IEEE 21st International Conference on Information Technology
Based Higher Education and Training (ITHET 2024)
Special Theme: The Future of Education, Powered by IT and AI
6 - 8 November 2024 | Paris, France
Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
Please note that all times are shown in the time zone of the conference. The current conference time is: 3rd Jan 2025, 08:36:24am CET
|
|
|
Session Overview |
| Session | ||
LEARNINGA 02
| ||
    | ||
ID: 211
/ LEARNINGA 02: 1
Learning Analytics in Higher Education (Full Paper) Topics: Innovative uses of technology for teaching and learning within higher education and training, The impact of technology on assessment practices in higher education, with particular interest in support for selfand peer-learning and evaluation, and the challenge of plagiarism and cheating., AI: Artificial Intelligence (DL, DS, ML and RL) in education Keywords: Academic Integrity, Large Language Models, Question Generation, Socratic Method, Computer Science Education F.Vintila, AVERT(Authorship Verification and Evaluation through Responsive Testing) Stuttgart University of Applied Sciences, Germany Bibliography
[1] J. Walker, “Measuring plagiarism: researching what students do, not what they say they do,” Studies in Higher Education, vol. 35, no. 1. Informa UK Limited, pp. 41–59, Nov. 17, 2009. doi: 10.1080/03075070902912994. [2] D. Ison, “Academic Misconduct and the Internet,” Scholarly Ethics and Publishing. IGI Global, pp. 22–51, 2019. doi: 10.4018/978-1-5225-8057-7.ch002. [3] N. Brunelle and J. R. Hott, “Ask Me Anything,” Proceedings of the 51st ACM Technical Symposium on Computer Science Education. ACM, Feb. 26, 2020. doi: 10.1145/3328778.3372658. [4] T. Marques, N. Reis, and J. Gomes, “A Bibliometric Study on Academic Dishonesty Research,” Journal of Academic Ethics, vol. 17, no. 2. Springer Science and Business Media LLC, pp. 169–191, Apr. 12, 2019. doi: 10.1007/s10805-019-09328-2. [5] Y. K. Dwivedi et al., “Opinion Paper: ‘So what if ChatGPT wrote it?’ Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy,” International Journal of Information Management, vol. 71. Elsevier BV, p. 102642, Aug. 2023. doi: 10.1016/j.ijinfomgt.2023.102642. [6] M. Alshater, “Exploring the Role of Artificial Intelligence in Enhancing Academic Performance: A Case Study of ChatGPT,” SSRN Electronic Journal. Elsevier BV, 2022. doi: 10.2139/ssrn.4312358. [7] E. Kasneci et al., “ChatGPT for good? On opportunities and challenges of large language models for education,” Learning and Individual Differences, vol. 103. Elsevier BV, p. 102274, Apr. 2023. doi: 10.1016/j.lindif.2023.102274. [8] D. R. E. Cotton, P. A. Cotton, and J. R. Shipway, “Chatting and cheating: Ensuring academic integrity in the era of ChatGPT,” Innovations in Education and Teaching International, vol. 61, no. 2. Informa UK Limited, pp. 228–239, Mar. 13, 2023. doi: 10.1080/14703297.2023.2190148. [9] S. W. Turner and S. Uludag, “Student perceptions of cheating in online and traditional classes,” 2013 IEEE Frontiers in Education Conference (FIE). IEEE, Oct. 2013. doi: 10.1109/fie.2013.6685007. [10] J. Manyrath, K. Kirubel, and T. Cruz, “Copy-Past Culture: Examining the Causes and Solutions to Source Code Plagiarism,” London Journal of Social Sciences, no. 6. UKEY Consulting and Publishing Ltd, pp. 49–55, Sep. 17, 2023. doi: 10.31039/ljss.2023.6.104. [11] J. Berrezueta-Guzman, M. Paulsen, and S. Krusche, “Plagiarism Detection and its Effect on the Learning Outcomes,” 2023 IEEE 35th International Conference on Software Engineering Education and Training (CSEE&T). IEEE, Aug. 2023. doi: 10.1109/cseet58097.2023.00021. [12] Ali, Asim M. El Tahir et al. “Overview and Comparison of Plagiarism Detection Tools.” Databases, Texts, Specifications, Objects (2011). Available: https://ceur-ws.org/Vol-706/poster22.pdf. [13] ] G. Lee, J. Kim, M. Choi, R.-Y. Jang, and R. Lee, “Review of Code Similarity and Plagiarism Detection Research Studies,” Applied Sciences, vol. 13, no. 20. MDPI AG, p. 11358, Oct. 16, 2023. doi: 10.3390/app132011358. [14] A. A. Pandit and G. Toksha, “Review of Plagiarism Detection Technique in Source Code,” International Conference on Intelligent Computing and Smart Communication 2019. Springer Singapore, pp. 393–405, Dec. 20, 2019. doi: 10.1007/978-981-15-0633-8_38. [15] M. Agrawal and D. K. Sharma, “A state of art on source code plagiarism detection,” 2016 2nd International Conference on Next Generation Computing Technologies (NGCT). IEEE, Oct. 2016. doi: 10.1109/ngct.2016.7877421. [16] R. C. Aniceto, M. Holanda, C. Castanho, and D. Da Silva, “Source Code Plagiarism Detection in an Educational Context: A Literature Mapping,” 2021 IEEE Frontiers in Education Conference (FIE). IEEE, Oct. 13, 2021. doi: 10.1109/fie49875.2021.9637155. [17] M. Horváth and E. Pietriková, “An Experimental Comparison of Three Code Similarity Tools on Over 1,000 Student Projects,” 2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, Jan. 25, 2024. doi: 10.1109/sami60510.2024.10432863. ID: 135
/ LEARNINGA 02: 2
Learning Analytics in Higher Education (Full Paper) Topics: Innovative uses of technology for teaching and learning within higher education and training, AI: Artificial Intelligence (DL, DS, ML and RL) in education, BD: Big Data and Data Analytics in education Keywords: learner characteristics, higher education area, generative AI, synthetic data, probabilistic models Probabilistic Machine Learning for Simulating Complex Learner Characteristics OTH Regensburg, Germany Bibliography
1. Nadimpalli, V., Bugert, F., Bittner, D., Hauser, F., Grabinger, L., Staufer, S., & Mottok, J. (2023). Towards personalized learning paths in adaptive learning management systems: bayesian modeling of psychological theories. In ICERI2023 Proceedings (pp. 4593-4603). IATED. 2. Nadimpalli, V., Hauser, F., Bittner, D., Grabinger, L., Staufer, S., & Mottok, J. (2023, June). Systematic Literature Review for the Use of AI Based Techniques in Adaptive Learning Management Systems. In Proceedings of the 5th European Conference on Software Engineering Education (pp. 83-92). 3. Nadimpalli, V., Fabrication and Analysis of the Properties of Coconut Shell Ash Reinforced Aluminum356 Composite, International Journal of Engineering and Research & Technology (IJERT) Volume07, Issue 09 (September – 2018). 4. Bugert, F., Staufer, S., Bittner, D., Nadimpalli, V. K., Ezer, T., Hauser, F., ... & Mottok, J. (2024, May). Ariadne's Thread for Unravelling Learning Paths: Identifying Learning Styles via Hidden Markov Models. In 2024 IEEE Global Engineering Education Conference (EDUCON) (pp. 1-7). IEEE. 5. Bugert, F., Grabinger, L., Bittner, D., Hauser, F., Nadimpalli, V., Staufer, S., & Prof. Dr. Mottok, J. (2023, June). Towards Learning Style Prediction based on Personality. In Proceedings of the 5th European Conference on Software Engineering Education (pp. 48-55). 6. Bittner, D., Nadimpalli, V., Grabinger, L., Ezer, T., Hauser, F., Mottok, J. H. (2024, June). Uncovering Learning Styles through Eye Tracking and Artificial Intelligence. In ETRA (pp. 70-1). 7. Röhrl, S., Staufer, S., Nadimpalli, V., Bugert, F., Hauser, F., Grabinger, L., ... & Mottok, J. (2024). Pythia-AI suggested Individual Learning Paths for Every Student. In INTED2024 Proceedings (pp. 2871-2880). IATED. 8. Staufer, S., Bugert, F., Hauser, F., Grabinger, L., Ezer, T., Nadimpalli, V., ... & Mottok, J. (2024). Tyche Algorithm: Markov Models for Generating Learning Paths in Learning Management Systems. In INTED2024 Proceedings (pp. 4195-4205). IATED. 9. Staufer, S., Hauser, F., Grabinger, L., Bittner, D., Nadimpalli, V., & Mottok, J. (2023). Learning elements in online learning management systems. In ICERI2023 Proceedings (pp. 3121-3130). IATED. 10. Hauser, F., Grabinger, L., Mottok, J., Jahn, S., & Nadimpalli, V. (2023, June). The Expert’s View: Eye Movement Modeling Examples in Software Engineering Education. In Proceedings of the 5th European Conference on Software Engineering Education (pp. 148-152). 11. Bittner, D., Hauser, F., Nadimpalli, V., Grabinger, L., Staufer, S., & Mottok, J. (2023, June). Towards eye tracking based learning style identification. In Proceedings of the 5th European Conference on Software Engineering Education (pp. 138-147). 12. Staufer, S., Hauser, F., Grabinger, L., Bittner, D., Nadimpalli, V., Bugert, F., ... & Mottok, J. (2024). Learning Elements in LMS-A Survey Among Students. In INTED2024 Proceedings (pp. 4224-4231). IATED. ID: 161
/ LEARNINGA 02: 3
Learning Analytics in Higher Education (Full Paper) Topics: Higher education as it is changing with the advent of pervasive information technology, Virtual laboratories, classroom, universities, Changing delivery patterns and asynchronous learning., IoT: Smart technologies and applications in education Keywords: Engineering Education, Mixed Reality, Virtual Learning Factories, Upskilling, Industry 4.0/5.0 and Education 4.0 Breaking Barriers: Enhancing Engineering Knowledge Transfer in Higher Education Through Mixed Reality Solutions Univeristy of Malta, Malta ID: 121
/ LEARNINGA 02: 4
Learning Analytics in Higher Education (Full Paper) Topics: Higher education as it is changing with the advent of pervasive information technology, AI: Artificial Intelligence (DL, DS, ML and RL) in education Keywords: Predictive Learning Analytics, Ethical Learning Analytics, Learning Management Systems, Moodle, AI audit The System Admin's Perspective: A Discussion on AI in Education with LMS Admins HTW University of Applied Sciences Berlin, Germany Bibliography
Fernsel, L., Kalff, Y., & Simbeck, K. (2024). Where Is the Evidence? A Plugin for Auditing Moodle's Learning Analytics. In CSEDU (2) (pp. 262-269). Bültemann, M., Simbeck, K., Rzepka, N., & Kalff, Y. (2024). Sustainable Learning Analytics: Measuring and Understanding the Drivers of Energy Consumption of AI in Education. In CSEDU (1) (pp. 272-279). Schauer, S., & Simbeck, K. (2024). AI Literacy for Cultural and Design Studies. In CSEDU (2) (pp. 39-50). | ||
Contact and Legal Notice · Contact Address:  Privacy Statement · Conference: ITHET 2024 |
Conference Software: ConfTool Pro 2.6.151+TC © 2001–2024 by Dr. H. Weinreich, Hamburg, Germany |