The Cambridge Handbook of the Learning Sciences: Educational Data Mining and Learning Analytics

700 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Seven Affordances of Computer-Supported Collaborative Learning: How to Support Collaborative Learning? How Can Technologies Help? (2016, background): “One promising approach for making structuring more appropriate to particular groups may come in the form of appropriate learning analytics, based on the big data generated in many CSCL environments (Baker & Siemens, 2014).”
Stupid Tutoring Systems, Intelligent Humans (2016, background): “I have written a lot of it (Baker & Yacef, 2009; Baker, 2010; Baker and Siemens 2014).”
Utilising learning analytics to support study success in higher education: a systematic review (2020, background): “Prescriptive analytics deploy algorithms mainly to predict study success and whether students will complete courses, as well as recommending any immediate interventions necessary (Baker and Siemens 2015).”
Designing educational technologies in the age of AI: A learning sciences-driven approach (2019, methodology): “In addition, the data itself is becoming easier to use, thanks to some standardized data formats, and better analytical tools to manage and explore large datasets (Baker & Siemens, 2014, Karkalas & Mavrikis, 2016).”