Although previous literature indicates that parents and siblings each provide key support for Latinx adolescents' academic success, most studies have not considered how parents and siblings work as a system to support adolescents in science. Informed by theories on family systems and family influence on youth's achievement and education, this study aimed to (a) identify what Latinx adolescents believed were the most helpful ways that parents and siblings supported them in science, and (b) explore whether family science support varied based on parents' science education. Using a qualitative approach, semi-structured interviews from 90 Latinx adolescents (mean age = 15.54 years; 38% girls; 84% born in the U.S.) were analyzed using inductive and deductive approaches. We found that parents and siblings supported Latinx adolescents in science through various home-based strategies: active engagement (classwork help and monitoring), academic socialization (encouragement, conversations about the future, and advice) and providing resources (material and social resources). Adolescents mentioned their older siblings were particularly helpful in providing class-specific support based on the science classes that they had previously taken. Additionally, our findings suggest that siblings relied more on classwork help from only older siblings in families where parents did not take any high school science classes compared to families where parents took some high school science classes. Overall, this study highlights the complementary science support that parents and siblings provide Latinx adolescents and the valuable role that siblings can play in Latinx families when parents have limited science education.

Collaborative problem-solving (CPS) is ubiquitous in everyday life, including work, family, leisure activities, etc. With collaborations increasingly occurring remotely, next-generation collaborative interfaces could enhance CPS processes and outcomes with dynamic interventions or by generating feedback for after-action reviews. Automatic modeling of CPS processes (called facets here) is a precursor to this goal. Accordingly, we build automated detectors of three critical CPS facets—construction of shared knowledge, negotiation and coordination, and maintaining team function—derived from a validated CPS framework. We used data of 32 triads who collaborated via a commercial videoconferencing software, to solve challenging problems in a visual programming task. We generated transcripts of 11,163 utterances using automatic speech recognition, which were then coded by trained humans for evidence of the three CPS facets. We used both standard and deep sequential learning classifiers to model the human-coded facets from linguistic, task context, facial expressions, and acoustic–prosodic features in a team-independent fashion. We found that models relying on nonverbal signals yielded above-chance accuracies (area under the receiver operating characteristic curve, AUROC) ranging from.53 to.83, with increases in model accuracy when language information was included (AUROCS from.72 to.86). There were no advantages of deep sequential learning methods over standard classifiers. Overall, Random Forest classifiers using language and task context features performed best, achieving AUROC scores of.86,.78, and.79 for construction of shared knowledge, negotiation/coordination, and maintaining team function, respectively. We discuss application of our work to real-time systems that assess CPS and intervene to improve CPS outcomes. © 2021, The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature.

This article is in response to the review entitled Identifying potential types of guidance for supporting student inquiry when using virtual and remote labs in science: a literature review (Zacharia et al. in Educ Technol Res Dev 63(2): 257-302). As COVID-19 alerts us to shift science education to digital when in-person schooling is not viable, one approach to facilitate this shift, as reviewed by Zacharia et al. (2015), is to involve students in computer supported inquiry learning (CoSIL) with appropriate guidance. As CoSIL guidance is critical to student success in CoSIL, Zacharia et al. (2015) contribute to our knowledge by systematically reviewing the forms and the efficacy of such guidance tools that are associated with each phase of scientific inquiry. With such knowledge we may develop decent guidance so that students can experience scientific inquiry virtually as they used to do in-person. Zacharia et al. (2015) indicated that the various guidance tools had increased the ease of use of CoSIL but failed in personalizing CoSIL to individual students. I agree with Zacharia et al. that the personalization of CoSIL guidance is vital. Further, I argue that the emergent machine learning may significantly increase the personalization of CoSIL without burdening teachers. I conclude the essay with suggestions to further investigate the cognitive needs of students in CoSIL and integrate the content, CoSIL, and guidance tools, as a way to move forward the personalization of CoSIL.

Teacher dashboards in mathematics classrooms tend to provide teachers with information on student performance that are often linked to classroom management systems, online course systems, or peer-tutoring software. Teacher dashboards also tend to emphasize features that support teachers using a transition or direct instruction model. In our approach, we iteratively designed, developed, tested, and refined a teacher dashboard that is linked to a student digital collaborative environment with an embedded problem-based mathematics curriculum. In this study, we investigate teacher dashboard features that support teacher enactment of problem-based mathematics curriculum embedded in a digital collaborative platform. We report on design principles that guided the development of three teacher dashboard features: (1) monitoring evidence of student thinking in real-time or after class, (2) accessing workspace for whole-class discussions of the problem, and (3) creating and sending just-in-time supports. The pedagogical advantages and challenges teachers face throughout the iterative development process are also discussed. Evidence from observational data and teacher interviews suggests that the organic synergism generated from the student and teacher digital platform offers several ways that teachers are provided with new and timely information from teacher dashboards that supports problem-based mathematics teaching.

Game-based learning environments are designed to provide effective and engaging learning experiences for students. Predictive student models use trace data extracted from students' in-game learning behaviors to unobtrusively generate early assessments of student knowledge and skills, equipping game-based learning environments with the capacity to anticipate student outcomes and proactively deliver adaptive scaffolding or notify instructors. Reflection is a key component of self-regulated learning, and it is critical in effective learning. However, there is currently limited work exploring the utility of reflection for inducing accurate predictive student models. This article presents a predictive student modeling framework that leverages natural language responses to in-game reflection prompts to predict student learning outcomes in a game-based learning environment for middle school microbiology, CRYSTAL ISLAND. With data from a pair of classroom studies involving 118 middle school students, we investigate the accuracy of early prediction models that utilize features extracted from student trace data combined with word embedding-based representations (i.e., GloVe, ELMo) of student reflection responses. We evaluate the accuracy of the predictive models over time using data from incremental segments of each student's interaction with the game-based learning environment, and we compare against models that omit student reflection features. Results reveal that models encoding students' natural language reflections with ELMo word embeddings yield significantly improved accuracy compared to other representations, with the greatest accuracy demonstrated by an ensemble of predictive models. We discuss the implications of these results for the design of game-based learning environments.

Combinatorial proof is an important topic both for combinatorics education and proof education researchers, but relatively little has been studied about the teaching and learning of combinatorial proof. In this paper, we focus on one specific phenomenon that emerged during interviews with mathematicians and students who were experienced provers as they discussed and engaged in combinatorial proof. In particular, participants used a wide variety of cognitive models to interpret multiplication by a constant when reasoning about binomial identities, some of which seemed to be more (or less) effective in helping produce a combinatorial proof. We present these cognitive models and describe episodes that illustrate implications of these cognitive models for our participants' work on proving binomial identities. Our findings both inform research on combinatorial proof and highlight the importance of understanding subtleties of the familiar operation of multiplication.

This paper addresses two aspects of integrating mathematics education with engineering education that may address persistence of engineering majors (and STEM majors more broadly): an emphasis on modeling as a vehicle for more authentic learning activity (Niss et al. 2007), and the need for measures that can support academic units' efforts to collect local data about student attainment of program goals. In this paper, we contribute: (1) a measure for modeling self-efficacy and its corresponding design process; (2) a measure for modeling competency and its corresponding design process; (3) a preliminary analysis of the relationship between modeling competency and self-efficacy. We argue that such instruments address a genuine need of engineering departments (as well as STEM education researchers) to have a means for collecting local data on students' modeling self-efficacy and competency.

Computational thinking and activity are vital aspects of what it means to conduct scientific and mathematical work. In light of this, some propose that students' mathematical education should include an integration of computing into their mathematical experiences, giving students opportunities to engage with computational tools as they reason about mathematical concepts. In this commentary, we make a case that the international RUME community should focus on studying the integration of computing in research in undergraduate mathematics education. We situate this discussion within existing literature. Then, we suggest ways in which researchers can incorporate ideas related to computing, and we propose ideas for how investigations into computing might practically be incorporated into our already-existing research foci. Ultimately, we hope to motivate other members of the RUME community to join us in what we consider to be a timely and exciting endeavor.

A growing body of work has shown that two specific study strategies help explain differences in learning and achievement in gateway courses: spacing (breaking up study sessions across multiple days) and self-testing (actively recalling information from memory). However, it is still unclear whether the benefits of these strategies are applicable in more advanced biology courses, and whether promoting effective study practices in these courses (spacing and self-testing) is related to increased use of these practices and greater learning outcomes. We studied two senior-level microbiology courses that were taught by the same instructor. Using a quasi-experimental design, one course additionally received a light-touch study skills intervention, where the instructor introduced the concepts of spacing and self-testing while also providing reminders to students about utilizing these strategies. We found that, while the intervention was not related to increased use of spacing and self-testing, both strategies were positively related to learning, as measured by the final course grade. Results from multiple regression analyses revealed that engaging in spacing throughout the course was the most consistent predictor of final course grade, even after accounting for other study strategies, demographic characteristics, and prior academic achievement. Our results add to the literature emphasizing the importance of spacing in increasing students’ achievement in STEM courses. © Springer Nature Switzerland AG 2021.

EQUIP is a free, customizable observation protocol for tracking patterns of student participation in STEM classrooms (https://www.equip.ninja). EQUIP generates data analytics that are disaggregated by student social markers (e.g., race, gender), which makes it a useful tool for tracking patterns of inequity in student participation. However, prior studies have not yet established how many observations are needed to create a representative picture of instruction. In this study, we use g-theory and simulations with Cramer’s V to analyze observations from 20 undergraduate mathematics instructors to determine how many classroom observations are needed, and how this differs by individual codes. We found that Gender could achieve stability in just a few observations, whereas codes such as Instructor Response, Instructor Solicitation Type, and Instructor Solicitation Method required nearly 20 observations. Thus, we recommend that users account for their specific context and needs with EQUIP when determining the ideal number of observations to conduct, using this research as a baseline. We also compare the g-study and simulations approaches, bringing up new methodological questions for the field. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021.