Leveraging big data to help each learner and accelerate learning science

Teachers College Record

Volumn 119, Issue 3, 2017, Pages

  Winne, Philip H ^a  

^a SIMON FRASER UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85016648383     PISSN: 01614681     EISSN: 14679620     Source Type: Journal    
DOI: None     Document Type: Article

References (23)

1. American Psychological Association. (2010). Publication Manual of the American Psychological Association (6th ed.). Washington, DC: Author.
2. Association for Psychological Science. (n.d.) Registered Replication Reports [Web page]. Retrieved from http://www.psychologicalscience.org/publications/replication
3. Bannert, M., Reimann, P., & Sonnenberg, C. (2013). Process mining techniques for analysing patterns and strategies in students' self-regulated learning. Metacognition Learning, 9(2), 161-185. doi:10.1007/s11409-013-9107-6
4. Deschryver, M. (2014). Higher order thinking in an online world: Toward a theory of webmediated knowledge synthesis. Teachers College Record, 116(12), 1-44.
5. Fahad, A., Alshatri, N., Tari, Z., Alamri, A., Khalil, I., Zomaya, A. Y., & Bouras, A. (2014). A survey of clustering algorithms for big data: Taxonomy and empirical analysis. IEEE Transactions on Emerging Topics in Computing, 2(3), 267-279. doi:10.1109/tetc.2014.2330519
6. Holcombe, A. (2013, March 3). Registered Replication Reports are open for submissions! [Web log post]. Retrieved from https://alexholcombe.wordpress.com/2013/03/03/ registered-replication-reports-are-open-for-submissions/
7. Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: A meta-analysis. Journal of Educational Psychology, 106(4), 901-918.
8. Rao, P. (1971). Some notes on misspecification in multiple regressions. The American Statistician, 25(5), 37-39.
9. Roll, I., & Winne, P. H. (2015). Understanding, evaluating, and supporting self-regulated learning using learning analytics. Journal of Learning Analytics, 2(1), 7-12.
10. Selwyn, N. (2008). An investigation of differences in undergraduates' academic use of the internet. Active Learning in Higher Education, 9(11), 11-22.
11. Shadish, W. R., Cook, T. D., & Campbell, D. T. (2001). Experimental and quasi-experimental designs for generalized causal inference. Boston, MA: Houghton Mifflin.
12. Sottilare, S., Graesser, A., Hu, X., & Holden, H. (Eds.). (2013). Design recommendations for intelligent tutoring systems. Orlando, FL: U.S. Army Research Laboratory.
13. Stanley, D. J., & Spence, J. R. (2014). Expectations for replications: Are yours realistic? Perspectives on Psychological Science, 9(3), 305-318.
14. Steenbergen-Hu, S., & Cooper, H. (2013). A meta-analysis of the effectiveness of intelligent tutoring systems on K-12 students' mathematical learning. Journal of Educational Psychology, 105(4), 970-987.
15. van der Aalst, W. M. P. (2014). Process mining in the large: A tutorial. In E. Zimányi, Ed., Business intelligence: Third European summer school, eBISS 2013, Dagstuhl Castle, Germany, July 7-12, 2013, tutorial lectures (pp. 33-76). Basel, Switzerland: Springer.
16. van Rijmenam, M. (2013, August 6). Why the 3V's are not sufficient to describe big data [Web log post]. Retrieved from https://datafloq.com/read/3vs-sufficient-describe-big-data/166 What Works Clearinghouse (n.d.) Procedures and standards handbook, version 3.0. Retrieved from https://ies.ed.gov/ncee/wwc/Docs/referenceresources/wwc_procedures_v3_0_ standards_handbook.pdf.
17. Winne, P. H. (1982). Minimizing the black box problem to enhance the validity of theories about instructional effects. Instructional Science, 11, 13-28.
18. Winne, P. H. (1987). Why process-product research cannot explain process-product findings and a proposed remedy: The cognitive mediational paradigm. Teaching and Teacherb Education, 3, 333-356.
19. Winne, P. H. (1992). State-of-the-art instructional computing systems that afford instruction and bootstrap research. In M. Jones and P. H. Winne (Eds.), Adaptive learning environments: Foundations and frontiers (pp. 349-380). Berlin, Germany: Springer-Verlag.
20. Winne, P. H. (2006). How software technologies can improve research on learning and bolster school reform. Educational Psychologist, 41, 5-17.
21. Winne, P. H. (2010). Improving measurements of self-regulated learning. Educational Psychologist, 45, 267-276.
22. Winne, P. H, & Baker, R. S. J. d. (2013). The potentials of educational data mining for researching metacognition, motivation and self-regulated learning. Journal of Educational Data Mining, 5(1), 1-8.
23. Winne, P. H., & Perry, N. E. (2000). Measuring self-regulated learning. In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 531-566). Orlando, FL: Academic Press.