doi.org/10.35940/ijrte.b3510.078219
A., O., & O, B. (2020). An Iris Recognition and Detection System Implementation. In International Journal of Inventive Engineering and Sciences (Vol. 5, Issue 8, pp. 8–10). https://doi.org/10.35940/ijies.h0958.025820