Detecting and Addressing Frustration in a Serious Game for Military Training

Abstract

Tutoring systems that are sensitive to affect show considerable promise for enhancing student learning experiences. Creating successful affective responses requires considerable effort both to detect student affect and to design appropriate responses to affect. Recent work has suggested that affect detection is more effective when both physical sensors and interaction logs are used, and that context-sensitive design of affective feedback is necessary to enhance engagement and improve learning. In this paper, we provide a comprehensive report on a multi-part study that integrates detection, validation, and intervention into a unified approach. This paper examines the creation of both sensor-based and interaction-based detectors of student affect, producing successful detectors of student affect. In addition, it reports results from an investigation of motivational feedback messages designed to address student frustration, and investigates whether linking these interventions to detectors improves outcomes. Our results are mixed, finding that self-efficacy enhancing interventions based on interaction-based affect detectors enhance outcomes in one of two experiments investigating affective interventions. This work is conducted in the context of the GIFT framework for intelligent tutoring, and the TC3Sim game-based simulation that provides training for first responder skills.

Special Issue on the Generalized Intelligent Framework for Tutoring (GIFT): Creating a Stable and Flexible Platform for Innovations in AIED Research

“…& teamwork tutoring -focused on enhancing coordination, cooperation, and communication among individuals on a team to achieve a shared goal (Salas 2015). & collaborative learning -Ba situation in which two or more people learn or attempt to learn something together^ (Dillenbourg 1999, p. 1) which may or may not require more than one member to accomplish once learned.…”

“…While GIFT has been used as an ITS authoring and experimental tool across a variety of task domains including more traditional topics in science, technology, education, and mathematics (STEM) (e.g., Renduchitnala and Matthews 2017;Warta 2017), this special issue focused on three distinctive topics: 1) sensitivity to learner affect (DeFalco et al 2017), adaptive training of psychomotor tasks (Goldberg et al 2017), and 3) adaptive instruction of teams (Fletcher and Sottilare 2017;Gilbert et al 2017;Sottilare et al 2017aSottilare et al , 2017b. Although each article describes a functional capability of significant importance to military training and education, we propose that the processes described therein could easily be transferred and applied to parallel civilian training and educational domains.…”

“…In this special issue, DeFalco et al (2017) presents the entire process of developing an affect-sensitive system, including affect detection research, the embedding of the resultant models in a running system, and the use of those models to drive affective interventions. This paper shows that GIFT can be used to obtain data for detector development, and that the resultant modelsboth models based on student interaction and models based on physical sensors -can be embedded back into GIFT for reuse.…”

“…Finally, GIFT was used to run an experimental study investigating the impacts of affective interventions on a variety of student outcomes. The example provided by DeFalco et al (2017) could easily form a template for future research to examine the sensitivity of instructional methods and to conduct validation studies or to examine the impact of various adaptive instructional tools or methods.…”

“…The AIED literature has focused heavily on ITSs as collaboration support for groups attempting to solve problems or learn knowledge/skills together (Adamson et al 2014). Dillenbourg (1999, p. 1) defined collaborative learning as Ba situation in which two or more people learn or attempt to learn something together^.…”