The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index; none of the 12 sources its claims cite is checked for support yet.
A Personalization Effect in Multimedia Learning: Students Learn Better When Words Are in Conversational Style Rather Than Formal Style.
Abstract
Students received a personalized or nonpersonalized version of a narrated animation explaining how the human respiratory system works. The narration for the nonpersonalized version was in formal style, whereas the narration for the personalized version was in conversational style in which “the ” was changed to “your ” in 12 places. In 3 experiments, students who received the personalized version scored significantly higher on transfer tests but not on retention tests than did students who received the nonpersonalized version. The results are consistent with a cognitive theory of multimedia learning in which personalization causes students to actively process the incoming material. Suppose you are sitting at your computer, exploring a Web site on health information. You click on a link about how the human respiratory system works, and you see a short presentation on the screen. The presentation consists of an animation depicting the processes of inhaling air into the lungs, exchanging oxygen from the lungs into the bloodstream and carbon dioxide from the blood-stream into the lungs, and exhaling air out of the body. The presentation also consists of a corresponding narration spoken in a
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
Across three experiments, college students who learned from a narrated animation about the respiratory system with conversational wording (“your” instead of “the” in 12 places) performed better on transfer tests than students who heard formal wording, but not on retention tests. The authors interpret this personalization effect as evidence that conversational style prompts deeper processing of multimedia material.
Key findings
- In Experiment 1, the personalized group scored higher on transfer than the nonpersonalized group (M = 7.1, SD = 2.4 vs. M = 6.0, SD = 1.8), t(60) = 2.170, p = .034, effect size = 0.65; retention did not differ significantly (both M = 11.3; effect size = −0.02).
- In Experiment 2, the personalized group again scored higher on transfer (M = 7.8, SD = 2.4 vs. M = 5.6, SD = 2.0), t(25) = 2.516, p = .019, effect size = 1.07; retention did not differ significantly (M = 8.2 vs. M = 9.0; effect size = −0.21).
- In Experiment 3, the personalized group scored higher on transfer (M = 6.2, SD = 2.0 vs. M = 4.5, SD = 2.3), t(30) = 2.177, p = .037, effect size = 0.72; retention did not differ significantly (M = 11.1 vs. M = 9.6; effect size = 0.37).
- Interest measures did not show a statistically significant personalization effect: in Experiment 3, interest ratings were M = 5.1 vs. M = 4.5, t(30) = 1.56, p = .129, effect size = 0.55; in Experiment 2, no participants in either group showed facial expressions during learning.
Methods
- Three experiments used a personalized or nonpersonalized version of a roughly 60-s narrated animation on how the human respiratory system works; the only difference was changing “the” to “your” in 12 places in a 100-word narration spoken in a male voice.
- Participants were 62 college students in Experiment 1 (29 personalized, 33 nonpersonalized), 27 college students in Experiment 2 (14 personalized, 13 nonpersonalized), and 32 paid adults in Experiment 3 (17 personalized, 15 nonpersonalized).
- After viewing, participants completed a retention test and five transfer test sheets; retention and transfer were scored out of 20 idea units or acceptable answers by two raters with consensus. Experiment 2 video recorded faces and tallied smiles; Experiment 3 added 1–7 difficulty and interest ratings. Main analyses were t tests with effect sizes.
Limitations
- The paper states that its interest measures were problematic: smiling in Experiment 2 was insensitive, and the interest rating in Experiment 3 did not reach significance, so either the measures were defective or the interest hypothesis needs revision.
- The paper states that direct measures of cognitive processing during learning are missing and calls for further work, including possible fMRI measures, to test whether personalization primes deeper processing.
- The paper notes that spatial ability data in Experiments 1 and 3 were not used because there were not enough participants, and that an additional study similar to Experiment 1 was not reported because of possible data coding errors. Evident from the text: samples were small, participants were from university subject pools, and the lesson was a single short topic, limiting generalizability.
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Claims the paper makes
20 Own interpretation · 11 Own result · 8 Not checked yet · 4 Own method · 2 No basis given
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
“In 3 experiments, students who received the personalized version scored significantly higher on transfer tests but not on retention tests than did students who received the nonpersonalized version.”
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
“In 3 experiments, students who received the personalized version scored significantly higher on transfer tests but not on retention tests than did students who received the nonpersonalized version.”
plain- 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.
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 mean difficulty rating of the personalized group ( M = 5.9, SD = 1.3) was not significantly different from the mean difficulty rating of the nonpersonalized group ( M = 5.8, SD = 1.2), t (30) = 0.324, p = .748, effect size = 0.11.”
plain- 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.
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 mean score on the retention test was not significantly different for the two groups, t (30) = 1.087, p = .286, yielding an effect size of 0.37.”
plain- 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.
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 mean score on the transfer test was significantly greater for the personalized group than for the nonpersonalized group, t (30) = 2.177, p = .037, yielding an effect size of 0.72.”
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
“The mean score on the transfer test was significantly greater for the personalized group than for the nonpersonalized group, t (60) = 2.170, p = .034, yielding an effect size of 0.65.”
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
“The mean score on the retention test was not significantly different for the two groups, t (25) = 0.594, p = .557, yielding an effect size of -0.21.”
plain- 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.
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 mean score on the transfer test was significantly greater for the personalized group than for the nonpersonalized group, t (25) = 2.516, p = .019, yielding an effect size of 1.07.”
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
“the mean score on the interest rating was not significantly greater for the personalized group ( M = 5.1, SD = 1.2) than for the nonpersonalized group ( M = 4.5, SD = 1.1), t (30) = 1.56, p = .129, yielding an effect size of 0.55.”
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
“None of the participants in either group showed any facial expressions during learning”
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. 13
- It may state two separate claims as one. 9
- The quote only partly states this claim. 1
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
“The deeper processing results in more meaningful learning as indicated by better transfer test performance.”
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“and (b) to increase the learner's interest (Harp & Mayer, 1998; Mayer, Sobko, & Mautone, 2003; Moreno & Mayer, 2000; Renninger, Hidi, & Krapp, 1992), thus causing the learner to use the available capacity for deep processing during learning.”
What the sources say
- Not checked yetcites Harp & Mayer, 1998
- Not checked yetcites Mayer, Sobko, & Mautone, 2003
- Not checked yetcites Moreno & Mayer, 2000
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.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In contrast to research on cognitive load, there is not yet a large research base concerning design principles that increase the learner's interest in multimedia learning.”
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
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
“Using the self as a reference point increases the learner's interest, which in turn encourages the learner to use available cognitive capacity for active cognitive processing of the incoming information during learning.”
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“The two most important paths toward fostering meaningful learning are (a) to design multimedia instructional messages in ways that reduce cognitive load (Mayer &Moreno, 2003; Paas, Renkl, & Sweller, 2003; Van Merrienboer, Kirschner, & Kester, 2003), thus making more capacity available for deep cognitive processing during learning”
What the sources say
- Not checked yetcites Mayer &Moreno, 2003
- Not checked yetcites Paas, Renkl, & Sweller, 2003
- Not checked yetcites Van Merrienboer, Kirschner, & Kester, 2003
plaindirect- 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
“Using the self as a reference point increases the learner's interest, which in turn encourages the learner to use available cognitive capacity for active cognitive processing of the incoming information during learning.”
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“In a previous set of studies, Moreno and Mayer (2000) found that personalization improved transfer performance on an environmental science educational game and on learning from a multimedia lesson on lightning formation.”
What the sources say
- Not checked yetcites Moreno and Mayer (2000)
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
“Overall, the experiment did not adequately test Prediction 2.”
plain- 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.
The quote only partly states this 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
“Making a seemingly minor change to 12 words (i.e., changing 'the' to 'your') in a multimedia lesson had a large effect on students' subsequent performance on tests of transfer-yielding effect sizes of 0.65, 1.07, and 0.72 across the three experiments, respectively.”
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.
In the paper
“transfer is generally recommended as a better measure of learner understanding than retention (Anderson et al., 2001).”
What the sources say
- Not checked yetcites Anderson et al., 2001
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check 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 be too vague to check against evidence.
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
“Participants were tested in groups of 1 to 4 and were randomly assigned to a treatment group.”
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
“Overall, this set of experiments helps establish the robustness of the personalization effect and explores the conditions for the personalization effect (i.e., even a very modest change in the wording of the lesson is effective).”
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
“The tests were scored by two raters; disagreements were resolved through consensus.”
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.
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
“Given the somewhat unexpected effects reported by Moreno and Mayer, it was worthwhile to determine whether similar effects could be obtained in a new contextlearning about the human respiratory system. The overwhelming answer is yes, greatly adding to the credibility of the personalization effect.”
boosteddirect- 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
“The only difference between the personalized and nonpersonalized presentations was that 'the' (in the nonpersonalized version) was changed to 'your' (in the personalized version) at 12 places in the narration.”
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.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Examples of techniques to increase learner interest in computerbased multimedia presentations include using a human voice rather than a machine voice (voice principle) and using words in a conversational style rather than a formal style (personalization principle).”
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: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
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
“Overall, there is not statistically significant evidence that personalization affects students' ratings of interest, but there is a trend in the predicted direction.”
plain- 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.
It may state two separate claims as one.
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
“The interest rating findings of Experiment 3 were more encouraging and suggestive than the findings related to smiling in Experiment 2, in that the interest measure yielded a larger difference in the predicted direction, this time approaching significance.”
hedged- 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.
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
“By using multiple measures, we were able to determine that although both groups remembered equivalent amounts of the presented text, the personalized group was better able to apply the material to new situations.”
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.
In the paper
“In addition, the personalization treatments used by Moreno and Mayer were somewhat heavy-handed and perhaps idiosyncratic, including adding a lot of new sentences to the instructional material.”
What the sources say
- Not checked yetcites Moreno and Mayer
hedged- 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.
The paper relies on this without support.
Both the personalized and nonpersonalized groups have adequate cognitive capacity for basic processing of the material and will pay attention to and encode the material.
“The rationale for this prediction is that both groups have adequate cognitive capacity for basic processing of the material and they both will pay attention to material and encode it”
The paper relies on this without support.
Both the personalized and nonpersonalized groups have adequate cognitive capacity for deeper processing.
“although both groups also have adequate cognitive capacity for deeper processing”
The paper relies on this without support.
Students in the personalized group will be more likely to use their cognitive capacity for organizing the material and integrating it with prior knowledge because they are more interested in understanding the material.
“students in the personalized group will be more likely to use that capacity for organizing the material and integrating it with prior knowledge because they are more interested in understanding the material.”
The paper relies on this without support.
The cognitive theory of multimedia learning correctly describes that personalization increases the learner's interest, which increases effort and deeper processing, leading to improved transfer performance.
“One answer to this question comes from the cognitive theory of multimedia learning: Personalization increases the learner's interest, increased interest causes the learner to exert more effort to engage in active cognitive processing during learning, and an increase in active cognitive processing during learning results in deeper learning, which is manifested in improved transfer performance.”
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?
In the citing paper
“For example, people learn more deeply when the words in a multimedia presentation are in conversational style rather than formal style (the personalization principle: e.g., Mayer et al. 2004;Moreno and Mayer 2004)”
From An Evolutionary Upgrade of Cognitive Load Theory: Using the Human Motor System and Collaboration to Support the Learning of Complex Cognitive Tasks (2012) · cites this paper as Mayer et al. 2004
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.
- Not confirmedt(25) = 0.594reported p = .557 · recomputed p = .558
“t (25) = 0.594, p = .557”
- Consistentt(60) = 2.17reported p = .034 · recomputed p = .034
“t (60) = 2.170, p = .034”
- Consistentt(25) = 2.516reported p = .019 · recomputed p = .019
“t (25) = 2.516, p = .019”
- Consistentt(30) = 1.087reported p = .286 · recomputed p = .286
“t (30) = 1.087, p = .286”
- Consistentt(30) = 2.177reported p = .037 · recomputed p = .037
“t (30) = 2.177, p = .037”
- Consistentt(30) = 1.56reported p = .129 · recomputed p = .129
“t (30) = 1.56, p = .129”
- Consistentt(30) = 0.324reported p = .748 · recomputed p = .748
“t (30) = 0.324, p = .748”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Self-check results appear here once an independent check has confirmed them.
Citations
Cites
(2001) · Cited by 13,930
Mayer, R. E. (2009) · doi:10.1017/cbo9780511811678 · Cited by 7,800
Self-citationMayer, R. (2020) · doi:10.1017/9781316941355 · Cited by 7,777
Self-citationNine Ways to Reduce Cognitive Load in Multimedia Learning
Mayer, R. E., & Moreno, R. (2003) · Educational Psychologist · doi:10.1207/s15326985ep3801_6 · Cited by 4,207
Self-citationCognitive Load Theory and Instructional Design: Recent Developments
Paas, F., Renkl, A., & Sweller, J. (2003) · Educational Psychologist · doi:10.1207/s15326985ep3801_1 · Cited by 3,041
Mayer, R. E. (2001) · doi:10.1017/cbo9781139164603 · Cited by 2,304
Self-citationThe Role of interest in Learning and Development
Renninger, K. A., Hidi, S. E., Krapp, A., & Renninger, A. (2014) · Psychology Press eBooks · doi:10.4324/9781315807430 · Cited by 1,611
Mayer, R. E., & Sims, V. K. (1994) · Journal of Educational Psychology · doi:10.1037/0022-0663.86.3.389 · Cited by 1,191
Self-citation(1999) · Cited by 944
Taking the Load Off a Learner's Mind: Instructional Design for Complex Learning
van Merrienboer, J. J. G., Kirschner, P. A., & Kester, L. (2003) · Educational Psychologist · doi:10.1207/s15326985ep3801_2 · Cited by 857
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How Multimedia Can Improve Learning and Instruction
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Contextualization in Perspective
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Reviews in instructional video
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The personalization effect in multimedia learning: The influence of dialect
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Does language matter in multimedia learning? Personalization principle revisited.
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Schneider, S., Nebel, S., Pradel, S., & Rey, G. D. (2014) · Computers in Human Behavior · doi:10.1016/j.chb.2014.10.052 · Cited by 26
Rickley, M., & Kemp, P. S. (2020) · The Electronic Journal of e-Learning · doi:10.34190/ejel.19.3.2297 · Cited by 28
Cromley, J. G., & Chen, R. (2025) · Educational Research Review · doi:10.1016/j.edurev.2025.100730 · Cited by 25
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Principles Based on Social Cues in Multimedia Learning
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Schrader, C., Reichelt, M., & Zander, S. (2018) · Educational Technology Research and Development · doi:10.1007/s11423-018-9588-8 · Cited by 24
Ciccone, A., & Hounslow, L. (2019) · Journal of University Teaching and Learning Practice · doi:10.53761/1.16.1.11 · Cited by 24
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Dutke, S., Grefe, A. C., & Leopold, C. (2015) · European Journal of Psychology of Education · doi:10.1007/s10212-015-0281-6 · Cited by 22
Carmel, A., Cornelius‐Schecter, A., Frankel, B., Jannat‐Khah, D., Sinha, S., Pelzman, F. N., & Safford, M. M. (2019) · Patient Education and Counseling · doi:10.1016/j.pec.2019.03.001 · Cited by 21
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Bowden, R. (2007) · Indiana Magazine of History (Indiana University) · Cited by 21
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Crook, C. K., & Cluley, R. (2009) · Learning Media and Technology · doi:10.1080/17439880903141570 · Cited by 20
Désiron, J. C., Bétrancourt, M., & de Vries, E. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.592509 · Cited by 21
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Schworm, S., & Stiller, K. D. (2012) · Intelligent Decision Technologies · doi:10.3233/idt-2012-0127 · Cited by 18
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Kim, T. S., Latzke, M., Bragg, J., Zhang, A. X., & Chang, J. C. (2023) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3586183.3606770 · Cited by 17
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Koch, J., Salamonson, Y., Du, H., Andrew, S., Frost, S. A., Dunncliff, K., & Davidson, P. M. (2011) · Journal of Nursing Education · doi:10.3928/01484834-20110331-02 · Cited by 16
Metadata
- Authors
- Mayer, R. E., Fennell, S., Farmer, L., & Campbell, J.
- Affiliations
- University of California, Santa Barbara (US)
- ORCID
- Mayer, Richard E.
- Venue
- Journal of Educational Psychology
- Published
- 2004-06
- Volume
- 96
- Issue
- 2
- Pages
- 389-395
- ISSN
- 0022-0663 (ISSN-L)
- 1939-2176
- OpenAlex
- W2096394886
- Semantic Scholar
- 13bc860cf6f55b6f221d447b65c42733cd80fbc7
- Cited by
- 298
- References
- 14
- Citations by year
- 2026: 11 · 2025: 13 · 2024: 9 · 2023: 13 · 2022: 11 · 2021: 22 · 2020: 11 · 2019: 17 · 2018: 18 · 2017: 14 · 2016: 13 · 2015: 19 · 2014: 21 · 2013: 22 · 2012: 14
- FWCI
- 8.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Educational Psychology (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Psychology › Developmental and Educational Psychology › Learning Styles and Cognitive Differences
- Keywords
- personalization, conversational style, formal style, narrative style, multimedia learning, cognitive theory of multimedia learning, transfer, retention, respiratory system, active processing
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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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