Lisa Anthony

Evaluation of multimodal input for entering mathematical equations on the computer

Current standard interfaces for entering mathematical equations on computers are arguably limited and cumbersome. Mathematics notations have evolved to aid visual thinking and yet text-based interfaces relying on keyboard-and-mouse input do not take advantage of the natural two-dimensional aspects of math. Due to its similarities to paper-based mathematics, pen-based handwriting input may be faster, more efficient, and more preferable for entering mathematics on computers. This paper presents an empirical study that tests this hypothesis. We also explored a multimodal input method combining handwriting and speech because we hypothesize that it may enhance computer recognition and aid user cognition. Novice users were indeed faster, more efficient and enjoyed the handwriting modality more than a standard keyboard-and-mouse mathematics interface, especially as equation length and complexity increased. The multimodal handwriting-plus-speech method was faster and better liked than the keyboard-and-mouse method and was not much worse than handwriting alone.

An Approach to Capturing Structure, Behavior, and Function of Artifacts in Computer-Aided Design

This paper presents an approach to computer-aided design (C that unites ideas from design with three-dimensional layouts knowledge engineering. Our goal is to capture the structure, havior, and function of CAD artifacts. We describe a software t based on this approach, the conceptual understanding and pr typing (CUP) environment, for capturing the design intent inh ent in the design process and authoring design semantics in viously created artifacts. CUP records design ideas, based functional, geometric, and knowledge-based relationships am components in an electromechanical assembly. This design kn edge is stored using ontologies defined in XML. The goal of work is to enable users to navigate intricate product data a design knowledge bases. @DOI: 10.1115/1.1385826 #

A paradigm for handwriting-based intelligent tutors

This paper presents the interaction design of, and demonstration of technical feasibility for, intelligent tutoring systems that can accept handwriting input from students. Handwriting and pen input offer several affordances for students that traditional typing-based interactions do not. To illustrate these affordances, we present evidence, from tutoring mathematics, that the ability to enter problem solutions via pen input enables students to record algebraic equations more quickly, more smoothly (fewer errors), and with increased transfer to non-computer-based tasks. Furthermore our evidence shows that students tend to like pen input for these types of problems more than typing. However, a clear downside to introducing handwriting input into intelligent tutors is that the recognition of such input is not reliable. In our work, we have found that handwriting input is more likely to be useful and reliable when context is considered, for example, the context of the problem being solved. We present an intelligent tutoring system for algebra equation solving via pen-based input that is able to use context to decrease recognition errors by 18% and to reduce recognition error recovery interactions to occur on one out of every four problems. We applied user-centered design principles to reduce the negative impact of recognition errors in the following ways: (1) though students handwrite their problem-solving process, they type their final answer to reduce ambiguity for tutoring purposes, and (2) in the small number of cases in which the system must involve the student in recognition error recovery, the interaction focuses on identifying the students problem-solving error to keep the emphasis on tutoring. Many potential recognition errors can thus be ignored and distracting interactions are avoided. This work can inform the design of future systems for students using pen and sketch input for math or other topics by motivating the use of context and pragmatics to decrease the impact of recognition errors and put user focus on the task at hand.

Adapting handwriting recognition for applications in algebra learning

In this paper we report the progress of our ongoing project exploring the adaptation of handwriting recognition-based interfaces for applications in intelligent tutoring systems for students learning algebra equation-solving. The research is motivated by the hypothesis that handwriting as an input modality may be able to provide significant advantages over typing in the mathematics learning domain. We review the literature of existing handwriting systems for mathematic applications and evaluations of handwriting recognition accuracy. We describe our approach and report results to date in exploring the use of handwriting recognition in interfaces for math learning, from both a technical and a pedagogical perspective. We have found that handwriting input can provide benefits to students learning math, and continue to pursue further technical and pedagogical enhancements.

Student Question-Asking Patterns in an Intelligent Algebra Tutor

Cognitive Tutors are proven effective learning environments, but are still not as effective as one-on-one human tutoring. We describe an environment (ALPS) designed to engage students in question-asking during problem solving. ALPS integrates Cognitive Tutors with Synthetic Interview (SI) technology, allowing students to type free-form questions and receive pre-recorded video clip answers. We performed a Wizard-of-Oz study to evaluate the feasibility of ALPS and to design the question-and-answer database for the SI. In the study, a human tutor played the SI’s role, reading the students’ typed questions and answering over an audio/video channel. We examine the rate at which students ask questions, the content of the questions, and the events that stimulate questions. We found that students ask questions in this paradigm at a promising rate, but there is a need for further work in encouraging them to ask deeper questions that may improve knowledge encoding and learning.

Toward Next-Generation, Intelligent Tutors: Adding Natural Handwriting Input

This article explores handwriting recognition-based interfaces in intelligent tutoring systems for students learning algebra equations.

Towards the Application of a Handwriting Interface for Mathematics Learning

We believe handwriting input may be able to provide significant advantages over typing, especially in the mathematics learning domain. The use of handwriting may result in decreased extraneous cognitive load on students, and it may provide better support for the two-dimensional spatial components of mathematics when compared to existing typing-based tools. Here we report progress towards the application of a handwriting interface for mathematics learning. We introduce a prototype system that allows students to use handwriting input to solve algebraic equations in an intelligent tutor. We discuss strategies to improve the existing handwriting system and apply it to math learning. Although the recognition accuracy of current handwriting engines may not be at a level suitable for use by students, we hypothesize that this may be realistically improved via advance training of the engine on a large corpus, as well as via techniques similar to co-training