The Potentials of Educational Data Mining for Researching Metacognition, Motivation and Self-Regulated Learning

Our article introduces the Journal of Educational Data Mining's Special Issue on Educational Data Mining on Motivation, Metacognition, and Self-Regulated Learning. We outline general research challenges for data mining researchers who conduct investigations in these areas, the potential of EDM to advance research in this area, and issues in validating findings generated by EDM.

[1]  Philip H. Winne,et al.  The psychology of academic achievement. , 2010, Annual review of psychology.

[2]  D. Perkins,et al.  Rocky Roads to Transfer: Rethinking Mechanism of a Neglected Phenomenon , 1989 .

[3]  T. Goetz,et al.  Academic Emotions in Students' Self-Regulated Learning and Achievement: A Program of Qualitative and Quantitative Research , 2002 .

[4]  Arthur C. Graesser,et al.  Better to be frustrated than bored: The incidence, persistence, and impact of learners' cognitive-affective states during interactions with three different computer-based learning environments , 2010, Int. J. Hum. Comput. Stud..

[5]  B. Zimmerman,et al.  Handbook of Self-Regulation of Learning and Performance , 2011 .

[6]  B. Zimmerman,et al.  Self-regulated learning and academic achievement : theoretical perspectives , 2001 .

[7]  Kenneth R. Koedinger,et al.  A Response Time Model For Bottom-Out Hints as Worked Examples , 2008, EDM.

[8]  Dragan Gamberger,et al.  Combining Unsupervised and Supervised Machine Learning , 2001, AIME.

[9]  Arthur C. Graesser,et al.  Automatic detection of learner’s affect from conversational cues , 2008, User Modeling and User-Adapted Interaction.

[10]  Philip H. Winne,et al.  A Cognitive and Metacognitive Analysis of Self-Regulated Learning , 2011 .

[11]  Cristina Conati,et al.  Combining Unsupervised and Supervised Classification to Build User Models for Exploratory , 2009, EDM 2009.

[12]  Ryan Shaun Joazeiro de Baker,et al.  Detecting Student Misuse of Intelligent Tutoring Systems , 2004, Intelligent Tutoring Systems.

[13]  Sebastián Ventura,et al.  Educational Data Mining: A Review of the State of the Art , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[14]  Philip H. Winne,et al.  How Software Technologies Can Improve Research on Learning and Bolster School Reform , 2006 .

[15]  Beverly Park Woolf,et al.  Inferring learning and attitudes from a Bayesian Network of log file data , 2005, AIED.

[16]  Vincent Aleven,et al.  Toward Meta-cognitive Tutoring: A Model of Help Seeking with a Cognitive Tutor , 2006, Int. J. Artif. Intell. Educ..

[17]  Ryan S. Baker,et al.  The State of Educational Data Mining in 2009: A Review and Future Visions. , 2009, EDM 2009.

[18]  Neil T. Heffernan,et al.  Predicting College Enrollment from Student Interaction with an Intelligent Tutoring System in Middle School , 2013, EDM.

[19]  Philip H. Winne,et al.  Studying as self-regulated learning. , 1998 .

[20]  Kimberly E. Arnold.  Signals: Applying Academic Analytics. , 2010 .

[21]  Philip H. Winne,et al.  Self-regulated learning viewed from models of information processing. , 2001 .

[22]  T. O. Nelson.  Metamemory: A Theoretical Framework and New Findings , 1990 .

[23]  Albert T. Corbett,et al.  Does Help Help? Introducing the Bayesian Evaluation and Assessment Methodology , 2008, Intelligent Tutoring Systems.