Details
| Original language | English |
|---|---|
| Title of host publication | 2023 IEEE Congress on Evolutionary Computation, CEC 2023 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (electronic) | 9798350314588 |
| ISBN (print) | 979-8-3503-1459-5 |
| Publication status | Published - 2023 |
| Event | 2023 IEEE Congress on Evolutionary Computation, CEC 2023 - Chicago, United States Duration: 1 Jul 2023 → 5 Jul 2023 |
Abstract
This paper presents a genetic assessment agent and a student and machine co-learning model for high-school students' computational intelligence (CI) experience. We invited the IEEE CIS High School Outreach (HSO) subcommittee members of the years 2021-2022 to provide lectures at CIS activities and conferences and constructed a basic CI conceptual knowledge structure for high-school student learning. From 2021 to 2022 in Taiwan, we collected high-school students' learning data, including labels, attitudes, environment, and effort, from the CI&AI-FML platform using robots and learning tools, then processed the data using natural language processing (NLP) techniques to efficiently evaluate high-school students' learning state. We then applied three evolutionary computation techniques: genetic algorithm (GA), particle swarm optimization (PSO), and genetic algorithm neural network (GANN) in the proposed genetic assessment agent for the co-learning model, with learning performance regression analysis. In this paper, a CI&AI-FML human and machine co-learning Metaverse model is presented as a solution, which provides hands-on learning and experience while also supporting student-centered online learning during the COVID-19 pandemic. Students participated in the course during the 2022 Spring semester to learn basic CI concepts and experience CI applications through interaction with machines using the developed CI&AI- FML learning tools. The experimental results indicate that the genetic assessment agent with the GANN method has better performance in the student and machine co-learning model as compared to the other two methods, and it is effective for student and machine co-learning model construction.
Keywords
- CI Learning Concept, Genetic Assessment Agent, High-School Student CI Experience Model, Machine Learning, Natural Language Processing
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
- Mathematics(all)
- Computational Mathematics
- Mathematics(all)
- Control and Optimization
- Mathematics(all)
- Modelling and Simulation
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2023 IEEE Congress on Evolutionary Computation, CEC 2023. Institute of Electrical and Electronics Engineers Inc., 2023.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Genetic Assessment Agent for High-School Student and Machine Co-Learning Model Construction on Computational Intelligence Experience
AU - Lee, Chang Shing
AU - Wang, Mei Hui
AU - Chen, Chih Yu
AU - Yang, Fu Jie
AU - Dockhorn, Alexander
N1 - Funding Information: The authors would like to thank the financial support sponsored by the National Science and Technology Council (NSTC) of Taiwan under the grants MOST 111-2221-E-024-012 and MOST 111-2622-E-024-001.
PY - 2023
Y1 - 2023
N2 - This paper presents a genetic assessment agent and a student and machine co-learning model for high-school students' computational intelligence (CI) experience. We invited the IEEE CIS High School Outreach (HSO) subcommittee members of the years 2021-2022 to provide lectures at CIS activities and conferences and constructed a basic CI conceptual knowledge structure for high-school student learning. From 2021 to 2022 in Taiwan, we collected high-school students' learning data, including labels, attitudes, environment, and effort, from the CI&AI-FML platform using robots and learning tools, then processed the data using natural language processing (NLP) techniques to efficiently evaluate high-school students' learning state. We then applied three evolutionary computation techniques: genetic algorithm (GA), particle swarm optimization (PSO), and genetic algorithm neural network (GANN) in the proposed genetic assessment agent for the co-learning model, with learning performance regression analysis. In this paper, a CI&AI-FML human and machine co-learning Metaverse model is presented as a solution, which provides hands-on learning and experience while also supporting student-centered online learning during the COVID-19 pandemic. Students participated in the course during the 2022 Spring semester to learn basic CI concepts and experience CI applications through interaction with machines using the developed CI&AI- FML learning tools. The experimental results indicate that the genetic assessment agent with the GANN method has better performance in the student and machine co-learning model as compared to the other two methods, and it is effective for student and machine co-learning model construction.
AB - This paper presents a genetic assessment agent and a student and machine co-learning model for high-school students' computational intelligence (CI) experience. We invited the IEEE CIS High School Outreach (HSO) subcommittee members of the years 2021-2022 to provide lectures at CIS activities and conferences and constructed a basic CI conceptual knowledge structure for high-school student learning. From 2021 to 2022 in Taiwan, we collected high-school students' learning data, including labels, attitudes, environment, and effort, from the CI&AI-FML platform using robots and learning tools, then processed the data using natural language processing (NLP) techniques to efficiently evaluate high-school students' learning state. We then applied three evolutionary computation techniques: genetic algorithm (GA), particle swarm optimization (PSO), and genetic algorithm neural network (GANN) in the proposed genetic assessment agent for the co-learning model, with learning performance regression analysis. In this paper, a CI&AI-FML human and machine co-learning Metaverse model is presented as a solution, which provides hands-on learning and experience while also supporting student-centered online learning during the COVID-19 pandemic. Students participated in the course during the 2022 Spring semester to learn basic CI concepts and experience CI applications through interaction with machines using the developed CI&AI- FML learning tools. The experimental results indicate that the genetic assessment agent with the GANN method has better performance in the student and machine co-learning model as compared to the other two methods, and it is effective for student and machine co-learning model construction.
KW - CI Learning Concept
KW - Genetic Assessment Agent
KW - High-School Student CI Experience Model
KW - Machine Learning
KW - Natural Language Processing
UR - http://www.scopus.com/inward/record.url?scp=85168758842&partnerID=8YFLogxK
U2 - 10.1109/cec53210.2023.10254139
DO - 10.1109/cec53210.2023.10254139
M3 - Conference contribution
AN - SCOPUS:85168758842
SN - 979-8-3503-1459-5
BT - 2023 IEEE Congress on Evolutionary Computation, CEC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE Congress on Evolutionary Computation, CEC 2023
Y2 - 1 July 2023 through 5 July 2023
ER -