As scientific models of student thinking, learning progressions (LPs) have been evaluated in terms of one important, but limited, criterion: fit to empirical data. We argue that LPs are not empirically adequate, largely because they rely on problematic assumptions of theory-like coherence in students' thinking. Through an empirical investigation of physics teachers' interactions with an LP-based score report, we investigate 2 other criteria of good models: utility and generativity. When interacting with LP-based materials, teachers often adopted finer-grained perspectives (in contrast to the levels-based perspective of the LP itself) and used these finer-grained perspectives to formulate more specific, actionable instructional ideas than when they reasoned in terms of LP levels. However, although teachers did not use the LP-based materials in ways envisioned by LP researchers, the teachers' interactions with the score reports embodied how philosophers envision the fruitful use of good models of dynamic, complex systems. In particular, teachers took a skeptical, inquiring stance toward the LP, using it as an oversimplified starting place for generating and testing hypotheses about student thinking and using concepts from the model in ways that moved beyond the knowledge available in the LP. Thus, despite-and perhaps even because of-their empirical inadequacy, LPs have the potential to serve teachers as productive models in ways not envisioned by LP researchers: as tools for knowledge generation.

Addressing high demand for developmental math instruction and low rates of successful completing of the developmental coursework, with cost and space constraints, has been an ongoing challenge for post-secondary institutions. With advances in online instructional technology, particularly those based on artificial intelligence, web-based instruction is increasingly considered as a way to alleviate these burdens. This is among one of the first efforts that uses a quasi-experimental design to compare the academic outcomes of students who take a developmental mathematics course in a blended setting that combines face-to-face instruction with an online intelligent tutorial system, ALEKS, to the academic outcomes of students who take the same course in a fully online setting. Results suggest that students receiving online-only instruction perform worse on the final exam and receive lower course grades. However, a cost-effectiveness analysis suggests that fully online instruction has both a lower cost per student enrolled and a lower cost per student passing the course.

Ages 10-14 mark a period in which children develop a strong sense of whether science is 'for them,' a time that typically coincides with the start of middle school in the United States and their first exposure to more rigorous science classes and testing. Experiences with science in and out of school can shape children's motivation to choose science careers or participate in voluntary science classes later on, for better or worse. We explore the hypothesis that children who engage in more informal educational science experiences at the start of this period are more likely than their peers to obtain and maintain interest, curiosity, and mastery goals in science (together forming a construct called fascination). We measured 983 children's fascination with science at the beginning and middle of sixth grade. We found that the children who participated in informal science during this time were more likely to maintain or have greater fascination than at the start. These findings held while also controlling for many potentially confounding covariates and are robust across subgroups by gender and race/ethnicity. Further, the effects are largest for those children whose family generally supports their learning.

Early Warning Systems (EWS) and Early Warning Indictors (EWI) have recently emerged as an attractive domain for states and school districts interested in predicting student outcomes using data that schools already collect with the intention to better time and tailor interventions. However, current diagnostic measures used across the domain do not consider the du& issues of sensitivity and specificity of predictors, key components for considering accuracy. We apply signal detection theory using Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) analysis adapted from the engineering and medical domains, and using the pROC package in R. Using nationally generalizable data from the Education Longitudinal Study of 2002 (ELS:2002) we provide examples of applying ROC accuracy analysis to a variety of predictors of student outcomes, such as dropping out of high school, college enrollment, and postsecondary STEM degrees and careers.

We investigated how young children evaluate disagreements between two people and whether formal education affects this capacity. We compared 120 first graders tested during the 2014-2015 academic year, who received a direct instruction-based curriculum, with 112 first graders tested in the same school system during the 2016-2017 academic year, who received an inquiry-based curriculum. All children were given a belief reasoning task that tested their ability to evaluate disagreements about matters of fact, matters of interpretation, and matters of preference. Children's evaluations of disagreements about interpretations or preferences did not differ depending on curriculum. Children who received an inquiry-based curriculum were more likely to resolve disagreements concerning facts correctly than children who received a direct instruction-based curriculum. When asked to justify their responses to disagreements about facts, children who received the inquiry-based curriculum relied more on an examination of the state of the world. We suggest that an inquiry-based curriculum fosters a greater appreciation for how first-hand experiences can create knowledge.

Similar estimation biases appear in a wide range of quantitative judgments, across many tasks and domains. Often, these biases (those that occur, for example, when adults or children indicate remembered locations of objects in bounded spaces) are believed to provide evidence of Bayesian or rational cognitive processing, and are explained in terms of relatively complex Bayesian models (e.g., the Category Adjustment Model). Here, we suggest that some of these phenomena may be accounted for instead within a simpler alternative theoretical framework that has previously been found to explain bias in common numerical estimation tasks across development. We report data from university undergraduate students and 7- through 10-year-olds completing a speeded linear position reproduction task. Bias in both adults' and children's responses was effectively explained in terms of a relatively simple psychophysical model of proportion estimation. These data clearly show that the proportion estimation framework is a viable alternative to theories that explain biases as the result of a Bayesian cognitive adjustment process. We also discuss our view that these data are not easily reconciled with the requirements of the more complex Category Adjustment Model that assumes estimates should exhibit a central tendency bias.

Collaborative problem solving (CPS) has been deemed a critical twenty-first century competency for a variety of contexts. However, less attention has been given to work aimed at the assessment and acquisition of such capabilities. Recently large scale efforts have been devoted toward assessing CPS skills, but there are no agreed upon guiding principles for assessment of this complex construct, particularly for assessment in digital performance situations. There are notable challenges in conceptualizing the complex construct and extracting evidence of CPS skills from large streams of data in digital contexts such as games and simulations. In the current paper, we discuss how the in-task assessment framework (I-TAF), a framework informed by evidence-centered design, can provide guiding principles for the assessment of CPS in these contexts. We give specific attention to one aspect of I-TAF, ontologies, and describe how they can be used to instantiate the student model in evidence-centered design which lays out what we wish to measure in a principled way. We further discuss how ontologies can serve as an anchor representation for other components of assessment such as scoring rubrics, evidence identification, and task design.

Protein glycosylation is an essential posttranslational modification that affects a myriad of physiologic processes. Humans with genetic defects in glycosylation, which result in truncated glycans, often present with significant cardiac deficits. Acquired heart diseases and their associated risk factors were also linked to aberrant glycosylation, highlighting its importance in human cardiac disease. In both cases, the link between causation and corollary remains enigmatic. The glycosyltransferase gene, mannosyl (-1,3-)-glycoprotein -1,2-N-acetylglucosaminyltransferase (Mgat1), whose product, N-acetylglucosaminyltransferase 1 (GlcNAcT1) is necessary for the formation of hybrid and complex N-glycan structures in the medial Golgi, was shown to be at reduced levels in human end-stage cardiomyopathy, thus making Mgat1 an attractive target for investigating the role of hybrid/complex N-glycosylation in cardiac pathogenesis. Here, we created a cardiomyocyte-specific Mgat1 knockout (KO) mouse to establish a model useful in exploring the relationship between hybrid/complex N-glycosylation and cardiac function and disease. Biochemical and glycomic analyses showed that Mgat1KO cardiomyocytes produce predominately truncated N-glycan structures. All Mgat1KO mice died significantly younger than control mice and demonstrated chamber dilation and systolic dysfunction resembling human dilated cardiomyopathy (DCM). Data also indicate that a cardiomyocyte L-type voltage-gated Ca2+ channel (Ca-v) subunit (21) is a GlcNAcT1 target, and Mgat1KO Ca-v activity is shifted to more-depolarized membrane potentials. Consistently, Mgat1KO cardiomyocyte Ca2+ handling is altered and contraction is dyssynchronous compared with controls. The data demonstrate that reduced hybrid/complex N-glycosylation contributes to aberrant cardiac function at whole-heart and myocyte levels drawing a direct link between altered glycosylation and heart disease. Thus, the Mgat1KO provides a model for investigating the relationship between systemic reductions in glycosylation and cardiac disease, showing that clinically relevant changes in cardiomyocyte hybrid/complex N-glycosylation are sufficient to cause DCM and early death.Ednie, A. R., Deng, W., Yip, K.-P., Bennett, E. S. Reduced myocyte complex N-glycosylation causes dilated cardiomyopathy.

While many studies have examined the structure, validity, and reliability of the Force Concept Inventory, far less research has been performed on other conceptual instruments in widespread use in physics education research. This study performs a confirmatory analysis of the Conceptual Survey of Electricity and Magnetism (CSEM) guided by a theoretical model of expert understanding of electricity and magnetism. Multidimensional Item Response Theory (MIRT) with the discrimination matrix constrained to the theoretical model was used to investigate two large datasets (N-1 = 2014 and N-2 = 2657) from two research universities in the United States. The optimal model identified by MIRT was similar, but not identical, for the two datasets and had very good model fit with comparative fit indices of 0.975 and 0.984, respectively. The most parsimonious optimal model required 23 independent principles of electricity and magnetism and was significantly better fitting than a more general model dividing the CSEM into 6 general topics. The optimal models for the two samples were quite similar, sharing 22 of a possible 26 conceptual principles. Most of the overall item difficulties and discriminations were significantly different between the two samples; however, the rank order of the overall difficulty and discrimination were generally similar. There was much more similarity between the discrimination by item of the individual principles. Five items had a difficulty ranking that was substantially different between the two samples, indicating that while generally similar, relative difficulty does depend on the student population and instructional environment.

The use of machine learning and data mining techniques across many disciplines has exploded in recent years with the field of educational data mining growing significantly in the past 15 years. In this study, random forest and logistic regression models were used to construct early warning models of student success in introductory calculus-based mechanics (Physics 1) and electricity and magnetism (Physics 2) courses at a large eastern land-grant university. By combining in-class variables such as homework grades with institutional variables such as cumulative GPA, we can predict if a student will receive less than a B in the course with 73% accuracy in Physics 1 and 81% accuracy in Physics 2 with only data available in the first week of class using logistic regression models. The institutional variables were critical for high accuracy in the first four weeks of the semester. In-class variables became more important only after the first in-semester examination was administered. The student's cumulative college GPA was consistently the most important institutional variable. Homework grade became the most important in-class variable after the first week and consistently increased in importance as the semester progressed; homework grade became more important than cumulative GPA after the first in-semester examination. Demographic variables including gender, race or ethnicity, and first generation status were not important variables for predicting course grade.