Play offers an unparalleled opportunity for young children to gain cognitive skills in informal settings. Block play in particular-including interactions with parents around block constructions-teaches children about intrinsic spatial features of objects (size, shape) and extrinsic spatial relations. In turn, early spatial cognition paves the way for later competencies in math and science. We assessed 4- and 5-year-old children's spatial skill on a set of block-building constructions and examined mother-child block building interactions in 167 U.S. dyads from African American, Dominican, Mexican, and Chinese backgrounds. At both ages, children were instructed to copy several 3D block constructions, followed by a break during which mothers and children were left alone with the blocks. A form that contained pictures of test items was left on the table. Video-recordings of mother-child interactions during the break were coded for two types of building behaviors - test-specific construction (building structures on the test form) or free-form construction (building structures not on the test form). Chinese children outperformed Mexican, African American, and Dominican children on the block-building assessment. Further, Chinese and Mexican mother-child dyads spent more time building test-specific constructions than did African American and Dominican dyads. At an individual level, mothers' time spent building test-specific constructions at the 4-year (but not 5-year) assessment, but not mothers' initiation of block building interactions or verbal instructions, related to children's performance, when controlling for ethnicity. Ethnic differences in children's block-building performance and experiences emerge prior to formal schooling and provide a valuable window into sources of individual differences in early spatial cognition.

Although online courses are becoming increasingly popular in higher education, evidence is inconclusive regarding whether online students are likely to be as academically successful and motivated as students in face-to-face courses. In this study, we documented online and face-to-face students' academic motivation and outcomes in community college mathematics courses, and whether differences might vary based on student characteristics (i.e., gender, underrepresented ethnic/racial minority status, first-generation college status, and adult learner status). Over 2,400 developmental mathematics students reported on their math motivation at the beginning (Week 1) and middle (Weeks 3, 5) of the semester. Findings indicated that online students received lower grades and were less likely to pass from their courses than face-to-face students, with online adult learners receiving particularly low final course grades and pass rates. In contrast, online and face-to-face students did not differ on incoming motivation, with subgroup analyses suggesting largely similar patterns of motivation across student groups. Together, findings suggest that online and faceto-face students may differ overall in academic outcomes but not in their motivation or differentially based on student characteristics. Small but significant differences on academic outcomes across modalities (Cohen's ds = 0.17-0.28) have implications for community college students' success in online learning environments, particularly for adult learners who are most likely to be faced with competing demands.

Emotions are a core factor of learning. Studies have shown that multiple emotions are co-experienced during learning and have a significant impact on learning outcomes. The present study investigated the importance of multiple, co-occurring emotions during learning about human biology with MetaTutor, a hypermedia-based tutoring system. Person-centered as well as variable-centered approaches of cluster analyses were used to identify emotion clusters. The person-centered clustering analyses indicated three emotion profiles: a positive, negative and neutral profile. Students with a negative profile learned less than those with other profiles and also reported less usage of emotion regulation strategies. Emotion patterns identified through spectral co-clustering confirmed these results. Throughout the learning activity, emotions built a stable correlational structure of a positive, a negative, a neutral and a boredom emotion pattern. Positive emotion pattern scores before the learning activity and negative emotion pattern scores during the learning activity predicted learning, but not consistently. These results reveal the importance of negative emotions during learning with MetaTutor. Potential moderating factors and implications for the design and development of educational interventions that target emotions and emotion regulation with digital learning environments are discussed.

Under the guidance of a formal exemplar model of categorization, we conduct comparisons of natural-science classification learning across four conditions in which the nature of the training examples is manipulated. The specific domain of inquiry is rock classification in the geologic sciences; the goal is to use the model to search for optimal training examples for teaching the rock categories. On the positive side, the model makes a number of successful predictions: Most notably, compared with conditions involving focused training on small sets of training examples, generalization to novel transfer items is significantly enhanced in a condition in which learners experience a broad swath of training examples from each category. Nevertheless, systematic departures from the model predictions are also observed. Further analyses lead us to the hypothesis that the high-dimensional feature-space representation derived for the rock stimuli (to which the exemplar model makes reference) systematically underestimates within-category similarities. We suggest that this limitation is likely to arise in numerous situations in which investigators attempt to build detailed feature-space representations for naturalistic categories. A low-parameter extended version of the model that adjusts for this limitation provides dramatically improved accounts of performance across the four conditions. We outline future steps for enhancing the current feature-space representation and continuing our goal of using formal psychological models to guide the search for effective methods of teaching science categories.

Over the past ten years, research into students' emotions in educational environments has increased. Although researchers have called for more studies that rely on objective measures of emotional experience, limitations on utilizing multi-modal data sources exist. Studies of emotion and emotional regulation in classrooms traditionally rely on survey instruments, experience-sampling, artifacts, interviews, or observational procedures. These methods, while valuable, are mainly dependent on participant or observer subjectivity and is limited in its authentic measurement of students' real-time performance to a classroom activity or task. The latter, in particular, poses a stumbling block to many scholars seeking to objectively measure emotions and other related measures in the classroom, in real-time. The purpose of this work is to present a protocol to experimentally study students' real-time responses to exam experiences during an authentic assessment situation. For this, a team of educational psychologists, engineers, and engineering education researchers designed an experimental protocol that retained the limits required for accurate physiological sensor measurement, best-practices of salivary collection, and an authentic testing environment. In particular, existing studies that rely on physiological sensors are conducted in experimental environments that are disconnected from educational settings (e.g., Trier Stress Test), detached in time (e.g., before or after a task), or introduce analysis error (e.g., use of sensors in environments where students are likely to move). This limits our understanding of students' real-time responses to classroom activities and tasks. Furthermore, recent research has called for more considerations to be covered around issues of recruitment, replicability, validity, setups, data cleaning, preliminary analysis, and particular circumstances (e.g., adding a variable in the experimental design) in academic emotions research that relies on multi-modal approaches.

Using more than one programming language in the same project is common practice. Often, additional languages might be introduced to projects to solve specific issues. While the practice is common, it is unclear whether it has an impact on developer productivity. In this paper, we present a pilot study investigating what happens when programmers switch between programming languages. The experiment is a repeated measures double-blind randomized controlled trial with 3 groups with various kinds of code switching in a database context. Results provide a rigorous testing methodology that can be replicated by us or others and a theoretical backing for why these effects might exist from the linguistics literature. © Phillip Merlin Uesbeck and Andreas Stefik.

The stories of high-achieving African American mathematics students are gaining prominence in the research literature. In this multiple case study, I use a critical race theoretical frame to document and analyze the experiences of 4 mathematically persistent African American male students who earned undergraduate degrees in mathematics and subsequently enrolled in mathematics or mathematics education graduate programs. The findings reveal that these African American men drew from internal factors to influence their mathematical persistence and identified how racial microaggressions manifest themselves in postundergraduate contexts. Recommendations for practice, policy implications, and future research directions that emerged from this study are discussed to better understand African American men’s mathematics experiences. © 2019 by theational Council of Teachers of athematics.

Constructed responses can be used to assess the complexity of student thinking and can be evaluated using rubrics. The two most typical rubric types used are holistic and analytic. Holistic rubrics may be difficult to use with expert-level reasoning that has additive or overlapping language. In an attempt to unpack complexity in holistic rubrics at a large scale, we have developed a systematic approach called deconstruction. We define deconstruction as the process of converting a holistic rubric into defining individual conceptual components that can be used for analytic rubric development and application. These individual components can then be recombined into the holistic score which keeps true to the holistic rubric purpose, while maximizing the benefits and minimizing the shortcomings of each rubric type. This paper outlines the deconstruction process and presents a case study that shows defined concept definitions for a hierarchical holistic rubric developed for an undergraduate physiology-content reasoning context. These methods can be used as one way for assessment developers to unpack complex student reasoning, which may ultimately improve reliability and validation of assessments that are targeted at uncovering large-scale complex scientific reasoning. ©