Full metadata
Title
Personalized Learning in a Virtual Hands-on Lab Platform for Computer Science Education
Description
Personalized learning is gaining popularity in online computer science education due to its characteristics of pacing the learning progress and adapting the instructional approach to each individual learner from a diverse background. Among various instructional methods in computer science education, hands-on labs have unique requirements of understanding learners' behavior and assessing learners' performance for personalization. Hands-on labs are a critical learning approach for cybersecurity education. It provides real-world complex problem scenarios and helps learners develop a deeper understanding of knowledge and concepts while solving real-world problems. But there are unique challenges when using hands-on labs for cybersecurity education. Existing hands-on lab exercises materials are usually managed in a problem-centric fashion, while it lacks a coherent way to manage existing labs and provide productive lab exercising plans for cybersecurity learners. To solve these challenges, a personalized learning platform called ThoTh Lab specifically designed for computer science hands-on labs in a cloud environment is established. ThoTh Lab can identify the learning style from student activities and adapt learning material accordingly. With the awareness of student learning styles, instructors are able to use techniques more suitable for the specific student, and hence, improve the speed and quality of the learning process. ThoTh Lab also provides student performance prediction, which allows the instructors to change the learning progress and take other measurements to help the students timely. A knowledge graph in the cybersecurity domain is also constructed using Natural language processing (NLP) technologies including word embedding and hyperlink-based concept mining. This knowledge graph is then utilized during the regular learning process to build a personalized lab recommendation system by suggesting relevant labs based on students' past learning history to maximize their learning outcomes. To evaluate ThoTh Lab, several in-class experiments were carried out in cybersecurity classes for both graduate and undergraduate students at Arizona State University and data was collected over several semesters. The case studies show that, by leveraging the personalized lab platform, students tend to be more absorbed in a lab project, show more interest in the cybersecurity area, spend more effort on the project and gain enhanced learning outcomes.
Date Created
2021
Contributors
- Deng, Yuli (Author)
- Huang, Dijiang (Thesis advisor)
- Li, Baoxin (Committee member)
- Zhao, Ming (Committee member)
- Hsiao, Sharon (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
118 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.168452
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: Ph.D., Arizona State University, 2021
Field of study: Computer Science
System Created
- 2022-08-22 03:35:40
System Modified
- 2022-08-22 03:36:02
- 2 years 3 months ago
Additional Formats