A Study of Multiple Teacher Evaluation in the United States Based on Artificial Intelligence: Comparison of Danielson and Marzano Evaluation Models

Di Yuan

2022

Abstract

Since the 1980s, the research on educational evaluation in the United States ushered in a “multi-model peri-od”, and corresponding teacher evaluation models have emerged to seek a symbiosis between the development of teachers’ professionalism and the enhancement of students’ academic achievement. This paper takes the Danielson Framework for Teaching and the Marzano Teacher Evaluation Model by modern information technology as examples, analyzing their backgrounds, model content, similarities, and differences. It aims to provide references for reflecting on and promoting current teacher evaluation practices in China.

Paper Citation

in Harvard Style

Yuan D. (2022). A Study of Multiple Teacher Evaluation in the United States Based on Artificial Intelligence: Comparison of Danielson and Marzano Evaluation Models. In Proceedings of the 2nd International Conference on New Media Development and Modernized Education - Volume 1: NMDME; ISBN 978-989-758-630-9, SciTePress, pages 221-228. DOI: 10.5220/0011909400003613

in Bibtex Style

@conference{nmdme22,
author={Di Yuan},
title={A Study of Multiple Teacher Evaluation in the United States Based on Artificial Intelligence: Comparison of Danielson and Marzano Evaluation Models},
booktitle={Proceedings of the 2nd International Conference on New Media Development and Modernized Education - Volume 1: NMDME},
year={2022},
pages={221-228},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011909400003613},
isbn={978-989-758-630-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on New Media Development and Modernized Education - Volume 1: NMDME
TI - A Study of Multiple Teacher Evaluation in the United States Based on Artificial Intelligence: Comparison of Danielson and Marzano Evaluation Models
SN - 978-989-758-630-9
AU - Yuan D.
PY - 2022
SP - 221
EP - 228
DO - 10.5220/0011909400003613
PB - SciTePress