Skip to main navigation Skip to search Skip to main content

Why traditional expository teaching-learning approaches may founder? An experimental examination of neural networks in biology learning

  • Jun Ki Lee
  • , Yong Ju Kwon*
  • *Corresponding author for this work
  • Korea National University of Education
  • Massachusetts General Hospital

Research output: Contribution to journalJournal articlepeer-review

Abstract

Using functional magnetic resonance imaging (fMRI), this study investigates and discusses neurological explanations for, and the educational implications of, the neural network activations involved in hypothesis-generating and hypothesis-understanding for biology education. Two sets of task paradigms about biological phenomena were designed: hypothesis-generating and hypothesis-understanding, and 60 healthy participants performed the tasks. fMRI results showed that participants utilised different neural networks during hypothesis-generating and hypothesis-understanding, and these networks appeared to have specialised patterns. In other words, these two types of learning strategies related to hypotheses do not share the same brain region or network. Therefore, for biology learning, it might be concluded that hypothesis- generating and hypothesis-understanding are operating at differing neural network levels, which causes the difficulty students have generating hypotheses about complicated natural phenomena despite a huge amount of exposure to hypothesis-understanding during their learning.

Original languageEnglish
Pages (from-to)83-92
Number of pages10
JournalJournal of Biological Education
Volume45
Issue number2
DOIs
StatePublished - 2011.05.10

Keywords

  • Biology learning
  • FMRI
  • Hhypothesis-generating
  • Learning strategy
  • Neural network

Quacquarelli Symonds(QS) Subject Topics

  • Agriculture & Forestry
  • Education & Training

Fingerprint

Dive into the research topics of 'Why traditional expository teaching-learning approaches may founder? An experimental examination of neural networks in biology learning'. Together they form a unique fingerprint.

Cite this