Proceedings of the 2022 3rd International Conference on Artificial Intelligence and Education (IC-ICAIE 2022)

Research on Evidence-based Differentiated Instruction of NCOs

Authors
Guqing Liu1, *, Deqiang Ding1, Jinzhi Ran1, Qingsong Xie1, Tao Wang1
1The School of Information and Communication, National University of Defense Technology, Wuhan, 430030, China
*Corresponding author. Email: liuguqing123@126.com
Corresponding Author
Guqing Liu
Available Online 27 December 2022.
DOI
10.2991/978-94-6463-040-4_74How to use a DOI?
Keywords
Differentiated Instruction; Evidence-based Teaching; Non-commissioned Officers (NCOs) Instruction
Abstract

There is a problem in NCOs instruction that the teaching effect is poor due to the diversity of students’ background, so differentiated instruction is introduced. It is well known that the key to successful differentiated instruction lies in sound decision making, such as reasonable grouping and appropriate teaching strategies. Therefore, evidence-based differentiated instruction is proposed to achieve the best combination of teacher experience and instructional evidence which can provide data to support the placement assessment and diagnostic assessment of differentiated instruction, thus helping teachers to make scientifically decisions. There are two kinds of instructional evidence, the first is the traditional data from the literature, questionnaires, examinations, etc., and the second is the so-called intelligent evidence collected from rain classrooms, intelligent classrooms and simulation training platforms. Especially deep neural network technology is used in simulation training platforms to recommend personalized learning resources for students. Based on the above evidence, differentiated instructional practices in NCOs were conducted. Firstly, differentiated teaching objectives and teaching contents are determined; then coarse- and fine-granularity differentiated teaching are combined to carry out individualized instruction on the basis of gradated instruction; finally, a fair and reasonable summative examination are realized through graded tests. To verify the effectiveness of the method, a randomized controlled experiment was conducted, and the results showed that evidence-based differentiated instruction can effectively improve student performance.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2022 3rd International Conference on Artificial Intelligence and Education (IC-ICAIE 2022)
Series
Atlantis Highlights in Computer Sciences
Publication Date
27 December 2022
ISBN
10.2991/978-94-6463-040-4_74
ISSN
2589-4900
DOI
10.2991/978-94-6463-040-4_74How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

TY  - CONF
AU  - Guqing Liu
AU  - Deqiang Ding
AU  - Jinzhi Ran
AU  - Qingsong Xie
AU  - Tao Wang
PY  - 2022
DA  - 2022/12/27
TI  - Research on Evidence-based Differentiated Instruction of NCOs
BT  - Proceedings of the 2022 3rd International Conference on Artificial Intelligence and Education (IC-ICAIE 2022)
PB  - Atlantis Press
SP  - 486
EP  - 492
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-040-4_74
DO  - 10.2991/978-94-6463-040-4_74
ID  - Liu2022
ER  -