Adapt2Learn: A Toolkit for Configuring the Learning Algorithm for Adaptive Physical Tools for Motor-Skill Learning

被引：10

作者：

Turakhia, Dishita G. ^{[1
]}

Wong, Andrew ^{[1
]}

Qi, Yini ^{[1
]}

Blumberg, Lotta-Gili ^{[1
]}

Kim, Yoonji ^{[1
]}

Mueller, Stefanie ^{[1
]}

机构：

[1] MIT, CSAIL, Cambridge, MA 02139 USA

来源：

PROCEEDINGS OF THE 2021 ACM DESIGNING INTERACTIVE SYSTEMS CONFERENCE (DIS 2021) | 2021年

基金：

美国国家科学基金会;

关键词：

adaptive learning; physical interfaces; motor-skill learning; toolkit design;

D O I：

10.1145/3461778.3462128

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

A recent study on motor-skill training showed that adaptive training tools that use shape-change to adapt the training difficulty based on learners' performance can lead to higher learning gains. However, to date, no support tools exist to help designers create adaptive learning tools. Our formative study shows that developing the adaptive learning algorithm poses a particular challenge. To address this, we built Adapt2Learn, a toolkit that auto-generates the learning algorithm for adaptive tools. Designers choose their tool's sensors and actuators, Adapt2Learn then configures the learning algorithm and generates a microcontroller script that designers can deploy on the tool. Once uploaded, the script assesses the learner's performance via the sensors, computes the training difficulty, and actuates the tool to adapt the difficulty. Adapt2Learn's visualization tool then lets designers visualize their tool's adaptation and evaluate the learning algorithm. To validate that Adapt2Learn can generate adaptation algorithms for different tools, we built several application examples that demonstrate successful deployment.

引用

页码：1301 / 1312

页数：12