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Causal reasoning with causal graphs in educational technology research
by
Hicks, Ben
, Weidlich, Joshua
, Drachsler, Hendrik
in
Bias
/ Causal Models
/ Educational Objectives
/ Educational Principles
/ Educational research
/ Educational technology
/ Graphs
/ Individual Differences
/ Inferences
/ Influence of Technology
/ Instructional Innovation
/ Instructional Systems
/ Nonparametric Statistics
/ Research Design
/ Research Methodology
/ Researchers
/ Statistical Analysis
/ Structural equation modeling
/ Structural Equation Models
/ World Problems
2024
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Causal reasoning with causal graphs in educational technology research
by
Hicks, Ben
, Weidlich, Joshua
, Drachsler, Hendrik
in
Bias
/ Causal Models
/ Educational Objectives
/ Educational Principles
/ Educational research
/ Educational technology
/ Graphs
/ Individual Differences
/ Inferences
/ Influence of Technology
/ Instructional Innovation
/ Instructional Systems
/ Nonparametric Statistics
/ Research Design
/ Research Methodology
/ Researchers
/ Statistical Analysis
/ Structural equation modeling
/ Structural Equation Models
/ World Problems
2024
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While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Causal reasoning with causal graphs in educational technology research
by
Hicks, Ben
, Weidlich, Joshua
, Drachsler, Hendrik
in
Bias
/ Causal Models
/ Educational Objectives
/ Educational Principles
/ Educational research
/ Educational technology
/ Graphs
/ Individual Differences
/ Inferences
/ Influence of Technology
/ Instructional Innovation
/ Instructional Systems
/ Nonparametric Statistics
/ Research Design
/ Research Methodology
/ Researchers
/ Statistical Analysis
/ Structural equation modeling
/ Structural Equation Models
/ World Problems
2024
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Causal reasoning with causal graphs in educational technology research
Journal Article
Causal reasoning with causal graphs in educational technology research
2024
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Overview
Researchers tasked with understanding the effects of educational technology innovations face the challenge of providing evidence of causality. Given the complexities of studying learning in authentic contexts interwoven with technological affordances, conducting tightly-controlled randomized experiments is not always feasible nor desirable. Today, a set of tools is available that can help researchers reason about cause-and-effect, irrespective of the particular research design or approach. This theoretical paper introduces such a tool, a simple graphical formalism that can be used to reason about potential sources of bias. We further explain how causal graphs differ from structural equation models and highlight the value of explicit causal inference. The final section shows how causal graphs can be used in several stages of the research process, whether researchers plan to conduct observational or experimental research.
Publisher
Springer Nature B.V
Subject
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