Cited by 42Personal Information Management and User BehaviorVisual and Cognitive Learning ProcessesHuman-Automation Interaction and Safety

Interest level improves learning but does not moderate the effects of interruptions: An experiment using simultaneous multitasking

Conard, Maureen A., Marsh, Robert F.·2014·Learning and Individual Differences

Integrity signals

Registry

Crossref · 10.1016/j.lindif.2013.11.004

Cites

38 of 38 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Conard, M. A., & Marsh, R. F.
Affiliations
Sacred Heart University (US)
Corresponding author
Conard, Maureen A.
Venue
Learning and Individual Differences
Publisher
Elsevier BV
Published
2014-02
Volume
30
Pages
112-117
ISSN
1041-6080 (ISSN-L)
OpenAlex
W2079379498
Cited by
42
References
37
Citations by year
2026: 1 · 2025: 2 · 2024: 8 · 2023: 2 · 2022: 5 · 2021: 4 · 2020: 4 · 2019: 1 · 2018: 2 · 2017: 1 · 2016: 7 · 2015: 5
FWCI
8.76
Type
journal article
Language
en
Open access
closed
Locations
Learning and Individual Differences (publishedVersion)
Topics
  • Social Sciences › Decision Sciences › Information Systems and Management › Personal Information Management and User Behavior
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
  • Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
Keywords
multitasking, interruptions, instant messaging, interest level, learning, video presentation, individual differences, attention

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review