View in EDS

Media Literacy Education to Counter Truth Decay: An Implementation and Evaluation Framework. Research Reports. RR-A112-18

Saved in:
Bibliographic Details
Title: Media Literacy Education to Counter Truth Decay: An Implementation and Evaluation Framework. Research Reports. RR-A112-18
Language: English
Authors: Huguet, Alice, Pane, John F., Baker, Garrett, Hamilton, Laura S., Faxon-Mills, Susannah, RAND Education and Labor
Source: RAND Corporation. 2021.
Availability: RAND Corporation. P.O. Box 2138, Santa Monica, CA 90407-2138. Tel: 877-584-8642; Tel: 310-451-7002; Fax: 412-802-4981; e-mail: order@rand.org; Web site: http://www.rand.org
Peer Reviewed: N
Page Count: 96
Publication Date: 2021
Intended Audience: Policymakers; Teachers
Document Type: Reports - Research
Descriptors: Media Literacy, Program Implementation, Program Evaluation, Teaching Methods, Summative Evaluation, Reading Material Selection, Information Literacy, Technological Literacy, Expectation, Alignment (Education), Student Educational Objectives, Academic Standards, Formative Evaluation
ISBN: 978-1-977406-63-7
Abstract: Media literacy (ML) education has emerged as a promising approach to slowing the spread of "Truth Decay," described as the diminishing role that facts, data, and analysis play in political and civil discourse. Several factors contribute to Truth Decay, including a rapidly evolving information ecosystem and overburdened educational institutions. Many teachers believe their students lack the complex skills that are necessary to navigate today's information-saturated world. This gap--between students' existing competencies and those required to engage responsibly in a fast-paced media environment--could lead to negative consequences for individuals and society writ large. However, teachers report a lack of guidance around promoting ML education in their practice, and rigorous research about what kinds of ML education work best, and in what conditions, remains limited. This report presents a framework for implementing and evaluating ML educational efforts. Following an introduction to the framework, the authors discuss six steps of ML implementation and evaluation: setting ML learning expectations; identifying conditions that can influence ML instructional efforts; exploring instructional resources; identifying measures of ML competencies; monitoring progress; and finally, measuring the summative impacts of ML education on student learning. By bringing this information together for implementors--such as district decisionmakers and teachers--as well as evaluators, the authors emphasize the important connections between these too often separate groups.
Abstractor: As Provided
Entry Date: 2021
Access URL: https://www.rand.org/t/RRA112-18
Accession Number: ED614214
Database: ERIC
Description
Abstract:Media literacy (ML) education has emerged as a promising approach to slowing the spread of "Truth Decay," described as the diminishing role that facts, data, and analysis play in political and civil discourse. Several factors contribute to Truth Decay, including a rapidly evolving information ecosystem and overburdened educational institutions. Many teachers believe their students lack the complex skills that are necessary to navigate today's information-saturated world. This gap--between students' existing competencies and those required to engage responsibly in a fast-paced media environment--could lead to negative consequences for individuals and society writ large. However, teachers report a lack of guidance around promoting ML education in their practice, and rigorous research about what kinds of ML education work best, and in what conditions, remains limited. This report presents a framework for implementing and evaluating ML educational efforts. Following an introduction to the framework, the authors discuss six steps of ML implementation and evaluation: setting ML learning expectations; identifying conditions that can influence ML instructional efforts; exploring instructional resources; identifying measures of ML competencies; monitoring progress; and finally, measuring the summative impacts of ML education on student learning. By bringing this information together for implementors--such as district decisionmakers and teachers--as well as evaluators, the authors emphasize the important connections between these too often separate groups.
ISBN:978-1-977406-63-7