TY - JOUR
T1 - Inspecting Technology-Related Quality of Teaching Artifacts to Understand Teachers' Technology Adoption
AU - Zheng, Longwei
AU - Wang, Chao
AU - Liu, Tong
AU - Gu, Xiaoqing
N1 - Publisher Copyright:
© 2008-2011 IEEE.
PY - 2023/12/1
Y1 - 2023/12/1
N2 - Teachers' technology adoption primarily aims to integrate technology into the curriculum and to improve teaching and learning. Teaching artifacts encapsulate the attributes that can reflect teacher expertise in the authentic context. This study inspects the implicit quality of teaching artifacts to examine teachers' technology adoption throughout the evolution process of teaching artifacts. We demonstrate how patterns within the artificial synthesis of digital tools and teaching practice can be identified by examining the technology-specified quality of digital artifacts shaped by teachers of a primary school in Shanghai. To address the potential challenges of artifact analysis, we conduct a set of semiautomatic processes to manage the quality properties of teaching artifacts and measure quality indicators by training a series of automatic annotations. The artifact quality changes over nine semesters demonstrated that teachers increase technology flexibility, content variety, instruction diversity, and richness of artifacts as their technological understanding gradually improves. Other than the mainstream technology adoption research, this study revealed details regarding technology adoption: the introduction of new technology in schools should involve the enhancement of the technological understanding of teachers, the encouragement of teachers to adapt each component of their teaching practice to technological contexts, and the quality and use frequency of artifacts. Moreover, the proposed inspection of artifact quality can reflect the teacher's adoption process in an unconstrained environment. The artifact analytic approaches we created can contribute to the technology adoption from a more objective perspective.
AB - Teachers' technology adoption primarily aims to integrate technology into the curriculum and to improve teaching and learning. Teaching artifacts encapsulate the attributes that can reflect teacher expertise in the authentic context. This study inspects the implicit quality of teaching artifacts to examine teachers' technology adoption throughout the evolution process of teaching artifacts. We demonstrate how patterns within the artificial synthesis of digital tools and teaching practice can be identified by examining the technology-specified quality of digital artifacts shaped by teachers of a primary school in Shanghai. To address the potential challenges of artifact analysis, we conduct a set of semiautomatic processes to manage the quality properties of teaching artifacts and measure quality indicators by training a series of automatic annotations. The artifact quality changes over nine semesters demonstrated that teachers increase technology flexibility, content variety, instruction diversity, and richness of artifacts as their technological understanding gradually improves. Other than the mainstream technology adoption research, this study revealed details regarding technology adoption: the introduction of new technology in schools should involve the enhancement of the technological understanding of teachers, the encouragement of teachers to adapt each component of their teaching practice to technological contexts, and the quality and use frequency of artifacts. Moreover, the proposed inspection of artifact quality can reflect the teacher's adoption process in an unconstrained environment. The artifact analytic approaches we created can contribute to the technology adoption from a more objective perspective.
KW - Artifact quality
KW - automated artifact analyses
KW - phase-related technology adoption
KW - teachers' technology adoption
KW - teaching artifacts
UR - https://www.scopus.com/pages/publications/85149395190
U2 - 10.1109/TLT.2023.3244231
DO - 10.1109/TLT.2023.3244231
M3 - 文章
AN - SCOPUS:85149395190
SN - 1939-1382
VL - 16
SP - 940
EP - 954
JO - IEEE Transactions on Learning Technologies
JF - IEEE Transactions on Learning Technologies
IS - 6
M1 - 3244231
ER -