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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. 

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. 

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. 

Multidimensional photography can capture optical fields beyond the capability of conventional image sensors that measure only two-dimensional (2D) spatial distribution of light. By mapping a high-dimensional datacube of incident light onto a 2D image sensor, multidimensional photography resolves the scene along with other information dimensions, such as wavelength and time. However, the application of current multidimensional imagers is fundamentally restricted by their static optical architectures and measurement schemes—the mapping relation between the light datacube voxels and image sensor pixels is fixed. To overcome this limitation, we propose tunable multidimensional photography through active optical mapping. A high-resolution spatial light modulator, referred to as an active optical mapper, permutes and maps the light datacube voxels onto sensor pixels in an arbitrary and programmed manner. The resultant system can readily adapt the acquisition scheme to the scene, thereby maximising the measurement flexibility. Through active optical mapping, we demonstrate our approach in two niche implementations: hyperspectral imaging and ultrafast imaging.

 

In this Letter, we present a snapshot hyperspectral light field imaging system using a single camera. By integrating an unfocused light field camera with a snapshot hyperspectral imager, the image mapping spectrometer, we captured a five-dimensional (5D) ($x,y,u,v,\mathrm{\lambda <\#comment/>}$) ($x,y,$spatial coordinates;$u,v,$emittance angles;$\mathrm{\lambda <\#comment/>},$wavelength) datacube in a single camera exposure. The corresponding volumetric image ($x,y,z$) at each wavelength is then computed through a scale-depth space transform. We demonstrated the snapshot advantage of our system by imaging the spectral-volumetric scenes in real time.

 

An image mapping spectrometer (IMS) is a snapshot hyperspectral imager that simultaneously captures both the spatial ($x$,$y$) and spectral ($\mathrm{\lambda <\#comment/>}$) information of incoming light. The IMS maps a three-dimensional (3D) datacube ($x$,$y$,$\mathrm{\lambda <\#comment/>}$) to a two-dimensional (2D) detector array ($x$,$y$) for parallel measurement. To reconstruct the original 3D datacube, one must construct a lookup table that connects voxels in the datacube and pixels in the raw image. Previous calibration methods suffer from either low speed or poor image quality. We herein present a slit-scan calibration method that can significantly reduce the calibration time while maintaining high accuracy. Moreover, we quantitatively analyzed the major artifact in the IMS, the striped image, and developed three numerical methods to correct for it.