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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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"I Am So Overwhelmed I Don't Know Where to Begin!" Towards Developing Relationship-Based and Values-Based End-of-Life Data Planning Approaches
To support people at the end of life as they create management plans for their assets, planning approaches like estate planning are increasingly considering data. HCI scholarship has argued that developing more effective planning approaches to support end-of-life data planning is important. However, empirical research is needed to evaluate specific approaches and identify design considerations. To support end-of-life data planning, this paper presents a qualitative study evaluating two approaches to co-designing end-of-life data plans with participants. We find that asset-first inventory-centric approaches, common in material estate planning, may be ineffective when making plans for data. In contrast, heavily facilitated, mission-driven, relationship-centric approaches were more effective. This study expands previous research by validating the importance of starting end-of-life data planning with relationships and values, and highlights collaborative facilitation as a critical part of successful data planning approaches.
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- Award ID(s):
- 2048244
- PAR ID:
- 10528214
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400703300
- Page Range / eLocation ID:
- 1 to 14
- Subject(s) / Keyword(s):
- digital legacy end of life inheritance stewardship memorial memory death identity legacy heirlooms data planning
- Format(s):
- Medium: X
- Location:
- Honolulu HI USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3613904.3642250
