Educational data mining and learning analytics on fine-grained learner data is the most actively growing area in evaluation of learning environments¶

Claim · All claims
Evidence · 1 study · review r2 · q1
Currency · newest study 2015 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q1 i? Applying educational data mining and learning analytics to large quantities of fine-grained learner data is likely the most actively growing area in evaluation of learning environments. → Jim Greer & Mary Mark 2015

Evidence¶

Jim Greer & Mary Mark 2015¶

Jim Greer & Mary Mark. (2015). Evaluation Methods for Intelligent Tutoring Systems Revisited. Int J Artif Intell Educ 26:387–392. https://doi.org/10.1007/s40593-015-0043-2

q1 · i? · review · r2

The authors' assessment (type e) in the EDM/LA section: many systems gather fine-grained click-stream data, video logs, eye-tracking data, LMS log files and MOOC platform data, making this the fastest-growing evaluation area, citing Baker & Siemens (2014).

"Applying these techniques of edu- cational data mining and learning analytics to large quantities of fine grained learner data is likely the most actively growing area in evaluation of learning environments"

Discussion¶

Related Claims¶

• Basic evaluation techniques from the 1993 ITS evaluation-methods paper remain in use and apply to today's adaptive learning environments — a broader claim this one bears on
• Research favors fine-grained knowledge components, but fine granularity brings practical costs that can outweigh better data fit — related