A Feasible Study of a Deep Learning Model Supporting Human-Machine Collaborative Learning of Object-Oriented Programming

Due to the development of deep learning technology, its application in education has received increasing attention from researchers. Intelligent agents based on deep learning technology can perform higher-order intellectual tasks than ever. However, the high deployment cost of deep learning models h...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on learning technologies Ročník 17; s. 1 - 15
Hlavní autor: Wang, Feng Hsu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Codes
Collaborative learning
Cooperative Learning
Deep learning
Deep learning technology
Education
Feasibility studies
human-machine collaborative learning
Inference
Inferences
Intelligent agents
intelligent tutoring system
learning partnerships model
Learning Processes
Low speed
Machine learning
Man-machine systems
Natural language (computers)
Object oriented modeling
Object oriented programming
Partnerships
Programming
Programming profession
Students
ISSN:1939-1382, 2372-0050
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Due to the development of deep learning technology, its application in education has received increasing attention from researchers. Intelligent agents based on deep learning technology can perform higher-order intellectual tasks than ever. However, the high deployment cost of deep learning models has hindered their widespread application in education. In addition, there needs to be more research on applying deep learning technology in education. In this study, we develop an intelligent agent using a Performer-based encoder-decoder neural model to classify object-oriented programming (OOP) errors in student code and generate hint feedback in natural language to help students correct the code. This study investigates the feasibility of deploying this agent in an educational setting to support the learning of OOP. This study first examines the low-speed inference problem of the deep learning model. A fast inference algorithm is proposed for the model, which achieves a speedup of eighty times. This study further explores integrating a human-machine collaborative learning process with the deep learning agent. Students were surveyed about their perceptions of the agent in supporting learning. Student responses are interpreted within the learning partnerships model (LPM) framework to show how the agent's technical automation and autonomy features support student-agent learning partnerships. Finally, implications and suggestions for educational application and research of deep learning technology are presented.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1939-1382
2372-0050
DOI:10.1109/TLT.2022.3226345