The purpose of the two experiments was to investigate the potential effects of different types of visualizations and self-explanation prompts on learning human cardiovascular system in a multimedia environment. In Experiments 1 and 2, 70 and 44 college students were randomly assigned to one of the four conditions in a 2 × 2 factorial design with visualization type (animated vs. static) and prompting self-explanations (prompts vs. none) as the between subjects factors. The results of Experiment 1 revealed a positive effect of prompts on learning outcomes. Experiment 2, in which the no-prompts conditions were re-designed, partially replicated the results of Experiment 1. In addition, it revealed a significant interaction between the two factors on learning time, learning outcomes, and goal-driven self-explanations. The results of both experiments suggest that instructional designers should pay attention to learners' cognitive aspects, and utilize the combination of animated visualizations and self-explanation prompts when designing multimedia environments.
Get full access to this article
View all access options for this article.
References
1.
AlevenV. A.KoedingerK. R. (2002). An effective metacognitive strategy: Learning by doing and explaining with a computer-based cognitive tutor. Cognitive Science, 26, 147–179.
2.
ArguelA.JametE. (2009). Using video and static pictures to improve learning of procedural contents. Computers in Human Behavior, 25(2), 354–359.
3.
AtkinsonR. K.RenklA.MerrillM. M. (2003). Transitioning from studying examples to solving problems: Effects of self-explanation prompts and fading worked-out steps. Journal of Educational Psychology, 95(4), 774–783.
4.
AyresP.MarcusN.ChanC.QianN. (2009). Learning hand manipulative tasks: When instructional animations are superior to equivalent static representations. Computers in Human Behavior, 25(2), 348–353.
5.
BertholdK.EysinkT.RenklA. (2009). Assisting self-explanation prompts are more effective than open prompts when learning with multiple representations. Instructional Science, 37(4), 345–363.
6.
BertholdK.RenklA. (2009). Instructional aids to support a conceptual understanding of multiple representations. Journal of Educational Psychology, 101(1), 70–87.
7.
BoucheixJ.SchneiderE. (2009). Static and animated presentations in learning dynamic mechanical systems. Learning and Instruction, 19(2), 112–127.
8.
CatramboneR.SeayA. F. (2002). Using animation to help students learn computer algorithms. Human Factors, 44(3), 495–511.
CohenJ. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.
11.
de KoningB. B.TabbersH. K.RikersR. M. J. P.PaasF. (2010a). Improved effectiveness of cueing by self-explanations when learning from a complex animation. Applied Cognitive Psychology, 25, 183–194.
12.
de KoningB. B.TabbersH. K.RikersR. M. J. P.PaasF. (2010b). Learning by generating vs. receiving instructional explanations: Two approaches to enhance attention cueing in animations. Computers & Education, 55(2), 681–691.
13.
DunsworthQ.AtkinsonR. K. (2007). Fostering multimedia learning of science: Exploring the role of an animated agent's image. Computers & Education, 49(3), 677–690.
14.
GerjetsP.ScheiterK.CatramboneR. (2004). Designing instructional examples to reduce intrinsic cognitive load: Molar versus modular presentation of solution procedures. Instructional Science, 32(1–2), 33–58.
15.
GerjetsP.ScheiterK.CatramboneR. (2006). Can learning from molar and modular worked examples be enhanced by providing instructional explanations and prompting self-explanations?Learning and Instruction, 16(2), 104–121.
16.
GroßeC. S.RenklA. (2007). Finding and fixing errors in worked examples: Can this foster learning outcomes?Learning and Instruction, 17(6), 612–634.
17.
HartS. G.StavelandL. E. (1988). Development of NASA-TLX (task load index): Results of experimental and theoretical research. In HancockP. A.MeshkatiN. (Eds.), Human mental workload (pp. 139–183). Amsterdam, Holland: North-Holland.
18.
HegartyM. (1992). Mental animation: Inferring motion from static displays of mechanical systems. Journal of Experimental Psychology: Learning, Memory, and Cognition, 18(5), 1084–1102.
19.
HegartyM. (2004). Dynamic visualizations and learning: Getting to the difficult questions. Learning and Instruction, 14(3), 343–351.
20.
HegartyM.KrizS.CateC. (2003). The roles of mental animations and external animations in understanding mechanical systems. Cognition and Instruction, 21(4), 325–360.
21.
KimS.YoonM.WhangS. M.TverskyB.MorrisonJ. B. (2007). The effect of animation on comprehension and interest. Journal of Computer Assisted Learning, 23(3), 260–270.
22.
KozmaR. B. (1994). Will media influence learning? Reframing the debate. Educational Technology Research and Development, 42, 7–19.
23.
LaiS. (2000). Increasing associative learning of abstract concepts through audiovisual redundancy. Journal of Educational Computing Research, 23(3), 275–289.
24.
LargeA.BeheshtiJ.BreuleuxA.RenaudA. (1996). Effect of animation in enhancing descriptive and procedural texts in a multimedia learning environment. Journal of the American Society for Information Science, 47(6), 437–448.
25.
LinL.AtkinsonR. K. (2011). Using animations and visual cueing to support learning of scientific concepts and processes. Computers & Education, 56(3), 650–658.
26.
MayerR. E. (2010). Unique contributions of eye-tracking research to the study of leaning with graphics. Learning and Instruction, 20, 167–171.
27.
MayerR. E.DeleeuwK. E.AyresP. (2007). Creating retroactive and proactive interference in multimedia learning. Applied Cognitive Psychology. 21(6), 795–809.
28.
MayerR. E.DowG. T.MayerS. (2003). Multimedia learning in an interactive self-explaining environment: What works in the design of agent-based microworlds?Journal of Educational Psychology, 95(4), 806–812.
29.
MayerR. E.HegartyM.MayerS.CampbellJ. (2005). When static media promote active learning: Annotated illustrations versus narrated animations in multimedia instruction. Journal of Experimental Psychology: Applied, 11(4), 256–265.
30.
MichasI. C.BerryD. C. (2000). Learning a procedural task: Effectiveness of multimedia presentations. Applied Cognitive Psychology, 14(6), 555–575.
MünzerS.SeufertT.BrünkenR. (2009). Learning from multimedia presentations: Facilitation function of animations and spatial abilities. Learning and Individual Differences, 19(4), 481–485.
33.
PaasF.RenklA.SwellerJ. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38(1), 1–4.
34.
ParkO. C.GittelmanS. S. (1992). Selective use of animation and feedback in computer-based instruction. Educational Technology, Research, and Development, 40, 27–38.
35.
RenklA.AtkinsonR. K. (2002). Learning from examples: Fostering self-explanations in computer-based learning environments. Interactive Learning Environments, 10, 105–119.
36.
RieberL. P. (1990). Using computer animated graphics with science instruction with children. Journal of Educational Psychology, 82(1), 135–140.
37.
RoyM.ChiM. T. H. (2005). The self-explanation principle in multimedia learning. MayerR. E. (Ed.) (pp. 271–286). New York, NY: Cambridge University Press.
38.
RyanR. M. (1982). Control and information in the intrapersonal sphere: An extension of cognitive evaluation theory. Journal of Personality and Social Psychology, 43, 450–461.
39.
ScheiterK.GerjetsP.CatramboneR. (2006). Making the abstract concrete: Visualizing mathematical solution procedures. Computers in Human Behavior, 22(1), 9–25.
40.
SchnotzW.KurschnerC. (2007). A reconsideration of cognitive load theory. Educational Psychology Review, 19(4), 469–508.
41.
SwellerJ.van MerrienboerJ. J. G.PaasF. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251–296.
42.
ThompsonS. V.RidingR. J. (1990). The effect of animated diagrams on the understanding of a mathematical demonstration in 11- to 14-year-old pupils. British Journal of Educational Psychology, 60, 93–98.
43.
TverskyB.MorrisonJ. B.BetrancourtM. (2002). Animation: Can it facilitate?International Journal of Human-Computer Studies, 57(4), 247–262.
44.
WongA.MarcusN.AyresP.SmithL.CooperG. A.PaasF. (2009). Instructional animations can be superior to statics when learning human motor skills. Computers in Human Behavior, 25(2), 339–347.
45.
YangE.AndreT.GreenboweT. J. (2003). Spatial ability and the impact of visualization/animation on learning electrochemistry. International Journal of Science Education, 25(3), 329.
