Personalized Education through Individualized Pathways and Resources to Adaptive Control Theory-Inspired Scientific Education (iPRACTISE): Proof-of-Concept Studies for Designing and Evaluating Personalized Education

References

• Aiken, L. S., West, S. G., and Millsap, R. E. (2008), “Doctoral Training in Statistics, Measurement, and Methodology in Psychology: Replication and Extension of Aiken, West, Sechrest, and Reno’s (1990) Survey of PhD Programs in North America,” The American Psychologist, 63, 32–50. DOI: 10.1037/0003-066x.63.1.32.
   PubMed Web of Science ®Google Scholar
• Akaike, H. (1973), “Information Theory and an Extension of the Maximum Likelihood Principle,” in Second International Symposium in Information Theory, eds. B. N. Petrov, and F. Csáki, pp. 267–281, Budapest: Akadémiai Kiadó. DOI: 10.1007/978-1-4612-1694-0_15.
   Google Scholar
• Åström, K. J., and Murray, R. M. (2008), Feedback Systems: An Introduction for Scientists and Engineers, New Jersey: Princeton University Press. DOI: 10.2307/j.ctvcm4gdk.
   Google Scholar
• Bellman, R. (1964), “Control Theory,” Scientific American, 211, 186–200. DOI: 10.1038/scientificamerican0964-186.
   PubMedGoogle Scholar
• Brent, R. I., and Mueller, G. (1998), Just the Basics, Please: A Quick Review of Math for Introductory Statistics, Boston, MA: Addison-Wesley, Longman.
   Google Scholar
• Bryan, R. L., Kreuter, M. W., and Brownson, R. C. (2009), “Integrating Adult Learning Principles into Training for Public Health Practice,” Health Promotion Practice, 10, 557–563. DOI: 10.1177/1524839907308117.
   PubMedGoogle Scholar
• Cai, L. (2010), “Metropolis-Hastings Robbins-Monro Algorithm for Confirmatory Item Factor Analysis,” Journal of Educational and Behavioral Statistics, 35, 307–335. DOI: 10.3102/1076998609353115.
   Web of Science ®Google Scholar
• Chalmers, R. P. (2016), “Generating Adaptive and Non-Adaptive Test Interfaces for Multidimensional Item Response Theory Applications,” Journal of Statistical Software, 71, 1–39. DOI: 10.18637/jss.v071.i05.
   Web of Science ®Google Scholar
• Chang, W., Cheng, J., Allaire, J. J., Sievert, C., Schloerke, B., Xie, Y., et al. (2022), Shiny: Web Application Framework for R [Computer Software Manual] R package version 1.7.2.
   Google Scholar
• Chen, W. H., and Thissen, D. (1997), “Local Dependence Indices for Item Pairs Using Item Response Theory,” Journal of Educational and Behavioral Statistics, 22, 265–289. DOI: 10.2307/1165285.
   Web of Science ®Google Scholar
• Chen, X. (2014), “STEM Attrition: College Students’ Paths into and out of STEM Fields (NCES 2014-001),” Technical Report, National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. Washington, DC
   Google Scholar
• Choi, Y., and Mislevy, R. J. (2022), “Evidence Centered Design Framework and Dynamic Bayesian Network for Modeling Learning Progression in Online Assessment System,” Frontiers in Psychology, 13, 742956. DOI: 10.3389/fpsyg.2022.742956.
   PubMed Web of Science ®Google Scholar
• Chow, S., Lee, J., Hofman, A., van der Maas, H. L. J., Pearl, D. K., and Molenaar, P. C. M. (2022), “Control Theory Forecasts of Optimal Training Dosage to Facilitate Children’s Arithmetic Learning in a Digital Educational Application,” Psychometrika, 87, 559–592. DOI: 10.1007/s11336-021-09829-3.
   PubMed Web of Science ®Google Scholar
• Curran, P. J., and Hussong, A. M. (2009), “Integrative Data Analysis: The Simultaneous Analysis of Multiple Data Sets,” Psychological Methods, 14, 81–100. DOI: 10.1037/a0015914.
   PubMed Web of Science ®Google Scholar
• Ferrini-Mundy, J. (2013), “Driven by Diversity,” Science, 340, 278–278. DOI: 10.1126/science.1235521.
   PubMed Web of Science ®Google Scholar
• Flaugher, R. L., (2000), “Item Pools,” in Computerized Adaptive Testing: A Primer (2nd ed.), ed. H. Wainer, pp. 61–84, Mahwah, NJ: Lawrence Erlbaum. DOI: 10.4324/9781410605931-11.
   Google Scholar
• Flint, A. S., and Jaggers, W. (2021), “You Matter Here: The Impact of Asset-Based Pedagogies on Learning,” Theory into Practice, 60, 254–264. DOI: 10.1080/00405841.2021.1911483.
   Web of Science ®Google Scholar
• Gelman, A. (2013), “Ethics and Statistics: It’s Too Hard to Publish Criticisms and Obtain Data for Republication,” CHANCE, 26, 49– 52. DOI: 10.1080/09332480.2013.845455.
   Google Scholar
• Gelman, A., and Loken, E. (2016), “The Statistical Crisis in Science,” in The Best Writing on Mathematics, ed. M. Pitici, pp. 305–318, Princeton: Princeton University Press. DOI: 10.1515/9781400873371-028/.
   Google Scholar
• Goodwin, G., Seron, M. M., and Doná, J. A. (2005), Constrained Control and Estimation: An Optimisation Approach (1st ed.), London: Springer-Verlag. DOI: 10.1007/b138145/.
   Google Scholar
• Hiltz, S. R., and Turoff, M. (2005), “Education Goes Digital: The Evolution of Online Learning and the Revolution in Higher Education,” Communications of the ACM, 48, 59–64. DOI: 10.1145/1089107.1089139.
   Web of Science ®Google Scholar
• Hitlin, P. (2016), Research in the Crowdsourcing Age, a Case Study, Washington DC: Pew Research Center. Available at http://www.pewinternet.org/2016/07/11/research-in-the-crowdsourcing-age-a-case-study/
   Google Scholar
• Ioannidis, J. P. A. (2005), “Why Most Published Research Findings Are False,” PLoS Medicine, 2, e124. DOI: 10.1371/journal.pmed.0020124.
   PubMed Web of Science ®Google Scholar
• Kafai, Y. B., and Resnick, M., eds. (1996), Constructionism in Practice: Designing, Thinking, and Learning in a Digital World, Mahwah, NJ Lawrence Erlbaum Associates. DOI: 10.4324/9780203053492.
   Google Scholar
• Khan Academy. (2017), “Khan Academy,” available at https://www.khanacademy.org/ (accessed 20-October-2017).
   Google Scholar
• Klinkenberg, S., Straatemeier, M., and van der Maas, H. L. J. (2011), “Computer Adaptive Practice of Maths Ability Using a New Item Response Model for on the Fly Ability and Difficulty Estimation,” Computers & Education, 57, 1813–1824. DOI: 10.1016/j.compedu.2011.02.003.
   Web of Science ®Google Scholar
• Knowles, M. S. (1973), The Adult Learner: A Neglected Species (1st ed.), Houston, TX: Gulf Publishing.
   Google Scholar
• Knowles, M., Holton, E., and Swanson, R. (2011), The Adult Learner, London Routledge. DOI: 10.4324/9780080964249.
   Google Scholar
• Kwon, W. H., and Han, S. H. (2005), Receding Horizon Control: Model Predictive Control for State Models, London Springer. DOI: 10.1007/b136204.
   Google Scholar
• Linacre, J. (1994), “Sample Size and Item Calibration Stability,” Rasch Measurement Transactions, 7, 328.
   Google Scholar
• McArdle, J. J., Grimm, K. J., Hamagami, F., Bowles, R. P., and Meredith, W. (2009), “Modeling Life-Span Growth Curves of Cognition Using Longitudinal Data with Multiple Samples and Changing Scales of Measurement,” Psychological Methods, 14, 126–149. DOI: 10.1037/a0015857.
   PubMed Web of Science ®Google Scholar
• Nosek, B., Spies, J., and Motyl, M. (2012), “Scientific Utopia: II. Restructuring Incentives and Practices to Promote Truth over Publishability,” Perspectives on Psychological Science, 7, 615–631. DOI: 10.1177/1745691612459058.
   Web of Science ®Google Scholar
• Park, J. Y., Joo, S. H., Cornillie, F., van der Maas, H. L., and Van den Noortgate, W. (2019), “An Explanatory Item Response Theory Method for Alleviating the Cold-Start Problem in Adaptive Learning Environments,” Behavior Research Methods, 51, 895–909. DOI: 10.3758/s13428-018-1166-9.
   PubMed Web of Science ®Google Scholar
• Peck, R., Gould, R., and Utts, J. (2017), “Mathematics Prerequisites for Success in Introductory Statistics,” Technical Report, Charles A. Dana Center.
   Google Scholar
• Petty, N. W. (2010). “Creating YouTube Videos That Engage Students and Enhance Learning in Statistics and Excel,” in Data and Context in Statistics Education: Toward an Evidence-Based Society. Proceedings of the Eight International Conference Conference on Teaching Statistics (ICOTS-8), ed. C. Reading, Ljubljana, Slovenia: International Statistical Institute.
   Google Scholar
• Rasch, G. (1960), Probabilistic Models for Some Intelligence and Attainment Tests, Copenhagen: Danmarks Paedagogiske Institut.
   Google Scholar
• Rivera, D. E., Pew, M. D., and Collins, L. M. (2007), “Using Engineering Control Principles to Inform the Design of Adaptive Interventions: A Conceptual Introduction,” Drug and Alcohol Dependence, 88, S31–S40. DOI: 10.1016/j.drugalcdep.2006.10.020.
   PubMed Web of Science ®Google Scholar
• Rose, T. (2016), The End of Average: How We Succeed in a World that Values Sameness, San Francisco, CA: HarperOne. Available at http://www.harpercollins.com/9780062358363/the-end-of-average
   Google Scholar
• Schwarz, G. (1978), “Estimating the Dimension of a Model,” The Annals of Statistics, 6, 461–464. DOI: 10.1214/aos/1176344136.
   Web of Science ®Google Scholar
• Sclove, L. S. (1987), “Application of Model-Selection Criteria to Some Problems in Multivariate Analysis,” Psychometrika, 52, 333–343. DOI: 10.1007/BF02294360.
   Web of Science ®Google Scholar
• Shahidian, A., Lashgari, K., and Goodarzi, S. (2011), “Adult Teaching: Methods and Principles,” Nature and Science, 9, 94–97.
   Google Scholar
• Shiffler, R. E., and Adams, A. J. (1995), Just Enough Algebra for Students of Statistics.,” Pacific Grove, CA Duxbury Press.
   Google Scholar
• Thompson, N. A., and Weiss, D. J. (2011), “A Framework for the Development of Computerized Adaptive Tests,” Practical Assessment, Research and Evaluation, 16, 1–9. DOI: 10.7275/wqzt-9427.
   Google Scholar
• Tofghi, D., and Enders, C. K. (2007), “Identifying the Correct Number of Classes in Mixture Models,” in Advances in Latent Variable Mixture Models, eds. G. R. Hancock and K. M. Samulelsen, pp. 317–341, Greenwich, CT: Information Age.
   Google Scholar
• Wang, Q., Molenaar, P., Harsh, S., Freeman, K., Xie, J., Gold, C., Rovine, M., and Ulbrecht, J. (2014), “Personalized State-Space Modeling of Glucose Dynamics for Type 1 Diabetes Using Continuously Monitored Glucose, Insulin Dose, and Meal Intake: An Extended Kalman Filter Approach,” Journal of Diabetes Science and Technology, 8, 331–345. DOI: 10.1177/1932296814524080.
   PubMedGoogle Scholar
• Wang, S., Zhang, A., Messer, S., Wiesner, A., and Pearl, D. K. (2021), “Student Developed Shiny Applications for Teaching Statistics,” Journal of Statistics and Data Science Education, 29, 218–227. DOI: 10.1080/26939169.2021.1995545.
   Web of Science ®Google Scholar
• Weiss, D. J. (1982), “Improving Measurement Quality and Efficiency with Adaptive Testing,” Applied Psychological Measurement, 6, 473–492. DOI: 10.1177/014662168200600408.
   Web of Science ®Google Scholar
• Weiss, D. J., and Kingsbury, G. G. (1984), “Application of Computerized Adaptive Testing to Educational Problems,” Journal of Educational Measurement, 21, 361–375. DOI: 10.1111/j.1745-3984.1984.tb01040.x.
   Web of Science ®Google Scholar
• Wickham, H. (2016), ggplot2: Elegant Graphics for Data Analysis, New York: Springer-Verlag. Available at https://ggplot2.tidyverse.org.
   Google Scholar
• Wickham, H., François, R., Henry, L., and Müller, K. (2022), “Dplyr: A Grammar of Data Manipulation [Computer Software Manual],” available at https://dplyr.tidyverse.org, https://github.com/tidyverse/dplyr.
   Google Scholar
• Yoes, M. (1995), “An Updated Comparison of Micro-Computer Based Item Parameter Estimation Procedures Used with the 3-Parameter IRT Model,” Technical Report, Saint Paul, MN Assessment Systems Corporation.
   Google Scholar