This Innovate Practice full paper presents a cloud-based personalized learning lab platform. 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 learner's behavior and assessing learner's performance for personalization. However, it is rarely addressed in existing research. In this paper, we propose a personalized learning platform called ThoTh Lab specifically designed for computer science hands-on labs in a cloud environment. 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. With that in mind, 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. For example, instructors may provide more detailed instructions to help slow starters, while assigning more challenging labs to those quick learners in the same class. To evaluate ThoTh Lab, we conducted an experiment and collected data from an upper-division cybersecurity class for undergraduate students at Arizona State University in the US. The results show that ThoTh Lab can identify learning style with reasonable accuracy. By leveraging the personalized lab platform for a senior level cybersecurity course, our lab-use study also shows that the presented solution improves students engagement with better understanding of lab assignments, spending more effort on hands-on projects, and thus greatly enhancing learning outcomes.
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Knowledge Graph based Learning Guidance for Cybersecurity Hands-on Labs
Hands-on practice is a critical component of cybersecurity education.
Most of the existing hands-on exercises or labs 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. With the advantages
of big data and natural language processing (NLP) technologies,
constructing a large knowledge graph and mining concepts from unstructured
text becomes possible, which motivated us to construct
a machine learning based lab exercising plan for cybersecurity education.
In the research presented by this paper, we have constructed
a knowledge graph in the cybersecurity domain using NLP technologies
including machine learning based word embedding and
hyperlink-based concept mining. We then utilized the knowledge
graph during the regular learning process based on the following
approaches: 1. We constructed a web-based front-end to visualize
the knowledge graph, which allows students to browse and search
cybersecurity-related concepts and the corresponding interdependence
relations; 2. We created a personalized knowledge graph for
each student based on their learning progress and status; 3.We built
a personalized lab recommendation system by suggesting more relevant
labs based on students’ past learning history to maximize their
learning outcomes. To measure the effectiveness of the proposed
solution, we have conducted a use case study and collected survey
data from a graduate-level cybersecurity class. Our study shows
that, by leveraging the knowledge graph for the cybersecurity area
study, students tend to benefit more and show more interests in
cybersecurity area.
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- Award ID(s):
- 1723440
- NSF-PAR ID:
- 10193689
- Date Published:
- Journal Name:
- ACM Global Computing Education Conference (CompEd)
- Page Range / eLocation ID:
- 194 to 200
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Nowadays, real‐world learning modules become vital components in computer science and engineering in general and cybersecurity in particular. However, as student enrollments have been dramatically increasing, it becomes more challenging for a university/college to keep up with the quality of education that offers hands‐on experiment training for students thoroughly. These challenges include the difficulty of providing sufficient computing resources and keep them upgraded for the increasing number of students. In order for higher education institutions to conquer such challenges, some educators introduce an alternative solution. Namely, they develop and deploy virtual lab experiments on the clouds such as Amazon AWS and the Global Environment for Network Innovations (GENI), where students can remotely access virtual resources for lab experiments. Besides, Software‐Defined Networks (SDN) are an emerging networking technology to enhance the security and performance of networked communications with simple management. In this article, we present our efforts to develop learning modules via an efficient deployment of SDN on GENI for computer networking and security education. Specifically, we first give our design methodology of the proposed learning modules, and then detail the implementations of the learning modules by starting from user account creation on the GENI testbed to advanced experimental GENI‐enabled SDN labs. It is worth pointing out that in order to accommodate students with different backgrounds and knowledge levels, we consider the varying difficulty levels of learning modules in our design. Finally, student assessment over these pedagogical efforts is discussed to demonstrate the efficiency of the proposed learning modules.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3300115.3309531
