UMBC Staff Collection

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http://hdl.handle.net/11603/7733

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    Toward a New Paradigm: Learning Analytics 2.0
    (Springer, 2023-07-09) Penniston, Thomas
    Innovation and advancements have led to the ability of higher education administrators and innovators to use machine learning (ML) to identify student academic risk behavior patterns at increasingly early points within a semester. These models bring with them the promise to help prioritize allocation of finite resources and inform scalable interventions to promote learner success. However, it may be more difficult to prioritize student needs when the measures for which a university is held accountable and use ML to predict are not specific to learning. How do we best navigate the ethical waters to emphasize and support student growth while simultaneously addressing business reporting needs? To begin this transformation, it’s critical that we gather better, more meaningful direct measures to build the models we use to predict outcomes, even if it means sacrificing some level of predictive validity, and then use our intervention strategies to improve these specific behavioral inputs feeding the models.
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    GIFTS: Making Research Experiences Meaningful through Critical Self-Reflection
    (ASEE, 2023-06-25) DeCrescenzo, Peter; Jangha, Sunji
    In this Great Ideas for Teaching Students (GIFTS) paper, we offer learning outcomes that we are beginning to recognize from our eight-week research experience for undergraduates (REU). There are four characteristics that have been found to be essential to success in Science, Technology, Engineering, and Mathematics (STEM) fields: a strong sense of STEM identity, scientific self-efficacy, a sense of belonging, and a psychological sense of community. This is especially true for first-year and transfer students pursuing STEM undergraduate degrees. A variety of studies have been published that go into detail about why these characteristics in particular have such a significant effect on student performance and retention. This paper will present Critical Self-Reflection as a practical way to integrate development of these characteristics into student research experiences to foster experiential learning that goes beyond increasing technical skills. STEM students are not often trained to critically self-reflect on their experiences in classroom and research settings. An inability for undergraduates to reflect intentionally on their experiences creates greater risk for attrition from STEM disciplines. Curated reflective experiences in collaborative learning settings can offer professional development opportunities to enhance students’ social and technical communication skills. There are four phases within the scaffolded Critical Self-Reflection framework: Learning to Reflect, Reflection for Action, Reflection in Action, and Reflection on Action. When applying the evidence-based practice, STEM undergraduate researchers describe their perceptions via three activities: creating a legacy statement, participating in facilitated dialogue sessions, and writing curated reflection journal entries within an REU. Through critical self-reflection exercises, we are beginning to find growth of first-year and transfer STEM undergraduates in the following areas: understanding of their role in the lab; confidence in their researcher identity; expression of agency; observation and communication skills; and intentionality for action. Participating in this self-reflection allows students to make meaning of their experience enabling them to hone the aforementioned characteristics that creates a pathway from their undergraduate experience to undergraduate degree completion, graduate degree attainment, and to the STEM workforce.
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    Research Under the Same Roof
    (UMBC Magazine, 2020-09-04) Leyshon, Randianne
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    A quest to reconstruct Baltimore’s American Indian ‘reservation’
    (The Conversation, 2019-04-23) Jones, Ashley Minner
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