Jon Wetzel

CogSketch: Sketch Understanding for Cognitive Science Research and for Education

Sketching is a powerful means of working out and communicating ideas. Sketch understanding involves a combination of visual, spatial, and conceptual knowledge and reasoning, which makes it both challenging to model and potentially illuminating for cognitive science. This paper describes CogSketch, an ongoing effort of the NSF-funded Spatial Intelligence and Learning Center, which is being developed both as a research instrument for cognitive science and as a platform for sketch-based educational software. We describe the idea of open-domain sketch understanding, the scientific hypotheses underlying CogSketch, and provide an overview of the models it employs, illustrated by simulation studies and ongoing experiments in creating sketch-based educational software.

CogSketch: open-domain sketch understanding for cognitive science research and for education

In this paper, we describe CogSketch, an open-domain sketch understanding system built on the nuSketch architecture. CogSketch captures the multi-modal, unconstrained nature of sketching by focusing on reasoning over recognition. We describe this approach, as well as two application domains for CogSketch: cognitive modeling, and education.

Learning How to Construct Models of Dynamic Systems: An Initial Evaluation of the Dragoon Intelligent Tutoring System

Constructing models of dynamic systems is an important skill in both mathematics and science instruction. However, it has proved difficult to teach. Dragoon is an intelligent tutoring system intended to quickly and effectively teach this important skill. This paper describes Dragoon and an evaluation of it. The evaluation randomly assigned students in a university class to either Dragoon or baseline instruction that used Dragoon as an editor only. Among students who did use their systems, the tutored students scored reliably higher (p < .021, d = 1.06) on the post-test than the students who used only the conventional editor-based instruction.

The design and development of the dragoon intelligent tutoring system for model construction: lessons learned

ABSTRACT This paper describes Dragoon, a simple intelligent tutoring system which teaches the construction of models of dynamic systems. Modelling is one of seven practices dictated in two new sets of educational standards in the U.S.A., and Dragoon is one of the first systems for teaching model construction for dynamic systems. Dragoon can be classified as a step-based tutoring system that uses example-tracing, an explicit pedagogical policy and an open learner model. Dragoon can also be used for computer-supported collaborative learning, and provides tools for classroom orchestration. This paper describes the features, user interfaces, and architecture of Dragoon; compares and contrasts Dragoon with other intelligent tutoring systems; and presents a brief overview of formative and summative evaluations of Dragoon in both high school and college classes. Of four summative evaluations, three found that students who used Dragoon learned more about the target system than students who did equivalent work without Dragoon.

Increasing Student Confidence in Engineering Sketching via a Software Coach

Sketching is an important skill for engineering design students. A serious problem found by Northwestern instructors is that students are afraid to sketch. We are tackling this problem by developing the Design Coach, which enables students to practice explaining their designs via a combination of sketching and language, to reduce their anxiety about communicating via sketching. This paper summarizes the overall operation of the Design Coach and reports on a classroom experiment providing evidence that that the Design Coach does in fact reduce student anxiety, compared to a control group that did not use it.

ElectronixTutor: An Intelligent Tutoring System with Multiple Learning Resources for Electronics.

BackgroundThe Office of Naval Research (ONR) organized a STEM Challenge initiative to explore how intelligent tutoring systems (ITSs) can be developed in a reasonable amount of time to help students learn STEM topics. This competitive initiative sponsored four teams that separately developed systems that covered topics in mathematics, electronics, and dynamical systems. After the teams shared their progress at the conclusion of an 18-month period, the ONR decided to fund a joint applied project in the Navy that integrated those systems on the subject matter of electronic circuits. The University of Memphis took the lead in integrating these systems in an intelligent tutoring system called ElectronixTutor. This article describes the architecture of ElectronixTutor, the learning resources that feed into it, and the empirical findings that support the effectiveness of its constituent ITS learning resources.ResultsA fully integrated ElectronixTutor was developed that included several intelligent learning resources (AutoTutor, Dragoon, LearnForm, ASSISTments, BEETLE-II) as well as texts and videos. The architecture includes a student model that has (a) a common set of knowledge components on electronic circuits to which individual learning resources contribute and (b) a record of student performance on the knowledge components as well as a set of cognitive and non-cognitive attributes. There is a recommender system that uses the student model to guide the student on a small set of sensible next steps in their training. The individual components of ElectronixTutor have shown learning gains in previous decades of research.ConclusionsThe ElectronixTutor system successfully combines multiple empirically based components into one system to teach a STEM topic (electronics) to students. A prototype of this intelligent tutoring system has been developed and is currently being tested. ElectronixTutor is unique in its assembling a group of well-tested intelligent tutoring systems into a single integrated learning environment.

Spatial reasoning in comparative analyses of physics diagrams

Spatial reasoning plays a critical role in STEM problem solving. Physics assessments, for example, are rich in diagrams and pictures, which help people understand concrete physical scenarios and abstract aspects of physical reasoning. In this paper we describe a system that analyzes sketched diagrams to solve qualitative physics problems from a popular physics textbook. Causal models describing each problem are formulated via visual and conceptual analyses of the sketched diagrams. We use a combination of qualitative and quantitative reasoning to solve vector addition, tension, and gravitation ranking problems in the introductory chapters of the book.

A Preliminary Evaluation of the Usability of an AI-Infused Orchestration System.

Artificial intelligence (AI) holds great promise for improving classroom orchestration—the teacher’s management of a classroom workflow that mixes small group, individual, and whole class activities. Although we have developed an orchestration system, named FACT, that uses AI, we were concerned that usability issues might decrease its effectiveness. We conducted an analysis of classroom video recordings that classified and compared the time FACT students spent to the time spent by students using paper versions of the same lessons. FACT wasted half the time that paper did. However FACT students spent slightly more time off task and had difficulties referring to objects on shared documents.

The Effect of Digital Versus Traditional Orchestration on Collaboration in Small Groups

We are developing an intelligent orchestration system named FACT (Formative Assessment using Computational Technology). Orchestration refers the teacher’s management of a face-to-face classroom workflow that mixes small group, individual and whole class activities. FACT is composed of an unintelligent Media system and an intelligent Analysis system. Although the Analysis system, which is still being refined, is designed to increase collaboration, prior work suggests that the Media system could possibly harm collaboration. Thus, we conducted an evaluation of the FACT Media system in classrooms, comparing it against traditional classrooms. We coded videos of small groups in order to measure their collaboration. The FACT Media system did no harm: the distribution of collaboration codes in FACT classrooms is statistically similar to the distribution in traditional classrooms. This null result is welcome news and sets the stage for testing the benefits of the Analysis system.

How Should Knowledge Composed of Schemas be Represented in Order to Optimize Student Model Accuracy

Most approaches to student modeling assume that students’ knowledge can be represented by a large set of knowledge components that are learned independently. Knowledge components typically represent fairly small pieces of knowledge. This seems to conflict with the literature on problem solving which suggests that expert knowledge is composed of large schemas. This study compared several domain models for knowledge that is arguably composed of schemas. The knowledge is used by students to construct system dynamics models with the Dragoon intelligent tutoring system. An evaluation with 52 students showed that a relative simple domain model, that assigned one KC to each schema and schema combination, sufficed and was more parsimonious than other domain models with similarly accurate predictions.