Do universal social and emotional learning programs for secondary school students enhance the competencies they address? A systematic review

While universal school‐based social and emotional learning (SEL) programs claim to target various SEL competencies, earlier reviews have not provided a clear overview of the competencies in question. We therefore wished to identify the competencies targeted in SEL programs for secondary school students. We also aimed to examine the effects of these programs on SEL competencies and psychosocial health. The specific SEL competencies directly addressed in the programs' primary learning targets were identified based on the Collaborative for Academic, Social and Emotional Learning (CASEL) framework for SEL. Five bibliographic databases (PubMed, PsychINFO, Education Resources Information Center (ERIC), Applied Science Premier (ASP), and Web of Science) were searched for relevant research papers published between 2014 and 2018. We included 40 studies that investigated 32 programs focusing on two or more SEL competencies. While most programs targeted four or five of the SEL competencies, many of the included studies did not measure the programs' effects on all the competencies targeted. Our results showed that the SEL programs had substantial effects on the SEL competencies they addressed and on psychosocial health. Although the programs focused predominantly on self‐management skills and relationship skills, the largest summary effects were found for self‐awareness and social awareness.     

A Model-Based Inquiry Sequence as a Heuristic to Evaluate Students’ Emotional,Behavioural, and Cognitive Engagement

Research in Science Education - In this study we address the need to promote student engagement with school science and the need to measure a more comprehensive conception of engagement considering...     

The use of reverse engineering to analyse student computer programs

In this paper, an explanatory program understander that can analyse student solutions for programming assignments is described. The approach uses a number of reverse engineering techniques and is grounded on a cognitive model of procedural programming knowledge. It will be shown that, through the use of reverse engineering techniques, a lot of feedback (e.g. on errors) can be generated without having to use any prior knowledge on what the program is supposed to do. When such information is available, problem-specific feedback can also be generated. Furthermore, the use of the cognitive programming concepts allows one to specify the errors in an understandable way using exactly the same words and concepts as human programmers. This opens interesting possibilities for other uses of the proposed program understanding technique described in this article.     

Balance in Positive Emotional Expressivity Across School Contexts Relates to Kindergartners’ Adjustment

Positive emotional expressivity has been associated with increased social competence and decreased maladjustment in childhood. However, a few researchers have found null or even positive associations between positive emotional expressivity and maladjustment, which suggests that there may be nuanced associations of positive expressivity, perhaps as a function of the social context in which it is expressed. We examined whether observed positive emotional expressivity balance across peer-oriented/recreational and learning contexts predicted kindergarten children’s adjustment (N = 301). Research Findings: Higher positive expressivity during lunch/recess compared to positive expressivity in the classroom was associated with lower teacher–student conflict, externalizing behaviors, and depressive symptoms. In addition, overall positive emotional expressivity predicted lower externalizing behaviors as well as lower depressive and anxiety symptoms. Practice or Policy: The results suggest the importance of assessing observed positive emotional expressivity in context as a potential indicator of children’s maladjustment risk and the need for children to adapt their emotions to different contexts. Implications for assessing and supporting positive emotional expression balance and training emotional regulation in school are discussed.     

Heterogeneity in the Effects of College Course Placement

Traditionally colleges have relied on standalone non-credit-bearing developmental education (DE) to support students academically and ensure readiness for college-level courses. As emerging evidence has raised concerns about the effectiveness of DE courses, colleges and states have been experimenting with approaches that place students into credit-bearing coursework more quickly. To better understand which types of students might be most likely to benefit from being placed into college-level math coursework, this study examines heterogeneity in the causal effects of placement into college-level courses using a regression discontinuity design and administrative data from the state of Texas. We focus on student characteristics that are related to academic preparation or might signal a student’s likelihood of success or need for additional support and might therefore be factors considered for placement into college-level courses under “holistic advising” or “multiple measures” initiatives. We find heterogeneity in outcomes for many of the measures we examined. Students who declared an academic major designation, had bachelor’s degree aspirations, tested below college readiness on multiple subjects, were designated as Limited English Proficiency (LEP), and/or were economically disadvantaged status were more likely to benefit from placement into college-level math. Part-time enrollment or being over the age of 21 were associated with reduced benefits from placement into college-level math. We do not find any heterogeneity in outcomes for our high school achievement measure, three or more years of math taken in high school.

     

Dielectrophoretic differentiation of mouse ovarian surface epithelial cells, macrophages, and fibroblasts using contactless dielectrophoresis

Ovarian cancer is the leading cause of death from gynecological malignancies in women. The primary challenge is the detection of the cancer at an early stage, since this drastically increases the survival rate. In this study we investigated the dielectrophoretic responses of progressive stages of mouse ovarian surface epithelial (MOSE) cells, as well as mouse fibroblast and macrophage cell lines, utilizing contactless dielectrophoresis (cDEP). cDEP is a relatively new cell manipulation technique that has addressed some of the challenges of conventional dielectrophoretic methods. To evaluate our microfluidic device performance, we computationally studied the effects of altering various geometrical parameters, such as the size and arrangement of insulating structures, on dielectrophoretic and drag forces. We found that the trapping voltage of MOSE cells increases as the cells progress from a non-tumorigenic, benign cell to a tumorigenic, malignant phenotype. Additionally, all MOSE cells display unique behavior compared to fibroblasts and macrophages, representing normal and inflammatory cells found in the peritoneal fluid. Based on these findings, we predict that cDEP can be utilized for isolation of ovarian cancer cells from peritoneal fluid as an early cancer detection tool.     

Correlation analysis of performance measures for multi-label classification

In many important application domains, such as text categorization, scene classification, biomolecular analysis and medical diagnosis, examples are naturally associated with more than one class label, giving rise to multi-label classification problems. This fact has led, in recent years, to a substantial amount of research in multi-label classification. In order to evaluate and compare multi-label classifiers, researchers have adapted evaluation measures from the single-label paradigm, like Precision and Recall; and also have developed many different measures specifically for the multi-label paradigm, like Hamming Loss and Subset Accuracy. However, these evaluation measures have been used arbitrarily in multi-label classification experiments, without an objective analysis of correlation or bias. This can lead to misleading conclusions, as the experimental results may appear to favor a specific behavior depending on the subset of measures chosen. Also, as different papers in the area currently employ distinct subsets of measures, it is difficult to compare results across papers. In this work, we provide a thorough analysis of multi-label evaluation measures, and we give concrete suggestions for researchers to make an informed decision when choosing evaluation measures for multi-label classification.