Difference between revisions of "Lab study proof-of-concept for handwriting vs typing input for learning algebra equation-solving"
Lisa-Anthony (Talk | contribs) (→Annotated Bibliography) |
Lisa-Anthony (Talk | contribs) (→Descendants) |
||
Line 69: | Line 69: | ||
=== Descendants === | === Descendants === | ||
− | + | ||
+ | None. | ||
=== Annotated Bibliography === | === Annotated Bibliography === |
Revision as of 21:34, 22 April 2007
Lisa Anthony, Jie Yang, Kenneth R. Koedinger
Contents
Summary Table
PIs | Lisa Anthony, Jie Yang, & Ken Koedinger |
Other Contributers | Research Programmers/Associates: Thomas Bolster (Research Associate, CMU HCII) |
Study Start Date | August 1, 2005 |
Study End Date | October 8, 2005 |
LearnLab Site | n/a |
LearnLab Course | n/a |
Number of Students | 48 |
Total Participant Hours | 1200 |
DataShop | No |
Abstract
This laboratory experiment compared differences in learning that occur depending on the modality of input during algebra equation solving. Students copied and studied a worked-out algebra example line by line before then solving an analogous problem while referring to the example. One-third of the students entered their input into a plain text box (keyboard condition), another third entered their input into a blank writing space (handwriting condition), and the final third entered their input in the writing space while also speaking the steps out loud (handwriting-plus-speaking).
The hypothesis of this study was that, in addition to previously seen usability advantages of handwriting over typing in terms of speed and user satisfaction, handwriting would also provide learning advantages. We hypothesize two interrelated factors would be responsible for these advantages: (1) the improved support of handwriting for 2D mathematics notations such as fractions and exponents which can be difficult to represent and manipulate via the keyboard; and (2) the decrease in extraneous and irrelevant cognitive load due to removing the overhead a cumbersome menu-based interface for mathematics can provide.
Preliminary results indicate that the handwriting students finished in about half the time that the keyboard students took (14.7 minutes vs 27.0 minutes) and yet they performed just as well on the post-test. More detailed analyses are in progress on isolating the effects of modality on learning rate and/or learning efficiency.
Glossary
Forthcoming, but will probably include
- Sample worked-out-example and/or screenshot of interaction in handwriting and typing
- Learning rate/efficiency
Research question
How is robust learning affected by the modality of the generated input of students, specifically comparing handwriting and typing?
Background & Significance
Prior work has found that handwriting can be faster and more liked by users than using a keyboard and mouse for entering mathematics on the computer [1]. Anecdotal evidence suggests that students take a long time to learn an interface, possibly because it interferes with learning the goal concept. If handwriting can be shown to provide robust learning gains over traditional interfaces for mathematics, it may be possible to improve intelligent tutoring systems for mathematics by incorporating handwriting interfaces; students will be faster, more engaged and more deeply involved in knowledge construction during the learning process.
Independent Variables
One independent variable was used:
- Modality of input: handwriting, typing, or handwriting-plus-speaking.
Hypothesis
The handwriting modality has been shown to be faster than typing for mathematics [1], and this corresponding speed-up in the classroom implies that more detailed study of current topics or further study of more advanced topics is possible than students otherwise would be able to achieve. In addition, students' cognitive overhead during writing should be less than typing, in which they must spend time to think about how to generate the desired input, whereas in handwriting this would come more naturally due to long practice.
Dependent variables
- Near transfer, immediate: During training, examples alternated with problems, and the problems were solved in one of the 3 modalities/conditions. Each problem was similar to the example that preceded it, so performance on it is a measure of normal learning (near transfer, immediate testing). Analyses of log data to determine error rate during training are in progress of being analyzed.
- Near transfer, retention: After the session the students were given a 20-minute post-test consisting of problems isomorphic to those seen in the session. Handwriting students and typing students both achieved similar pre-post gains, but handwriting-plus-speaking students achieved much lower gains.
- Far transfer: No far transfer items were included.
- Acceleration of future learning: No acceleration of future learning measures were included in this laboratory study.
Findings
Final findings in progress.
Explanation
This study is part of the Refinement and Fluency cluster (was Coordinative Learning) and addresses two of the 9 core assumptions: (1) fluency from basics: for true fluency, higher level skills must be grounded on well-practiced lower level skills; and (2) immediacy of feedback: a corollary of the emphasis on in vivo evaluation, scheduling, and explicit instruction is the idea that immediate feedback, which is a strong point of computerized instruction, facilitates learning.
The fluency from basics element in this study is relevant to the idea that students and teachers use handwritten notations in math class extensively on paper tests and when working on the chalkboard. Learning a new interface is not the goal of a math classroom, but rather learning the concepts and operations is. Thus, extraneous cognitive load of students is increased while learning the interface and learning the math conpete for resources.
The immediacy of feedback issue is not present in this study but rather in the overall project which doesn't have a node yet.
Not sure if this should be both CL and RF or just CL or just RF. We don't have two instructional activities or sources of information as CL requires...
Descendants
None.
Annotated Bibliography
[1] Anthony, Lisa; Yang, Jie; Koedinger, Kenneth R. (2005) "Evaluation of Multimodal Input for Entering Mathematical Equations on the Computer." ACM Conference on Human Factors in Computing Systems (CHI 2005), Portland, OR, 4 Apr 2005, pp. 1184-1187.
[2] Anthony, Lisa; Yang, Jie; Koedinger, Kenneth R. (2007) "Benefits of Handwritten Input for Students Learning Algebra Equation Solving." To appear in Proceedings of International Conference on Artificial Intelligence in Education (AIEd 2007).
Further Information
Plans for June 2007-December 2007
- This study has been completed, analyzed, submitted, and accepted for publication.
- The next steps in this line of research are relevant to add link here which is currently ongoing.