The present investigation examines how personal self-regulation (presage variable) and regulatory teaching (process variable of teaching) relate to learning approaches, strategies for coping with stress, and self-regulated learning (process variables of learning) and, finally, how they relate to performance and satisfaction with the learning process (product variables). The objective was to clarify the associative and predictive relations between these variables, as contextualized in two different models that use the presage-process-product paradigm (the Biggs and DEDEPRO models). A total of 1101 university students participated in the study. The design was cross-sectional and retrospective with attributional (or selection) variables, using correlations and structural analysis. The results provide consistent and significant empirical evidence for the relationships hypothesized, incorporating variables that are part of and influence the teaching–learning process in Higher Education. Findings confirm the importance of interactive relationships within the teaching–learning process, where personal self-regulation is assumed to take place in connection with regulatory teaching. Variables that are involved in the relationships validated here reinforce the idea that both personal factors and teaching and learning factors should be taken into consideration when dealing with a formal teaching–learning context at university.

One way to view ‘equitable pedagogy’ is through an opportunity to learn (OTL) lens, meaning that regardless of race, class, or culture, a student has access to rigorous and meaningful content, as well as appropriate resources and instruction necessary to learn and demonstrate understanding of that content. Assessment holds a unique position in the classroom in that it can both uncover whether inequitable conditions exist (i.e., performance gaps, denied OTL) and provide an OTL by mediating communication between teacher and students regarding learning progress and what is important to learn. Nevertheless, individuals entering teacher education programs often hold deficit views toward marginalized students, such as Language Minorities (LMs), believe that assessment strictly serves to evaluate learning, and do not do consider how language and culture influence student thinking–views supplanting assessment’s role at supporting an equitable pedagogy for LMs. Through surveys, interviews, program artifacts, and classroom observation, I report on a case study of one pre-service physics teacher, Dean, to depict how his expertise at assessing science did evolve throughout his yearlong teacher education program in terms of (a) becoming more knowledgeable of the role of language and (b) developing a belief in incorporating ‘discourse’ while assessing science. Within the case study, I analyze one particular episode from Dean’s teaching practicum to highlight remaining challenges for pre-service teachers to integrate science and language in classroom assessment—namely, interpreting students’ use of language along with their understanding of core science ideas. The findings underscore the need for connecting language and equity issues to content-area assessment in teacher preparation.

Background: Efforts to improve the care of patients with chronic disease in primary care settings have been mixed. Application of a complex adaptive systems framework suggests that this may be because implementation efforts often focus on education or decision support of individual providers, and not on the dynamic system as a whole. We believe that learning among clinic group members is a particularly important attribute of a primary care clinic that has not yet been well-studied in the health care literature, but may be related to the ability of primary care practices to improve the care they deliver.
To better understand learning in primary care settings by developing a scale of learning in primary care clinics based on the literature related to learning across disciplines, and to examine the association between scale responses and chronic care model implementation as measured by the Assessment of Chronic Illness Care (ACIC) scale.

Methods: Development of a scale of learning in primary care setting and administration of the learning and ACIC scales to primary care clinic members as part of the baseline assessment in the ABC Intervention Study. All clinic clinicians and staff in forty small primary care clinics in South Texas participated in the survey.

Results: We developed a twenty-two item learning scale, and identified a five-item subscale measuring the construct of reciprocal learning (Cronbach alpha 0.79). Reciprocal learning was significantly associated with ACIC total and sub-scale scores, even after adjustment for clustering effects.

Conclusions: Reciprocal learning appears to be an important attribute of learning in primary care clinics, and its presence relates to the degree of chronic care model implementation. Interventions to improve reciprocal learning among clinic members may lead to improved care of patients with chronic disease and may be relevant to improving overall clinic performance.

Recently, multistage testing (MST) has been adopted by several important large-scale testing programs and become popular among practitioners and researchers. Stemming from the decades of history of computerized adaptive testing (CAT), the rapidly growing MST alleviates several major problems of earlier CAT applications. Nevertheless, MST is only one among all possible solutions to these problems. This article presents a new adaptive testing design, “on-the-fly assembled multistage adaptive testing” (OMST), which combines the benefits of CAT and MST and offsets their limitations. Moreover, OMST also provides some unique advantages over both CAT and MST. A simulation study was conducted to compare OMST with MST and CAT, and the results demonstrated the promising features of OMST. Finally, the “Discussion” section provides suggestions on possible future adaptive testing designs based on the OMST framework, which could provide great flexibility for adaptive tests in the digital future and open an avenue for all types of hybrid designs based on the different needs of specific tests.

This article explores a variety of contemporary picturebooks that offer the picturebook as an object of construction, as a character, and as portals to other narrative levels. These metafictive devices break the traditional boundaries between fictional and real worlds by involving the reader in the narrative, talking to them directly, and requiring them to actively participate in the construction of the narrative. In addition, we offer some ways these picturebooks may be integrated into the elementary reading curriculum.

The current debate over graduate rate calculations and results has glossed over the relationship between student migration and the accuracy of various graduation rates proposed over the past five years. Three general grade-based graduation rates have been proposed recently, and each has a parallel version that includes an adjustment for migration, whether international, internal to the U.S., or between different school sectors. All of the adjustment factors have a similar form, allowing simulation of estimates from real data, assuming different unmeasured net migration rates. In addition, a new age-based graduation rate, based on mathematical demography, allows the simulation of estimates on a parallel basis using data from Virginia's public schools.

Both the direct analysis and simulation demonstrate that graduation rates can only be useful with accurate information about student migration. A discussion of Florida's experiences with longitudinal cohort graduation rates highlights some of the difficulties with the current status of the oldest state databases and the need for both technical confidence and definitional clarity. Meeting the No Child Left Behind mandates for school-level graduation rates requires confirmation of transfers and an audit of any state system for accuracy, and basing graduation rates on age would be a significant improvement over rates calculated using grade-based data.

This paper presents a Bayesian framework for evaluative classification. Current education policy debates center on arguments about whether and how to use student test score data in school and personnel evaluation. Proponents of such use argue that refusing to use data violates both the public’s need to hold schools accountable when they use taxpayer dollars and students’ right to educational opportunities. Opponents of formulaic use of test-score data argue that most standardized test data is susceptible to fatal technical flaws, is a partial picture of student achievement, and leads to behavior that corrupts the measures.

A Bayesian perspective on summative ordinal classification is a possible framework for combining quantitative outcome data for students with the qualitative types of evaluation that critics of high-stakes testing advocate. This paper describes the key characteristics of a Bayesian perspective on classification, describes a method to translate a naïve Bayesian classifier into a point-based system for evaluation, and draws conclusions from the comparison on the construction of algorithmic (including point-based) systems that could capture the political and practical benefits of a Bayesian approach. The most important practical conclusion is that point-based systems with fixed components and weights cannot capture the dynamic and political benefits of a reciprocal relationship between professional judgment and quantitative student outcome data.