Predicting User Performance and Learning in Human--Computer Interaction with the Herbal Compiler

Author:

Paik Jaehyon1,Kim Jong W.2,Ritter Frank E.3,Reitter David3

Affiliation:

1. LG Electronics, Seoul, South Korea

2. University of Central Florida

3. The Pennsylvania State University, PA, USA

Abstract

We report a way to build a series of GOMS-like cognitive user models representing a range of performance at different stages of learning. We use a spreadsheet task across multiple sessions as an example task; it takes about 20--30 min. to perform. The models were created in ACT-R using a compiler. The novice model has 29 rules and 1,152 declarative memory task elements (chunks)—it learns to create procedural knowledge to perform the task. The expert model has 617 rules and 614 task chunks (that it does not use) and 538 command string chunks—it gets slightly faster through limited declarative learning of the command strings and some further production compilation; there are a range of intermediate models. These models were tested against aggregate and individual human learning data, confirming the models’ predictions. This work suggests that user models can be created that learn like users while doing the task.

Funder

O N R

d t r a

Publisher

Association for Computing Machinery (ACM)

Subject

Human-Computer Interaction

Cited by 15 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3