Abstract
As Artificial Intelligence (AI) continues to revolutionize education, the use of AI to complete online assessments poses a considerable challenge to assessment analytics. With a goal to support and improve the assessment process, assessment analytics involves deriving meaningful insights from assessment data related to student knowledge and instructional effectiveness. However, when students utilize machine learning to complete online assessments, the validity of assessment data as an accurate representation of student knowledge diminishes. This chapter explores the strategic use of online formative assessments to both enhance understanding of student knowledge and deter the inappropriate use of AI. While there is always a risk of academic dishonesty, online formative assessments offer a unique avenue for teachers to gather empirical evidence of student engagement, current comprehension, and critical thinking. By adopting the PICS framework for personalized, informal, constructivist, and short online formative assessments, educators can harness assessment analytics to gain valid and valuable insights into student knowledge. This approach facilitates proactive engagement with students who may be grappling with content and tempted to seek assistance from artificial intelligence, fostering meaningful conversations that aim to address learning challenges and promote academic integrity in online education.
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References
Baleni, Z. G. (2015). Online formative assessment in higher education: Its pros and cons. Electronic Journal of e-Learning, 13(4), 228–236.
Bell, B., & Cowie, B. (2001). The characteristics of formative assessment in science education. Science Education, 85(5), 536–553. https://doi.org/10.1002/sce.1022
Bostrom, N., & Yudkowsky, E. (2018). The ethics of artificial intelligence. In Artificial intelligence safety and security (pp. 57–69). Chapman and Hall/CRC. https://doi.org/10.1201/9781351251389-4
Bretag, T., Harper, R., Burton, M., Ellis, C., Newton, P., van Haeringen, K., et al. (2019). Contract cheating and assessment design: Exploring the relationship. Assessment & Evaluation in Higher Education, 44(5), 676–691. https://doi.org/10.1080/02602938.2018.1527892
De Kleijn, R. A., Bouwmeester, R. A., Ritzen, M. M., Ramaekers, S. P., & Van Rijen, H. V. (2013). Students’ motives for using online formative assessments when preparing for summative assessments. Medical Teacher, 35(12), e1644–e1650. https://doi.org/10.3109/0142159X.2013.826794
Dergaa, I., Chamari, K., Zmijewski, P., & Saad, H. B. (2023). From human writing to artificial intelligence generated text: Examining the prospects and potential threats of ChatGPT in academic writing. Biology of Sport, 40(2), 615–622. https://doi.org/10.5114/biolsport.2023.125623
Dewey, J. D. (1916). Democracy and education. Macmillan.
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., et al. (2023). “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, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Fletcher, J. M., Francis, D. J., Morris, R. D., & Lyon, G. R. (2005). Evidence-based assessment of learning disabilities in children and adolescents. Journal of Clinical Child and Adolescent Psychology, 34(3), 506–522. https://doi.org/10.1207/s15374424jccp3403_7
Francke, E., & Bennett, A. (2019, October). The potential influence of artificial intelligence on plagiarism: A higher education perspective. In European Conference on the Impact of Artificial Intelligence and Robotics (ECIAIR 2019) (pp. 131–140).
Gardner, J., O’Leary, M., & Yuan, L. (2021). Artificial intelligence in educational assessment: ‘Breakthrough? Or buncombe and ballyhoo?’. Journal of Computer Assisted Learning, 37(5), 1207–1216. https://doi.org/10.1111/jcal.12577
Gaytan, J., & McEwen, B. C. (2007). Effective online instructional and assessment strategies. The American Journal of Distance Education, 21(3), 117–132. https://doi.org/10.1080/08923640701341653
Goldin, I., Narciss, S., Foltz, P., & Bauer, M. (2017). New directions in formative feedback in interactive learning environments. International Journal of Artificial Intelligence in Education, 27, 385–392. https://doi.org/10.1007/s40593-016-0135-7
Hanus, M. D., & Fox, J. (2015). Assessing the effects of gamification in the classroom: A longitudinal study on intrinsic motivation, social comparison, satisfaction, effort, and academic performance. Computers & Education, 80, 152–161. https://doi.org/10.1016/j.compedu.2014.08.019
Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459
IEEE-USA Board of Directors. (2017, February). Artificial intelligence research, development and regulation. Institute of Electrical and Electronics Engineers [IEEE] Position Statement. http://globalpolicy.ieee.org/wp-content/uploads/2017/10/IEEE17003.pdf
Jones, A., & Moreland, J. (2005). The importance of pedagogical content knowledge in assessment for learning practices: A case-study of a whole-school approach. Curriculum journal, 16(2), 193–206. http://dx.doi.org/10.1080/09585170500136044
Kleiman, G. (2022, September). AI has learned to write; can it help teach students to write well? [Working paper]. Stanford Accelerator for Learning. https://acceleratelearning.stanford.edu/app/uploads/2022/07/AI-has-learned-to-write-Can-it-help-teach-students-to-write-well-.pdf
Knight, S., Shum, S. B., & Littleton, K. (2014). Epistemology, assessment, pedagogy: Where learning meets analytics in the middle space. Journal of Learning Analytics, 1(2), 23–47. https://doi.org/10.18608/jla.2014.12.3
Koenka, A. C., & Anderman, E. M. (2019). Personalized feedback as a strategy for improving motivation and performance among middle school students. Middle School Journal, 50(5), 15–22. https://doi.org/10.1080/00940771.2019.1674768
Koohang, A., Riley, L., Smith, T., & Schreurs, J. (2009). E-learning and constructivism: From theory to application. Interdisciplinary Journal of E-Learning and Learning Objects, 5(1), 91–109. http://dx.doi.org/10.28945/66
Leskes, A., & Wright, B. D. (2005). The art & science of assessing general education outcomes: A practical guide. Association of American Colleges and Universities.
Manoharan, S. (2016). Personalized assessment as a means to mitigate plagiarism. IEEE Transactions on Education, 60(2), 112–119. https://doi.org/10.1109/TE.2016.2604210
Manoharan, S. (2019). Cheat-resistant multiple-choice examinations using personalization. Computers & Education, 130, 139–151. https://doi.org/10.1109/TE.2016.2604210
Martin, F., Bacak, J., Polly, D., & Dymes, L. (2021). A systematic review of research on K12 online teaching and learning: Comparison of research from two decades 2000 to 2019. Journal of Research on Technology in Education, 1–20. https://doi.org/10.1080/15391523.2021.1940396
Martinez, M. E., & Lipson, J. I. (1989). Assessment for learning. Educational Leadership, 46(7), 73–75.
McCallum, S., & Milner, M. M. (2021). The effectiveness of formative assessment: Student views and staff reflections. Assessment & Evaluation in Higher Education, 46(1), 1–16. https://doi.org/10.1080/02602938.2020.1754761
McMillan, J. H. (2013). Why we need research on classroom assessment. Sage. https://doi.org/10.4135/9781452218649.n1
Middleton, K. V. (2020). The longer-term impact of COVID-19 on K–12 student learning and assessment. Educational Measurement: Issues and Practice, 39(3), 41–44. https://doi.org/10.1111/emip.12368
Mijwil, M. M., Aggarwal, K., Mutar, D. S., Mansour, N., & Singh, R. S. (2022). The position of artificial intelligence in the future of education: An overview. Journal of Applied Sciences, 10(2). https://doi.org/10.24203/ajas.v10i2.6956
Office of Educational Technology. (2023, May). Artificial intelligence and the future of teaching and learning. https://www2.ed.gov/documents/ai-report/ai-report.pdf
Ouyang, F., Zheng, L., & Jiao, P. (2022). Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020. Education and Information Technologies, 27(6), 7893–7925. https://doi.org/10.1007/s10639-022-10925-9
Papamitsiou, Z., & Economides, A. A. (2016). An Assessment Analytics Framework (AAF) for enhancing students’ progress. In Formative assessment, learning data analytics and gamification (pp. 117–133). Academic. https://doi.org/10.1016/B978-0-12-803637-2.00007-5
Perkins, M. (2023). Academic integrity considerations of AI large language models in the post-pandemic era: ChatGPT and beyond. Journal of University Teaching & Learning Practice, 20(2), 40–51. https://doi.org/10.53761/1.20.02.07
Russell, M. (2011). Personalizing assessment. In T. Gray & H. Silver-Pacuilla (Eds.), Breakthrough teaching and learning: How educational and assistive technologies are driving innovation (pp. 111–126). Springer. https://doi.org/10.1007/978-1-4419-7768-7_7
Scanlan, C. L. (2006). Strategies to promote a climate of academic integrity and minimize student cheating and plagiarism. Journal of Allied Health, 35(3), 179–185.
Schwartz, D. L., Lindgren, R., & Lewis, S. (2009). Constructivism in an age of non-constructivist assessments. In S. Tobias & T. M. Duffy (Eds.), Constructivist instruction: Success or failure? (pp. 34–61). Routledge/Taylor & Francis.
Seo, K., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021). The impact of artificial intelligence on learner–instructor interaction in online learning. International Journal of Educational Technology in Higher Education, 18, 1–23. https://doi.org/10.1186/s41239-021-00292-9
Snelling, J. (2021, June). It’s time to re-evaluate K-12 assessments. International Society for Technology in Education [ISTE]. https://www.iste.org/explore/professional-development/its-time-re-evaluate-k-12-assessments
St-Onge, C., Ouellet, K., Lakhal, S., Dubé, T., & Marceau, M. (2022). COVID-19 as the tipping point for integrating e-assessment in higher education practices. British Journal of Educational Technology, 53(2), 349–366. https://doi.org/10.1111/bjet.13169
Strzelecki, A. (2023). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interactive Learning Environments, 1–14. https://doi.org/10.1080/10494820.2023.2209881
Sudakova, N. E., Savina, T. N., Masalimova, A. R., Mikhaylovsky, M. N., Karandeeva, L. G., & Zhdanov, S. P. (2022). Online formative assessment in higher education: Bibliometric analysis. Education Sciences, 12, 209.
Tallent-Runnels, M. K., Thomas, J. A., Lan, W. Y., Cooper, S., Ahern, T. C., Shaw, S. M., & Liu, X. (2006). Teaching courses online: A review of the research. Review of Educational Research, 76(1), 93–135. https://doi.org/10.3102/00346543076001093
Time. (2023, January). Schools shouldn’t ben access to ChatGPT. https://time.com/6246574/schools-shouldnt-ban-access-to-chatgpt
United Nations Educational, Scientific and Cultural Organization [UNESCO]. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. http://repositorio.minedu.gob.pe/bitstream/handle/20.500.12799/6533/Artificial%20intelligence%20in%20education%20challenges%20and%20opportunities%20for%20sustainable%20development.pdf
Vázquez-Cano, E. (2021). Artificial intelligence and education: A pedagogical challenge for the 21st century. Educational Process: International Journal, 10(3), 7–12. https://doi.org/10.22521/edupij.2021.103.1
Watson, G., & Sottile, J. (2010). Cheating in the digital age: Do students cheat more in online courses? Online Journal of Distance Learning Administration, 13(1).
Woo, D. J., Wang, Y., Susanto, H., & Guo, K. (2023). Understanding English as a foreign language students’ idea generation strategy for creative writing with natural language generation tools. Journal of Educational Computing Research. https://doi.org/10.1177/07356331231175999
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Riegel, C. (2024). Leveraging Online Formative Assessments Within the Evolving Landscape of Artificial Intelligence in Education. In: Sahin, M., Ifenthaler, D. (eds) Assessment Analytics in Education. Advances in Analytics for Learning and Teaching. Springer, Cham. https://doi.org/10.1007/978-3-031-56365-2_18
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