DISTRIBUTIVE PARTITIONING OPERATION IN MATHEMATICAL SITUATIONS INVOLVING FRACTIONAL QUANTITIES

In the present study, we describe a participating student’s (Carol’s) distributive partitioning scheme and operations along with Steffe’s and his colleagues’ studies about children’s constructions of fraction knowledge as a particular model of mathematical learning. Analysis of Carol’s mathematical behaviors indicates that an operationally common mathematical behavior (distributive partitioning operation) was revealed in various mathematical problem situations such as fraction multiplication, fraction division, and multiplicative transformation between fractional quantities. It both provides a rationale for why becoming versed in one mathematical subject could facilitate working with another mathematical subject and also implies the necessity of describing and defining students’ mathematical behaviors from an operational view of knowledge, which might lead to building foundations of a substantial cognitive map for students’ mathematical development.     

Testing the specificity of environmental risk factors for developmental outcomes

Developmental theories often assume that specific environmental risks affect specific outcomes. Canonical Correlation Analysis was used to test whether 28 developmental outcomes (measured at 11–15 years) share the same early environmental risk factors (measured at 0–3 years), or whether specific outcomes are associated with specific risks. We used data from the UK Millennium Cohort Study (N = 10,376, 51% Female, 84% White) collected between 2001 and 2016. A single environment component was mostly sufficient for explaining cognition and parent-rated behavior outcomes. In contrast, adolescents’ alcohol and tobacco use were specifically associated with their parents’, and child-rated mental health was weakly associated with all risks. These findings suggest that with some exceptions, many different developmental outcomes share the same early environmental risk factors.     

Global pedigree and national imperative: Hong Kong universities’ response to China’s grand strategies

Higher Education - Literature offers a theoretical framework exemplifying the inherent tensions between “becoming Chinese” and “remaining global” in the evolution of the...     

The Effects of Perfectionism and Dispositional Self-focused Attention of Novice Counselor on Psychological Burnout: Moderated Mediating Effects of Cognitive Emotion Regulation Strategies

International Journal for the Advancement of Counselling - The current study investigated the mediation effect of self-focused attention on the relationship between South Korean novice...     

Teaching Science as a Process,Not a Set of Facts

Science & Education - The widespread misperception of science as a deliverer of irrefutable facts, rather than a deliberative process, is undermining public trust in science. Science education...     

Cheating among elementary school children: A machine learning approach

Academic cheating is common, but little is known about its early emergence. It was examined among Chinese second to sixth graders (N = 2094; 53% boys, collected between 2018 and 2019) using a machine learning approach. Overall, 25.74% reported having cheated, which was predicted by the best machine learning algorithm (Random Forest) at a mean accuracy of 81.43%. Cheating was most strongly predicted by children's beliefs about the acceptability of cheating and the observed prevalence and frequency of peer cheating at school. These findings provide important insights about the early development of academic cheating, and how to promote academic integrity and limit cheating before it becomes entrenched. The present research demonstrates that machine learning can be effectively used to analyze developmental data.     

Language and reading impairments are associated with increased prevalence of non-right-handedness

Handedness has been studied for association with language-related disorders because of its link with language hemispheric dominance. No clear pattern has emerged, possibly because of small samples, publication bias, and heterogeneous criteria across studies. Non-right-handedness (NRH) frequency was assessed in N = 2503 cases with reading and/or language impairment and N = 4316 sex-matched controls identified from 10 distinct cohorts (age range 6–19 years old; European ethnicity) using a priori set criteria. A meta-analysis (N_cases = 1994) showed elevated NRH % in individuals with language/reading impairment compared with controls (OR = 1.21, CI = 1.06–1.39, p = .01). The association between reading/language impairments and NRH could result from shared pathways underlying brain lateralization, handedness, and cognitive functions.     

Disambiguation and co-authorship networks of the U.S. patent inventor database (1975–2010)

Research into invention, innovation policy, and technology strategy can greatly benefit from an accurate understanding of inventor careers. The United States Patent and Trademark Office does not provide unique inventor identifiers, however, making large-scale studies challenging. Many scholars of innovation have implemented ad-hoc disambiguation methods based on string similarity thresholds and string comparison matching; such methods have been shown to be vulnerable to a number of problems that can adversely affect research results. The authors address this issue contributing (1) an application of the Author-ity disambiguation approach (0170 and 0175) to the US utility patent database, (2) a new iterative blocking scheme that expands the match space of this algorithm while maintaining scalability, (3) a public posting of the algorithm and code, and (4) a public posting of the results of the algorithm in the form of a database of inventors and their associated patents. The paper provides an overview of the disambiguation method, assesses its accuracy, and calculates network measures based on co-authorship and collaboration variables. It illustrates the potential for large-scale innovation studies across time and space with visualizations of inventor mobility across the United States. The complete input and results data from the original disambiguation are available at (http://dvn.iq.harvard.edu/dvn/dv/patent); revised data described here are at (http://funglab.berkeley.edu/pub/disamb_no_postpolishing.csv); original and revised code is available at (https://github.com/funginstitute/disambiguator); visualizations of inventor mobility are at (http://funglab.berkeley.edu/mobility/).