Ecological momentary assessment of digital literacy: Influence of fluid and crystallized intelligence, domain-specific knowledge, and computer usage
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Domain-specific knowledge is a key predictor variable for comprehension/literacy.
“As known from traditional comprehension studies and studies on other literacies (e.g., ICT literacy), domain-specific knowledge is a key predictor variable (see for example Moehring et al., 2016).”
From Effects of Explainable Artificial Intelligence on trust and human behavior in a high-risk decision task (2023) · cites this paper as Moehring et al., 2016
The null results for AI knowledge and domain-specific knowledge could be due to inadequate measurement instruments.
“As mentioned before, the null results for AI knowledge and domainspecific knowledge contradicting previous research (e.g., Greiff et al., 2014; Moehring et al., 2016) could be due to inadequate measurement instruments”
From Effects of Explainable Artificial Intelligence on trust and human behavior in a high-risk decision task (2023) · cites this paper as Moehring et al., 2016
Cites
Socioeconomic Status and Academic Achievement: A Meta-Analytic Review of Research
Sirin, S. R. (2005) · Review of Educational Research · doi:10.3102/00346543075003417 · Cited by 4,687 · see citing works
Chase, W. G., & Simon, H. A. (1973) · Cognitive Psychology · doi:10.1016/0010-0285(73)90004-2 · Cited by 4,479 · see citing works
How to Use a Monte Carlo Study to Decide on Sample Size and Determine Power
Muthén, L. K., & Muthén, B. O. (2002) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1207/s15328007sem0904_8 · Cited by 2,362 · see citing works
Google Effects on Memory: Cognitive Consequences of Having Information at Our Fingertips
Sparrow, B., Liu, J., & Wegner, D. M. (2011) · Science · doi:10.1126/science.1207745 · Cited by 1,762 · see citing works
Mayer, R. E., Heiser, J., & Lonn, S. (2001) · Journal of Educational Psychology · doi:10.1037/0022-0663.93.1.187 · Cited by 1,029 · see citing works
Prior knowledge, reading skill, and text cohesion in the comprehension of science texts
Ozuru, Y., Dempsey, K., & McNamara, D. S. (2009) · Learning and Instruction · doi:10.1016/j.learninstruc.2008.04.003 · Cited by 428 · see citing works
Deary, I. J., & Batty, G. D. (2007) · Journal of Epidemiology and Community Health · doi:10.1136/jech.2005.039206 · Cited by 121 · see citing works
Greiff, S., Kretzschmar, A., Müller, J. C., Spinath, B., & Martin, R. (2014) · Journal of Educational Psychology · doi:10.1037/a0035426 · Cited by 49 · see citing works
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Leichtmann, B., Humer, C., Hinterreiter, A., Streit, M., & Mara, M. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107539 · Cited by 200 · see citing works
Self-citation
- Authors
- Moehring, A., Schroeders, U., Leichtmann, B., & Wilhelm, O.
- Affiliations
- University of Bamberg (DE)
- Universität Ulm (DE)
- Venue
- Intelligence
- Publisher
- Elsevier BV
- Published
- 2016-11
- Volume
- 59
- Pages
- 170-180
- ISSN
- 0160-2896 (ISSN-L)
- OpenAlex
- W2541729175
- Cited by
- 16
- References
- 8
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 1 · 2023: 1 · 2022: 3 · 2021: 3 · 2020: 2 · 2019: 2 · 2018: 1
- FWCI
- 1.26
- Type
- journal article
- Language
- en
- Update notices
- erratum (2017-01-01)
- Open access
- closed
- Locations
- Intelligence (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Abilities and Testing
- Physical Sciences › Computer Science › Information Systems › Digital literacy in education
- Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
- Keywords
- digital literacy, fluid intelligence, crystallized intelligence, domain knowledge, computer use, web information retrieval, health knowledge, individual differences, ecological momentary assessment, vocational high school students, adults
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.
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