MATHEMATICAL REASONING WITH ANIMATED
WORKED-OUT EXAMPLES
Alexander Salle
Bielefeld University (Germany)
This paper examines how the processing of interactive and animated worked-out
examples in the domain of fractions is affected by the application of self-explanation
prompts. 56 German sixth-graders were videotaped while they were working pairwise
in an individual learning environment and especially while they were processing the
worked-out examples. In contrast to previous research papers that focus on learning
strategies and learning success, the present work analyses the occurrence of
self-explanations and mathematical reasoning activities. First results reveal that
working with animated worked-out examples elicits spontaneous mathematical
reasoning activities. In particular, self-explanation prompts amplify this connection.
THEORETICAL FRAMEWORK
Animated worked-out examples
Worked-out examples consist of a task and its solution. Many researchers showed the
benefits of those examples in the early knowledge acquisition-phase in structural domains
like mathematics (e.g. Sweller et al. 1985). In contrast, worked-out examples
lose their effectiveness with increasing expertise (Kalyuga et al. 2003).
The results of studies examining animated and interactive worked-out examples
however are controversial and varying from context to context (Betrancourt 2005). A
meta-review of Tversky et al. (2002) describes the benefits of animations only in concrete
domains where phenomena are observable. Scheiter et al. (2009) argue that also
in abstract domains like mathematics animations can have positive effects.
The construction of worked-out examples is a crucial factor for their helpfulness in
learning activities. A lot of study results provide guidelines for the construction of
worked-out examples. Most of their arguments are based on Cognitive Load Theory
(Sweller et al. 1998).
Apart from those guidelines, the mathematical contents of the examples often remain
on a calculatory level. Many studies use algebra and probability theory examples (e.g.
Sweller 1985, Große 2005 etc.). Furthermore a lot of studies do not publish the used
worked-out examples although their construction could have huge influence on future
empirical studies (Betrancourt 2005).
Self-Explanations
Another crucial point for the effectiveness of worked-out examples is the learner’s
activity. Chi et al. (1989) demonstrate that students who actively process the example
by combining information within the material, filling in missing information or
comparing new information with existing knowledge have a higher learning outcome
than students who do not show such self-explanations. However, spontaneous
self-explanations occur rarely and often worked-out examples are processed passively
MATHEMATICAL REASONING WITH ANIMATEDWORKED-OUT EXAMPLESAlexander SalleBielefeld University (Germany)This paper examines how the processing of interactive and animated worked-outexamples in the domain of fractions is affected by the application of self-explanationprompts. 56 German sixth-graders were videotaped while they were working pairwisein an individual learning environment and especially while they were processing theworked-out examples. In contrast to previous research papers that focus on learningstrategies and learning success, the present work analyses the occurrence ofself-explanations and mathematical reasoning activities. First results reveal thatworking with animated worked-out examples elicits spontaneous mathematicalreasoning activities. In particular, self-explanation prompts amplify this connection.THEORETICAL FRAMEWORKAnimated worked-out examplesWorked-out examples consist of a task and its solution. Many researchers showed thebenefits of those examples in the early knowledge acquisition-phase in structural domainslike mathematics (e.g. Sweller et al. 1985). In contrast, worked-out exampleslose their effectiveness with increasing expertise (Kalyuga et al. 2003).The results of studies examining animated and interactive worked-out exampleshowever are controversial and varying from context to context (Betrancourt 2005). Ameta-review of Tversky et al. (2002) describes the benefits of animations only in concretedomains where phenomena are observable. Scheiter et al. (2009) argue that also
in abstract domains like mathematics animations can have positive effects.
The construction of worked-out examples is a crucial factor for their helpfulness in
learning activities. A lot of study results provide guidelines for the construction of
worked-out examples. Most of their arguments are based on Cognitive Load Theory
(Sweller et al. 1998).
Apart from those guidelines, the mathematical contents of the examples often remain
on a calculatory level. Many studies use algebra and probability theory examples (e.g.
Sweller 1985, Große 2005 etc.). Furthermore a lot of studies do not publish the used
worked-out examples although their construction could have huge influence on future
empirical studies (Betrancourt 2005).
Self-Explanations
Another crucial point for the effectiveness of worked-out examples is the learner’s
activity. Chi et al. (1989) demonstrate that students who actively process the example
by combining information within the material, filling in missing information or
comparing new information with existing knowledge have a higher learning outcome
than students who do not show such self-explanations. However, spontaneous
self-explanations occur rarely and often worked-out examples are processed passively
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