Embellishing Problem-Solving Examples with Deep Structure Information Facilitates Transfer

Appreciation of problem structure is critical to successful learning. Two experiments investigated effective ways of communicating problem structure in a computer-based learning environment and tested whether verbal instruction is necessary to specify solution steps, when deep structure is already embellished by instructional examples. Participants learned to solve algebra-like problems and then solved transfer problems that required adjustment of learned procedures. Experiment 1 demonstrated that verbal instruction helped learning by reducing learners' floundering, but its positive effect disappeared in the transfer. More importantly, students transferred better when they studied with examples that emphasized problem structure rather than solution procedure. Experiment 2 showed that verbal instruction was not necessarily more effective than nonverbal scaffolding to convey problem structure. Final understanding was determined by transparency of problem structure regardless of presence of verbal instruction. However, verbal instruction had a positive impact on learners by having them persist through the task, and optimal instructional choices were likely to differ depending on populations of learners.     

Opinion Versus Knowledge: The Influence of Testimony Format on Children’s Judgments in Morally Relevant Contexts

Research Findings: To test children’s use of testimony of others, 3 – 9 years (N = 227) made judgments about a potential peer transgression in which the intentions of the protagonist were ambiguous, after hearing two different forms of testimony. The 2 forms of testimony were (a) opposing opinion-based testimony from an adult authority versus a peer consensus group and (b) knowledge-based testimony (eyewitness testimony) that was counter to the participants’ initial judgments. Findings revealed that when testimony was presented in an opinion-based format, children were likely to side with the opinion that reflected their own interpretation of the peer encounter, regardless of whether the opinion came from a peer consensus or an adult authority. However, when knowledge-based testimony was introduced in support of the opposite of children’s initial interpretation of the ambiguous peer encounter, children most often changed their initial judgment to align with the new testimony. That is, children used knowledge-based testimony but not opinion-based testimony to evaluate a potential transgression. Practice or Policy: These findings demonstrate that the way in which testimony is delivered to children has a direct influence on their decision making about peer interactions and has relevance for teacher–student discourse in the classroom.     

Novel model reference adaptive control laws for improved transient dynamics and guaranteed saturation constraints

In classical model reference adaptive control (MRAC), the adaptive rates must be tuned to meet multiple competing objectives. Large adaptive rates guarantee rapid convergence of the trajectory tracking error to zero. However, large adaptive rates may also induce saturation of the actuators and excessive overshoots of the closed-loop system’s trajectory tracking error. Conversely, low adaptive rates may produce unsatisfactory trajectory tracking performances. To overcome these limitations, in the classical MRAC framework, the adaptive rates must be tuned through an iterative process. Alternative approaches require to modify the plant’s reference model or the reference command input. This paper presents the first MRAC laws for nonlinear dynamical systems affected by matched and parametric uncertainties that constrain both the closed-loop system’s trajectory tracking error and the control input at all times within user-defined bounds, and enforce a user-defined rate of convergence on the trajectory tracking error. By applying the proposed MRAC laws, the adaptive rates can be set arbitrarily large and both the plant’s reference model and the reference command input can be chosen arbitrarily. The user-defined rate of convergence of the closed-loop plant’s trajectory is enforced by introducing a user-defined auxiliary reference model, which converges to the trajectory tracking error obtained by applying the classical MRAC laws before its transient dynamics has decayed, and steering the trajectory tracking error to the auxiliary reference model at a rate of convergence that is higher than the rate of convergence of the plant’s reference model. The ability of the proposed MRAC laws to prescribe the performance of the closed-loop system’s trajectory tracking error and control input is guaranteed by barrier Lyapunov functions. Numerical simulations illustrate both the applicability of our theoretical results and their effectiveness compared to other techniques such as prescribed performance control, which allows to constrain both the rate of convergence and the maximum overshoot on the trajectory tracking error of uncertain systems.     

Measure of opportunity: assessing equitable conditions to learn twenty-first century thinking skills

Learning Environments Research - Many school change efforts aim to foster conditions for the development of students’ twenty-first century skills in an equitable and comprehensive way, but...     

Overlaps of multiple database retrieval and citation tracking in dementia care research: a methodological study

Objective:We aimed to determine overlaps and optimal combination of multiple database retrieval and citation tracking for evidence synthesis, based on a previously conducted scoping review on facilitators and barriers to implementing nurse-led interventions in dementia care.Methods:In our 2019 scoping review, we performed a comprehensive literature search in eight databases (CENTRAL, CINAHL, Embase, Emcare, MEDLINE, Ovid Nursing Database, PsycINFO, and Web of Science Core Collection) and used citation tracking. We retrospectively analyzed the coverage and overlap of 10,527 retrieved studies published between 2015 and 2019. To analyze database overlap, we used cross tables and multiple correspondence analysis (MCA).Results:Of the retrieved studies, 6,944 were duplicates and 3,583 were unique references. Using our search strategies, considerable overlaps can be found in some databases, such as between MEDLINE and Web of Science Core Collection or between CINAHL, Emcare, and PsycINFO. Searching MEDLINE, CINAHL, and Web of Science Core Collection and using citation tracking were necessary to retrieve all included studies of our scoping review.Conclusions:Our results can contribute to enhancing future search practice related to database selection in dementia care research. However, due to limited generalizability, researchers and librarians should carefully choose databases based on the research question. More research on optimal database retrieval in dementia care research is required for the development of methodological standards.     

Exploring Daily Physical Activity and Nutrition Patterns in Early Learning Settings: Snapshots of Young Children in Head Start,Primary, and After-School Settings

The purpose of this research project was to gain a greater understanding of daily routines of 4–7 year olds regarding physical activity and nutrition practices in typical early learning environments. The settings selected for this observational study included Head Start, primary, and after-school learning environments in a city in the southeast. Specifically, this exploratory study focused on the research question: What are typical daily physical activity and nutrition patterns in community-based early learning settings for young children 4–7 years of age in the United States? Two overarching themes were identified through data analysis: restriction of activity and restriction of physical movement. Results indicated that children in elementary school settings had fewer opportunities for indoor and outdoor physical activity than children in other settings and that children seek to be physically active and will do so when given the opportunity. Increasing time for both structured and unstructured physical activity and play for children ages 4–7 in early learning settings was recommended by the researchers.