Jyun‐Chen Chen
Biographic Data
| ID | 9116161 |
|---|---|
| NAME | Jyun‐Chen Chen |
| GIVEN NAMES | Jyun‐Chen |
| FAMILY NAME | Chen |
| SIGNATURE | CHEN J C |
| AFFILIATIONS | National Taiwan Normal University |
| ORCID | 0000-0001-7560-8350 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 7 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2014 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Beyond all-or-nothing
Purpose During hands-on science, technology, engineering, and mathematics (STEM) learning, the Predict-Observe-Explain-Do (POED) model provides a systematic framework to enhance learning effectiveness. When hands-on STEM learning is integrated with content and language integrated learning (CLIL), it may deepen content knowledge and develop English proficiency.Design Drawing on embodied learning theory, we examined the effects of integrating the C…
Using gesture recognition with the memory strategy to improve preschoolers’ learning performance, motor skills, and executive function
Gesture recognition can create an interactive environment in which to train children to control their thoughts, gestures, and body postures when performing learning activities. This study integrated gesture recognition and a memory strategy to develop a physical learning activity for preschoolers to improve their English learning performance, motor skills (namely, stability and movement), and executive functions (namely, working memory). The stud…
Developing an Interdisciplinary Hands‐On Learning Activity With the 6E Model to Improve Students' STEM Knowledge, Learning Motivation and Creativity
Using knowledge building and flipped learning to enhance students' learning performance in a hands‐on STEM activity
A study on the effects of using gamification with the 6E model on high school students’ computer programming self-efficacy, IoT knowledge, hands-on skills, and behavioral patterns
Developing a cycle-mode Poed model and using scientific inquiry for a practice activity to improve students’ learning motivation, learning performance, and hands-on ability
“Learning by doing” involves completing a practice activity through the method of inquiry. This study combined the predict-observe-explain (POE) inquiry method and hands-on doing processes to develop a cycle-mode POED (predict-observe-explain-do) model with an e-learning system to help students complete a practice activity using scientific inquiry. The study selected 60 sixth-grade students, who were divided into the experimental group (cycle-mod…
The influence of collaborative learning games within different devices on student’s learning performance and behaviours
This study demonstrates the effectiveness of using multi-touch tabletop collaborative game (MTCG) as a collaborative learning platform, in which multiple students can play games using a digital surface. The learning performance of participants is also explored, along with their related behaviours and their experiences. Consisting of 49 Taipei elementary school fifth graders ranging from 11 to 12 years old, the study participants came from two cla…
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The influence of collaborative learning games within different devices on student’s learning performance and behaviours
This study demonstrates the effectiveness of using multi-touch tabletop collaborative game (MTCG) as a collaborative learning platform, in which multiple students can play games using a digital surface. The learning performance of participants is also explored, along with their related behaviours and their experiences. Consisting of 49 Taipei elementary school fifth graders ranging from 11 to 12 years old, the study participants came from two cla…
Developing a cycle-mode Poed model and using scientific inquiry for a practice activity to improve students’ learning motivation, learning performance, and hands-on ability
“Learning by doing” involves completing a practice activity through the method of inquiry. This study combined the predict-observe-explain (POE) inquiry method and hands-on doing processes to develop a cycle-mode POED (predict-observe-explain-do) model with an e-learning system to help students complete a practice activity using scientific inquiry. The study selected 60 sixth-grade students, who were divided into the experimental group (cycle-mod…
A study on the effects of using gamification with the 6E model on high school students’ computer programming self-efficacy, IoT knowledge, hands-on skills, and behavioral patterns
Using knowledge building and flipped learning to enhance students' learning performance in a hands‐on STEM activity
Using gesture recognition with the memory strategy to improve preschoolers’ learning performance, motor skills, and executive function
Gesture recognition can create an interactive environment in which to train children to control their thoughts, gestures, and body postures when performing learning activities. This study integrated gesture recognition and a memory strategy to develop a physical learning activity for preschoolers to improve their English learning performance, motor skills (namely, stability and movement), and executive functions (namely, working memory). The stud…
Developing an Interdisciplinary Hands‐On Learning Activity With the 6E Model to Improve Students' STEM Knowledge, Learning Motivation and Creativity
Beyond all-or-nothing
Purpose During hands-on science, technology, engineering, and mathematics (STEM) learning, the Predict-Observe-Explain-Do (POED) model provides a systematic framework to enhance learning effectiveness. When hands-on STEM learning is integrated with content and language integrated learning (CLIL), it may deepen content knowledge and develop English proficiency.Design Drawing on embodied learning theory, we examined the effects of integrating the C…
Computer Science (6 works) · Mathematics education (6 works) · Psychology (6 works) · Artificial Intelligence (4 works) · Educational technology (4 works) · Educational Games and Gamification (3 works) · Innovative Teaching and Learning Methods (3 works) · Multimedia (3 works) · Blended Learning (2 works) · Human–computer interaction (2 works)