The expertise reversal effect pertinent to visuals in learning has gained attraction among the researchers over the past decade (Zheng & Greenberg, 2018, Zheng & Gardner, 2020; Gupta & Zheng, 2020; Kalyuga et al., 2012), largely because of the fascinating observations about how expertise may influence the outcome of visual learning (Gegenfurtner et al., 2011). Kalyuga (2009) pointed out that learning procedures and techniques that are beneficial for low-prior-knowledge learners may become relatively inefficient for high-prior-knowledge learners. In a longitudinal study, Kalyuga et al. (1998) investigated the redundancy effect between high- and low-prior-knowledge learners. They found that when diagrams were embedded in the text, novices learned the content well. After the learners underwent intensive training, however, a reversal effect was observed: Diagram-alone materials generated a higher level of performance on the subsequent tests. Kalyuga et al. explained that at the beginning, novices may not have constructed schemata to understand the complex content. Therefore, the text with the diagram helped the novices comprehend the content. However, as the learners gained more knowledge, their learning actually became hindered when additional text was added, since it was then unnecessary and redundant in learning. Kalyuga et al. (2012) argued that when this redundant information cannot be ignored, interference with learning occurs resulting in high extraneous cognitive load as well as a misalignment between learners’ effort and task difficulty. Lee et al. (2006) conducted a study on task complexity (high vs. low), visual representation (symbol vs. icon), and prior knowledge (high vs. low). An expertise reversal effect was observed. Low-prior-knowledge learners performed better with symbolic and iconic visual representations, whereas high-prior-knowledge learners performed better with symbolic visual representations only. Lee et al. explained that the multiple visuals were necessary scaffolds for low-prior-knowledge learners who lacked adequate schemata. However, these same scaffolds became redundant to high-prior-knowledge learners and consequently hindered their learning. The current study therefore investigated the role of expertise in visual learning.

Three predictions were made to guide the present study.

Prediction 1: Learners with annotation will outperform these without annotation as measured by learners’ comprehension, problem solving, and three types of cognitive load.

Prediction 2: There will be an interaction between annotation and abstract/concrete visual representations as measured by learners’ comprehension, problem solving, and three types of cognitive load.

Prediction 3: Learners’ performance in annotation and abstract/concrete visual representations will be affected by their expertise in the domain area. Specifically, high-prior-knowledge learners will perform better in the abstract visual representation with annotation (AA) condition, since abstract visuals facilitate the understanding of the underlying structure of the problems. In contrast, low-prior-knowledge learners will perform better in the concrete visual representation with annotation (CA) condition, because the extra visual support from both concrete visuals and annotation will facilitate schema development for the novices.

Two studies were conducted to test the above predictions. Study 1 explored a two-way interaction between annotation and visual representation with a focus on the difference between annotation and non-annotation in abstract and concrete visual representations. Study 2 tested a three-way interaction between visual representation (abstract vs. concrete), annotation (annotation vs. non-annotation), and prior knowledge (high vs. low). The purpose was to understand how visual representation and annotation may be influenced by learners’ prior knowledge.

Study 1

To test predictions 1 and 2, Study 1 investigated (a) the differences between annotation and non-annotation and (b) the relationship between annotation (annotation vs. non-annotation) and visual representations (abstract vs. concrete) in science learning.

Methodology

Subjects and design

Participants (N = 108) were recruited from a Research I university in the western USA. The average age of the subjects was 21.5 (SD = 1.60). Of 108 subjects, 49% (n = 53) were males and 51% (n = 55) were females. About 57.4% (n = 62) were white, 4.6% (n = 5) were African American, 20.4% (n = 22) were Hispanic, 14.8% (n = 16) were Asian, and 2.8% (n = 3) were other. A 2 × 2 between-subjects factorial design was employed with visual representation (abstract vs. concrete) and annotation (annotation vs. non-annotation) as the independent variables, and comprehension, problem solving, and cognitive load scores (intrinsic, extraneous, and germane) as dependent variables. The pretest scores on learners’ prior knowledge of electrical circuitry were entered as a covariate. Four conditions were created. They included Abstract visual + Annotation (AA), Abstract visual + Non-annotation (ANA), Concrete visual + Annotation (CA), and Concrete visual + Non-annotation (CNA). Subjects were randomly assigned to one of the four conditions. A family-wise alpha level of 0.05 was adopted for all analyses.

Learning materials

The learning materials were created with Adobe DX to support interactive computer-based learning in electrical circuitry. The content was adapted from a textbook by Herman (2016). The learning materials covered the concepts of electric circuit (e.g., parallel and multiple resistance in an electric circuit) with electric circuit problems requiring the learners to solve them with Ohm’s law. The learning materials had built-in annotation support where the participants were able to click the annotation button to get the resources (e.g., formula for calculating the resistance) and enter their own notes if needed.

Measurement

The measurement for Study 1 included prior knowledge test (PKT), the posttest, and cognitive load questionnaire (CLQ), the details of which are described as follows.

Prior knowledge test (PKT) The PKT consisted of 10 items aiming to test learners’ prior knowledge in electric circuitry. The test included basic concepts from elements of electric circuit (e.g., current, voltage) to the types of electric circuits (e.g., series, parallel). The maximum score one could obtain was 10 points. The PKT was adapted from a screen test in Herman’s (2016) textbook. The items were reviewed by a panel of experts whose feedback was incorporated in the finalization of the instrument. The internal consistency for the current study for the PKT was α = 0.806, suggesting good reliability for measuring the prior knowledge on the subject.

The posttest The posttests consisted of a comprehension test and problem-solving test. The comprehension test had ten questions assessing learners’ understanding of the concepts and principles related to electrical circuit such as explaining the difference between parallel and multiple resistance in electric circuit. The maximum score one could obtain on the comprehension test was 10 points. The problem-solving test included five near transfer problems with a maximum of 10 points possible for the entire test. In the problem-solving test, learners were asked to solve a problem based on a given condition. The learner would calculate, for example, the level of resistance in the electric current using Ohm’s law and then find a solution for the proper functioning of the electric circuit. The inter-item reliabilities for comprehension and problem solving were α = 0.812 and α = 0.721, respectively, showing medium to high reliabilities. Figure 3 provides an example of a problem-solving test item.

Fig. 3
figure 3

A sample of problem-solving item

Cognitive load questionnaire (CLQ) A self-report questionnaire that evaluates the cognitive load in learning was used. The 11-point Likert-scale questionnaire (N = 10) was adapted from Leppink et al. (2013) to measure three types of cognitive load: intrinsic load (IL) items 1–3, extraneous load (EL) items 4–6, and germane load (GL) items 7–10. Examples of the questions include “The topic covered in the electric circuit material was very complex” (IL), “The instruction and explanation during the learning were very ineffective” (EL), and “The annotation with visuals really enhanced my understanding of the content covered” (GL) (see Appendix for the whole questionnaire).

The instrument reports medium to high reliabilities with intrinsic load α = 0.81, extraneous load α = 0.75, and germane load α = 0.82. The current study reported similar reliabilities with intrinsic load α = 0.81, extraneous load α = 0.76, and germane load α = 0.84, all indicating good reliability.

Procedure

Participants were informed of the nature of the study and completed the consenting process before participating in the study. They were then randomly assigned to one of the four learning conditions: AA, ANA, CA, and CNA. The participants completed a demographic survey and a prior knowledge test. They were then told to log onto the computer to start the learning session that included the electrical circuitry materials followed by a practice session. At the end of practice session, the participants were asked to complete a posttest that consisted of comprehension and problem-solving subtests. Finally, the CLQ was administered. The entire study took about one and a half hours. The data were aggregated for final analyses.

Results

Statistical assumptions were evaluated and met. The MANCOVA was performed using SPSS v. 26 with annotation (annotation vs. non-annotation) and visualization (abstract vs. concrete) as independent variables and comprehension, problem solving, and three cognitive load scores as dependent variables. As the raw scores for the three cognitive load measures varied due to the differing number of questions in each load category (IL = 3, EL = 3, GL = 4), Z-scores were calculated to allow for meaningful comparisons among the outcomes. Prior knowledge scores were entered as a covariant in the final analyses. Table 1 presents the descriptive statistics with means and standard deviations.

Table 1 Descriptive statistics with means and standard deviations for study 1 (N = 108)

The results of multivariate tests show prior knowledge as a covariant was significant λ = 0.59, p < 0.001, ƞ2 = 0.40. Main effects were observed for annotation λ = 0.69, p < 0.001, ƞ2 = 0.30 and visual representation λ = 0.70, p < 0.001, ƞ2 = 0.29. The follow-up between-subjects tests revealed that there was a significant difference in annotation measured by problem solving F(1, 107) = 7.38, p < 0.01, ƞ2 = 0.06, but not by comprehension F(1, 107) = 3.33, p = 0.07.

It was found that learners who studied in the AA condition generally outperformed these in the CA condition (Fig. 4). There was a significant interaction between visual representation and annotation λ = 0.76, p < 0.001, ƞ2 = 0.23 as measured by comprehension F(1, 107) = 11.73, p < 0.001, ƞ2 = 0.10, problem solving F(1, 107) = 6.41, p < 0.05, ƞ2 = 0.05 and germane cognitive load F(1, 107) = 8.89, p < 0.01, ƞ2 = 0.07, but not by intrinsic cognitive load F(1, 107) = 1.56, p = 0.213 and extraneous cognitive load F(1, 107) = 3.83, p = 0.053, suggesting a connection between germane cognitive load and performance.

Fig. 4
figure 4

The interaction between annotation and visual representation as measured by comprehension and problem solving

As expected, learners experienced higher extraneous cognitive load without annotation than with annotation F(1, 107) = 39.11, p < 0.001, ƞ2 = 0.27. A significant difference in intrinsic cognitive load was observed for visual representation F(1, 107) = 5.48, p < 0.05, ƞ2 = 0.05 where learners experienced higher intrinsic load in concrete visual representation than in abstract visual representation conditions. Finally, germane cognitive load was significant for annotation F(1, 107) = 8.17, p < 0.01, ƞ2 = 0.07. The interaction between annotation and visual representation was significant F(1, 107) = 8.89, p < 0.01, ƞ2 = 0.07.

As expected, learners experienced higher extraneous cognitive load without annotation than with annotation F(1, 107) = 39.11, p < 0.001, ƞ2 = 0.27. A significant difference in intrinsic cognitive load was observed for visual representation F(1, 107) = 5.48, p < 0.05, ƞ2 = 0.05, indicating learners experienced higher intrinsic load in concrete visual representation than in abstract visual representation conditions. Finally, germane cognitive load was significant for annotation F(1, 107) = 8.17, p < 0.01, ƞ2 = 0.07 revealing the relationship between germane load and annotation. There was a significant interaction between annotation and visual representation F(1, 107) = 8.89, p < 0.01, ƞ2 = 0.07 suggesting that the types of visual representation were related to the presence of annotation in science learning.

Regardless of the significant interaction between visual representation and annotation, the results, however, remained inconclusive. As Kalyuga (2007) noted, the effects of instructional strategies may differ relative to learners’ prior knowledge. Given the significance of prior knowledge as a covariant in Study 1, a follow-up study that examined the impact of prior knowledge on visual representation and annotation was called for.

Study 2

Two hundred and twenty-seven participants were recruited from the same university. Of 227 participants, 59% (n = 135) were females and 41% (n = 92) were males. The average age of the subjects was 22.5 (SD = 1.72). About 63% (n = 143) were white, 7% (n = 16) were African American, 9.7% (n = 22) were Hispanic, 15% (n = 34) were Asian, and 5.3% (n = 12) were other.

Methodology

The design and measurement in Study 2 were similar to these in Study 1. The materials in learning and practice sessions were the same as these in Study 1.

Procedure

The procedure in Study 2 was almost the same as Study 1 except that the participants were divided into high- and low-prior-knowledge groups based on the pretest and then randomly assigned to one of the AA, ANA, CA, and CNA conditions.

Defining high- and low-prior-knowledge learners Two different methods were considered when defining high- and low-prior-knowledge learners. They were: median split method and tri-split method. The median split method finds the median point and splits a continuous variable like prior knowledge into half (Rucker et al., 2015). The drawback of median split method is that it arbitrarily defines the participants who are one position above and below the median point as high- or low-prior-knowledge learners which, as Liu and Reed (1994) point out, may significantly skew the results. McClelland et al. (2015) warn that median-split method is likely to increase Type II error. In contrast to median split method, Liu and Reed (1994) proposed a tri-split method that divided the participants into upper-third quarter, middle-third quarter, and lower-third quarter. It eliminates the middle-third quarter and keeps only the upper and lower third quarters in its final analysis. Since the tri-split method eliminates middle one-third sample, it clearly creates the high and low categories by retaining top and bottom one-third samples, thus avoiding artificially labelling the samples as high or low and minimizing the risk of Type II error. Based on the results of the pretest (N = 227, M = 5.54, б = 1.56), the participants were divided into high-, low-, and middle-prior-knowledge groups with those who scored one standard deviation above the mean as high-prior-knowledge learners (n = 81, m = 7.35, s = 0.50) and those scored one standard deviation below the mean as low-prior-knowledge learners (n = 82, m = 3.84, s = 0.37). The middle group (n = 64, m = 5.45, s = 0.53) were eliminated from the final analysis.

Results

To test Prediction 3, a three-way ANOVA was performed using SPSS v. 26 with annotation (annotation vs. non-annotation), visual representation (abstract vs. concrete), and prior knowledge (high vs. low) as independent variables and comprehension, problem solving and CL scores as dependent variables. Table 2 presents the descriptive statistics with means and standard deviations for Study 2.

Table 2 Descriptive statistics with means and standard deviations for study 2 (N = 163)

The multivariate tests revealed a main effect for the interaction among prior knowledge, annotation, and visual representation λ = 0.695, p < 0.001, ƞ2 = 0.31. The follow-up analysis showed a significant main effect for annotation λ = 0.569, p < 0.001, ƞ2 = 0.43, visual representation λ = 0.707, p < 0.001, ƞ2 = 0.29, and prior knowledge λ = 0.848, p < 0.001, ƞ2 = 0.15. Prior knowledge was significantly interacted with visual representation λ = 0.678, p < 0.001, ƞ2 = 0.32 and annotation λ = 0.906, p < 0.05, ƞ2 = 0.09. The interaction between annotation and visualization was significant λ = 0.729, p < 0.001, ƞ2 = 0.23.

The results of between-subjects tests revealed that high-prior-knowledge learners performed better in the AA condition, whereas low-prior-knowledge learners performed better in the CA condition with a significant 3-way interaction by comprehension F(1, 162) = 14.77, p < 0.001, ƞ2 = 0.08 and problem solving F(1, 162) = 7.37, p < 0.01, ƞ2 = 0.04. Prior knowledge significantly interacted with visual representation by comprehension F(1, 162) = 24.32, p < 0.001, ƞ2 = 0.13 and problem solving F(1, 162) = 6.57, p < 0.05, ƞ2 = 0.04. However, it significantly interacted with annotation by problem solving only F(1, 162) = 5.02, p < 0.05, ƞ2 = 0.03 (Fig. 5).

Fig. 5
figure 5

The interaction among annotation, visual representation, and prior knowledge by comprehension and problem solving

Regarding cognitive load, a significant 3-way interaction was observed as measured by extraneous F(1, 162) = 39.40, p < 0.001, ƞ2 = 0.20 and germane load F(1, 162) = 6.19, p < 0.05, ƞ2 = 0.04. Noticeable differences were found between high- and low-prior-knowledge learners in terms of conditions. The high-prior-knowledge learners showed a lower intrinsic load in the AA condition than in the CA condition. In contrast, the low-prior-knowledge learners had a higher intrinsic load in the AA condition than in the CA condition. In terms of extraneous load, the high-prior-knowledge learners had low extraneous load in both AA and CA conditions, whereas the low-prior-knowledge learners showed a high extraneous load in the AA condition and a low extraneous load in the CA condition. Finally, the high-prior-knowledge learners showed a higher germane load in the CA condition compared to the AA condition. For low-prior-knowledge learners, the germane load was high in the CA condition but very low in the AA condition (Fig. 6).

Fig. 6
figure 6

The interaction among annotation, visual representation, and prior knowledge by intrinsic, extraneous, and germane cognitive load

Both intrinsic F(1, 162) = 12.24, p < 0.01, ƞ2 = 0.07 and extraneous load F(1, 162) = 31.86, p < 0.001, ƞ2 = 0.17 was significant for the interaction between prior knowledge and visual representation. Finally, extraneous load was significant for the interaction between prior knowledge and annotation F(1, 162) = 10.60, p < 0.01, ƞ2 = 0.06.

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