{ENTER}ing the time series {SPACE}: Uncovering the writing process through keystroke analyses

Laura K. Allen; Matthew E. Jacovina; Mihai Dascalu; Rod D. Roscoe; Kevin M. Kent; Aaron D. Likens; Danielle S. McNamara

{ENTER}ing the time series {SPACE}: Uncovering the writing process through keystroke analyses

Laura K. Allen, Matthew E. Jacovina, Mihai Dascalu, Rod D. Roscoe, Kevin M. Kent, Aaron D. Likens, Danielle S. McNamara

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

Abstract

This study investigates how and whether information about students’ writing can be recovered from basic behavioral data extracted during their sessions in an intelligent tutoring system for writing. We calculate basic and time-sensitive keystroke indices based on log files of keys pressed during students’ writing sessions. A corpus of prompt-based essays was collected from 126 undergraduates along with keystrokes logged during the session. Holistic scores and linguistic properties of these essays were then automatically calculated using natural language processing tools. Results indicated that keystroke indices accounted for 76% of the variance in essay quality and up to 38% of the variance in the linguistic characteristics. Overall, these results suggest that keystroke analyses can help to recover crucial information about writing, which may ultimately help to improve student models in computer-based learning environments.

Original language	English (US)
Pages	22-29
Number of pages	8
State	Published - 2016
Event	9th International Conference on Educational Data Mining, EDM 2016 - Raleigh, United States Duration: Jun 29 2016 → Jul 2 2016

Conference

Conference	9th International Conference on Educational Data Mining, EDM 2016
Country/Territory	United States
City	Raleigh
Period	6/29/16 → 7/2/16

Keywords

Feedback
Intelligent tutoring systems
Keystrokes
Natural language processing
Temporality
Writing

ASJC Scopus subject areas

Computer Science Applications
Information Systems

Cite this

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title = "{ENTER}ing the time series {SPACE}: Uncovering the writing process through keystroke analyses",

abstract = "This study investigates how and whether information about students{\textquoteright} writing can be recovered from basic behavioral data extracted during their sessions in an intelligent tutoring system for writing. We calculate basic and time-sensitive keystroke indices based on log files of keys pressed during students{\textquoteright} writing sessions. A corpus of prompt-based essays was collected from 126 undergraduates along with keystrokes logged during the session. Holistic scores and linguistic properties of these essays were then automatically calculated using natural language processing tools. Results indicated that keystroke indices accounted for 76% of the variance in essay quality and up to 38% of the variance in the linguistic characteristics. Overall, these results suggest that keystroke analyses can help to recover crucial information about writing, which may ultimately help to improve student models in computer-based learning environments.",

keywords = "Feedback, Intelligent tutoring systems, Keystrokes, Natural language processing, Temporality, Writing",

author = "Allen, {Laura K.} and Jacovina, {Matthew E.} and Mihai Dascalu and Roscoe, {Rod D.} and Kent, {Kevin M.} and Likens, {Aaron D.} and McNamara, {Danielle S.}",

note = "Funding Information: The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305A120707 to Arizona State University. The opinions expressed are those of the authors and do not represent views of thenIstitetroueth.U.S epDartment ofdEucation. Publisher Copyright: {\textcopyright} 2016 International Educational Data Mining Society. All rights reserved.; 9th International Conference on Educational Data Mining, EDM 2016 ; Conference date: 29-06-2016 Through 02-07-2016",

year = "2016",

language = "English (US)",

pages = "22--29",

}

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T1 - {ENTER}ing the time series {SPACE}

T2 - 9th International Conference on Educational Data Mining, EDM 2016

AU - Allen, Laura K.

AU - Jacovina, Matthew E.

AU - Dascalu, Mihai

AU - Roscoe, Rod D.

AU - Kent, Kevin M.

AU - Likens, Aaron D.

AU - McNamara, Danielle S.

N1 - Funding Information: The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305A120707 to Arizona State University. The opinions expressed are those of the authors and do not represent views of thenIstitetroueth.U.S epDartment ofdEucation. Publisher Copyright: © 2016 International Educational Data Mining Society. All rights reserved.

PY - 2016

Y1 - 2016

N2 - This study investigates how and whether information about students’ writing can be recovered from basic behavioral data extracted during their sessions in an intelligent tutoring system for writing. We calculate basic and time-sensitive keystroke indices based on log files of keys pressed during students’ writing sessions. A corpus of prompt-based essays was collected from 126 undergraduates along with keystrokes logged during the session. Holistic scores and linguistic properties of these essays were then automatically calculated using natural language processing tools. Results indicated that keystroke indices accounted for 76% of the variance in essay quality and up to 38% of the variance in the linguistic characteristics. Overall, these results suggest that keystroke analyses can help to recover crucial information about writing, which may ultimately help to improve student models in computer-based learning environments.

AB - This study investigates how and whether information about students’ writing can be recovered from basic behavioral data extracted during their sessions in an intelligent tutoring system for writing. We calculate basic and time-sensitive keystroke indices based on log files of keys pressed during students’ writing sessions. A corpus of prompt-based essays was collected from 126 undergraduates along with keystrokes logged during the session. Holistic scores and linguistic properties of these essays were then automatically calculated using natural language processing tools. Results indicated that keystroke indices accounted for 76% of the variance in essay quality and up to 38% of the variance in the linguistic characteristics. Overall, these results suggest that keystroke analyses can help to recover crucial information about writing, which may ultimately help to improve student models in computer-based learning environments.

KW - Feedback

KW - Intelligent tutoring systems

KW - Keystrokes

KW - Natural language processing

KW - Temporality

KW - Writing

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M3 - Paper

AN - SCOPUS:85027513354

SP - 22

EP - 29

Y2 - 29 June 2016 through 2 July 2016

ER -

{ENTER}ing the time series {SPACE}: Uncovering the writing process through keystroke analyses

Abstract

Conference

Keywords

ASJC Scopus subject areas

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