Development of CLASSALI: An Online Learning Tool and Academic Performance Report for Makati Public Elementary Schools

Purpose–The main focus of the study is to develop an asynchronous framework ready for online classes of Makati Public Elementary Schools with Academic Performance Report to help users identify students at academic risk and adjust educational strategies to meet student's academic needs.CLASSALI is equipped with five major features namely: (1) Virtual Classroom, (2) Online Quiz, (3) Grading System, (4) CLASSALI Forum, and (5) Data Analytics Reports.Method–Researchers conduct an online assessment that has been tested by fifteen IT Practitioners and fifteen Non-IT Participants including admin, teachers, students, and parents. The data collected during the evaluation were computed for the analysis and efficiency of the system. Results–The system gathered reviews mostly with “Excellent” remarks.Conclusion–The system was found relevant nowadays since a new normal setting (i.e., pure online classes) due to the COVID-19 pandemic.Recommendations–In the future analysis, some research points should be given moreattention. First and foremost, the developed learning management systemshould be implemented in other schoolsin the Philippines.In the future to further examine the stability and generalization of the developed learning management system. Practical Implications–It helps significant users such as teachers, school administrators, and parents rapidly identify students at academic risk and adjust educational strategies to meet students' needs.

Identifying the Impact of Culturally Relevant Pedagogy: Evidence of Academic Risk-Taking in Culturally and Economically Diverse Nova Scotia Classrooms

This article reports on findings from a qualitative research study investigating ways to support learners from populations who have been historically underserved by the Nova Scotia education system, particularly African Nova Scotian and Mi’kmaq learners, and learners who experience poverty. Working with middle school teachers located in rural schools with a proven track record of enabling students to succeed and thrive, we spent two years in their classrooms observing and documenting pedagogical practices in the teaching of science and social studies. The results of this research not only complement what is known about how to support vulnerable learners in diverse school contexts, but also provide insights into how these teachers created conditions in which students felt able to take risks academically. The findings of this study show how the idea of academic risk-taking can complement, and expand, scholarship on culturally relevant pedagogy.

Identifying and Addressing Social Determinants of Learning during the COVID-19 Pandemic

The COVID-19 pandemic has negatively impacted the health of people from communities of color and people of limited socioeconomic means in a disproportionate way due to social determinants of health (SDoH). The Centers for Disease Control defines SDoH as the “conditions in the places where people live, learn, work, and play that affect a wide range of health and quality-of life-risks and outcomes.” A related construct, social determinants of learning (SDoL), includes contextual conditions and variables that impact students’ ability to optimally participate in their education, including academic and clinical development. SDoL directly impact students’ ability to participate in the educational process. During the COVID-19 pandemic, students struggling with SDoH and, by extension SDoL, may be more likely to have sick family members, caregiving responsibilities, food and housing insecurity, and obligations to supplement lost family wages. SDoL are also influenced by individual experiences within and outside of the classroom. Beyond bringing this matter to the attention of our profession, especially clinical and academic educators, we must take action to reach and support students who are at higher academic risk due to the SDoL. The purpose of this paper is to: (1) define SDoL, (2) explain how SDoL are impacting DPT and physical therapist assistant students, and (3) discuss actions that physical therapists and physical therapist assistants can take to mitigate the effects of SDoL on current DPT and physical therapist assistant students.

The Effects of Co-Teaching Zones of Regulation on Elementary Students’ Social, Emotional, and Academic Risk Behaviors

Student misbehavior is a significant concern in the current classroom. Teachers nationwide have implemented several approaches to reduce student misbehavior, including School-wide Positive Behavior Interventions and Supports (PBIS) and co-teaching. However, since misbehaver still disrupts learning, research is still needed to find a classroom intervention that will reduce misbehavior in a classroom to prevent students from under achieving, disruption of peers’ learning and teacher burnout. Zones of Regulation focuses on self-regulation, while addressing sensory processing, executive functioning, and emotional regulation. This experimental study tested the effects of co-taught Zones of Regulation on students’ Social, Academic, and Emotional Behavior Risk Screener (SAEBRS). Fifty-six early elementary students (48% female) were assigned randomly to either the experimental group, which received co-taught direct instruction with Zones of Regulation or the control group, which received standard instruction with a morning meeting. It was predicted that the experimental group would score higher on the SAEBRS than the control group. Although results showed no statistical difference on the SAEBRS scores between the two groups, more students in the experimental group moved from “at risk” to “not at risk”. This study suggests additional research needs to be conducted to determine if co-taught Zones of Regulation instruction is an effective intervention for reducing misbehavior.

Evaluation of multiple transcriptomic gene risk signatures in male breast cancer

Male breast cancer (BCa) is a rare disease accounting for less than 1% of all breast cancers and 1% of all cancers in males. The clinical management is largely extrapolated from female BCa. Several multigene assays are increasingly used to guide clinical treatment decisions in female BCa, however, there are limited data on the utility of these tests in male BCa. Here we present the gene expression results of 381 M0, ER+ve, HER2-ve male BCa patients enrolled in the Part 1 (retrospective analysis) of the International Male Breast Cancer Program. Using a custom NanoString™ panel comprised of the genes from the commercial risk tests Prosigna®, OncotypeDX®, and MammaPrint®, risk scores and intrinsic subtyping data were generated to recapitulate the commercial tests as described by us previously. We also examined the prognostic value of other risk scores such as the Genomic Grade Index (GGI), IHC4-mRNA and our prognostic 95-gene signature. In this sample set of male BCa, we demonstrated prognostic utility on univariate analysis. Across all signatures, patients whose samples were identified as low-risk experienced better outcomes than intermediate-risk, with those classed as high risk experiencing the poorest outcomes. As seen with female BCa, the concordance between tests was poor, with C-index values ranging from 40.3% to 78.2% and Kappa values ranging from 0.17 to 0.58. To our knowledge, this is the largest study of male breast cancers assayed to generate risk scores of the current commercial and academic risk tests demonstrating comparable clinical utility to female BCa.

The Counseling Center Assessment of Psychological Symptoms-62 (CCAPS-62) and College Academic Outcomes

College students experiencing psychological distress are at risk for negative academic outcomes. The Counseling Center Assessment of Psychological Symptoms-62 (CCAPS-62) is a symptom inventory designed for and widely used in college counseling centers. However, the relationships between the CCAPS-62 and functional outcomes in the college environment have not been examined. This study examined the validity of the CCAPS-62 in predicting term grade point average (GPA) and dropout. Data from 297 first-year students at a university’s counseling center were analyzed using multiple regression to determine associations between CCAPS-62 subscales, term GPA, and dropout within the subsequent three academic years. Results show that academic distress was predictive of all academic outcomes in the expected directions, social anxiety was associated with higher term GPA and retention, and hostility was associated with lower term GPA and dropout. Results demonstrated support for the instrument’s predictive validity in the identification of students at academic risk.

Modelos predictivos de riesgo académico en carreras de computación con minería de datos educativos

Los problemas de bajo rendimiento académico y rezago son recurrentes en instituciones educativas de nivel superior, especialmente al inicio de los estudios universitarios. En el contexto local, análisis diagnósticos han mostrado altos índices de reprobación y bajo rendimiento académico. En este trabajo, se utilizaron datos sociodemográficos y resultados de exámenes de admisión de 415 alumnos de las carreras del área de computación de la Universidad Autónoma de Yucatán (México), inscritos entre 2016 y 2019. El objetivo es generar modelos predictivos de riesgo académico, empleando métodos de la minería de datos educativa, que sirvan como herramientas de detección temprana de condiciones de riesgo académico y faciliten el despliegue de estrategias de intervención educativa. Se siguieron las etapas del Proceso de Extracción de Conocimiento en Bases de Datos, concretamente, se aplicaron técnicas de clasificación para el análisis, obtención y validación de los modelos. Los resultados muestran que el mejor modelo corresponde al algoritmo LMT, con un valor de precisión de 75.42% y un 0.805 para el área bajo la curva ROC. Se logró identificar a los mejores atributos predictores, particularmente las pruebas del examen de ingreso a licenciatura fueron muy significativas. Se propone el desarrollo de herramientas informáticas para la detección precoz de riesgo académico y estrategias de intervención educativa oportuna. The problems of poor academic performance and lag are recurrent in higher-level educational institutions, especially at the beginning of university studies. The early detection of academic risk conditions enables the implementation of educational intervention measures to address factors of poor school performance, associated with the particular contexts of the students. The purpose of this study was to generate predictive models of academic risk, using educational data mining methods, specifically classification or prediction techniques, for the analysis, obtaining and validation of the models. The data used correspond to admission exam results and sociodemographic data of 415 students of the computer science majors at the Autonomous University of Yucatán (Mexico), enrolled between 2016 and 2019. The results show that the best model corresponding to the algorithm of LMT classification, with a precision value of 75.42% and 0.805 for the area under the ROC curve. It was possible to identify the best predictive attributes, particularly the bachelor entrance exam tests were very significant. The development of computer tools for the early detection of academic risk and strategies for timely educational intervention is proposed.