By: Tanja Labudovic on August 6th, 2014
Personalized Learning | Teachers | Blended Learning | Classrooms
More learning is happening online and education data is growing quickly. Big amounts of education data are empowering the Educational Data Mining (EDM) research field which develops methods for exploring data that come from various educational environments. The increase of educational data, and its use, is of special interest to me as an Engineer at Ed Elements who gets to work on developing a platform that uses this data to improve student outcomes.
There are many applications of EDM but I will just name a few: recommendations for students, providing feedback for instructors, predicting student performance, detecting undesirable student behaviors, grouping students, constructing coursework, planning and scheduling, analysis and visualization of data[1]. With these applications EDM methods are of great value for a Personalized Learning Platform like ours, Highlight, so today I am taking a break from working on continuously improving our platform to share some high level, short descriptions of some of the most-used EDM methods.
For details about EDM research and methods please refer to references [2],[4],[5] below.
Today we will focus on several methods. Some are standard data mining methods, widely used in mining educational data: prediction methods (classification, regression analysis), clustering, relationship mining methods (association rule mining, correlation mining, sequential pattern mining), outlier detection method and social network analysis. Other methods are specific to the EDM field: Distillation of data for human judgment, discovery with models, knowledge tracing. I have a few examples below, geared to the non statisticians out there:
We are very enthusiastic about using EDM methods to help teachers personalize learning for their students efficiently, to respond to students needs, and to improve learning and teaching performance. Have a question? Let us know!
References:
[1] Romero C, Ventura S. Educational Data Mining: A Review of the State-of-the-Art. IEEE Transactions Syst Man Cybern C: Appl Rev 2010, 40:601–618.
[2] Baker, R., Siemens, G. Educational data mining and learning analytics. To appear in Sawyer, K. (Ed.)Cambridge Handbook of the Learning Sciences: 2nd Edition.
[3] Corbett A, Anderson J. Knowledge tracing: modeling the acquisition of procedural knowledge. User Model User-Adapted Interact 1995, 4:253–278.
[4] Baker, R.S.J.d., Yacef, K. (2009) The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining, 1 (1), 3-17
[5] Coursera hosted the course “Big Data in Education” which covers all of these methods in detail. Archives of the course are online.
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