Investigating children's deep learning of the tree life cycle using mobile technologies

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

This study investigates children's problem-solving activities during mobile learning in an outdoor summer camp setting. We designed a mobile application to support children on trails at a nature center to apply strategies for decision making about tree life cycles. We analyzed video records of 10 groups (9 dyads and 1 triad) of children (ages 9–12) using primarily a thematic qualitative analysis of learning episodes. We analyzed how children used problem-solving strategies to identify and capture the tree cycle with the help of mobile tablets. We found that our mobile learning experience and its external representations supported the following: (1) engagement in deep learning in the natural setting as evidenced by coordinating decisions with photographic evidence; (2) use of procedural or tactical strategies to approach the problem; and (3) use of real-time decision making strategies about tree life cycles.

Original languageEnglish (US)
Pages (from-to)470-479
Number of pages10
JournalComputers in Human Behavior
Volume87
DOIs
StatePublished - Oct 1 2018

Fingerprint

Life Cycle Stages
Life cycle
Decision making
Learning
Technology
Decision Making
Mobile Applications
Tablets
Deep learning
Life Cycle
Mobile Technology
Problem Solving
Mobile Learning

All Science Journal Classification (ASJC) codes

  • Arts and Humanities (miscellaneous)
  • Human-Computer Interaction
  • Psychology(all)

Cite this

@article{39465d4ef29d4b67bae57e7f37048bbe,
title = "Investigating children's deep learning of the tree life cycle using mobile technologies",
abstract = "This study investigates children's problem-solving activities during mobile learning in an outdoor summer camp setting. We designed a mobile application to support children on trails at a nature center to apply strategies for decision making about tree life cycles. We analyzed video records of 10 groups (9 dyads and 1 triad) of children (ages 9–12) using primarily a thematic qualitative analysis of learning episodes. We analyzed how children used problem-solving strategies to identify and capture the tree cycle with the help of mobile tablets. We found that our mobile learning experience and its external representations supported the following: (1) engagement in deep learning in the natural setting as evidenced by coordinating decisions with photographic evidence; (2) use of procedural or tactical strategies to approach the problem; and (3) use of real-time decision making strategies about tree life cycles.",
author = "Choi, {Gi Woong} and Land, {Susan Mary} and Heather Zimmerman",
year = "2018",
month = "10",
day = "1",
doi = "10.1016/j.chb.2018.04.020",
language = "English (US)",
volume = "87",
pages = "470--479",
journal = "Computers in Human Behavior",
issn = "0747-5632",
publisher = "Elsevier Limited",

}

Investigating children's deep learning of the tree life cycle using mobile technologies. / Choi, Gi Woong; Land, Susan Mary; Zimmerman, Heather.

In: Computers in Human Behavior, Vol. 87, 01.10.2018, p. 470-479.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Investigating children's deep learning of the tree life cycle using mobile technologies

AU - Choi, Gi Woong

AU - Land, Susan Mary

AU - Zimmerman, Heather

PY - 2018/10/1

Y1 - 2018/10/1

N2 - This study investigates children's problem-solving activities during mobile learning in an outdoor summer camp setting. We designed a mobile application to support children on trails at a nature center to apply strategies for decision making about tree life cycles. We analyzed video records of 10 groups (9 dyads and 1 triad) of children (ages 9–12) using primarily a thematic qualitative analysis of learning episodes. We analyzed how children used problem-solving strategies to identify and capture the tree cycle with the help of mobile tablets. We found that our mobile learning experience and its external representations supported the following: (1) engagement in deep learning in the natural setting as evidenced by coordinating decisions with photographic evidence; (2) use of procedural or tactical strategies to approach the problem; and (3) use of real-time decision making strategies about tree life cycles.

AB - This study investigates children's problem-solving activities during mobile learning in an outdoor summer camp setting. We designed a mobile application to support children on trails at a nature center to apply strategies for decision making about tree life cycles. We analyzed video records of 10 groups (9 dyads and 1 triad) of children (ages 9–12) using primarily a thematic qualitative analysis of learning episodes. We analyzed how children used problem-solving strategies to identify and capture the tree cycle with the help of mobile tablets. We found that our mobile learning experience and its external representations supported the following: (1) engagement in deep learning in the natural setting as evidenced by coordinating decisions with photographic evidence; (2) use of procedural or tactical strategies to approach the problem; and (3) use of real-time decision making strategies about tree life cycles.

UR - http://www.scopus.com/inward/record.url?scp=85049927163&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85049927163&partnerID=8YFLogxK

U2 - 10.1016/j.chb.2018.04.020

DO - 10.1016/j.chb.2018.04.020

M3 - Article

VL - 87

SP - 470

EP - 479

JO - Computers in Human Behavior

JF - Computers in Human Behavior

SN - 0747-5632

ER -