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Computer science, cybersecurity education, and microcredentials are becoming more pervasive in all levels of the educational system. The purpose of this study was partnering with precollegiate teachers: (1) to investigate the self-efficacy of 30 precollegiate teacher participants towards computer science before, during, and after three iterations of a cybersecurity microcredential, and (2) to make changes to the cybersecurity microcredential to improve its effectiveness. The authors explored what teachers need in a microcredential. The first Cohort (n = 5) took the microcredential sequence over 28 days in the summer of 2020, the second Cohort (n = 16) took it over 42 days in the fall of 2020, and the third Cohort (n = 9) took it over 49 days in the summer of 2021. The authors investigated three research questions and used a systems thinking approach while developing, evaluating, and implementing the research study. The researchers used quantitative methods in the collection of a self-efficacy subscale survey to assess whether the precollegiate teachers' beliefs about computer science changed, and then used qualitative methods when conducting semi-structured teacher participant interviews to address the research questions. The findings show that the precollegiate teachers' self-efficacy scores towards computer science increased, and that there are areas in need of attention, such as resources and implementation, when creating microcredentials. The implications of this research include the importance of purposefully crafting microcredentials and professional developments, including aspects of creating effective partnerships.

This study compares three pre-collegiate teacher professional learning and development (PLD) integrated science, technology, engineering, and mathematics (STEM) experiences framed in astronomy. The study is set in the western United States (USA) and involves 60 pre-collegiate teachers (in the USA these are K-12 teachers) over the course of three years (June 2014-May 2017). During the PLDs, astronomy acted as a vehicle for pre-collegiate STEM teachers to increase their STEM content knowledge as well as create and implement integrated STEM classroom lessons. The authors collected quantitative and qualitative data to address five research questions and embraced social constructionism as the theoretical framework. Findings show that STEM pre-collegiate teachers are largely engaged with integrated STEM PLD content and embrace astronomy content and authentic science. Importantly, they need time to practice, interpret, translate, and use the integrated STEM content in classroom lessons. Recommendations for PLD STEM teacher support are provided. Implications of this study are vast, as gaps in authentic science, utilizing astronomy, PLD structure, and STEM integration are ripe for exploration.

Research on innovative, integrated outreach programs guided three separate week-long outreach camps held across two summers (2018 and 2019). These camps introduced computer science through real-world applications and hands-on activities, each dealing with cybersecurity principles. The camps utilized low-cost hardware and free software to provide a total of 84 students (aged 10 to 18 years) a unique learning experience. Based on feedback from the 2018 camp, a new pre/post survey was developed to assess changes in participant knowledge and interest. Student participants in the 2019 iteration showed drastic changes in their cybersecurity content recall (33% pre vs. 96% post), cybersecurity concept identification within real-world scenarios, and exhibited an increased ability to recognize potential cybersecurity threats in their every-day lives (22% pre vs. 69% post). Finally, students' self-reported interest-level before and after the camp show a positive increase across all student participants, with the number of students who where highly interested in cybersecurity more than doubling from 31% pre-camp to 65% post-camp. Implications for educators are large as these activities and experiences can be interwoven into traditional schooling as well as less formal camps as pure computer science or through integrated STEM.

There is a clear call for pre-collegiate students in the United States to become literate in computer science (CS) concepts and practices through integrated, authentic experiences and instruction. Yet, a majority of in-service and pre-service pre-collegiate teachers (instructing children aged five to 18) lack the fundamental skills and self-efficacy to adequately and effectively integrate CS into existing curricula. In this study, 30 pre-collegiate teachers who represent a wide band of experience, grade-levels, and prior CS familiarity participated in a 16-day professional development (PD) course to enhance their content knowledge and self-efficacy in integrating CS into existing lessons and curricula. Using both qualitative and quantitative methodology, a social constructivist approach guided the researchers in the development of the PD, as well as the data collection and analysis on teacher content knowledge and perceptions through a mixed-methods study. Ultimately, participants were introduced to CS concepts and practices through NetLogo, which is a popular multi-agent simulator. The results show that although the pre-collegiate teachers adopted CS instruction, the CS implementation within their curricula was limited to the activities and scope of the PD with few adaptations and minimal systemic change in implementation behaviors.

For 2 weeks in the summer of 2018, K-12 STEM teachers (n = 40) attended a professional development (PD) that included four sessions focused on computer science modeling with follow-up academic year sessions; however, overall, the teachers did not meet expectations about what modeling means or how to utilize it. To examine why, the authors looked at the teachers' discourse. Using three theories to connect to the practice (terministic screens, schema theory, and concepts of capital) the authors analyzed the surveys, interviews, and email reflections to explore participants' concept of models and the potential difficulties of implementing computer modeling in their classrooms. Five general concepts of models are presented. Findings show that the term model was interpreted differently by the PD's faculty team and participants. Further, the authors found that the majority of presenters held differing theories of models than the participants. Participant concepts of models did improve slightly. Implications are provided showcasing articulated keys for delivering PD that assists in eliminating discursive and theoretical issues. Included are considerations for STEM teacher educators, PD providers, and K-12 teachers.

This article showcases STEM as an interdisciplinary field in which the disciplines strengthen and support each other (not as separate science, technology, engineering, and mathematics disciplines). The authors focus on an open-ended, complex problem--water quality--as the primary teaching and learning task. The participants, middle school female students (aged 9-15 years), interacted in an informal educational setting (i.e., Girl Scouts) on a research project investigating river quality following the river's restoration. The community, including Girl Scout participants, leaders, parents, university faculty, graduate students, and others, utilized an action research (AR) approach when interacting with the participants. Methods such as observational field notes, focus groups, and collected artifacts were commonly employed. The authors describe the history of STEM and AR leading to authentic science research projects through eight engineering skills/practices (incorporating science, technology, and mathematics) and showcase participant interactions, implementation, and community engagement in the STEM water quality river project. Findings indicate that informal engineering based projects can serve as opportunities for participants to connect with integrated STEM. Implications include the need for engaging participants in informal authentic science to support traditional school STEM learning and encouraging community engagement in integrated STEM to support traditional K-12 classroom instruction.

The measure of diffusion, the property of dissipating patterns and statistical structures in cryptographic transformations, serves as a valuable heuristic for assessing the obscurity of patterns that could lead to collisions. As with many cryptographic hash functions, SHA-256 is thought to exhibit the property of diffusion. While SHA-256's diffuse output is loosely documented, even less is known about how the diffusion rate changes across the 64 rounds in its compression function and how the algorithm's individual sub-functions contribute to the overall diffusion. The diffusion of the unmodified compression function is initially measured using the Strict Avalanche Criterion (SAC), with the aim of understanding the alteration in diffusion across the 64 rounds of compression. The level to which sub-functions affect diffusion is subsequently measured, enabling potential prioritization of these sub-functions in future collision attacks. To accomplish this, the compression function is modified by removing sub-functions, and the diffusion of these new variants is measured. While the SAC measurements of each function eventually plateau close to the 50% target, no function, including the unmodified compression function, strictly meets the SAC, and multiple variant functions diffuse at comparatively slower rates. [ABSTRACT FROM AUTHOR]

This article explores one group's use of action research (AR) and lesson study in three US university-level computer science courses affecting 85 undergraduate computer science and engineering students. It offers an overview of three areas including AR, lesson study, and engineering soft skills. The research group identified a problem: undergraduate engineering student soft skill understanding. The group collaborated to enhance participant engagement with this problem by utilizing one lesson focused on soft skills over three research lesson iterations in three distinct university semesters (2017-2018). Field notes were the main data collection method. Observational field notes were recorded in the three research lesson iterations, and themes from the notes were identified. The group valued peer interactions, and the research lesson adaptations as well as pedagogical changes were based on evidence. Four themes were identified, and participant comments were considered as the lessons became more focused . The research group argues that AR and lesson study are separate and distinct ventures based on this yearlong experience, which is counter to some other literature presented. Implications are large for all stakeholders interested in AR, lesson study, soft skill utilization, conflict management, and participant learning impact. Possibilities for enhanced participant engagement exists with honed lesson pedagogy.

The control flow of a program represents valuable and sensitive information; in embedded systems, this information can take on even greater value as the resources, control flow, and execution of the system have more constraints and functional implications than modern desktop environments. Early works have demonstrated the possibility of recovering such control flow through power-based side-channel attacks in tightly constrained environments; however, they relied on meaningful differences in computational states or data dependency to distinguish between states in a state machine. This work applies more advanced machine learning techniques to state machines which perform identical operations in all branches of control flow. Complete control flow is recovered with 99% accuracy even in situations where 97% of work is outside of the control flow structures. This work demonstrates the efficacy of these approaches for recovering control flow information; continues developing available knowledge about power-based attacks on program control flow; and examines the applicability of multiple standard machine learning models to the problem of classification over power-based side-channel information. [ABSTRACT FROM AUTHOR]

This article details a new, free resource for continuous video assessment named YouDemo. The tool enables real time rating of uploaded YouTube videos for use in science, technology, engineering, and mathematics (STEM) education and beyond. The authors discuss trends of preservice science teachers' assessments of self- and peer-created videos using the tool. The trends were identified from over 900 assessments of 170 videos, with over 131 unique users. Included in this data set is a 2-year study focusing on 27 preservice science teachers (from a 5-year study of 76 total science preservice teachers) and their use of the tool. The authors collected both quantitative (numerical scores) and qualitative data (open-ended questions) from the 27 participants. Findings show that (a) rating two metrics had a non-zero bias between the two metrics; (b) preservice teachers found continuous video rating beneficial in enabling video assessment, promoting critical thinking, and increasing engagement; and (c) preservice teacher's self-assessment was uncorrelated with their peers' assessment. Additionally, the elements to enable skill improvement were met, including (a) a well defined task, (b) a challenging task, (c) immediate feedback, (d) error correction, and (e) practice. Implications include improvement in preservice teacher reflection and discussions, especially related to STEM content and pedagogy.