Proceedings of the Third Annual Virginia Tech Center for Human-Computer Interaction Research Experience for Undergraduates (REU) Symposium

Virginia Tech's Center for Human-Computer Interaction presents the project abstracts for the REU ’08 symposium. The REU (Research Experience for Undergraduates) program provides undergraduate students from various universities with the opportunity to spend eight weeks at Virginia Tech, working with our faculty and graduate students on research projects using the state-of-the-art technology and laboratories assembled here. The REU program is sponsored by a National Science Foundation grant IIS-0552732

The Role of Geospatial Thinking and Geographic Skills in Effective Problem Solving with GIS: K-16 Education

Effective use of a Geographic Information System (GIS) is hampered by the limited geospatial reasoning abilities of students. The ability to reason with spatial relations, more specifically apply geospatial concepts, including the identification of spatial patterns and spatial associations, is important to geographic problem solving in a GIS context. This dissertation examines the broad influence of three factors on GIS problem solving: 1) affection towards computers, geography, and mathematics, 2) geospatial thinking, as well as 3) geographic skills. The research was conducted with 104 students in Waterloo, Ontario, Canada. Students were drawn from four educational levels: grade 9 students, 13 to 14 years of age; 1st year undergraduate university students, 3rd and 4th year undergraduate geography majors; and geography students at the graduate level ranging from 22 to 32 years of age. The level of affection is measured with modified scales borrowed from psychology. Results show that students in general exhibit positive sentiments toward computers and geography but less so towards mathematics. Spatial thinking and knowledge of geospatial concepts are measured by a 30-item scale differentiating among spatial thinkers along a novice-expert continuum. Scores on the scale showed an increase in spatial reasoning ability with age, grade, and level of education, such that grade 9 students averaged 7.5 out of 30 while the mean score of graduate students was 20.6. The final exercise assessed pertinent skills to geography namely inquiry, data collection, and analysis. In general, there was a positive correlation in the scores such that the skill proficiency increased with grade. Related analysis found three factors that affect problem-solving performance with a GIS. These include age, geographic skills (inquiry and analysis), and geospatial thinking (subscales analysis, representation, comprehension, and application). As well, the relationship(s) between performance on the geospatial scale and the observed problem-solving sequences and strategies applied on a GIS was examined. In general, students with lower scores were more apt to use basic visualization (zoom/measure tools) or buffer operations, while those with higher scores used a combination of buffers, intersection, and spatial queries. There were, however, exceptions as some advanced students used strategies that overly complicated the problem while others used visualization tools alone. The study concludes with a discussion on future research directions, followed by a series of pencil and paper games aimed to develop spatial thinking within a geographic setting

Computer Assisted Learning in Obstetric Ultrasound

Ultrasound is a dynamic, real-time imaging modality that is widely used in clinical obstetrics. Simulation has been proposed as a training method, but how learners performance translates from the simulator to the clinic is poorly understood. Widely accepted, validated and objective measures of ultrasound competency have not been established for clinical practice. These are important because previous works have noted that some individuals do not achieve expert-like performance despite daily usage of obstetric ultrasound. Underlying foundation training in ultrasound was thought to be sub-optimal in these cases. Given the widespread use of ultrasound and the importance of accurately estimating the fetal weight for the management of high-risk pregnancies and the potential morbidity associated with iatrogenic prematurity or unrecognised growth restriction, reproducible skill minimising variability is of great importance. In this thesis, I will investigate two methods with the aim of improving training in obstetric ultrasound. The initial work will focus on quantifying operational performance. I collect data in the simulated and clinical environment to compare operator performance between novice and expert performance. In the later work I developed a mixed reality trainer to enhance trainee’s visualisation of how the ultrasound beam interacts with the anatomy being scanned. Mixed reality devices offer potential for trainees because they combine real-world items with items in the virtual world. In the training environment this allows for instructions, 3-dimensional visualisations or workflow instructions to be overlaid on physical models. The work is important because the techniques developed for the qualification of operator skill could be combined in future work with a training programme designed around educational theory to give trainee sonographers consistent feedback and instruction throughout their training

Integration of Abductive and Deductive Inference Diagnosis Model and Its Application in Intelligent Tutoring System

This dissertation presents a diagnosis model, Integration of Abductive and Deductive Inference diagnosis model (IADI), in the light of the cognitive processes of human diagnosticians. In contrast with other diagnosis models, that are based on enumerating, tracking and classifying approaches, the IADI diagnosis model relies on different inferences to solve the diagnosis problems. Studies on a human diagnosticians\u27 process show that a diagnosis process actually is a hypothesizing process followed by a verification process. The IADI diagnosis model integrates abduction and deduction to simulate these processes. The abductive inference captures the plausible features of this hypothesizing process while the deductive inference presents the nature of the verification process. The IADI diagnosis model combines the two inference mechanisms with a structure analysis to form the three steps of diagnosis, mistake detection by structure analysis, misconception hypothesizing by abductive inference, and misconception verification by deductive inference. An intelligent tutoring system, Recursive Programming Tutor (RPT), has been designed and developed to teach students the basic concepts of recursive programming. The RPT prototype illustrates the basic features of the IADI diagnosis approach, and also shows a hypertext-based tutoring environment and the tutoring strategies, such as concentrating diagnosis on the key steps of problem solving, organizing explanations by design plans and incorporating the process of tutoring into diagnosis

Upper extremity biomechanics in native and non-native signers

abstract: Individuals fluent in sign language who have at least one deaf parent are considered native signers while those with non-signing, hearing parents are non-native signers. Musculoskeletal pain from repetitive motion is more common from non-natives than natives. The goal of this study was twofold: 1) to examine differences in upper extremity (UE) biomechanical measures between natives and non-natives and 2) upon creating a composite measure of injury-risk unique to signers, to compare differences in scores between natives and non-natives. Non-natives were hypothesized to have less favorable biomechanical measures and composite injury-risk scores compared to natives. Dynamometry was used for measurement of strength, electromyography for ‘micro’ rest breaks and muscle tension, optical motion capture for ballistic signing, non-neutral joint angle and work envelope, a numeric pain rating scale for pain, and the modified Strain Index (SI) as a composite measure of injury-risk. There were no differences in UE strength (all p≥0.22). Natives had more rest (natives 76.38%; non-natives 26.86%; p=0.002) and less muscle tension (natives 11.53%; non-natives 48.60%; p=0.008) for non-dominant upper trapezius across the first minute of the trial. For ballistic signing, no differences were found in resultant linear segment acceleration when producing the sign for ‘again’ (natives 27.59m/s2; non-natives 21.91m/s2; p=0.20). For non-neutral joint angle, natives had more wrist flexion-extension motion when producing the sign for ‘principal’ (natives 54.93°; non-natives 46.23°; p=0.04). Work envelope demonstrated the greatest significance when determining injury-risk. Natives had a marginally greater work envelope along the z-axis (inferior-superior) across the first minute of the trial (natives 35.80cm; non-natives 30.84cm; p=0.051). Natives (30%) presented with a lower pain prevalence than non-natives (40%); however, there was no significant difference in the modified SI scores (natives 4.70 points; non-natives 3.06 points; p=0.144) and no association between presence of pain with the modified SI score (r=0.087; p=0.680). This work offers a comprehensive analysis of all the previously identified UE biomechanics unique to signers and helped to inform a composite measure of injury-risk. Use of the modified SI demonstrates promise, although its lack of association with pain does confirm that injury-risk encompasses other variables in addition to a signer’s biomechanics.Dissertation/ThesisDoctoral Dissertation Exercise and Nutritional Sciences 201

Modelling student errors in physics problem-solving

The motivation for this work has been the development of knowledge about the behaviour of human problem-solvers that would enable an intelligent machine tutor to be designed. In the domain of Newtonian Mechanics, this breaks down into two necessary sub-tasks; how do people decide what equation to generate; and what do they produce when they do try to generate an equation? Although these are psychologically separate questions, an automatic tutor for the domain would need to make use of both kinds of knowledge. Therefore, strategies for controlling search in physics problem-solving are investigated, and a computational model of erroneous solutions is described. Experimental data is used to evaluate the model. Errors in the domain are classified, and the behaviour of problem-solvers predicted under certain circumstances. Prediction of Novice errors is a crucial ability for an intelligent tutorial system, and the error analysis implemented in the NEWT program is the main contribution of this thesis. The investigation has two principal aims: (1) To develop a model that allows a student's future behaviour to be predicted from an analysis of his past actions. It is argued that this is a necessary prerequisite for the construction of an intelligent tutorial system. (2) To identify the psychological mechanisms used by problem-solvers working in the domain. The thesis attempts to achieve these aims in two main ways: (1) A computer program called NEWT has been constructed, which solves problems of Newtonian Mechanics correctly, or in one of a number of erroneous ways. This allows human errors to be matched, classified, and in some cases predicted. (2) An analysis of published data leads to the formulation of a control strategy termed "planstacking". This is compared to alternative control strategies, and shown to explain existing data more adequately. The program is evaluated both as a psychological theory, and as a proposed student model for use in a computer-based tutorial system. The NEWT program was developed from the MECHO program written by Bundy, Byrd, Luger, Mellish and Palmer (1979), at the Department of Artificial Intelligence, Edinburgh University. This program was adapted to produce erroneous problem solutions by the inclusion of procedures to implement malrules observed in the domain

Assistive Teaching of Motor Control Tasks to Humans

Recent works on shared autonomy and assistive-AI technologies, such as assistive robot teleoperation, seek to model and help human users with limited ability in a fixed task. However, these approaches often fail to account for humans' ability to adapt and eventually learn how to execute a control task themselves. Furthermore, in applications where it may be desirable for a human to intervene, these methods may inhibit their ability to learn how to succeed with full self-control. In this paper, we focus on the problem of assistive teaching of motor control tasks such as parking a car or landing an aircraft. Despite their ubiquitous role in humans' daily activities and occupations, motor tasks are rarely taught in a uniform way due to their high complexity and variance. We propose an AI-assisted teaching algorithm that leverages skill discovery methods from reinforcement learning (RL) to (i) break down any motor control task into teachable skills, (ii) construct novel drill sequences, and (iii) individualize curricula to students with different capabilities. Through an extensive mix of synthetic and user studies on two motor control tasks -- parking a car with a joystick and writing characters from the Balinese alphabet -- we show that assisted teaching with skills improves student performance by around 40% compared to practicing full trajectories without skills, and practicing with individualized drills can result in up to 25% further improvement. Our source code is available at https://github.com/Stanford-ILIAD/teachingComment: 22 pages, 14 figures, NeurIPS 202