Handwriting Algebra Tutor
Lisa Anthony, Jie Yang, Kenneth R. Koedinger
Much work has been done in the field on modality of presentation: that is, how to best present instructional materials to students, both in the classroom and on the computer. This project’s focus is instead of modality of generation: how the modality in which students produce their solutions interacts with the cognitive processes involved in conceptualizing and working through a solution.
Most intelligent tutoring systems have relied on standard computer interface paradigms using the keyboard and mouse, even in mathematics. These interfaces are often idiosyncratic and quirky in their support for standard math notations such as two-dimensional constructs like fractions and exponents. The spatial relationships between characters has inherent and important meaning in mathematics, far more so than in other forms of writing, yet computer typing interfaces are ill-equipped to deal with them. Freeform handwriting input may be uniquely suited to entering mathematics input on the computer by virtue of its natural support for spatially variegated notations and its lack of constraints imposed on students producing a solution (allowing the problem-solving process to benefit from foundational fluency in handwriting).
Therefore, this project’s goal is to explore the ways in which the use of handwriting recognition in the interfaces of intelligent tutoring systems, already shown to be highly effective learning environments, can lead to improved learning gains. Our work has already shown that handwriting provides usability benefits in that speed of entry increases, user error decreases, and user satisfaction increases. We have also shown preliminary evidence that handwriting also provides learning benefits, in that students solving the same problems by handwriting as others who were typing took only half the time as their typing counterparts.
One concern with the use of handwriting in intelligent tutoring systems, however, is that recognition technology is not perfect. To the extent that we cannot be confident of correctly recognizing what the student is writing, we cannot provide detailed, step-targeted feedback. Therefore, a trade-off is clear between development effort to improve recognition accuracy and need to support step-targeted feedback. We attempt to address this trade-off by using worked examples, providing a sort of feed-forward instead and providing opportunities for students to use coordination of the given problem and the given example to improve their knowledge. To this end, this project consists of a number of studies that explore what the advantages of using handwriting are, what cognitive and learning factors contribute to these advantages, and how we can leverage these advantages in real tutoring systems with minimal technical development cost.
- Lab study proof-of-concept for handwriting vs typing input for learning algebra equation-solving
- Effect of adding simple worked examples to problem-solving in algebra learning
- Summative evaluation of enhanced handwriting interface with step-targeted feedback
 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.
 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).