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Sensemaking is conceptualized as a trajectory to develop better understanding and is advocated as one of the fundamental practices in science education. However, the field is lacking of a framework to view the prolonged process of sensemaking that starts from a raise of uncertainty of a target phenomenon to a grasping of a better understanding of a target phenomenon. The process requires teachers to recognize the role of scientific uncertainty in different phases of sensemaking and develop responsive instructional supports to help students navigate the uncertainties. With an attention on student scientific uncertainty as a potential driver of the trajectory of sensemaking, this study aims to identify different phases of sensemaking that can be developed with students’ scientific uncertainty. This study especially attends to two types of scientific uncertainty—conceptual and epistemic uncertainties. Conceptual uncertainty refers to student struggle of using conceptual understanding (e.g., mastery of content and everyday knowledge) to respond to an encountered phenomenon. Epistemic uncertainty emerges from struggles in using epistemic understanding to generate new ideas. Based on the multiple case study method, we examined sensemaking activities in two Korean science classrooms and one American science classroom and identified three phases of sensemaking: (a) focusing on a driving question related to a target phenomenon, (b) delving into multiple resources to develop plausible explanation(s), and (c) examining the successfulness of the new understanding and concretizing it. Based on the findings, we discuss two emerging themes. First, sensemaking progresses through three distinctive phases driven by students’ dynamically evolving scientific uncertainty. Second, attending to both epistemic and conceptual uncertainties can support developing sensemaking coherent with students’ view.more » « lessFree, publicly-accessible full text available June 1, 2025
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Building a skilled cybersecurity workforce is paramount to building a safer digital world. However, the diverse skill set, constantly emerging vulnerabilities, and deployment of new cyber threats make learning cybersecurity challenging. Traditional education methods struggle to cope with cybersecurity's rapidly evolving landscape and keep students engaged and motivated. Different studies on students' behaviors show that an interactive mode of education by engaging through a question-answering system or dialoguing is one of the most effective learning methodologies. There is a strong need to create advanced AI-enabled education tools to promote interactive learning in cybersecurity. Unfortunately, there are no publicly available standard question-answer datasets to build such systems for students and novice learners to learn cybersecurity concepts, tools, and techniques. The education course material and online question banks are unstructured and need to be validated and updated by domain experts, which is tedious when done manually. In this paper, we propose CyberGen, a novel unification of large language models (LLMs) and knowledge graphs (KG) to generate the questions and answers for cybersecurity automatically. Augmenting the structured knowledge from knowledge graphs in prompts improves factual reasoning and reduces hallucinations in LLMs. We used the knowledge triples from cybersecurity knowledge graphs (AISecKG) to design prompts for ChatGPT and generate questions and answers using different prompting techniques. Our question-answer dataset, CyberQ, contains around 4k pairs of questions and answers. The domain expert manually evaluated the random samples for consistency and correctness. We train the generative model using the CyberQ dataset for question answering task.
Free, publicly-accessible full text available March 25, 2025 -
In college cybersecurity education, problem-based learning has been introduced to promote student agency in solving a complex problem. However, a dilemma of balancing the student agency persist and previous research has focused on students’ cognitive, metacognitive, and regulatory to enhance the efficacy of PBL. Given the importance of students’ self-awareness of their agency, this study suggests a concept of meta-agency as an essential learner characteristic that influences the effectiveness of student agency in PBL. Four dimensions of meta-agency, perceptions of productive struggle, expectation alignment between instructor and students, strategies for regulating agency, and familiarity with PBL tasks, were qualitatively explored with student interview data. Features of meta-agency and how students’ meta-agency level develop through cybersecurity PBL sessions were further investigated.more » « less
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In problem-based learning (PBL), individual differences in students’ use of metacognition and self-regulation skills exist and calls for extensive research in postsecondary STEM education. This study focuses on students’ uncertainty management in PBL. A scale of the uncertainty management in PBL (UM-PBL) was developed. Exploratory factor analysis was conducted and showed that the UM-PBL has substantial reliability and a total of 14 items across three constructs of a) perception of uncertainty in learning to solve problems, b) self-efficacy in and c) strategy for uncertainty management. Gender differences in the first two constructs were found, confirming its known-group validation. Students’ problem-solving scores were positively correlated with scores of the first two constructs, suggesting its predictability of its relationship with academic performance.more » « less
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Knowledge graphs gained popularity in recent years and have been useful for concept visualization and contextual information retrieval in various applications. However, constructing a knowledge graph by scraping long and complex unstructured texts for a new domain in the absence of a well-defined ontology or an existing labeled entity-relation dataset is difficult. Domains such as cybersecurity education can harness knowledge graphs to create a student-focused interactive and learning environment to teach cybersecurity. Learning cybersecurity involves gaining the knowledge of different attack and defense techniques, system setup and solving multi-facet complex real-world challenges that demand adaptive learning strategies and cognitive engagement. However, there are no standard datasets for the cybersecurity education domain. In this research work, we present a bottom-up approach to curate entity-relation pairs and construct knowledge graphs and question-answering models for cybersecurity education. To evaluate the impact of our new learning paradigm, we conducted surveys and interviews with students after each project to find the usefulness of bot and the knowledge graphs. Our results show that students found these tools informative for learning the core concepts and they used knowledge graphs as a visual reference to cross check the progress that helped them complete the project tasks.more » « less