The incubation effect among students playing an educational game for physics

The incubation effect (IE) is a problem-solving phenomenon composed of three phases: pre-incubation where one fails to solve a problem; incubation, a momentary break where time is spent away from the unsolved problem; and post-incubation where the unsolved problem is revisited and solved. Literature...

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Vydáno v:Research and practice in technology enhanced learning Ročník 16; číslo 1; s. 1 - 21
Hlavní autoři: Talandron-Felipe, May Marie P., Rodrigo, Ma. Mercedes T.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Singapore Springer Singapore 07.07.2021
Springer
Asia-Pacific Society for Computers in Education (APSCE)
The Asia-Pacific Society for Computers in Education (APSCE)
Témata:
CAI
Clustering
Computer assisted instruction
Computer-based learning environment
Education
Educational Games
Educational Technology
Electronic Learning
Incubation effect
Intermittent conscious work
Learning
Learning and Instruction
Learning Processes
Literature reviews
Long Term Memory
LSTM
Physics
Physics Playground
Playgrounds
Problem Solving
Regression models
School environment
Short Term Memory
Students
Physics Playground
Computer-based learning environment
LSTM
Intermittent conscious work
Incubation effect
ISSN:1793-7078, 1793-2068, 1793-7078
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Shrnutí:The incubation effect (IE) is a problem-solving phenomenon composed of three phases: pre-incubation where one fails to solve a problem; incubation, a momentary break where time is spent away from the unsolved problem; and post-incubation where the unsolved problem is revisited and solved. Literature on IE was limited to experiments involving traditional classroom activities. This initial investigation showed evidence of IE instances in a computer-based learning environment. This paper consolidates the studies on IE among students playing an educational game called Physics Playground and presents further analysis to examine the incidence of post-incubation or the revisit to a previously unsolved problem. Prior work, which focused on predicting successful outcomes, includes a coarse-grained IE model developed with logistic regression on aggregated data and an improved model which leveraged long short-term memory (LSTM) combined with dimensionality reduction visualization technique and clustering on fine-grained data. The additional analysis which aims to understand factors that may trigger the post-incubation phase also used fine-grained data and LSTM to create a revisit model. Results show that time elapsed relative to the activity period and encountering a problem with a similar solution during incubation were possible factors in revisiting previously unsolved problems.
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ISSN:1793-7078
1793-2068
1793-7078
DOI:10.1186/s41039-021-00171-x