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1. PhD Progression: a micro-credentialing program motivates and supports PhD students’ professional development at a US University

   https://doi.org/10.1108/SGPE-07-2023-0070  

   Gnangnon, Bénédicte ; Li, Kuang ; Rezaei, Dena Fatemeh ; Marks, Mishonne Maryann ; Dellarocas, Chrysanthos ; Hokanson, Sarah Chobot ; Goldman, Sasha B ( July 2024 , Studies in Graduate and Postdoctoral Education)

   This study aims to present the evaluation of a competency-based online professional development training program, PhD Progression, tied to a digital badge system, created to support PhD students across fields.

   This study took place at Boston University, a large, nonprofit, Carnegie Classified R1 research-intensive institution located in the northeastern region of the USA. Through internal campus collaborations, the authors developed a PhD core capacities framework. Building from this framework, the authors designed the first learning level of the program and ran a pilot study with PhD students from various fields and at different stages of their PhD. Using surveys and focus groups, the authors collected both quantitative and qualitative data to evaluate this program.

   The quantitative and qualitative data show that the majority of the PhD student participants found the contents of the competency-based training program useful, appropriate for building skills and knowledge and therefore relevant for both their degree progress and their future job. Gaining digital badges significantly increased their motivation to complete training modules.

   This type of resource is scalable to other institutions that wish to provide self-paced professional development support to their PhD students while rewarding them for investing time in building professional skills and enabling them to showcase these skills to potential employers.

   This study demonstrates, for the first time, that tying a digital badging system to a competency-based professional development program significantly motivates PhD students to set professional development goals and invest time in building skills.

    

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2. Leveraging community-based assets in coaching, wellness and mentorship to increase the African American and Latinx math teacher workforce

   https://doi.org/10.1108/SUP-09-2023-0033  

   Warner, Sean S. ( December 2023 , School-University Partnerships)

   Hoppey, David ; Hall, Katrina ; Lynch, Megan (Ed.)

   There is some evidence to suggest that the historical challenge associated with recruiting and retaining Black and Brown Science, Technology, Engineering and Math (STEM) collegians is tied to early their teaching and learning experiences in Mathematics. This paper describes an National Science Foundation (NSF) funded project (NSF #2151043) whose goal is to attract, prepare and retain math teachers of color in high need school districts ensure that those teachers remain in the field long enough to make a meaningful impact on the minds and hearts of BIPOC students who are often, extrinsically, and intrinsically, discouraged from pursuing careers in STEM professions.

   This mixed-methods study, which began in the summer of 2023, seeks to recruit, prepare, support and retain nineteen (19) Black and Brown math teachers for two (2) high need urban school districts. The expectancy value theory will be used to explain the performance, persistence, and choices of the teachers, while grounded theory will be utilized to understand the impact of the intensive mentorship and wellness coaching that applied over the first year of their preservice preparation and subsequent in-service years.

   Measures of project efficacy won’t begin until 2025 and as such there are no findings or implications to draw from for the study at this time.

   The intention of this paper is to augment the body of knowledge on recruiting and retaining Black and Brown math teachers for urban schools where the need for quality STEM teachers is critical.

    

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3. Taking stock of campus mentoring ecosystems: a peer assessment dialogue exercise

   https://doi.org/10.1108/IJMCE-09-2022-0072  

   Packard, Becky Wai-Ling ; Montgomery, Beronda L. ; Mondisa, Joi-Lynn ( September 2023 , International Journal of Mentoring and Coaching in Education)

   The purpose of this study was to examine the experiences of multiple campus teams as they engaged in the assessment of their science, technology, engineering and mathematics (STEM) mentoring ecosystems within a peer assessment dialogue exercise.

   This project utilized a qualitative multicase study method involving six campus teams, drawing upon completed inventory and visual mapping artefacts, session observations and debriefing interviews. The campuses included research universities, small colleges and minority-serving institutions (MSIs) across the United States of America. The authors analysed which features of the peer assessment dialogue exercise scaffolded participants' learning about ecosystem synergies and threats.

   The results illustrated the benefit of instructor modelling, intra-team process time and multiple rounds of peer assessment. Participants gained new insights into their own campuses and an increased sense of possibility by dialoguing with peer campuses.

   This project involved teams from a small set of institutions, relying on observational and self-reported debriefing data. Future research could centre perspectives of institutional leaders.

   The authors recommend dedicating time to the institutional assessment of mentoring ecosystems. Investing in a campus-wide mentoring infrastructure could align with campus equity goals.

   In contrast to studies that have focussed solely on programmatic outcomes of mentoring, this study explored strategies to strengthen institutional mentoring ecosystems in higher education, with a focus on peer assessment, dialogue and learning exercises.

    

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4. Using network visualizations to engage elementary students in locally relevant data literacy

   https://doi.org/10.1108/ILS-06-2023-0069  

   Zhou, Mengxi ; Steinberg, Selena ; Stiso, Christina ; Danish, Joshua A. ; Craig, Kalani ( December 2023 , Information and Learning Sciences)

   This study aims to explore how network visualization provides opportunities for learners to explore data literacy concepts using locally and personally relevant data.

   The researchers designed six locally relevant network visualization activities to support students’ data reasoning practices toward understanding aggregate patterns in data. Cultural historical activity theory (Engeström, 1999) guides the analysis to identify how network visualization activities mediate students’ emerging understanding of aggregate data sets.

   Pre/posttest findings indicate that this implementation positively impacted students’ understanding of network visualization concepts, as they were able to identify and interpret key relationships from novel networks. Interaction analysis (Jordan and Henderson, 1995) of video data revealed nuances of how activities mediated students’ improved ability to interpret network data. Some challenges noted in other studies, such as students’ tendency to focus on familiar concepts, are also noted as teachers supported conversations to help students move beyond them.

   To the best of the authors’ knowledge, this is the first study the authors are aware of that supported elementary students in exploring data literacy through network visualization. The authors discuss how network visualizations and locally/personally meaningful data provide opportunities for learning data literacy concepts across the curriculum.

    

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5. Explainable deep neural network for in-plain defect detection during additive manufacturing

   https://doi.org/10.1108/RPJ-05-2023-0157  

   Kumar, Deepak ; Liu, Yongxin ; Song, Houbing ; Namilae, Sirish ( September 2023 , Rapid Prototyping Journal)

   The purpose of this study is to develop a deep learning framework for additive manufacturing (AM), that can detect different defect types without being trained on specific defect data sets and can be applied for real-time process control.

   This study develops an explainable artificial intelligence (AI) framework, a zero-bias deep neural network (DNN) model for real-time defect detection during the AM process. In this method, the last dense layer of the DNN is replaced by two consecutive parts, a regular dense layer denoted (L1) for dimensional reduction, and a similarity matching layer (L2) for equal weight and non-biased cosine similarity matching. Grayscale images of 3D printed samples acquired during printing were used as the input to the zero-bias DNN.

   This study demonstrates that the approach is capable of successfully detecting multiple types of defects such as cracks, stringing and warping with high accuracy without any prior training on defective data sets, with an accuracy of 99.5%.

   Once the model is set up, the computational time for anomaly detection is lower than the speed of image acquisition indicating the potential for real-time process control. It can also be used to minimize manual processing in AI-enabled AM.

   To the best of the authors’ knowledge, this is the first study to use zero-bias DNN, an explainable AI approach for defect detection in AM.

    

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