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1. Peer mentors' experiences in an online STEM peer mentoring program: “beacons of light”

   https://doi.org/10.1108/IJMCE-03-2023-0033  

   Wendt, Jillian L. ; Jones, Vivian O. ( April 2024 , International Journal of Mentoring and Coaching in Education)

   Racially and ethnically minoritized (REM) women continue to be underrepresented in science, technology, engineering and mathematics (STEM) programs and careers. Peer mentoring is one strategy that can support their participation. This study explores the experiences of Black women peer mentors in an online peer mentoring program at two historically Black institutions.

   A qualitative case study approach was utilized to explore the impact of an online peer mentoring program on peer mentors' STEM self-efficacy, sense of community, STEM identity and intent to persist in STEM.

   Analysis identified five themes relating to peer mentors' experiences in the program: (1) an “I can do this” approach: confidence and self-efficacy; (2) utility of like others; (3) “beacons of light”: intersecting and malleable identities; (4) skills development and (5) motivation and reciprocity. Further, challenges of the online relationship were shared.

   The study contributes to the body of knowledge by demonstrating the utility of an online peer mentoring model among women mentors enrolled in STEM programs at two historically Black institutions. The findings support those who are historically marginalized in participating in and remaining in STEM.

    

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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. Safeguarding trust in a digital ecosystem

   https://doi.org/10.1108/JBS-08-2023-0157  

   Aguiar, Marcos ; Kiderman, Jeff ; Shekar, Harsha Chandra ; Schilke, Oliver ( November 2023 , Journal of Business Strategy)

   The purpose of this paper is to elaborate the significance of safeguards in digital ecosystems and their role in generating trust among participants. This paper argues that the right mix and number of safeguards are crucial for an ecosystem’s growth and success. It offers ecosystem orchestrators concrete guidelines for how to implement and monitor safeguards.

   This research is based on both consulting experience and publicly available information on several digital ecosystems.

   This research conceptualizes safeguards as precautionary mechanisms that mandate or promote desirable behavior in an effort to engender trust among ecosystem participants. Safeguards can take various forms, including passwords, escrow, user privacy controls, ratings and reviews and policies and contracts. Striking the right balance of safeguards – neither too few nor too many – is crucial for ecosystem orchestrators. This paper identifies the factors that determine the optimal mix of safeguards, including the power asymmetry between sellers and buyers, the sophistication of participants, the nature of transactions, the cost of negative outcomes and the cost-benefit tradeoff.

   To the best of the authors’ knowledge, this study is one of the first to illuminate the relationship between safeguards and trust in the context of digital ecosystem. It is also one of the few attempts to provide managerial guidance for ecosystem designers trying to structure their platform for trust.

    

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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. 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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