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Metacognitive Management of Attention in Online Learning

Authors :
Matthew Jensen Hays
Scott Richard Kustes
Elizabeth Ligon Bjork
Source :
Journal of Intelligence, Vol 12, Iss 4, p 46 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Performance during training is a poor predictor of long-term retention. Worse yet, conditions of training that produce rapidly improving performance typically do not produce long-lasting, generalizable learning. As a result, learners and instructors alike can be misled into adopting training or educational experiences that are suboptimal for producing actual learning. Computer-based educational training platforms can counter this unfortunate tendency by providing only productive conditions of instruction—even if they are unintuitive (e.g., spacing instead of massing). The use of such platforms, however, introduces a different liability: being easy to interrupt. An assessment of this possible liability is needed given the enormous disruption to modern education brought about by COVID-19 and the subsequent widespread emergency adoption of computer-based remote instruction. The present study was therefore designed to (a) explore approaches for detecting interruptions that can be reasonably implemented by an instructor, (b) determine the frequency at which students are interrupted during a cognitive-science-based digital learning experience, and (c) establish the extent to which the pandemic and ensuing lockdowns affected students’ metacognitive ability to maintain engagement with their digital learning experiences. Outliers in time data were analyzed with increasing complexity and decreasing subjectivity to identify when learners were interrupted. Results indicated that only between 1.565% and 3.206% of online interactions show evidence of learner interruption. And although classroom learning was inarguably disrupted by the pandemic, learning in the present, evidence-based platform appeared to be immune.

Details

Language :
English
ISSN :
20793200
Volume :
12
Issue :
4
Database :
Directory of Open Access Journals
Journal :
Journal of Intelligence
Publication Type :
Academic Journal
Accession number :
edsdoj.5985c92d67d430d8089dd2c449e5b43
Document Type :
article
Full Text :
https://doi.org/10.3390/jintelligence12040046