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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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Personalized Learning in a Virtual Hands-on Lab Platform for Computer Science Education
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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- Award ID(s):
- 1723440
- PAR ID:
- 10193690
- Date Published:
- Journal Name:
- IEEE Frontiers in Education Conference (FIE)
- Page Range / eLocation ID:
- 1 to 8
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract BackgroundWith the increasing popularity of distance education, how to engage students in online inquiry‐based laboratories remains challenging for science teachers. Current remote labs mostly adopt a centralized model with limited flexibility left for teachers' just‐in‐time instruction based on students' real‐time science practices. ObjectivesThe goal of this research is to investigate the impact of a non‐centralized remote lab on students' cognitive and behavioural engagement. MethodsA mixed‐methods design was adopted. Participants were the high school students enrolled in two virtual chemistry classes. Remote labs 2.0, branded as Telelab, supports a non‐centralized model of remote inquiry that can enact more interactive hands‐on labs anywhere, anytime. Teleinquiry Instructional Model was used to guide the curriculum design. Students' clickstreams logs and instruction timestamps were analysed and visualized. Multiple regression analysis was used to determine whether engagement levels influence their conceptual learning. Behavioural engagement patterns were corroborated with survey responses. Results and ConclusionsWe found approximate synchronizations between student–teacher–lab interactions in the heatmap. The guided inquiry enabled by Telelab facilitates real‐time communications between instructors and students. Students' conceptual learning is found to be impacted by varying engagement levels. Students' behavioural engagement patterns can be visualized and fed to instructors to inform learning progress and enact just‐in‐time instruction. ImplicationsTelelab offers a model of remote labs 2.0 that can be easily customized to live stream hands‐on teleinquiry. It enhances engagement and gives participants a sense of telepresence. Providing a customizable teleinquiry curriculum for practitioners may better prepare them to teach inquiry‐based laboratories online.more » « less
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Computer labs are commonly used in computing education to help students reinforce the knowledge obtained in classrooms and to gain hands-on experience on specific learning subjects. While traditional computer labs are based on physical computer centers on campus, more and more virtual computer lab systems (see, e.g., [1, 2, 3, 4]) have been developed that allow students to carry out labs on virtualized resources remotely through the internet. Virtual computer labs make it possible for students to use their own computers at home, instead of relying on computer centers on campus to work on lab assignments. However, they also make it difficult for students to collaborate, due to the fact that students work remotely and there is a lack of support of sharing and collaboration. This is in contrast to traditional computer labs where students naturally feel the presence of their peers in a physical lab room and can easily work together and help each other if needed. Funded by NSF’s Division of Undergraduate Education, this project develops a collaborative virtual computer lab (CVCL) environment to support collaborative learning in virtual computer labs. The CVCL environment leverages existing open source collaboration tools and desktop sharing technologies and adds new functions unique to virtual computer labs to make it easy for students to collaborate while working on computer labs remotely. It also implements several collaborative lab models to support different forms of collaboration in both formal and informal settings. We have developed the main functions of the CVCL environment and begun to use it in classes in the Computer Science (CS) department at Georgia State University. While the original project focuses on computer labs in its traditional sense, the issue of lack of collaboration applies to much broader learning settings where students work on tasks or assignments on computers, with or without being associated with a lab environment. Due to the high mobility of students in modern campuses and the fact that many learning activities are carried out over the Internet, computer-based learning increasingly happen in students’ personal spaces (e.g., homes, apartments), as opposed to public learning spaces (e.g., laboratories, libraries). In these personal spaces, it is difficult for students to get help from classmates or teaching assistants (TAs) when encountering problems. As a result, collaborative learning is difficult and rare. This is especially true for urban universities such as Georgia State University where a significant portion of students are part-time students and/or commute. To address this issue, we intend to broaden the concept of “virtual computer lab” to include general computer based learning happening in “virtual space,” which is any location where people can meet using networked digital devices [5]. Virtual space is recognized as an increasingly important part of “learning spaces” and asks for support from both the technology aspect and learning theory aspect [5]. Collaborative learning environments that support remote collaboration in virtual computer labs would fill an important need in this broader trend.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/FIE.2018.8659291
