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The higher education community continues to pursue solutions to the alarming number of science, technology, engineering, and math (STEM) undergraduates leaving their degree programs. This qualitative study investigated the experiences of 12 STEM scholarship recipients in a near-peer-mentored social support group at a large Midwestern university. The goal of this study was to investigate the scholars’ challenges and supports prior to and while participating in a weekly peer group through the lens of the Phenomenological Variant of Ecological Systems Theory model. This case study triangulated the experiences of the peer group participants using pre-group individual interviews, peer leader reflections, and a focus group. The pre-group interviews revealed that the participants experienced challenges associated with the rigor of their courses, self-imposed pressure, and unsupportive relationships. Supports for their persistence prior to the peer group included their internal drive to achieve their goals and supportive relationships, particularly with family. The focus group revealed that the peer group provided a non-academic space to connect with peers, facilitated sense of belonging, and normalized their struggle as STEM majors, broadening their perception of science identity. Paradoxically, although participants highlighted personal disclosure as key to promoting social support, they indicated their greatest challenge in the peer group was discomfort with sharing.

 

Given the importance of fresh water, we investigated undergraduate students’ understanding of water flow and its consequences. We probed introductory geology students’ pre-instruction knowledge using a classroom management system at two large research-intensive universities. Open-ended clicker questions, where students click directly on diagrams using their smart device (e.g., cell phone, tablet) to respond, probed students’ predictions about: (1) groundwater movement and (2) velocity and erosion in a river channel. Approximately one-third of students correctly identified groundwater flow as having lateral and vertical components; however, the same number of students identified only vertical components to flow despite the diagram depicting enough topographic gradient for lateral flow. For rivers depicted as having a straight channel, students correctly identified zones of high velocity. However, for curved river channels, students incorrectly identified the inside of the bend as the location of greatest erosion and highest velocity. Systematic errors suggest that students have mental models of water flow that are not consistent with fluid dynamics. The use of students’ open-ended clicks to reveal common errors provided an efficient tool to identify conceptual challenges associated with the complex spatial and temporal processes that govern water movement in the Earth system. 

There is a critical inconsistency in the literature on analogical retrieval. On the one hand, a vast set of laboratory studies has found that people often fail to retrieve past experiences that share deep relational commonalities, even when they would be useful for reasoning about a current problem. On the other hand, historical studies and naturalistic research show clear evidence of remindings based on deep relational commonalities. Here, we examine a possible explanation for this inconsistency—namely, that remindings based on relational principles increase as a function of expertise. To test this claim, we devised a simple analogy‐generation task that can be administered across a wide range of expertise. We presented common events as the bases from which to generate analogies. Although the events themselves were unrelated to geoscience, we found that when the event was explainable in terms of a causal principle that is prominent in geoscience, expert geoscientists were likely to spontaneously produce analogies from geoscience that relied on the same principle. Further, for these examples, prompts to produce causal analogies increased their frequency among nonscientists and scientists from another domain, but not among expert geoscientists (whose spontaneous causal retrieval levels were already high). In contrast, when the example was best explained by a principle outside of geoscience, all groups required prompting to produce substantial numbers of analogies based on causal principles. Overall, this pattern suggests that the spontaneous use of causal principles is characteristic of experts. We suggest that expert scientists adopt habitual patterns of encoding according to the key relational principles in their domain, and that this contributes to their propensity to spontaneously retrieve relational matches. We discuss implications for the nature of expertise and for science instruction and assessment.

 

The construct of active learning permeates undergraduate education in science, technology, engineering, and mathematics (STEM), but despite its prevalence, the construct means different things to different people, groups, and STEM domains. To better understand active learning, we constructed this review through an innovative interdisciplinary collaboration involving research teams from psychology and discipline-based education research (DBER). Our collaboration examined active learning from two different perspectives (i.e., psychology and DBER) and surveyed the current landscape of undergraduate STEM instructional practices related to the modes of active learning and traditional lecture. On that basis, we concluded that active learning—which is commonly used to communicate an alternative to lecture and does serve a purpose in higher education classroom practice—is an umbrella term that is not particularly useful in advancing research on learning. To clarify, we synthesized a working definition of active learning that operates within an elaborative framework, which we call the construction-of-understanding ecosystem. A cornerstone of this framework is that undergraduate learners should be active agents during instruction and that the social construction of meaning plays an important role for many learners, above and beyond their individual cognitive construction of knowledge. Our proposed framework offers a coherent and actionable concept of active learning with the aim of advancing future research and practice in undergraduate STEM education.