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Sample_sleep-and-grades.docxSample excerpt

Sleep and academic performance in college studentsPage 4 of 15

2. Sleep and academic performance

Sleep problems are common on campus: about half of college students report daytime sleepiness, and 70% do not get enough sleep (Hershner & Chervin, 2014, p. 73). A meta-analysis found that sleepiness, sleep quality, and sleep duration are all significantly, if modestly, related to school performance, with sleepiness showing the strongest link (Dewald et al., 2010, p. 179).

Objective data point the same way. In a study that gave wearable activity trackers to students in an introductory chemistry class, sleep measures accounted for nearly 25% of the variance in academic performance, while sleep on the single night before a test showed no relation to the result (Okano et al., 2019, p. 1). Studies that restricted or optimized sleep saw performance worsen or improve accordingly (Curcio et al., 2006, p. 323).

References

  • Curcio, G., Ferrara, M., & De Gennaro, L. (2006). Sleep loss, learning capacity and academic performance. Sleep Medicine Reviews, 10(5), 323–337. https://doi.org/10.1016/j.smrv.2005.11.001
  • Dewald, J. F., Meijer, A. M., Oort, F. J., Kerkhof, G. A., & Bögels, S. M. (2010). The influence of sleep quality, sleep duration and sleepiness on school performance in children and adolescents: A meta-analytic review. Sleep Medicine Reviews, 14(3), 179–189. https://doi.org/10.1016/j.smrv.2009.10.004
  • Hershner, S. D., & Chervin, R. D. (2014). Causes and consequences of sleepiness among college students. Nature and Science of Sleep, 6, 73–84. https://doi.org/10.2147/NSS.S62907
  • Okano, K., Kaczmarzyk, J. R., Dave, N., Gabrieli, J. D. E., & Grossman, J. C. (2019). Sleep quality, duration, and consistency are associated with better academic performance in college students. npj Science of Learning, 4(1), Article 16. https://doi.org/10.1038/s41539-019-0055-z

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Paper_ai-in-education.docxWord · editable✓ 98% human
Artificial intelligence in educationPage 7 of 24

3. Opportunities and Potential: Artificial intelligence in education

Artificial intelligence is changing how individual learning can be organized. Such AI systems can guide learners one-on-one while also supporting teachers and policymakers in their decisions (Hwang et al., 2020, p. 1). This makes a level of individual support possible that used to be out of reach for lack of time and staff.

A systematic review assigns the applications to four fields, including adaptive systems and personalization as well as intelligent tutoring systems (Zawacki-Richter et al., 2019, p. 1). The underlying goal is to model previously implicit knowledge about learning so that systems can work with it (Luckin et al., 2016, p. 18).

How this benefit can be unlocked pedagogically at scale and across subjects, however, remains an open question (Zawacki-Richter et al., 2019, p. 1). The following sections therefore examine the conditions for effective use.

Citation style: APA 7 · every source with a page number
Artificial intelligence in educationPage 23 of 24

References

Hattie, J. (2009). Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement. London: Routledge. doi.org/10.4324/9780203887332 ✓ real
Holmes, W., Bialik, M. & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston: Center for Curriculum Redesign. ✓ real
Hwang, G.-J., Xie, H., Wah, B. W. & Gašević, D. (2020). Vision, challenges, roles and research issues of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100001. doi.org/10.1016/j.caeai.2020.100001 ✓ real
Luckin, R., Holmes, W., Griffiths, M. & Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. London: Pearson. edu.google.com/pdfs/Intelligence-Unleashed-Publication.pdf ✓ real
Zawacki-Richter, O., Marín, V. I., Bond, M. & Gouverneur, F. (2019). Systematic Review of Research on Artificial Intelligence Applications in Higher Education. International Journal of Educational Technology in Higher Education, 16(39). doi.org/10.1186/s41239-019-0171-0 ✓ real
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Such AI systems can guide learners one-on-one while also supporting teachers and policymakers in their decisions (Hwang et al., 2020, p. 1).

✓ verified · checked against the originalcomputer systems can provide personalized guidance, supports, or feedback to students as well as assisting teachers or policymakers in making decisions.Hwang et al. 2020, Computers and Education: Artificial Intelligence, p. 1 · doi.org/10.1016/j.caeai.2020.100001

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