Handbook of Learning Analytics

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———. 2017b. “Leveraging Big Data to Help Each Learner and Accelerate Learning Science.” Teachers College Record 119 (3): 1–24.

———. 2020a. “Commentary: A Proposed Remedy for Grievances About Self-Report Methodologies.” Frontline Learning Research 8 (3): 164–73.

———. 2020b. “Construct and Consequential Validity for Learning Analytics Based on Trace Data.” Computers in Human Behavior 112: 106457. https://doi.org/10.1016/j.chb.2020.106457.

———. 2020c. “Open Learner Models Working in Symbiosis with Self-Regulating Learners: A Research Agenda.” International Journal of Artificial Intelligence in Education, 1–14.

Winne, Philip H., and Ryan S. J. d. Baker. 2013. “The Potentials of Educational Data Mining for Researching Metacognition, Motivation and Self-Regulated Learning.” Journal of Educational Data Mining 5 (1): 1–8.

Winne, Philip H., and Zahia Marzouk. 2019. “Learning Strategies and Self-Regulated Learning.” In The Cambridge Handbook of Cognition and Education, edited by Dunlosky John and Katherine A. Rawson, 696–715. Cambridge University Press. https://doi.org/10.1017/9781108235631.028.

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Winne, Philip H., and Nancy E. Perry. 2000. “Measuring Self-Regulated Learning.” In Handbook of Self-Regulation, edited by Monique Boekaerts, Moshe Zeidner, and Paul R. Pintrich, 531–66. Elsevier.

Zhou, Mingming, Yabo Xu, John C. Nesbit, and Philip H. Winne. 2010. “Sequential Pattern Analysis of Learning Logs: Methodology and Applications.” In Handbook of Educational Data Mining, edited by Cristobal Romero, Sebastián Ventura, Mykola Pechenizkiy, and Ryan S. J. d. Baker, 107–21. Chapman& Hall/CRC.