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Information about what visual elements in instructional animations enhance learning via mediating effects of elevated engagement is largely lacking. In this study, high school students (n = 41) interacted in a laboratory with a roughly 6-minute-long, black-and-white, instructional animation with "emotionally" enhanced graphics. The topic was biological wastewater treatment. The enhancements included a) adding static faces for two schematic visual elements and b) changing the neutral appearance of a fish and a river bed to funny appearances. The participants' learning outcomes (assessed by retention and transfer tests) and the participants' state engagement (indexed by generalized positive affect and flow levels) were compared to data from a group of comparable students interacting with the same animation with neutral graphics (n = 37) in a previous study. The two groups did not differ in state engagement (flow: d = -0.16; positive affect: d = 0.03) and transfer test scores (d = 0.13), but there was a small trend favoring the enhanced graphics in the retention test, when corrected for pretest scores (η2 = 0.038; p = .095). Qualitative data suggest that the graphical enhancements might serve as memory cues during the test phase for some participants. The small effect of the enhanced graphics on retention may thus be of cognitive, rather than of affective, origin. This study demonstrates the importance of considering emotional manipulations in future research. [ABSTRACT FROM AUTHOR]

Computerized animations, simulations and games are useful tools for supporting acquisition of mental models. Various personal characteristics, such as prior knowledge and spatial abilities, can influence, in various ways, effectivity of learning from these materials. In comparative studies with between-subject design that investigate learning effects of these materials, it is important to control for these variables because they should be taken as covariates in case the two (or more) research groups are not sampled equally (which happens even in the case of random assignment of participants to the research groups). In addition, it would be useful to have interventions that measure these variables with as few items as possible; to avoid unbearably long questionnaires. In this initial exploratory study we investigate if mathematical selfefficacy, measured by a single question, and self-assessed ability of acquiring mental models (SAAMM), also measured by a single question, predicts learning outcomes; as concerns mental models acquisition. Re-analyzing data from our four recent studies on one of the well-known principles of multimedia learning, personalization principle (N = 75, 85, 76, 41; college students with diverse background), we show that mathematical self-efficacy and SAAMM are moderately correlated (r = .32 - .40) and indeed related to learning outcomes, measured by transfer tests (r = .22 - .57 and .28 - .48, respectively). However, the reasons behind these relationships seem to be complex and diverse, and at least partly dependent on treatments' characteristics. For a complex simulation using graphs and resembling an educational computer game, this relationship can be, to a large extent, explained by mutual relationships between graphing skills, frequency of game-playing, mathematical self-efficacy, SAAMM, and learning outcomes. For a short animation on an electrophysical topic, it can be explained by mutual links between prior electrophysical knowledge, mathematical self-efficacy, SAAMM, and learning outcomes. Only for a short animation on a math/physics-unrelated topic, we could not explain the relationship between mathematical selfefficacy, SAAMM, and learning outcomes by a third variable (however, the graphing test was not administered in this case). In general, this study indicates that our two questions for assessing mathematical self-efficacy and SAAMM are promising instruments for measuring variables that should be controlled for in studies on learning effects of computerized materials with between-subject design, but more research is needed to pin down details. [ABSTRACT FROM AUTHOR]