Tool for Oversight Syllabuses and Feedback

Tool for Oversight Syllabuses and Feedback

  IJETT-book-cover           
  
© 2024 by IJETT Journal
Volume-72 Issue-2
Year of Publication : 2024
Author : Ritu Sodhi, Jitendra Choudhary, Ruby Bhatt
DOI : 10.14445/22315381/IJETT-V72I2P110

How to Cite?

Ritu Sodhi, Jitendra Choudhary, Ruby Bhatt, "Tool for Oversight Syllabuses and Feedback," International Journal of Engineering Trends and Technology, vol. 72, no. 2, pp. 82-91, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I2P110

Abstract
The creation of curricula and syllabi is a crucial component of the educational system. The process of developing curricula involves industries as well. The study has been done to oversee syllabuses. Work has been done in the area of sentence-similar tests. The prior study does not compare the syllabuses of the same courses, nor does it offer the designer of the syllabus advice for the topics that might be included in it. This paper suggests a mechanism for developing and maintaining the syllabus. Also, it will consider industry feedback. This tool compares feedback from various industries after receiving input from them and makes recommendations for subjects and contents that syllabus creators might want to include in their curriculum. This study used the streamlit framework and spacy for semantic comparison. This study will create a tool to oversee the syllabus. Universities can solicit input from industries using this technology on two different levels. Initially, it asks for advice on courses the institution can include in the curriculum. Second, it can accept comments regarding the subject matter of any course. Industry can advise the university on the newest/upcoming innovations. After receiving feedback from various industries, this model compares the feedback and provides the curriculum designer with recommendations for topics that might be covered in the syllabus. In this study, the actual industry feedback from 10 industry experts has been taken and semantically compared their feedback on contents, calculated the weightage of topics, and displayed the topics in decreasing order of weightage. Conclusion: The model for creating a curriculum and putting it into practice will be provided by this research, allowing us to follow a procedure. It bridges the gap between academia and industry.

Keywords
Curriculum, Comparison, Feedback, Industry, Syllabus, University.

References
[1] Yoshihiro Matsunaga et al., “A Web Syllabus Crawler and its Efficiency Evaluation,” Proceedings of the International Symposium on Information Science and Electrical Engineering, Fukuoka, Japan, pp. 565-568, 2003.
[Google Scholar] [Publisher Link]
[2] Xiaoyan Yu et al., “Using Automatic Metadata Extraction to Build a Structured Syllabus Repository,” International Conference on Asian Digital Libraries. Looking Back 10 Years and Forging New Frontiers, pp. 337-346, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Manas Tungare et al., “Towards a Syllabus Repository for Computer Science Courses,” Proceedings of the 38th SIGCSE Technical Symposium on Computer Science Education, pp. 55-59, 2007.
[CrossRef] [Google Scholar] [Publisher Link]
[4] M'hammed Abdous, and Wu He, “A Design Framework for Syllabus Generator,” Journal of Interactive Learning Research, vol. 19, no. 4, pp. 541-550, 2008.
[Google Scholar] [Publisher Link]
[5] Arash Joorabchi, and Abdulhussain E. Mahdi, “An Automated Syllabus Digital Library System for Higher Education in Ireland,” The Electronic Library, vol. 27, no. 4, pp. 640-658, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Nakul Rathod, and Lillian Cassel, “Building a Search Engine for Computer Science Course Syllabi,” Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 77-86, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Bassam Hussein et al., “A Web-Based University Courses Syllabi Generator,” Computer Engineering and Intelligent Systems, vol. 6, no. 11, pp. 1-7, 2015.
[Google Scholar] [Publisher Link]
[8] Hyunsook Chung, and Jeongmin Kim, “An Ontological Approach for Semantic Modeling of Curriculum and Syllabus in Higher Education,” International Journal of Information and Education Technology, vol. 6, no. 5, pp. 365-369, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Emanuel Guberovic et al., “In Search of a Syllabus: Comparing Computer Science Courses,” 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia, pp. 588-592, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Maedeh Mosharraf, and Fattaneh Taghiyareh, “Automatic Syllabus-Oriented Remixing of Open Educational Resources Using AgentBased Modeling,” IEEE Transactions on Learning Technologies, vol. 13, no. 2, pp. 297-311, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Courtney Corley, and Rada Mihalcea, “Measuring the Semantic Similarity of Texts,” Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment, pp. 13-18, 2005.
[Google Scholar] [Publisher Link]
[12] Palakorn Achananuparp, Xiaohua Hu, and Xiajiong Shen, “The Evaluation of Sentence Similarity Measures,” International Conference on Data Warehousing and Knowledge Discovery, Berlin, Heidelberg, pp. 305-316, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Catia Pesquita et al., “Semantic Similarity in Biomedical Ontologies,” PLOS Computational Biology, vol. 5, no. 7, pp.1-12, 2009.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Vasile Rus et al., “Semilar: A Semantic Similarity Toolkit,” Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, Sofia, Bulgaria, pp. 63-168, 2013.
[Google Scholar] [Publisher Link]
[15] Wael H. Gomaa, and Aly A. Fahmy, “A Survey of Text Similarity Approaches,” International Journal of Computer Applications, vol. 68, no. 13, pp. 13-18, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Thabet Slimani, “Description and Evaluation of Semantic Similarity Measures Approaches,” International Journal of Computer Applications, vol. 80, no. 10, pp. 25-33, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Issa Atoum, Ahmed Otoom, and Narayanan Kulathuramaiyer, “A Comprehensive Comparative Study of Word and Sentence Similarity Measures,” International Journal of Computer Applications, vol. 135, no. 1, pp. 10-17, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Victor Saquicela et al., “Similarity Detection among Academic Contents through Semantic Technologies and Text Mining,” IWSW 2018 - International Workshop on Semantic Web, pp.1-12, 2018.
[Google Scholar] [Publisher Link]
[19] Qingyu Chen et al., “Sentence Similarity Measures Revisited: Ranking Sentences in PubMed Documents,” Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, Washington, DC, USA, pp. 531- 532, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Zhe Quan et al., “An Efficient Framework for Sentence Similarity Modeling,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 27, no. 4, pp. 853-865, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Xiaofang Liao, and Zijiang Zhu, “Classification of Natural Language Semantic Relations Under Deep Learning,” 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications( AEECA), Dalian, China, pp. 1025-1027, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Artem A. Maksutov et al., “Knowledge Base Collecting Using Natural Language Processing Algorithms,” 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), St. Petersburg and Moscow, Russia, pp. 405-407, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Hamed Jelodar et al., “A Collaborative Framework Based for Semantic Patients-Behavior Analysis and Highlight Topics Discovery of Alcoholic Beverages in Online Healthcare Forums,” Journal of Medical Systems, vol. 44, no. 101, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Xiaolong Wang, Xingtong Dong, and Shuxin Chen, “Text Duplicated-Checking Algorithm Implementation Based on Natural Language Semantic Analysis,” 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China, pp. 732-735, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Shuang Peng et al., “Enhanced-RCNN: An Efficient Method for Learning Sentence Similarity,” Proceedings of the Web Conference 2020, Taipei, Taiwan, pp. 2500-2506, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Jiapeng Wang, and Yihong Dong, “Measurement of Text Similarity: A Survey,” Information, vol. 11, no. 9, pp. 1-17, 2020.
[Google Scholar] [Publisher Link]
[27] Dastan Hussen Maulud et al., “A State of Art for Semantic Analysis of Natural Language Processing,” Qubahan Academic Journal, vol. 1, no. 2, pp. 21-28, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Ritika Singh, and Satwinder Singh, “Text Similarity Measures in News Articles by Vector Space Model Using NLP,” Journal of The Institution of Engineers (India): Series B, vol. 102, pp. 329-338, 2021.
[CrossRef] [Google Scholar] [Publisher Link]