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This convergent mixed method study investigates learner engagement during a blended, transmedia curriculum called CryptoComics, which is designed to teach 3rd-5th graders about cryptology and cybersecurity. Curriculum design is presented through the lens of four engagement facilitators: (1) anchoring the curriculum with a comic book, (2) blending digital and unplugged media, (3) supporting situational interest via a transmedia narrative, and (4) designing for social-cultural relevance. Latent profile analysis is used to develop profiles of learner engagement using quantitative indicators of cognitive and emotional engagement collected across 204 students at 13 implementation sites in the Eastern U.S. Qualitative indicators of engagement include teacher weekly check-ins submitted by 17 teachers working at the 13 implementation sites, student interviews, and classroom observations of 26 students participating in the curriculum at two local sites. Quantitative and qualitative results converge to suggest the majority of the students participating in the curriculum were highly engaged cognitively and emotionally. Qualitative data (1) suggest some third graders may be less cognitively engaged due to challenging content, (2) provide evidence for how the design of the blended, transmedia curriculum supported, and some cases hindered, engagement, (3) highlight the importance of transitions between blended learning components in facilitating engagement, and (4) uncover questions regarding one of the quantitative measures selected as an indicator of cognitive engagement.

K-12 teachers and students are vulnerable to cybersecurity attacks and mostly ill-prepared to deal with them. The COVID-19 pandemic has only increased these risks because of the reliance on digital technology in education and increased free time young children and adolescents spend online. Simultaneously, the U.S. is facing an extreme shortage of cybersecurity professionals. Given the rise of cyberattacks and the need for cybersecurity professionals, a concerted effort to prepare preservice teachers to integrate cybersecurity education across the K-12 curriculum is needed. In our vision for 2025, all preservice teachers across the country are prepared to integrate age-appropriate cybersecurity concepts, skills and career awareness in the curriculum regardless of their content area or grade level specialization. We propose a repository of stand-alone activities and full curricula developed through collaboration among K-12 educators, teacher educators, and cybersecurity experts that could be adopted across teacher education programs. We use the elementary grades as a context for providing examples of some activities that might be included in the repository. We also provide recommendations for developing such a repository and for individual teacher educators who want integrate cybersecurity education in preservice teacher education right now.

AbstractThis study presents a novel framework to evaluate the usability and user experience of e-learning platforms, focusing on the Duolingo language learning app. By integrating advanced Natural Language Processing (NLP) techniques such as Large Language Models (LLMs), Latent Dirichlet Allocation (LDA), and sentence embedding models, we identified thirteen key usability and user experience topics from a large dataset of user reviews. Our findings reveal that Duolingo is highly rated since users value its enjoyable learning experiences and effectiveness. However, technical issues, lack of explanations, and frustrating features were identified as areas for improvement. The study demonstrates the effectiveness of our framework in analyzing user feedback at scale, offering valuable insights for improving user experience in e-learning platforms and other domains. [ABSTRACT FROM AUTHOR]

This article describes a novel method for quantifying fixation disparity and evaluates its role in visuospatial cognition during an authentic learning task, specifically, the determination of molecule chirality in organic chemistry involving mental rotation and pattern comparison. The first study examined the influence of molecular model dimensionality (2D vs. 3D) on chirality determination performance and visual attention of 55 participants. The second study explored how the sustained playing of the tile‐matching game Mahjong, a pattern comparison game, can affect visual attention and visuospatial performance during the chirality determination task of 59 participants. Fixation disparity was one of the eye tracking variables explored. Both studies revealed that (1) individuals with higher fixation disparity underperformed on the chirality task, which involves mental rotation and pattern comparison, and (2) fixation disparity improved over time in participants who played Mahjong. This work provides important implications for using fixation disparity as a possible biomarker of visuospatial performance. We introduced a new method to measure fixation disparity and studied its relationship with visuospatial cognition during organic chemistry chirality learning tasks. Two studies show that poor fixation disparity is associated with low task performance, and fixation disparity and task performance can be improved over time by playing a Mahjong game. Our findings suggest that fixation disparity may serve as a possible index of visuospatial cognition and should be further investigated in different educational contexts and tasks. [ABSTRACT FROM AUTHOR]

The article reports on the study that examines the touchless (T-less) mouse, a human-machine interface (HMI) device for non-contact gesture inputs which is used by moving a finger. The study shows that the average completion time with the mouse is longer than the one with conventional mouse. Results of the study also indicate the potential use of T-less mouse as an alternative for conventional mouse in special situations.

In an effort toward standardization, this paper evaluates the performance of five eye-movement classification algorithms in terms of their assessment of oculomotor fixation and saccadic behavior. The results indicate that performance of these five commonly used algorithms vary dramatically, even in the case of a simple stimulus-evoked task using a single, common threshold value. The important contributions of this paper are: evaluation and comparison of performance of five algorithms to classify specific oculomotor behavior; introduction and comparison of new standardized scores to provide more reliable classification performance; logic for a reasonable threshold-value selection for any eye-movement classification algorithm based on the standardized scores; and logic for establishing a criterion-based baseline for performance comparison between any eye-movement classification algorithms. Proposed techniques enable efficient and objective clinical applications providing means to assure meaningful automated eye-movement classification. [ABSTRACT FROM PUBLISHER]