R. Baker, A. Corbett, and K. Koedinger. Proceedings of the 23rd Annual Conference of the Cognitive Science, page 45-50. Routledge, (2001)
Abstract
The use of graphs to represent and reason about data is of growing importance in pre-high school mathematics curricula.This study examines middle school students’ skills in reasoning about three graphical representations: histograms, scatterplots and stem-and-leaf plots. Students were asked to interpret graphs, select an appropriate graph type to represent a relationship and to generate graphs. Accuracy levels varied substantially across the three tasks and three graph types. Theoverall pattern of results is largely explained by the varying ease of transfer of student knowledge from a simpler graph type, based on surface similarity.
%0 Conference Paper
%1 Baker2001
%A Baker, Ryan Shaun
%A Corbett, Albert T.
%A Koedinger, Kenneth R.
%B Proceedings of the 23rd Annual Conference of the Cognitive Science
%D 2001
%E Moore, Johanna D.
%E Stenning, Keith
%I Routledge
%K graphs statistics
%P 45-50
%T Toward a Model of Learning Data Representations
%X The use of graphs to represent and reason about data is of growing importance in pre-high school mathematics curricula.This study examines middle school students’ skills in reasoning about three graphical representations: histograms, scatterplots and stem-and-leaf plots. Students were asked to interpret graphs, select an appropriate graph type to represent a relationship and to generate graphs. Accuracy levels varied substantially across the three tasks and three graph types. Theoverall pattern of results is largely explained by the varying ease of transfer of student knowledge from a simpler graph type, based on surface similarity.
%@ 0-8058-4152-0
@inproceedings{Baker2001,
abstract = {The use of graphs to represent and reason about data is of growing importance in pre-high school mathematics curricula.This study examines middle school students’ skills in reasoning about three graphical representations: histograms, scatterplots and stem-and-leaf plots. Students were asked to interpret graphs, select an appropriate graph type to represent a relationship and to generate graphs. Accuracy levels varied substantially across the three tasks and three graph types. Theoverall pattern of results is largely explained by the varying ease of transfer of student knowledge from a simpler graph type, based on surface similarity.},
added-at = {2011-09-19T12:14:49.000+0200},
author = {Baker, Ryan Shaun and Corbett, Albert T. and Koedinger, Kenneth R.},
biburl = {https://www.bibsonomy.org/bibtex/204f6e0013abe23e8ceb8c3a9fba1ef4f/voj},
booktitle = {Proceedings of the 23rd Annual Conference of the Cognitive Science},
editor = {Moore, Johanna D. and Stenning, Keith},
interhash = {bd6a6b4c540b5c3892a53e873ea89cd4},
intrahash = {04f6e0013abe23e8ceb8c3a9fba1ef4f},
isbn = {0-8058-4152-0},
keywords = {graphs statistics},
pages = {45-50},
publisher = {Routledge},
timestamp = {2011-09-19T12:14:49.000+0200},
title = {Toward a Model of Learning Data Representations},
year = 2001
}