The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index; none of the 16 sources its claims cite is checked for support yet.
An expertise reversal effect of segmentation in learning from animated worked-out examples
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Written from the paper’s full text.
TL;DR
This experiment found an expertise reversal effect of segmentation in animated worked-out examples: segmented animations were more efficient than continuous animations for students with lower prior knowledge, but not for students with higher prior knowledge. The effect appeared on mental effort and instructional efficiency, not on transfer test performance.
Key findings
- Segmented animations were more efficient than continuous animations at one standard deviation below the mean prior knowledge: near transfer, b = 0.39, t(71) = 2.60, p = .01; far transfer, b = 0.33, t(71) = 2.20, p = .03. At one standard deviation above the mean, this difference disappeared: near, b = −0.05, t(71) = −0.36, p = .72; far, b = −0.10, t(71) = −0.67, p = .51.
- Mental effort during instruction showed a significant prior knowledge × condition interaction, b = 0.38, t(71) = 2.06, p = .04. At lower prior knowledge, continuous examples were marginally higher in mental effort than segmented ones, b = −0.32, t(71) = −1.98, p = .05; at higher prior knowledge, continuous examples decreased in mental effort, b = −0.49, t(71) = −2.71, p = .01, while segmented examples remained almost equal, b = −0.02, t(71) = −0.11, p = .91.
- No significant interaction was found for near or far transfer performance. The regression models were significant for near transfer, F(3, 71) = 8.61, p = .00, adjusted R² = .24, and far transfer, F(3, 71) = 6.01, p = .00, adjusted R² = .17.
- Efficiency regression models were significant for near transfer, F(3, 71) = 7.13, p = .00, adjusted R² = .20, and far transfer, F(3, 71) = 6.70, p = .00, adjusted R² = .19, with significant interaction coefficients for near, b = −0.35, t(71) = −2.07, p = .04, and far, b = −0.34, t(71) = −2.00, p = .05.
Methods
- Participants were 76 Dutch secondary education students (53% female; mean age = 16.16, SD = 0.80), randomly assigned to segmented or continuous animated worked-out examples. One participant was excluded for missing values, leaving 37 in the segmented and 38 in the continuous condition.
- A computer-based Flash learning environment administered a prior knowledge test (4 multiple-choice and 8 open items; max score 12), eight animated worked-out examples on probability calculation (computer-paced, approximately 2 min each), transfer tests (8 open near-transfer and 4 open far-transfer items), and a 9-point mental effort rating after each animated example. Segmented examples were divided into 5–7 segments with 2-s pauses and a slightly darkened screen; continuous examples were shown as one stream.
- Regression analyses entered prior knowledge (centered), condition (0 = continuous, 1 = segmented), and their interaction simultaneously as predictors. Follow-up tests examined condition effects at one standard deviation below and above the mean prior knowledge. Efficiency was calculated using the Paas and Van Merriënboer formula with test performance and mental effort invested during learning.
Limitations
- The authors state that the pauses between segments created a difference in learning time between conditions, with segmented students having more time per animated example. They note that no additional information was provided during pauses and that 2 s is probably too short for reflection, but future studies should control time-on-task.
- The authors state that the study did not reveal the cognitive processes underlying the expertise reversal effect, and suggest future research using cued retrospective reporting combined with eye movements.
- The authors state that it is unclear whether the results generalize to animations for declarative knowledge about natural processes or mechanical systems. They also suggest that the limited range of prior knowledge—most participants were not complete novices or full experts—may explain why no interaction was found on performance.
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Claims the paper makes
18 Own result · 10 Not checked yet · 5 Own interpretation · 4 Own method
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The regression coefficients for the interaction terms were not significant, however.”
§ An expertise reversal effect of segmentation in learning from animated worked-out examples > 3. Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“whereas the efficiency of segmented, animated examples increased more slowly and only marginally significant, near: b = 0.24, t(71) = 1.79, p = .08,far: b = .25,t(71) = 1.85, p = .07.”
§ An expertise reversal effect of segmentation in learning from animated worked-out examples > 3. Results
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“and so were the regression coefficients for the interaction term, near: b = À0.35,t(71) = À2.07,p = .04;far: b = À0.34,t(71) = À2.00,p = .05.”
§ An expertise reversal effect of segmentation in learning from animated worked-out examples > 3. Results
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The regression coefficients for the interaction terms were not significant, however.”
§ An expertise reversal effect of segmentation in learning from animated worked-out examples > 3. Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At one standard deviation below the mean the difference in mental effort invested in studying continuous, animated examples was marginally higher than in studying segmented ones, b = À0.32,t(71) = À1.98,p = .05.”
§ An expertise reversal effect of segmentation in learning from animated worked-out examples > 3. Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“With higher levels of prior knowledge, the efficiency of continuous, animated examples increased significantly, near: b = 0.68, t(71) = 4.04, p = .00,far: b = 0.68, t(71) = 4.00, p = .00”
§ An expertise reversal effect of segmentation in learning from animated worked-out examples > 3. Results
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“With higher levels of prior knowledge, the efficiency of continuous, animated examples increased significantly, near: b = 0.68, t(71) = 4.04, p = .00,far: b = 0.68, t(71) = 4.00, p = .00”
§ An expertise reversal effect of segmentation in learning from animated worked-out examples > 3. Results
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As a consequence, the difference in efficiency between the two conditions had disappeared at one standard deviation above the mean, near: b = À0.05,t(71) = À0.36,p = .72,far: b = À0.10,t(71) = À0.67,p = .51.”
§ An expertise reversal effect of segmentation in learning from animated worked-out examples > 3. Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“With higher levels of prior knowledge, the mental effort invested in studying continuous, animated examples decreased, b = À0.49,t(71) = À2.71,p = .01”
§ An expertise reversal effect of segmentation in learning from animated worked-out examples > 3. Results
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The regression models for performance on near and far transfer were both significant, near: F(3, 71) = 8.61, p = .00,adjusted R 2 = .24;far: F(3, 71) = 6.01, p = .00,adjusted R 2 = .17.”
§ An expertise reversal effect of segmentation in learning from animated worked-out examples > 3. Results
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Probably not a substantial claim. 10
- It may state two separate claims as one. 6
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The regression model for mental effort during instruction was marginally significant, F(3, 71) = 2.57, p = .06,adjusted R 2 = .06”
§ An expertise reversal effect of segmentation in learning from animated worked-out examples > 3. Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“A possible limitation of this study was that the pauses between segments led to a difference in learning time between the conditions: students in the segmented condition had more time for each animated example than students in the continuous condition.”
§ An expertise reversal effect of segmentation in learning from animated worked-out examples > 4. Discussion
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Efficiency on both the near and the far transfer test was calculated using the formula developed by Paas and Van Merriënboer (1993)”
§ An expertise reversal effect of segmentation in learning from animated worked-out examples > 2.4. Scoring
What the sources say
- Not checked yetcites Paas & Van Merriënboer, 1993 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Working memory has a limited capacity (Baddeley, 2003) and has time constraints (Barrouillet & Camos, 2007), as a consequence of which only two or three information elements can be simultaneously processed.”
§ An expertise reversal effect of segmentation in learning from animated worked-out examples > 1.1. Effects of segmentation and learner expertise
What the sources say
- Not checked yetcites Baddeley, 2003 source
- Not checked yetcites Barrouillet & Camos, 2007
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Note though, that during these pauses no additional information was provided (only the last frame of the previous segment was visible)”
§ An expertise reversal effect of segmentation in learning from animated worked-out examples > 4. Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Cognitive Load Theory (Sweller, Van Merriënboer, & Paas, 1998;Van Merriënboer & Sweller, 2005) distinguishes between three additive types of cognitive load imposed on working memory by maintaining and processing information: Extraneous (i.e., ineffective) and germane (i.e., effective) cognitive load imposed by the design of instructional materials, and intrinsic cognitive load, imposed by the”
§ An expertise reversal effect of segmentation in learning from animated worked-out examples > 1.1. Effects of segmentation and learner expertise
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This suggests that only for students with lower levels of prior knowledge, segmentation successfully reduces the high cognitive load imposed by animations and leads to more efficient learning.”
§ An expertise reversal effect of segmentation in learning from animated worked-out examples > 4. Discussion
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“They were randomly assigned to either the segmented or the continuous, animated worked-out examples condition.”
§ An expertise reversal effect of segmentation in learning from animated worked-out examples > 2.1. Participants and design
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“A prior knowledge test consisting of four multiple-choice and eight open items about probability calculation (see Wouters et al., 2009Wouters et al., , 2010) ) was used to measure students' prior knowledge.”
§ An expertise reversal effect of segmentation in learning from animated worked-out examples > 2.2.2. Prior knowledge test
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“Immediately after studying each animated example, students rated how much mental effort they invested in studying it on a 9-point subjective rating scale ranging from (1) very, very low mental effort to ( 9) very, very high mental effort (Paas, 1992).”
§ An expertise reversal effect of segmentation in learning from animated worked-out examples > 2.2.5. Mental effort
What the sources say
- Not checked yetcites Paas, 1992 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This experiment with animated worked-out examples showed an expertise reversal effect of segmentation: segmented animations were more efficient than continuous animations (i.e., equal test performance with lower investment of mental effort during learning) for students with lower levels of prior knowledge, but not for students with higher levels of prior knowledge.”
- Also stated in: § 4. Discussion · hedged
“In sum, this study provides some evidence that an expertise reversal effect of segmentation in animated worked-out examples occurs.”
§ An expertise reversal effect of segmentation in learning from animated worked-out examples > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § 4. Discussion · hedged
In the paper
“He found a positive effect of segmentation of a simulation for students with lower levels of prior knowledge, but no effect for students with higher levels of prior knowledge on one of the tests.”
§ An expertise reversal effect of segmentation in learning from animated worked-out examples > 1.1. Effects of segmentation and learner expertise
What the sources say
- Not checked yetcites Schnotz, 2002
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Segmentation has been proposed as a means to reduce the high load occurring due to the transience of animations (e.g., Ayres & Paas, 2007;Mayer & Moreno, 2003;Schnotz & Lowe, 2008).”
§ An expertise reversal effect of segmentation in learning from animated worked-out examples > 1.1. Effects of segmentation and learner expertise
What the sources say
- Not checked yetcites Ayres & Paas, 2007
- Not checked yetcites Mayer & Moreno, 2003 source
- Not checked yetcites Schnotz & Lowe, 2008
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Data from one participant had to be excluded due to too many missing values, which resulted in 37 students in the segmented and 38 in the continuous condition.”
§ An expertise reversal effect of segmentation in learning from animated worked-out examples > 2.1. Participants and design
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper relies on this without support.
A 2 s pause is too short for processes such as reflection.
“and that 2 s is probably too short for processes such as reflection.”
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?
The cited source says what the claim attributes to it.
In the citing paper
“The segmentation effect was reported by Spanjers et al. (2011), who found that segmented animations (i.e. segmented in parts with pauses in between) were more efficient than continuous animations for novice learners, but not for learners with higher levels of prior knowledge.”
From Cognitive Architecture and Instructional Design: 20 Years Later (2019) · cites this paper as Spanjers et al. (2011)
Statistics recomputed
Every reported test result, recomputed from its test statistic and degrees of freedom. A result shows as not adding up only once an independent check has confirmed it.
- ConsistentF(3, 71) = 8.61reported p = 0 · recomputed p < .001
“F(3, 71) = 8.61, p = .00”
- ConsistentF(3, 71) = 6.01reported p = 0 · recomputed p = .001
“F(3, 71) = 6.01, p = .00”
- ConsistentF(3, 71) = 2.57reported p = .06 · recomputed p = .061
“F(3, 71) = 2.57, p = .06”
- Consistentt(71) = 2.06reported p = .04 · recomputed p = .043
“t(71) = 2.06, p = .04”
- Consistentt(71) = -1.98reported p = .05 · recomputed p = .052
“t(71) = À1.98,p = .05”
- Consistentt(71) = -2.71reported p = .01 · recomputed p = .008
“t(71) = À2.71,p = .01”
- Consistentt(71) = -0.11reported p = .91 · recomputed p = .913
“t(71) = À0.11,p = .91”
- Consistentt(71) = 0.98reported p = .33 · recomputed p = .330
“t(71) = 0.98, p = .33”
- ConsistentF(3, 71) = 7.13reported p = 0 · recomputed p < .001
“F(3, 71) = 7.13, p = .00”
- ConsistentF(3, 71) = 6.7reported p = 0 · recomputed p < .001
“F(3, 71) = 6.70, p = .00”
- Consistentt(71) = -2.07reported p = .04 · recomputed p = .042
“t(71) = À2.07,p = .04”
- Consistentt(71) = -2reported p = .05 · recomputed p = .049
“t(71) = À2.00,p = .05”
- Consistentt(71) = 2.6reported p = .01 · recomputed p = .011
“t(71) = 2.60, p = .01”
- Consistentt(71) = 2.2reported p = .03 · recomputed p = .031
“t(71) = 2.20, p = .03”
- Consistentt(71) = 4.04reported p = 0 · recomputed p < .001
“t(71) = 4.04, p = .00”
- Consistentt(71) = 4reported p = 0 · recomputed p < .001
“t(71) = 4.00, p = .00”
- Consistentt(71) = 1.79reported p = .08 · recomputed p = .078
“t(71) = 1.79, p = .08”
- Consistentt(71) = 1.85reported p = .07 · recomputed p = .068
“t(71) = 1.85, p = .07”
- Consistentt(71) = -0.36reported p = .72 · recomputed p = .720
“t(71) = À0.36,p = .72”
- Consistentt(71) = -0.67reported p = .51 · recomputed p = .505
“t(71) = À0.67,p = .51”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
“the regression coefficient for the interaction term was significant, b = 0.38, t(71) = 2.06, p = .04.”
§ An expertise reversal effect of segmentation in learning from animated worked-out examples > 3. Results
Reported 0.38 · recomputed 0.47
Citations
Cites
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Cognitive Architecture and Instructional Design
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Working memory: Looking back and looking forward
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Nine Ways to Reduce Cognitive Load in Multimedia Learning
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Cognitive Load Theory and Complex Learning: Recent Developments and Future Directions
van Merriënboer, J. J. G., & Sweller, J. (2005) · Educational Psychology Review · doi:10.1007/s10648-005-3951-0 · Cited by 2,206
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Research works printed but not linked: 1
- Schnotz, W. (2002). Enabling, facilitating and inhibiting effects in learning from animated pictures. Paper presented at the International Workshop on Dynamic Visualizations and Learning, Tübingen, Germany. Downloaded on 31 July 2008 from http://www.iwm-kmrc.de/workshops/visualization/schnotz.pdf.
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Example-Based Learning: Integrating Cognitive and Social-Cognitive Research Perspectives
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Cognitive load theory, the transient information effect and e-learning
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Does cognitive load moderate the seductive details effect? A multimedia study
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Effects of segmentation and pacing on procedural learning by video
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A Theoretical Analysis of How Segmentation of Dynamic Visualizations Optimizes Students' Learning
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Self-citationContemporary cognitive load theory research: The good, the bad and the ugly
Kirschner, P. A., Ayres, P., & Chandler, P. (2011) · Computers in Human Behavior · doi:10.1016/j.chb.2010.06.025 · Cited by 144
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Metadata
- Authors
- Spanjers, I. A. E., Wouters, P., van Gog, T., & van Merriënboer, J. J. G.
- Affiliations
- Maastricht University (NL)
- Utrecht University (NL)
- Corresponding author
- Spanjers, Ingrid A.E.
- Venue
- Computers in Human Behavior
- Publisher
- Elsevier BV
- Published
- 2011-01
- Volume
- 27
- Issue
- 1
- Pages
- 46-52
- ISSN
- 0747-5632 (ISSN-L)
- OpenAlex
- W2059227461
- Semantic Scholar
- 733a638bd03ef6d298932615e83045a9fffc115b
- Cited by
- 175
- References
- 35
- Citations by year
- 2026: 8 · 2025: 12 · 2024: 8 · 2023: 7 · 2022: 8 · 2021: 27 · 2020: 15 · 2019: 10 · 2018: 13 · 2017: 11 · 2016: 9 · 2015: 4 · 2014: 13 · 2013: 10 · 2012: 10
- FWCI
- 12.56
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Computers in Human Behavior (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion, other-oa)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- DSpace (Open University in the Netherlands) (submittedVersion, cc-by-nc-nd)
- DSpace (Open University in the Netherlands) (submittedVersion, other-oa)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- expertise reversal effect, segmentation, cognitive load, prior knowledge, cognitive effort, instructional design
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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.
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