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Teaching Reform of Principles and Applications of the Linux Operating System Course Based on Multi-Theory Integration and Artificial Intelligence

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• Volume 12, Issue 2, Mar. 2025

Year:

2025

Type of Publication:

Article

Keywords:

Constructivism, Bloom's Taxonomy, Cognitive Load Theory, Feedback Theory, AI

Authors:

• Liu, Lei

Journal:

IJIRES

Volume:

12

Number:

2

Pages:

19-29

Month:

March

ISSN:

2349-5219

BibTex:

@article{2071, author = "Lei Liu", abstract = "This paper develops a framework by integrating constructivism, Bloom{\&}#039;s Taxonomy, cognitive load theory, and feedback theory with artificial intelligence (AI) to meet the increasing demand for Linux expertise in data science education. We revamp the teaching methodology of the course Principles and Applications of the Linux Operating System by implementing a task-driven approach, layered learning, cognitive load reduction, timely feedback, and collaborative simulations of big data projects. Evaluation results exhibit substantial enhancements in students{\&}#039; practical skills, self-directed learning capabilities, and collaborative capacities. This framework offers instructors a systematic, scalable strategy to develop Linux proficiency aligned with the changing needs of data science professionals.", issn = "23495219", journal = "IJIRES", keywords = "Constructivism, Bloom{\&}#039;s Taxonomy, Cognitive Load Theory, Feedback Theory, AI", month = "March", number = "2", pages = "19-29", title = "{T}eaching {R}eform of {P}rinciples and {A}pplications of the {L}inux {O}perating {S}ystem {C}ourse {B}ased on {M}ulti-{T}heory {I}ntegration and {A}rtificial {I}ntelligence", volume = "12", year = "2025", }

Abstract:

This paper develops a framework by integrating constructivism, Bloom's Taxonomy, cognitive load theory, and feedback theory with artificial intelligence (AI) to meet the increasing demand for Linux expertise in data science education. We revamp the teaching methodology of the course Principles and Applications of the Linux Operating System by implementing a task-driven approach, layered learning, cognitive load reduction, timely feedback, and collaborative simulations of big data projects. Evaluation results exhibit substantial enhancements in students' practical skills, self-directed learning capabilities, and collaborative capacities. This framework offers instructors a systematic, scalable strategy to develop Linux proficiency aligned with the changing needs of data science professionals.

Full text: IJIRES_2071_FINAL.pdf Online version [Bibtex]