Thoughts and information captured from Ryan Baker’s Presentation as part of LAK12 videocast:
- Educational Data Mining: improve research, improving learning models (Journal of Ed Psychology). It predicts the future. Change the future [tough for me to digest, maybe should say: change the future failure into success rather than change the future]
- Resources: Journal of EDM, Intl EDM Society
- EDM & LAK:
- Similarities: understand learning through study of large data; improving education and research; drives planning, decision making and manual/automated intervention.
- LAK=include automated discovery, EDM=putting human judgement in the automated discovery [still confusing to me]
- LAK=understand the system, EDM=focus on components and the relationship between them
- LAK=inform instructors and learners, EDM=automated adaptation
- LAK=focus on needs of multiple stakeholders; EDM=focus on model generalization
- EDM Methods: Prediction (classification, regression, density estimation), Clustering [I oppose this], Relationship mining [I relate to this], Distillation, Discovery [I need to know more about it to align it to my research and interest].
- Knowledge Engineering [?]
- Vision: predict student success based on analysing data generated by the students. Data obtained from: course selection data, cognitive tutor log data, grade data, AST data, Khan Academy log, State Std exams, SAT Career interest, Strong Interest Inventory, MSLQ Survey. [missing soft data like intelligence indicators, interest indicators, strengths indicators, stimuli profiling, communication profiling, etc… will they be handled by LAK?]
- [I did not sense the drive to help students to discover their learning strengths although I have read a lot about changing students attitudes!]
- Learning indicators: correctness or incorrectness,
Slide 29: Sample of correlation between behaviour and EDM indicators
Slide 30: Sample of correlation between action and gaming