東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Into the Black Box: Using Data Minin...

Kerr, Deirdre Song.

Linked to FindBook

Google Book

Amazon

博客來

Into the Black Box: Using Data Mining of In-Game Actions to Draw Inferences from Educational Technology about Students' Math Knowledge.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Into the Black Box: Using Data Mining of In-Game Actions to Draw Inferences from Educational Technology about Students' Math Knowledge./
Author:	Kerr, Deirdre Song.
Description:	131 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-06(E), Section: A.
Contained By:	Dissertation Abstracts International75-06A(E).
Subject:	Education, Technology of. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3613716
ISBN:	9781303770708

Into the Black Box: Using Data Mining of In-Game Actions to Draw Inferences from Educational Technology about Students' Math Knowledge.
Kerr, Deirdre Song.

Into the Black Box: Using Data Mining of In-Game Actions to Draw Inferences from Educational Technology about Students' Math Knowledge. - 131 p.

Source: Dissertation Abstracts International, Volume: 75-06(E), Section: A.

Thesis (Ed.D.)--University of California, Los Angeles, 2014.

Educational video games have the potential to be used as assessments of student understanding of complex concepts. However, the interpretation of the rich stream of complex data that results from the tracking of in-game actions is so difficult that it is one of the most serious blockades to the use of educational video games or simulations to assess student understanding, and there is currently no systematic approach to extracting relevant data from log files. This study attempts to determine whether data mining techniques can be used to extract information from log files that allows for the formation of testable hypotheses. The log files in this study come from an educational video game teaching students about the identification of fractions. The three data mining techniques used in this study were: cluster analysis, sequence mining, and classification.

ISBN: 9781303770708Subjects--Topical Terms:

1018012
Education, Technology of.

Into the Black Box: Using Data Mining of In-Game Actions to Draw Inferences from Educational Technology about Students' Math Knowledge.
LDR:03648nam a2200313 4500 001 1962427
005 20140805141058.5
008 150210s2014 ||||||||||||||||| ||eng d
020 $a 9781303770708
035 $a (MiAaPQ)AAI3613716
035 $a AAI3613716
040 $a MiAaPQ $c MiAaPQ
100 1 $a Kerr, Deirdre Song. $3 2098504
245 1 0 $a Into the Black Box: Using Data Mining of In-Game Actions to Draw Inferences from Educational Technology about Students' Math Knowledge.
300 $a 131 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-06(E), Section: A.
500 $a Adviser: Noreen M. Webb.
502 $a Thesis (Ed.D.)--University of California, Los Angeles, 2014.
520 $a Educational video games have the potential to be used as assessments of student understanding of complex concepts. However, the interpretation of the rich stream of complex data that results from the tracking of in-game actions is so difficult that it is one of the most serious blockades to the use of educational video games or simulations to assess student understanding, and there is currently no systematic approach to extracting relevant data from log files. This study attempts to determine whether data mining techniques can be used to extract information from log files that allows for the formation of testable hypotheses. The log files in this study come from an educational video game teaching students about the identification of fractions. The three data mining techniques used in this study were: cluster analysis, sequence mining, and classification.
520 $a Cluster analysis was used to examine the individual actions each student took in a given attempt to solve each game level. This led to the identification of valid solution strategies students used and mathematical errors and game-related errors students made as they tried to solve game levels. Sequence mining was used to examine the change in strategy use as each student moved through the game levels. This led to the identification of strategy sequences representing different paths students took to arrive at the correct solution. Classification was used to examine the change in the number of attempts each student required to solve the levels in each stage. This led to the identification of performance trajectories representing improvement, decline, or lack of change in performance over time.
520 $a To demonstrate the usefulness of the extracted information and provide initial evidence that the interpretation of the extracted information was valid, testable hypotheses from the results of each data mining technique were generated examining whether the grouping of students resulting from each of the three data mining techniques differed significantly on paper-and-pencil pretest scores, posttest scores, or the gain in scores between pretest and posttest. The groups resulting from each of the three data mining techniques differed significantly on pretest and posttest scores, with students in groups interpreted as representing lower performance demonstrating lower performance on the tests and students in groups interpreted as representing higher performance demonstrating higher performance on the tests. However, none of the three techniques led to the identification of groups of students that differed significantly on the gain in scores between pretest and posttest.
590 $a School code: 0031.
650 4 $a Education, Technology of. $3 1018012
650 4 $a Education, Tests and Measurements. $3 1017589
650 4 $a Education, General. $3 1019158
690 $a 0710
690 $a 0288
690 $a 0515
710 2 $a University of California, Los Angeles. $b Education 0249. $3 2036364
773 0 $t Dissertation Abstracts International $g 75-06A(E).
790 $a 0031
791 $a Ed.D.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3613716