The Better You Feel the Better You Learn: Do Warm Colours and Rounded Shapes Enhance Learning Outcome in Multimedia Learning?

Table 2

Regression of performance after learning (i.e., comprehension and transfer) on condition and positive and negative affect measured before learning .

Predictors	Comprehension			Transfer
	b	t	p	b	t	p
Condition only model
Intercept	6.480	22.24	<.001	4.020	25.11	<.001
Condition	0.307	0.78	.437	0.390	1.81	.074
Main effects model
Intercept	6.567	23.55	<.001	4.051	25.50	<.001
Condition	0.149	0.40	.693	0.333	1.55	.124
Positive affect	0.035	2.75	.007	0.009	−1.17	.246
Negative affect	−0.038	−2.15	.034	−0.017	−1.74	.085
Interaction model
Intercept	6.633	24.11	<.001	4.040	25.30	<.001
Condition	0.129	.35	.729	0.336	1.56	.121
Positive affect	0.049	2.51	.013	−0.003	−0.31	.760
Negative affect	−0.078	−2.98	.004	−0.019	−1.25	.216
Condition × positive affect	−0.027	−1.05	.297	0.021	1.38	.169
Condition × negative affect	0.074	2.12	.036	0.001	0.07	.946
Final model
Intercept	6.619	24.08	<.001	4.055	26.14	<.001
Condition	0.133	.36	.721	0.330	1.58	.118
Positive affect	0.034	2.66	.009	−0.012	−1.01	.317
Negative affect	−0.081	−3.11	.002	−0.011	−1.03	.305
Condition × negative affect	0.077	2.22	.029	—	—	—
Condition × positive affect	—	—	—	0.032	2.11	.038
Positive × negative affect	—	—	—	0.002	2.40	.018

Note. Condition was dummy-coded using the neutral affect condition as the reference group. Positive and negative affect were centered. Dashes are indicating that the predictor was not entered into the regression model.