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1. Engaging elementary students in data science practices

   https://doi.org/10.1108/ILS-06-2023-0062  

   Adisa, Ibrahim Oluwajoba ; Herro, Danielle ; Abimbade, Oluwadara ; Arastoopour_Irgens, Golnaz ( December 2023 , Information and Learning Sciences)

   This study is part of a participatory design research project and aims to develop and study pedagogical frameworks and tools for integrating computational thinking (CT) concepts and data science practices into elementary school classrooms.

   This paper describes a pedagogical approach that uses a data science framework the research team developed to assist teachers in providing data science instruction to elementary-aged students. Using phenomenological case study methodology, the authors use classroom observations, student focus groups, video recordings and artifacts to detail ways learners engage in data science practices and understand how they perceive their engagement during activities and learning.

   Findings suggest student engagement in data science is enhanced when data problems are contextualized and connected to students’ lived experiences; data analysis and data-based decision-making is practiced in multiple ways; and students are given choices to communicate patterns, interpret graphs and tell data stories. The authors note challenges students experienced with data practices including conflict between inconsistencies in data patterns and lived experiences and focusing on data visualization appearances versus relationships between variables.

   Data science instruction in elementary schools is an understudied, emerging and important area of data science education. Most elementary schools offer limited data science instruction; few elementary schools offer data science curriculum with embedded CT practices integrated across disciplines. This research assists elementary educators in fostering children's data science engagement and agency while developing their ability to reason, visualize and make decisions with data.

    

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2. 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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3. Tapping into early PhD aspirations to advance gender equity in computing: predicting PhD interest among upward transfer students

   https://doi.org/10.1108/SGPE-06-2023-0057  

   Blaney, Jennifer M ; Feldon, David F ; Litson, Kaylee ( February 2024 , Studies in Graduate and Postdoctoral Education)

   Supporting community college transfer students represents a critical strategy for broadening participation in STEM. In addition to being a racially diverse group, students who pursue STEM degrees by way of community college report frequent interests in graduate study and academic careers. Thus, supporting and expanding transfer students’ PhD interests can help to diversify the STEM professoriate. This study aims to identify the experiences that predict PhD interests among students who transferred into the computer science major from a community college.

   Relying on longitudinal survey data from over 150 community college transfer students throughout their first year at their receiving four-year university, we used regression analysis to identify the post-transfer college experiences that predict early interest in PhDs.

   We found that receiving information about PhDs from a professor strongly predicted PhD interest among transfer students. Relationships with other variables indicate that the provision of information about graduate school was more likely to occur for students who participated in undergraduate research experiences than for those participating in internships. Descriptive data document inequities in who has access to these types of experiences.

   This paper provides new insight into how STEM departments can develop targeted efforts to ensure that information about PhD training is equitably available to all transfer students. Working to ensure that faculty equitably communicate with students about PhD opportunities may go a long way in countering potential deterrents among transfer students who may be interested in such pathways.

    

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4. 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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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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