Affect-Sensitive Instructional Strategies for Synchronous Distance Learning
Abstract
Synchronous distance learning does not provide nonverbal student feedback to the instructor indicating the students emotional state. Nonverbal emotive cues provide the instructor valuable information to adjust and adapt the pace and content of instruction to the students affective and cognitive states. The emerging technology of automated affect recognition provides an innovative approach to providing nonverbal instructional feedback. However, to take full advantage of this technology, an instructional system must not only detect the affective states of students but also respond appropriately to those states. Thus, the development of an affect-sensitive learning system must address three separate problems: (1) dynamically collect cognitive and affective information from the learner to assess affective state,(2) understand and model the implications that those affective states have on instruction, and (3) choose an appropriate instructional intervention for individual students and contexts. Once the intervention is deployed, student affect is reassessed and the cycle restarts. The objective of this report is to examine and assess the maturity of the science and technology behind the three problems (assess state, understand state, and determine intervention) and suggest how best affect-sensitive learning technologies can be deployed to enhance synchronous distance learning.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 2020
- Accession Number
- AD1146284
Entities
People
- Emily A. Fedele
- John E. Morrison
Organizations
- Institute for Defense Analyses