Evidence map›Paper›PMID 40443726›Full record

ArticleFrontiers in psychology2025

Componential modeling of argumentative essay writing from multiple online sources: a Bayesian network approach.

Anisha Singh, Yuting Sun, Patricia A Alexander, Hongyang Zhao

Abstract read
In one paragraph

Article in Frontiers in psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Anisha SinghDepartment of Psychology, San Francisco State University, San Francisco, CA, United States.
Yuting SunDepartment of Teaching, Learning & Curriculum, University of North Florida, Jacksonville, FL, United States.
Patricia A AlexanderDepartment of Human Development and Quantitative Methodology, University of Maryland, College Park, College Park, MD, United States.
Hongyang ZhaoDepartment of Educational Psychology, University of Illinois at Urbana-Champaign, Champaign, IL, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Writing argumentative essays using multiple sources is a critical skill for college students, yet it remains a significant challenge. Despite previous research acknowledging this difficulty, the specific dynamics of the argumentative essay writing process and where breakdowns occur remain unclear. Methods: College students wrote argumentative essays on a controversial topic after reading multiple documents. The data were fitted to two competing theory-based Bayesian networks, a method highly suited to the modeling of cognitive processes identified with argumentative writing. Results: The best-fitting model showed that the argumentative essay task is both initiated and sustained by higher-order integration components. This model lends support to the description of the process of argumentation writing from multiple documents put forth by the stage-based Integrated Framework of Multiple Texts. Further, we found that the process of argumentation falters due to students' inability to frame counterarguments and their non-optimal critical analysis. Discussion: This research not only enriches our understanding of the mechanics of argumentative writing from multiple sources, but the innovative Bayesian approach could lead to further refinement of the model by future researchers.

Indexed as

argumentationargumentative essay writingBayesian network analysiscollege studentsmultiple documentsmultiple source use

Identifiers

PMID40443726
PMCPMC12119471

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.