Evaluating Feedback Strategies for Virtual Human Trainers

TL;DR

This study compares two feedback strategies in a virtual training system, finding that fully corrective feedback improves performance and is preferred by users over suggestive feedback.

Contribution

The paper introduces and empirically evaluates two distinct feedback strategies for virtual trainers, demonstrating the effectiveness of correctness feedback.

Findings

Correctness feedback was preferred by participants.

Correctness feedback led to better task performance.

The virtual system was rated comparable to real interactions.

Abstract

In this paper we address feedback strategies for an autonomous virtual trainer. First, a pilot study was conducted to identify and specify feedback strategies for assisting participants in performing a given task. The task involved sorting virtual cubes according to areas of countries displayed on them. Two feedback strategies were specified. The first provides correctness feedback by fully correcting user responses at each stage of the task, and the second provides suggestive feedback by only notifying if and how a response can be corrected. Both strategies were implemented in a virtual training system and empirically evaluated. The correctness feedback strategy was preferred by the participants, was more effective time-wise, and was more effective in improving task performance skills. The overall system was also rated comparable to hypothetically performing the same task with real interactions.