Qualitative, quantitative, and data mining methods for analyzing log data to characterize students' learning strategies and behaviors

Ryan S.J.D. Baker; Janice D. Gobert; Wouter Van Joolingen; Roger Azevedo; Ido Roll; Michael São Pedro; Juelaila Raziuddin; Nathan Krach; Adriana M.J.B. De Carvalho; Jay Raspat; Vincent Aleven; Albert T. Corbett; Kenneth R. Koedinger; Mihaela Cocea; Arnon Hershkovitz; Amy Witherspoon; Amber Chauncey; Mihai Lintean; Zhiqiang Cai; Vasile Rus; Arthur Greesser

Qualitative, quantitative, and data mining methods for analyzing log data to characterize students' learning strategies and behaviors

Ryan S.J.D. Baker, Janice D. Gobert, Wouter Van Joolingen, Roger Azevedo, Ido Roll, Michael São Pedro, Juelaila Raziuddin, Nathan Krach, Adriana M.J.B. De Carvalho, Jay Raspat, Vincent Aleven, Albert T. Corbett, Kenneth R. Koedinger, Mihaela Cocea, Arnon Hershkovitz, Amy Witherspoon, Amber Chauncey, Mihai Lintean, Zhiqiang Cai, Vasile RusArthur Greesser

School of Education

Research output: Contribution to conference › Paper › peer-review

Abstract

This symposium addresses how different classes of research methods, all based upon the use of log data from educational software, can facilitate the analysis of students' learning strategies and behaviors. To this end, four multi-method programs of research are discussed, including the use of qualitative, quantitative-statistical, quantitative-modeling, and educational data mining methods. The symposium presents evidence regarding the applicability of each type of method to research questions of different grain sizes, and provides several examples of how these methods can be used in concert to facilitate our understanding of learning processes, learning strategies, and behaviors related to motivation, meta-cognition, and engagement.

Original language	English
Pages	45-52
Number of pages	8
State	Published - 2010
Event	9th International Conference of the Learning Sciences, ICLS 2010 - Chicago, IL, United States Duration: 29 Jun 2010 → 2 Jul 2010

Conference

Conference	9th International Conference of the Learning Sciences, ICLS 2010
Country/Territory	United States
City	Chicago, IL
Period	29/06/10 → 2/07/10

Cite this

Baker, R. S. J. D., Gobert, J. D., Van Joolingen, W., Azevedo, R., Roll, I., São Pedro, M., Raziuddin, J., Krach, N., De Carvalho, A. M. J. B., Raspat, J., Aleven, V., Corbett, A. T., Koedinger, K. R., Cocea, M., Hershkovitz, A., Witherspoon, A., Chauncey, A., Lintean, M., Cai, Z., ... Greesser, A. (2010). Qualitative, quantitative, and data mining methods for analyzing log data to characterize students' learning strategies and behaviors. 45-52. Paper presented at 9th International Conference of the Learning Sciences, ICLS 2010, Chicago, IL, United States.

@conference{318a22e7e6f04c839bd265af966e125b,

title = "Qualitative, quantitative, and data mining methods for analyzing log data to characterize students' learning strategies and behaviors",

abstract = "This symposium addresses how different classes of research methods, all based upon the use of log data from educational software, can facilitate the analysis of students' learning strategies and behaviors. To this end, four multi-method programs of research are discussed, including the use of qualitative, quantitative-statistical, quantitative-modeling, and educational data mining methods. The symposium presents evidence regarding the applicability of each type of method to research questions of different grain sizes, and provides several examples of how these methods can be used in concert to facilitate our understanding of learning processes, learning strategies, and behaviors related to motivation, meta-cognition, and engagement.",

author = "Baker, {Ryan S.J.D.} and Gobert, {Janice D.} and {Van Joolingen}, Wouter and Roger Azevedo and Ido Roll and {S{\~a}o Pedro}, Michael and Juelaila Raziuddin and Nathan Krach and {De Carvalho}, {Adriana M.J.B.} and Jay Raspat and Vincent Aleven and Corbett, {Albert T.} and Koedinger, {Kenneth R.} and Mihaela Cocea and Arnon Hershkovitz and Amy Witherspoon and Amber Chauncey and Mihai Lintean and Zhiqiang Cai and Vasile Rus and Arthur Greesser",

year = "2010",

language = "אנגלית",

pages = "45--52",

note = "null ; Conference date: 29-06-2010 Through 02-07-2010",

}

Baker, RSJD, Gobert, JD, Van Joolingen, W, Azevedo, R, Roll, I, São Pedro, M, Raziuddin, J, Krach, N, De Carvalho, AMJB, Raspat, J, Aleven, V, Corbett, AT, Koedinger, KR, Cocea, M, Hershkovitz, A, Witherspoon, A, Chauncey, A, Lintean, M, Cai, Z, Rus, V & Greesser, A 2010, 'Qualitative, quantitative, and data mining methods for analyzing log data to characterize students' learning strategies and behaviors', Paper presented at 9th International Conference of the Learning Sciences, ICLS 2010, Chicago, IL, United States, 29/06/10 - 2/07/10 pp. 45-52.

Qualitative, quantitative, and data mining methods for analyzing log data to characterize students' learning strategies and behaviors. / Baker, Ryan S.J.D.; Gobert, Janice D.; Van Joolingen, Wouter et al.

2010. 45-52 Paper presented at 9th International Conference of the Learning Sciences, ICLS 2010, Chicago, IL, United States.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - Qualitative, quantitative, and data mining methods for analyzing log data to characterize students' learning strategies and behaviors

AU - Baker, Ryan S.J.D.

AU - Gobert, Janice D.

AU - Van Joolingen, Wouter

AU - Azevedo, Roger

AU - Roll, Ido

AU - São Pedro, Michael

AU - Raziuddin, Juelaila

AU - Krach, Nathan

AU - De Carvalho, Adriana M.J.B.

AU - Raspat, Jay

AU - Aleven, Vincent

AU - Corbett, Albert T.

AU - Koedinger, Kenneth R.

AU - Cocea, Mihaela

AU - Hershkovitz, Arnon

AU - Witherspoon, Amy

AU - Chauncey, Amber

AU - Lintean, Mihai

AU - Cai, Zhiqiang

AU - Rus, Vasile

AU - Greesser, Arthur

PY - 2010

Y1 - 2010

N2 - This symposium addresses how different classes of research methods, all based upon the use of log data from educational software, can facilitate the analysis of students' learning strategies and behaviors. To this end, four multi-method programs of research are discussed, including the use of qualitative, quantitative-statistical, quantitative-modeling, and educational data mining methods. The symposium presents evidence regarding the applicability of each type of method to research questions of different grain sizes, and provides several examples of how these methods can be used in concert to facilitate our understanding of learning processes, learning strategies, and behaviors related to motivation, meta-cognition, and engagement.

AB - This symposium addresses how different classes of research methods, all based upon the use of log data from educational software, can facilitate the analysis of students' learning strategies and behaviors. To this end, four multi-method programs of research are discussed, including the use of qualitative, quantitative-statistical, quantitative-modeling, and educational data mining methods. The symposium presents evidence regarding the applicability of each type of method to research questions of different grain sizes, and provides several examples of how these methods can be used in concert to facilitate our understanding of learning processes, learning strategies, and behaviors related to motivation, meta-cognition, and engagement.

UR - http://www.scopus.com/inward/record.url?scp=84880558135&partnerID=8YFLogxK

M3 - ???researchoutput.researchoutputtypes.contributiontoconference.paper???

AN - SCOPUS:84880558135

SP - 45

EP - 52

Y2 - 29 June 2010 through 2 July 2010

ER -

Qualitative, quantitative, and data mining methods for analyzing log data to characterize students' learning strategies and behaviors

Abstract

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