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Feeling good, learning better? Effectivity ofan emotional design procedure in multimedialearning

Hannes Münchow & Maria Bannert

To cite this article: Hannes Münchow & Maria Bannert (2018): Feeling good, learning better?Effectivity of an emotional design procedure in multimedia learning, Educational Psychology, DOI:10.1080/01443410.2018.1524852

To link to this article: https://doi.org/10.1080/01443410.2018.1524852

Published online: 14 Nov 2018.

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Feeling good, learning better? Effectivity of an emotionaldesign procedure in multimedia learning

Hannes M€unchowa and Maria Bannertb

aDepartment of Educational Psychology, University of W€urzburg, W€urzburg, Germany; bTUM Schoolof Education, Technical University of Munich, M€unchen, Germany

ABSTRACTEmotional designing describes the elicitation of positive affectduring learning through specific design elements of the learningenvironment to enhance learning. This experimental study exam-ined the effectivity of an emotional design procedure on learningperformance. Moreover, the learner’s affective states before learn-ing were taken into consideration as possible moderators. 145university students learned for 20min either in a multimedia posi-tive affect inducing learning environment (n¼ 85) or a neutralmultimedia learning environment (n¼ 60). The Affect was meas-ured before, during, and after learning. Performance was testedafterwards. To control for possible confounding effects, achieve-ment motivation, emotion regulation, and situationalinterest were measured. In contrast to earlier findings, no super-iority effect of the emotional design procedure was found.Furthermore, the effectivity of the emotional design procedurewas not moderated by student’s prior effective states. However,there was a main influence of student’s positive affect on transferperformance.

ARTICLE HISTORYReceived 27 July 2017Accepted 13 September 2018

KEYWORDSAffect; learningperformance; emotionaldesign; multimedia learning

Introduction

Affective states have been accepted as important factors for succeeding and persistingin learning and achievement situations (see Pekrun & Stephens, 2011). Despite a multi-tude of correlational findings suggesting a strong relationship between affect andlearning performance, causal relations have not been investigated frequently. Recently,a string of studies on short-term learning situations outside the classroom used emo-tional design procedures in order to elicit affective states that are related to learninggains. According to these studies’ core assumptions, the design of the learning mater-ial can induce positive activating affect that enhances learning performance. Um,Plass, Hayward, and Homer (2012) developed a multimedia learning environment thatcombined warm colours and rounded human-like shapes as design elements in orderto induce a positive activating affect. Using this learning environment increased

CONTACT Hannes M€unchow hannes.muenchow@uni-wuerzburg.de Department of Educational Psychology,University of W€urzburg, R€ontgenring 10, 97070 W€urzburg, Germany.Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/cedp� 2018 Informa UK Limited, trading as Taylor & Francis Group

EDUCATIONAL PSYCHOLOGYhttps://doi.org/10.1080/01443410.2018.1524852

learning performance and achievement motivation compared to an effectively neutralcontrol condition. Similar affect induction procedures have been adopted in subse-quent studies in emotional design research (e.g. Heidig, M€uller, & Reichelt, 2015;Mayer & Estrella, 2014). However, findings on emotional designing so far have beenrather heterogeneous because of difficulties in eliciting the intended effective statesor a missing link between induced affect and learning outcomes.

Consequentially, subsequent studies need to provide explanatory approaches forthe heterogeneous results. Hence, inducing positive affect should foster learning out-comes regardless of the learners’ individual characteristics. Recent findings promote amore complex nature of the role positive activating affect plays in multimedia learning(M€unchow, Mengelkamp, & Bannert, 2017; Park, Kn€orzer, Plass, & Br€unken, 2015).According to these results, the effectiveness of emotional designing depends on one’saffective experiences before the affect induction starts: Strong positive affect prior tothe learning phase predicted higher learning transfer in the emotional design condi-tion (both studies) and better knowledge performance (only in Park et al., 2015).M€unchow et al. (2017) also found a negative relationship between negative affectprior to learning and learning performance for participants who were not learning inan affect-inducing learning environment. However, Park et al’s (2015) moderationeffect was not statistically significant in the omnibus test but rather in multiple com-parisons. Accordingly, the present study aims to replicate these interaction effects andto extend these findings to better understand how affective states influence multi-media learning.

Affect and multimedia learning

According to Pekrun (2006), affective states can influence learning performance dir-ectly and indirectly mediated through motivational processes. Different affective states(positive vs. negative; activating vs. deactivating) can have different impacts on thelearning process (Pekrun & Stephens, 2011). Positive as well as negative deactivatingaffective states as for instance relaxation or boredom are often related to impairedlearning outcomes (e.g. Aspinwall, 1998). Although it has been shown that negativeactivating affect such as anger or frustration can weaken performance due toenhanced task-irrelevant thinking (e.g. Pekrun et al., 2004), it is also indicated thatnegative activating affects like confusion can enhance learning gains (e.g. D’Mello &Graesser, 2014). Positive correlations are often found between positive activating affectand learning memory, task persistence, and creative problem solving (e.g. Isen,Daubman, & Nowicki, 1987) as well as learning gains in general (e.g. Nadler, Rabi, &Minda, 2010). Moreover, positive activating affect correlates positively with motiv-ational determinants of learning such as interest (e.g. Ainley & Hidi, 2014), intrinsicmotivation (e.g. Isen & Reeve, 2005), or learning enhancing achievement goal orienta-tions (Huang, 2011).

Nevertheless, empirical evidence still refers mainly to correlation studies that do notallow any causal interpretation. Moreover, most of these studies do not address affect-ive state in the context of multimedia learning which has become indispensable in thefield of instructional learning (Mayer, 2009). Multimedia learning is defined as

2 H. M€UNCHOW AND M. BANNERT

perceiving and processing information presented in more than one way, such as textsand pictures (Mayer & Moreno, 2003). Mayer’s cognitive theory of multimedia learning(CTML; 2009), the cognitive-affective theory of learning with media (CATLM; Moreno,2005; Moreno & Mayer, 2007), and most recently, Plass and Kaplan (2016) integratedcognitive-affective model of learning with multimedia (ICALM) are the most prominenttheoretical approaches. The CTML identifies several cognitive factors that describehow information is processed on different channels. The theory is supplemented bythe CATLM, which states that the learners’ information processing is influenced bymotivational and affective processes, learner characteristics (i.e. prior knowledge) aswell as meta-cognitive competencies. Moreover, the ICALM assumes that when infor-mation is processed cognitively, additional affective appraisals occur leading to aninseparably interweaving of cognition and affect during multimedia learning situations.Accordingly, variation in the instructional design of the learning material can improvethe affective-cognitive processing of the given textual and visual information.

Emotional design and multimedia learning

In recent years, emotional design procedures have been used in several studies result-ing in a variety of design principles in order to evoke affective states, for exampleusability (Heidig et al., 2015), decorative pictures (Schneider, Nebel, & Rey, 2016), ormusic (Kn€orzer, Br€unken, & Park, 2016). However, the influence of shapes, colours, andanthropomorphic design elements have been most frequently examined (e.g. Mayer &Estrella, 2014; Park et al., 2015; Plass, Heidig, Hayward, Homer, & Um, 2014). Most com-monly used design elements are bright and saturated colours of greater wavelengths(i.e. yellow, orange) as well as rounded shapes, human-like baby-face images, and goo-gly eyes. Several findings indicate positive relationships between these colours andpositive affect in children (e.g. Boyatzis & Varghese, 1994) and young adults (e.g.Hemphill, 1996). Moreover, studies by Palmer and colleagues (e.g. Palmer & Schloss,2010) show that higher levels of brightness and saturation are more likely preferred.Additionally, colours hues of higher wavelengths are related to stronger degrees ofpositive activation (Hamid & Newport, 1989; Stone & English, 1998), while colours onthe lower end of the spectrum are associated with tranquillity and calmness. However,Mehta and Zhu (2009) found that blue colour may strengthen one’s creativity.Findings concerning rounded shapes and anthropomorphic design elements are lessclear, but nonetheless present. Circled shapes have been found to predict an individu-al’s perception of feeling strong and powerful (Kim, Lee, & Choi, 2003). Berry andMcArthur (1985) argue that round shapes can elicit positive affect. Finally, anthropo-morphic design elements were found most effective in inducing positive activatingaffect in study 2 from Plass et al. (2014).

Nonetheless, replication studies of the initial results from Um et al. (2012) appearheterogeneously. In some of the studies the affect induction procedure was not suc-cessful (e.g. M€unchow et al., 2017; study 1 in Plass et al., 2014). Curiously enough, in astudy from Park et al. (2015) participants receiving no emotional design interventionreported increased levels of positive affect after learning while participants in the emo-tional design condition did not. Kn€orzer et al. (2016) on the other hand successfully

EDUCATIONAL PSYCHOLOGY 3

induced positive affect but did not find any links to increased learning performance.Then again, there were studies that did not successfully evoke positive affect butfound learning gains for participants who learned in the emotional design condition(e.g. Mayer & Estrella, 2014). Kn€orzer et al. (2016) even showed that negative activatingaffect was related to better learning outcomes. Nevertheless, there are findings sup-porting the results from Um et al. (2012) as, for example, the second study by Plasset al. (2014). The authors showed that anthropomorphic design elements were super-ior for eliciting positive activating affect. Conclusively, research on emotional designchallenge two major problems: First, insignificant changes of affective states duringlearning, and second, induced affect is not or only weakly associated with learn-ing gains.

Least probably, non-significant manipulation checks indicate that positive affectmight not be altered using emotional design procedures. As presented earlier, severalstudies have shown that emotional design elements (e.g. colours and anthropomor-phisms) influence affective states separately as well as combined (e.g. Hemphill, 1996;Park, Plass, & Br€unken, 2014; Um et al., 2012). Therefore, it is unlikely that emotionaldesign simply does not work. It may also be plausible that the effectiveness of theaffect induction is influenced by other variables. According to the CATLM (Moreno,2005), multimedia learning is also influenced by motivational constructs. The learnersmight also differ in their respective emotion regulation competencies, i.e. one’s abilityto detect self-related affective states and to deliberately generate and maintain func-tional affective experiences. Accordingly, participants with better emotion regulationcompetencies may be better in perceiving and truthfully expressing their currentaffective states. Hence, it might be worth to consider achievement motivation andemotion regulation as control variables in studies on emotional designing.

Alternatively, it might be the case that emotional design procedures elicit motiv-ational-affective states like interest rather than positive activating affect (e.g. Durik &Harackiewicz, 2007). Consequently, checking for unintended elicitation of suchconstructs can be fruitful for validating the emotional design procedure. Finally, non-significant manipulation checks may be a result of rather a methodological than a the-oretical issue. Most of the present study used pre-to-post experimental self-reports inorder to assess changes in affective states. However, it may be not sufficient to testfor changes in affective states during learning with only two points of measurement(before and after learning). It is also possible that the emotional design procedure iseffective at the beginning of the learning phase but that these effects decrease even-tually while learning. Additional points of measurement might increase certainty aboutthe process of the affect induction.

Furthermore, high levels of positive affect before the learning phase can predictlearning performance regardless of the affectivity of the learning material (Park et al.,2015). Extending these results, M€unchow et al. (2017) found different patterns of inter-action effects for different performance measures. Concerning the comprehension ofthe learning material, high negative affect prior to the learning phase was associatedwith learning impairments but only in the control condition. Participants in the emo-tional design condition did not perform worse even if negative affect before learningwas high. If tested for transfer, positive affect prior to learning predicted better

4 H. M€UNCHOW AND M. BANNERT

performance but only when participants were given an emotional design intervention.Similar effects showing that positive affect can enhance creative problem-solving skillshave been found frequently (e.g. Isen et al., 1987).

Research questions and hypotheses

Empirical findings on emotional design interventions in relatively short learning set-tings are discordant. Inconsistencies arise mainly regarding the effectivity of the affectinduction and regarding the relationship between induced affects and performance.Consequently, the present experiment uses an emotional design treatment in order toelicit positive activating affect that is equivalent to those used in earlier studies. Thepresent study further aims to replicate and extend earlier findings that indicate moder-ation effects of affective states prior to learning on the effect of the affect inductionand the relation between induced affect and learning outcomes (M€unchow et al.,2017; Park et al., 2015). Hence, the research questions of this experimental study are:

1. Can positive activating affect be increased when learning in emotional designlearning environment?

2. Can learning outcomes be increased when learning in an emotional design learn-ing environment?

3. Can these effects be moderated by the learners’ positive and negative affectbefore learning?

To reduce confounding, it is further intended to control for achievement goal orien-tations, emotion regulation competencies, and situational interest.

Method

Sample and experimental design

Data from 145 undergraduate German university students were analysed. The averageage was 20.2 years (SD¼ 2.6; ranging from 18 to 32 years). Students were mostlyfemale (69%), studying media communication (70.3%) or human-computer interaction(26.9%), and at the beginning of their studies (mean number of semesters was 1.6;SD¼ 1.0). Participants were tested in groups (0 to 10 participants) and were instructedto learn for 20min using a computer-based multimedia learning environment. Basedon random assignments, participants learned in a positive affect inducing learningenvironment (PA condition) or an effectively neutral one in the control condition (CG).Due to computerized complete randomization, participants were not completelyequally assigned (65 participants in the CG vs. 80 participants in the PA condition). Forthe same reason, there was a slight skew in the distribution of study courses betweenthe two conditions. However, there was no indication for differences between thestudy courses in any of the dependent variables in this study. Before learning, thelearners’ prior knowledge, achievement motivation, and emotion regulation competen-cies were tested as control variables. Positive and negative affect were assessed fourtimes: Before testing for prior knowledge (t1), directly before learning (t2), after half of

EDUCATIONAL PSYCHOLOGY 5

the learning time (10min) (t3), and immediately after learning (t4). Furthermore, inter-est was measured as a covariate at t2 and t4. Learning performance was tested afterlearning. The experiment’s duration was about 60minutes. Students were rewardedwith one hour of partial course credit.

Design of the learning environment

The multimedia learning environments were programmed using HTML 5 and PHPscript and were presented using the Mozilla Firefox version 38.0 web browser (MozillaProject, 2015). The content consisted of 12 nodes about the structures and functionsof different parts and organelles of eukaryotic cells (1.600 words written in Germanand 7 pictures overall). The learning topic was similar to previous studies on emotionaldesign to ensure comparability. Besides the browser’s forward and backward buttonson a menu on the left-hand side of the screen were used to navigate within the learn-ing environment. Additional linkages between technical terms that were overlappingon different nodes were implemented to facilitate navigation. The environments in thetwo conditions differed only in their emotional design, not in their contents. Brightand highly saturated colours of higher wavelengths (excluding red hues), roundedshapes, and anthropomorphic elements (see Figure 1) within pictures of the cells’structures were implemented in the environment of the PA condition. Similar designelements have been used successfully in previous studies (e.g. Plass et al., 2014).The learning environment in the CG used achromatic colours, sharp edges, and noanthropomorphic design elements. However, it was hoped that confounding due toperceptional features such as salience were reduced by varying the learning environ-ment between the two conditions only as much as necessary for implementing anaffect induction.

Measures

Prior knowledge

Prior knowledge was tested with 24 multiple-choice items - items asked for factualknowledge and core concepts of the upcoming learning content. The answers wererated on a three-stage rating scheme by two trained student assistants. If none of thecorrect answering options were chosen, zero points were given. Choosing at least oneof the correct answering option or selecting any wrong answering options in addition,one point was given. Two points were rated when the question was answered cor-rectly. The mean score of all 24 items was used as an index for prior knowledge. Thedata from one student was excluded from data analyses because of too high priorknowledge. Interrater reliability was sufficient with Cohen’s j¼ 0.72.

Affective states

For measuring affective states the PANAS (Watson, Clark, & Tellegen, 1988; Germanversion by Krohne, Egloff, Kohlmann, & Tausch, 1996) was used. The PANAS consists of10 items each to assess positive and negative affect. The items represent the

6 H. M€UNCHOW AND M. BANNERT

individual’s level of different emotionally charged states. Instead of a 5-point Likert-scale a visual analogue scale (VAS) ranging from 0¼‘not at all’ to 100¼‘completely’was used in this study. A VAS was chosen because of the more fine-grained detectionof even small changes in the variables (e.g. Joyce, Zutshi, Hrubes, & Mason, 1975).Depending on the current instructions, the PANAS can measure trait as well as stateeffects. In this study, a state version of the PANAS was used. Cronbach’s a in thisexperiment was at least 0.87.

Figure 1. Screenshots of (A) the multimedia learning environment to elicit positive affect by theuse of bright colours, rounded shapes, and anthropomorphic elements, (B) affectively neutral coun-terpart using achromatic colours and sharp edges.

EDUCATIONAL PSYCHOLOGY 7

Achievement goal orientations

The learners’ achievement gal orientations were assessed using the student versionof the SELLMO questionnaire (Spinath, Stiensmeier-Pelster, Sch€one, & Dickh€auser,2002). The SELLMO has 31 items on four scales (mastery goal orientation, approachand avoidance performance goal orientation, and work avoidance). However, the workavoidance scale was not required in this study. Hence, the SELLMO in this study had23 items. Again, a VAS similar to the one used for the PANAS was applied to ensurean accurate measurement of even small changes. Internal consistencies in this experi-ment were at least 0.85.

Interest

To check if the treatment has falsely induced situational interest instead of an affect,the interest subscale from the FAM questionnaire (Rheinberg, Vollmeyer, & Burns,2001) was assessed. The scale assesses the learner’s estimation of the importance andvalue of an upcoming or completed task (e.g. ‘I would work on such a task in myspare time’). According to the authors, the FAM is sufficiently reliable as well as intern-ally and externally valid. However, in this study the item ‘I like this kind of puzzles’was changed to ‘I like this kind of tasks’. As recommended by the authors, anotheritem was excluded because it referred to the specific task learners had to perform inRheinberg et al. (2001) validation studies. The interest scale in this study, therefore,consisted of four items. Again, the original Likert-scale was substituted by a VAS.Sufficient values of Cronbach’s a¼ 0.80 were assessed.

Emotion regulation

The learners’ emotion regulation competencies were assessed using the SEK-27(Berking & Znoj, 2008). The 27 items of the questionnaire refer to different aspects ofemotion regulation including perceiving and recognizing affective states consciously,accepting and tolerating affective states generously, and supporting oneself actively inorder to allow the adaption of further emotion regulation strategies. A VAS similar tothe ones used for the other questionnaires in this study was applied. The mean scoreof the SEK-27 serves as an indicator of the one’s general emotion regulation compe-tencies (Berking & Znoj, 2008) and was calculated in this study. The internal consist-ency for this score was 0.92.

Learning outcomes

Based on Bloom’s taxonomy of educational objectives (e.g. Bloom, Engelhart, Furst,Hill, & Krathwohl, 1956), the performance test consisted of three parts: Recall, know-ledge, and transfer. Participants were asked to remember the technical terms thatwere written in the learning environment. The total count correctly remembered termswas calculated as a measure for recall. Knowledge was measured using the same 24multiple-choice items as in the prior knowledge test. Two items were excluded due tonegative item selectivity. The remaining 22 items were rated by trained student assis-tants (Cohen’s j¼ 0.74) and averaged to indicate knowledge performance.

8 H. M€UNCHOW AND M. BANNERT

Transfer was measured with four open questions. Answers were rated on a 4-stage rat-ing scheme ranging from zero (‘wrong/no answer’) to three points (‘completely correctanswer’) per question. Mean scores served as an indicator of transfer performance.Ratings between two trained raters were highly correlated (average interrater reliabilityGoodman and Kruskal’s G¼0.97). All items for measuring learning performance werebased on the written content of the learning environment. The contents of the imagesused in the learning environments were not taken into account in order to excludeany advantages due to the different appearance of the images.

Procedure

After being welcomed, participants were told about the duration and procedure ofthe experiment (see Figure 2). Participants were then seated in front of a computerand given screen instructions for the rest of the experiment. First, participants wereasked for their current affective states, certain demographic variables, achievementgoal orientations, emotion regulation competencies, and their prior knowledge aboutthe learning topic (t1). Afterwards (t2), affective states were measured again to ruleout effective changes due to the prior knowledge test. Moreover, interest was

Figure 2. Procedure of the experiment.

EDUCATIONAL PSYCHOLOGY 9

assessed for the first time. Immediately after this, the learning phase started.Participants were instructed to ‘read the content of the learning environments care-fully and to memorize as much as possible’. Affective states were measured for thethird time after 10min of learning (t3). Immediately after learning, affective states andinterest were measured for the least time (t4). Finally, the learning performance testswere given. Questionnaires were administered using the SoSciSurvey open sourcequestionnaire tool by Leiner (2014).

Results

IBM SPSS Statistics 22 was used for calculating. Unless otherwise stated a 5% signifi-cance level was used. Before testing, outliers were visually searched for using boxplotsand excluded when values were beneath or above three standard deviations from themedian. Furthermore, normal distribution was checked visually using histograms andstatistically looking at skewness and kurtosis and conducting Kolmogorov-Smirnovtests. Homogeneity of variances and covariances were tested by conducting Levenne’stests or Box’s M tests. All assumptions were met in the analyses unless other-wise stated.

Preliminary analyses

A one-way MANOVA with mastery, performance approach, performance avoidancegoal orientations and the emotion regulation score as dependent variables was con-ducted to test for systematic differences between conditions in levels of achievementgoal orientations and emotion regulation competency (see Table 1 for descriptive sta-tistics). There was a non-significant Omnibus test on the effect of condition, Pillai’sTrace V¼ 0.01, F(2,142)¼ 0.31, p¼ .871, hp

2¼ 0.01. Accordingly, there were no signifi-cant effects in any of the univariate ANOVAs. Hence, there were no differences in thelearners’ motivational traits or emotion regulation competencies between the two con-ditions. To test for changes between t1 and t2 and to check for group differences att1 and t2 a one-way mixed MANOVA on affective states as dependent variables andcondition as between-subjects factor as well as times of measurement as within-sub-jects factor (baseline measure, t1 vs. measurement before the learning phase, t2) wasconducted (see Table 1 for descriptive data). Pillai’s Trace was significant for the maineffect of time of measurement, V¼ 0.29, F(2,142)¼ 28.54, p< .001, hp

2¼ 0.29.Following univariate analyses revealed a significant main effect of time of measure-ment on positive activating effect, F(1,143)¼ 57.43, p< .001, hp

2¼0.29, whichdecreased from t1 to t2 regardless of the learners’ condition. However, because therewas no significant main effect of condition, V¼ 0.00, F(2,142)¼ 0.08, p¼ .927,hp

2¼0.00, and no interaction effect, V¼ 0.02, F(2,142)¼ 1.61, p¼ .204, hp2¼ 0.02, posi-

tive and negative affective states did not change differently between the two condi-tions. It was also checked for differences in demographic variables to ensure asuccessful randomization. Neither age, t(143)¼�1.64, p¼ 0.103, nor the distribution ofgender, v2(1, n¼ 145)¼ 0.00, p¼ .950, did significantly differ between thetwo conditions.

10 H. M€UNCHOW AND M. BANNERT

Table1.

Descriptivestatisticsforthefullsample(n¼145)

andexperim

entalcon

ditio

ns.

Num

berof

items

Mina

Max

a

Full

sample(n¼145)

CG(n¼60)

PAcond

ition

(n¼85)

Internalconsistenciesb

MSD

MSD

MSD

Positiveaffect

t110

16.80

187.40

50.17

15.01

50.64

14.94

49.78

15.15

0.88

b

t210

12.40

187.00

41.72

18.19

42.11

18.89

41.40

17.70

0.93

b

t310

11.70

184.80

40.45

20.51

38.90

20.56

41.71

20.51

0.95

b

t410

11.30

177.80

37.73

19.56

36.49

20.11

38.74

19.17

0.94

b

Negativeaffect

t110

10.00

168.60

15.16

12.01

16.29

14.64

14.24

09.53

0.87

b

t210

10.00

158.90

16.90

14.01

16.28

14.54

17.40

13.64

0.89

b

t310

10.00

162.20

12.83

12.26

13.66

13.84

12.15

10.84

0.89

b

t410

10.00

157.10

12.25

11.50

13.24

11.91

11.45

11.18

0.88

b

Learnertraits

Mastery

GO

834.38

100.00

78.91

13.25

78.75

13.89

79.05

12.80

0.87

b

Perf.av.GO

817.14

196.29

54.75

17.72

30.55

18.92

32.40

16.97

0.90

b

Perf.app

r.GO

710.00

186.35

31.57

17.83

54.70

18.63

54.79

17.06

0.84

b

Emotionreg.

2732.68

195.14

66.04

12.80

66.96

12.16

65.29

13.33

0.92

b

Interest

t24

10.00

190.75

33.91

18.13

34.90

18.41

33.11

19.98

0.80

b

t44

10.00

193.50

29.63

19.42

30.55

21.83

28.89

19.40

0.86

b

Learning

performance

Recallc

–12.00

129.00

13.86

16.32

15.54

6.51

13.30

6.14

–Priorknow

ledg

ed24

1110.42

111.25

11.89

1110.16

1110.89

010.16

1110.89

010.15

0.74

e

Know

ledg

ed22

1110.64

111.50

11.16

1110.17

01.16

010.17

01.17

010.16

0.72

e

Transfer

241110.50

112.75

11.11

1110.65

01.08

010.62

01.14

010.68

0.97

f

Note.CG

:Con

trol

grou

p;PA

:Positive

affect;t1:

baseline;t2:b

eforethelearning

phase;t3:after

halfof

thelearning

time;t4:after

thelearning

phase;GO:g

oalo

rientation;

Perf.av.:p

er-

form

ance

avoidance;

Perf.appr.:performance

approach;Em

otionreg.:em

otionregu

latio

n;a Using

visual

analog

uescales

from

0–100except

forperformance

indicators;bCron

bach’s

alph

a;c Num

berof

mem

orized

correcttechnicalterm

s;d3-stageratin

gschemefrom

0to

2po

ints

averaged

forallitems;

e InterraterreliabilityCo

hen’sKapp

a;f Interrater

reliability

Goodm

anandKruskal’s

gamma.

EDUCATIONAL PSYCHOLOGY 11

Changes in affective states

In order to examine the changes of affective states while learning another mixedMANOVA with positive and negative affect at t2, t3, and t4 as within-person factorand condition as between-person factor was calculated (descriptive statistics are foundin Table 1). Again, the Omnibus tests was significant for time of measurement,V¼ 0.32, F(4,140)¼ 16.48, p<.001, hp

2¼0.32. Inspecting univariate analyses, declineswere found for positive, F(1.45,207.18)¼ 16.48, p< .001, hp

2¼0.32, and negative acti-vating affect, F(1.43,204.19)¼ 16.48, p< .001, hp

2¼0.32. Because of violations of thetest assumption of sphericity, degrees of freedom were Greenhouse-Geisser adjusted(e< 0.75; see e.g. Barcikowski & Robey, 1984). Based on additional post-hoc tests usingthe Bonferroni correction, declines in positive affect occurred only in the second10min of learning (from t3 to t4; p< .001) but not in the first 10min of learning (fromt2 to t3; p¼ .743). Contrarily, pairwise comparisons revealed a significant reduction ofnegative affect from t2 to t3 (p< .001), but not from t3 to t4 (p¼ .560). However, therewas neither a significant main effect of condition, V ¼ 0.00, F(2,142)¼ 0.18, p ¼ .836,hp

2¼ 0.00, nor a significant interaction effect, V¼ 0.02, F(4,140)¼ 1.07, p¼ .375,hp

2¼ 0.03. Hence, positive affect was stable for participants in the PA condition duringthe first 10min of learning at least at descriptive levels. In contrast, positive affect inthe control condition continuously decreased while learning (see Figure 3a). Yet, thisinteraction failed statistical significance, F(1.45,207.18)¼ 1.49, p¼ .228, hp

2¼ 0.01.Negative activating affect continuously decreased from the first to the last time ofmeasurement in both conditions (see Figure 3b).

Similar to previous analyses, changes in the learners’ levels of interest were testedwith another mixed ANOVA. Interest at t2 and t4 was used as within-person factorand condition was used as between-person factor. It was found that interestdecreased from t2 to t4 in both condition. This main effect of time was statistically sig-nificant, F(1,143)¼ 19.90, p< .001, hp

2¼ 0.12. Again, no main effect of condition,F(1,143)¼ 0.33, p¼ .565, hp

2¼ 0.00, or a significant interaction effect, F(1,143)¼ 0.00,p¼ .949, hp

2¼ 0.00, were found. Accordingly, there were no differences in the changeof interest between conditions. Hence, interest was not unintentionally influenced bythe emotional design procedure.

Figure 3. Scores of (A) positive and (B) negative affect using the PANAS scales for the treatmentconditions, PA¼ positive affect; t2¼ before the learning phase; t3¼ after half of the learning time;t4¼ after the learning phase.

12 H. M€UNCHOW AND M. BANNERT

Learning performance and its relation to affect

A stepwise multiple regression approach was used to test for differences in learningperformance. First, the learners’ condition (PA vs. CG) was dummy-coded and enteredas a predictor into the regression model to test for treatment effects (condition onlymodel) with the control condition being the reference condition. Second, positive andnegative affective states prior to the learning phase were included as additional pre-dictors (main effects model). Next, the interaction model added interaction terms ofcondition and initial affective states into the regression model. Each performance indi-cator was tested in a separate model to enter covariates as predictors in the regres-sion model (see Cohen, Cohen, West, & Aiken, 2003; Hayes, 2013). It was furtherchecked if the externally studentized residuals were normally distributed and if varian-ces were homogeneous using scatterplots, histograms, and Q-Q plots before testing(Eid, Gollwitzer, & Schmitt, 2015). Moreover, it was checked if there were no toleranceshigher than .10 in order to rule out collinearity between predictors. Table 1 showsmeans and standard deviations for learning performance measures. Table 2 shows thebeta weights for all measures.

Affect and recall

For recall, condition did not significantly predict knowledge of the learning material inthe condition only model, R2¼ 0.01, F(1,143)¼ 1.38, p¼ .242, f2¼ 0.01. Adding positiveand negative affective states prior to the learning phase into the model (main effectsmodel) did not significantly increase recall performance, DR2¼ 0.01, F(2,141)¼ 0.94,p¼ .394, R2¼ 0.02, F(3,141)¼ 1.09, p¼ .357, f2¼ 0.02. Entering first order interactionsbetween condition and initial affective states as predictors did not significantlyenhance the amount of explained variance as well, DR2¼ 0.02, F(2,139)¼ 1.33,p¼ .267. This interaction model did not predict recall performance significantly,R2¼ 0.04, F(5,139)¼ 1.19, p¼ .318, f2¼ 0.04. The interaction between positive and

Table 2. Regression of Performance on Condition and Affective States Measured beforeLearning (n¼ 145)

Recall Knowledge Transfer

Predictors b t p b t p b t p

Condition only modelIntercept 14.538 18.58 <.001 1.162 56.73 <.001 1.085 13.36 <.001Condition �1.238 �1.18 .242 0.012 0.043 .670 0.054 0.50 .619

Main effects modelIntercept 14.561 18.59 <.001 1.161 57.02 <.001 1.084 13.67 <.001Condition �1.224 �1.16 .248 0.014 0.51 .610 0.064 0.05 .548Positive affect 0.039 1.36 .177 0.001 1.15 .251 0.008 2.73 .007Negative affect 0.012 0.32 .748 �0.001 �1.50 .135 �0.004 �0.08 .334

Interaction modelIntercept 14.510 18.55 <.001 1.161 57.09 <.001 1.085 13.68 <.001Condition �1.163 �1.10 .272 0.015 0.04 .595 0.069 0.11 .520Positive affect 0.018 0.42 .673 0.000 �0.10 .924 0.003 0.09 .423Negative affect �0.043 �0.79 .433 0.000 �0.31 .756 �0.002 �0.04 .748Condition x positive affect 0.049 0.84 .401 0.002 1.17 .243 0.009 0.18 .138Condition x negative affect 0.111 1.47 .144 �0.002 �0.93 .357 �0.003 �0.04 .711

Note. Condition was dummy-coded using the neutral affect condition as the reference group. Positive and negativeaffect were centered.

EDUCATIONAL PSYCHOLOGY 13

negative activating affect as well as second order interactions were checked in orderto check for misspecification of the model. However, none of these interactions was asignificant predictor of recall.

Affect and knowledge

Analogous to recall, the condition only model did not predict knowledge performancesignificantly, R2¼ 0.00, F(1,143)¼ 0.18, p¼ .670, f2¼ 0.00. Furthermore, the proportionof variance explained by the regression model did not significantly increase when add-ing positive and negative affective prior to learning as covariates, DR2¼ 0.03,F(2,141)¼ 1.98, p¼ .139. This main effects model was not significant, R2¼ 0.03,F(3,141)¼ 1.38, p¼ .251, f2¼ 0.03. Again, the predictive power of the model did notincrease when first order interactions of condition and affective states prior to thelearning phase were added as predictors, DR2¼ 0.02, F(2,139)¼ 1.22, p¼ .298. Thus,the interaction model was not significant for knowledge, F(5,139)¼ 1.32, p¼ .259,f2¼ 0.05. For the sake of clarity, interaction effects between positive and negative acti-vating affects as well as second order interactions were tested. However, neither ofthem was a significant predictor of knowledge performance.

Affect and transfer

As before, the condition only model did not predict transfer performance, R2¼ 0.00,F(1,143)¼ 0.25, p¼ .619, f2¼ 0.05. When adding positive and negative affective statesprior to learning as covariates the model was significant and explained R2¼ 0.06 ofthe variance, F(3,141)¼ 3.07, p< .05, f2¼ 0.06. The increase in predictive power of theregression model was significant as well, DR2¼ 0.06, F(2,141)¼ 4.48, p¼ .013. An ana-lysis of regression coefficients in Table 2 indicates that positive activating affect signifi-cantly predicted transfer performance, i.e. the higher the positive activating affectbefore learning the better the learning outcomes in transfer, b¼ 0.008, t¼ 2.73,p< .05. However, taking first order interactions between condition and positive andnegative affective states into consideration, the interaction model did not increase thepredictive power of the regression model any further, DR2¼ 0.07, F(2,139)¼ 1.24,p¼ .293. This interaction model explained a significant amount of variance, R2¼ 0.08,F(5,139)¼ 2.35, p< .05, f2¼ 0.09. Nevertheless, none of the predictors was significant(see Table 2). As before, it was checked for second order interaction effects and inter-actions between positive and negative affect as well as interest prior to the learningphase. Neither of these regression models significantly predicted transfer performance.

Discussion

Previous studies on emotional design have found complex interaction effects betweeninduced positive activating affect and affective states before a certain period of deeplearning (e.g. M€unchow et al., 2017). The present study aimed to replicate and extendthese findings. In general, these assumptions were not confirmed. Despite there wasno interaction effect, a main effect of positive activating affect on transfer perform-ance was found. This effect has been reported in several correlation studies and

14 H. M€UNCHOW AND M. BANNERT

studies on emotional designing (e.g. Heidig et al., 2015). Moreover, the results of thepresent study indicate that the control variables that were measured, that is, thelearners’ differences in achievement motivation and emotion regulation competencies,did not influence the dependent variables. Moreover, an unintended induction ofinterest rather than positive affect is unlikely.

Differences between experimental conditions

Changes in affective states

This experiment’s emotional design elements (bright and highly saturated colours,rounded shapes, and anthropomorphic figures) were adopted from other studieswhich used them successfully (Plass et al., 2014). It was expected that positive affectat t3 and t4 was higher in the PA condition compared to the CG because of the emo-tional design intervention. Findings did not finally support these assumptions.However, there was a tendency of higher levels of positive activating affect after10min of learning in the PA condition at descriptive level. Albeit this effect did notmaintain over the second 10min of learning, positive affect on average was still higherin the PA condition. Contrarily, participants in the CG reported decreasing levels ofpositive affect from t2 to t4. Because this interaction was not significant, this study’sprimary results contrast initial findings of Um et al. (2012). Again, the present findingsare in line with several other studies that could not clearly enhance positive activatingaffect using an emotional design paradigm (e.g. Park et al., 2015). Furthermore, thepresent study negated the influence of confounding of interest, achievement goal ori-entations, and emotion regulation competencies that were associated with learningrelated positive activating affect.

Learning performance

There was no interaction effect of affective states before learning on learning perform-ance, which was contrary to the assumptions. Moreover, recall and knowledge werenot significantly predicted by either condition or affective states before learning.However, similar to various previous findings (e.g. Isen et al., 1987; Um et al., 2012) asignificant main effect of positive affect on transfer was found in the present experi-ment. Results that indicate negative influences of positive activating affect on learningoutcomes (e.g. Kn€orzer et al., 2016) could not be supported. Yet, contrary to otherstudies (e.g. M€unchow et al., 2017), there was no significant interaction effect in trans-fer learning despite the present study used an experimental design as well as an affectinduction procedure that was similar to previous studies. Moreover, the content of thelearning tests was based completely on the written information of the learning envi-ronments and it was ensured that the different appearance of the learning environ-ments’ images did not influence test performance. Like most of the previous works,the learning topic was derived from the field of biology. It is therefore unlikely toassume that the non-significant findings of the present experiment occurred becauseof different or inappropriate operationalisations.

EDUCATIONAL PSYCHOLOGY 15

Moreover, the measures that were used in this study were sufficiently reliable.Some of the instruments were even identical to those used in previous studies (e.g.the PANAS). It is therefore unlikely that lack of reliability or missing validity of theinstruments was a reason for the non-significant findings. Furthermore, several add-itional control variables including achievement goal orientations and emotion regula-tion were taken into consideration. The present data does not favour any confoundingeffects caused by these variables. Finally, a sample of nearly 150 participants is com-paratively high for an experimental study on learning performance. In fact, a priori cal-culations of the required sample size that were done with G�Power 3.1 (Faul,Erdfelder, Buchner, & Lang, 2009) showed that a total sample of 92 participants wouldhave been needed to detect similar sized moderation effects to those obtained byM€unchow et al. (2017). However, we found much smaller effect sizes in the presentstudy. Summing up, there are no compelling reasons to doubt the presented results.

Implications and limitations

Previous findings could not completely be replicated in the present experiment.However, due to the recurring main effect of positive activating affect on transfer per-formance it is suitable to assume that high positive activating affect can foster realworld learning situations in which learners have to apply knowledge into new con-texts. Furthermore, positive affect was stable when applying an emotional design pro-cedure at least for the first minutes of learning. Albeit there is a clear necessity toconduct more research, this may indicate that an emotional design procedure can pro-tect against declines in positive affect if properly applied and in small doses.

Similar to most works on emotional design in multimedia learning, self-report ques-tionnaires were used to assess affective states. However, affective experiences areformed by affective, motivational, cognitive, peripheral physiological, and expressivecomponents (Scherer, 1984; Shuman & Scherer, 2014) that have not been covered suf-ficiently in this experiment’s measures. This experiment measured affective states forthe first time not only before and after the learning phase but also in between.Nevertheless, questionnaires in general are vulnerable to distortions due to false mem-ories or social desirability. Moreover, conclusions on the dynamic changes in affectiveexperiences that occur during learning (see e.g. Graesser & D’Mello, 2012) are limited.Future research should collect data from different data sources such as behavioural orperipheral-physiological measures. One first attempt was done by Park and colleagues(Park et al., 2015) who found positive relationships between positive affect and read-ing fixation times using eye-tracking measures.

Again, Elliot and Maier (Elliot & Maier, 2007) doubt that colours of greater wave-lengths truly elicit physiological arousal. Accordingly, Mehta and Zhu (2009) foundthat colours of shorter wavelengths (in particular, colours of blue hue) increase cogni-tive creativity. Another important aspect includes the learners colour preferences,which could have influenced the way the learning environment was perceived andinterpreted by the learners. Hence, more studies on effects of different colours andthe role of individual colour preferences are needed (see also Heidig et al., 2015).

16 H. M€UNCHOW AND M. BANNERT

Conclusions

This study found a recurring main effect of positive affect on transfer, which indicatesa positive relationship between positive activating affect and performance.Nevertheless, research on emotional design paradigms so far has generated ratherinconsistent results. Accordingly, there are several studies reporting findings that arecontradictory to the initial results from Um et al. (2012). The present study thereforeranks amongst the variety of different results on the same matter. Several explanationswhy applying an emotional design procedure did not increase learning performancehave been addressed: Possible influences of theoretically as well as empirically derivedcovariates was controlled, an additional measurement of affective states in order toincrease the temporal resolution of the affect assessment was implemented, and itwas checked that the treatment did not falsely elicit interest. However, because noneof these alternative explanations could be verified, a valid reason for the heterogeneityof the empirical findings is still missing. Hence, it is rather unlikely that inducing posi-tive activating affect impairs learning outcome in shorter learning settings becausemost of the studies on emotional design do not find a negative relationship betweenpositive affect and learning performance. Accordingly, when it comes to practicalterms, emotional design is not harmful in the worst case while it can enhance learningoutcome at best.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Hannes M€unchow http://orcid.org/0000-0002-5393-534XMaria Bannert http://orcid.org/0000-0001-7045-2764

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