Effects of School Absences on GPAs for Disabled Students

2015 ◽  
Vol 4 (4) ◽  
pp. 165
Author(s):  
Elizabeth Christani ◽  
Laura Revetti ◽  
Ashleigh Sue Young ◽  
Karen H Larwin
Keyword(s):  
Academic Success ◽  
Students With Disabilities ◽  
National Data ◽  
Data Set ◽  
Content Areas ◽  
Core Content ◽  
School Absences ◽  
Health Related ◽  
Research Questions ◽  
Using Data

<table border="0" cellspacing="0" cellpadding="0"><tbody><tr><td width="387" valign="top"><p>Chronic absences, suspensions, and expulsions can all be detrimental to students’ GPAs. Students with disabilities have a disadvantage with learning and require additional services making it crucial that they are present in school. There are various reasons why students miss school and the study examined a few specific research questions. The current investigation examined students’ current GPA scores in the core content areas compared to the number of days absent from school, the frequency of health related school absences, and the number of days spent out of school due to suspensions and expulsions, using data from a national data set. This investigation examines whether or not attendance is related to students’ academic success, when specifically considering students with identified disabilities.</p></td></tr></tbody></table>

Do Community College Students Benefit from Federal Work-Study Participation?

2020 ◽  
Vol 122 (1) ◽  
pp. 1-36
Author(s):  
Hongwei Yu ◽  
Lyle Mckinney ◽  
Vincent D. Carales
Keyword(s):  
College Students ◽  
Community College ◽  
Academic Success ◽  
Community College Students ◽  
Selection Bias ◽  
Low Income ◽  
Work Study ◽  
Data Set ◽  
Self Selection ◽  
Research Questions

Background Prior studies suggest that Federal Work-Study (FWS) participation is positively associated with student learning, persistence, and academic achievement at four-year institutions. Limited research, however, has evaluated whether FWS participation improves academic success among students attending community colleges. Purpose and Research Questions The purpose of this study was to determine whether and how FWS participation impacted academic performance and enrollment outcomes among a racially/ethnically diverse sample of students attending a large, urban community college (UCC) system in Texas. There were two research questions: (1) What are the characteristics of students at UCC who participated in FWS, compared with their peers who did not participate? (2) After controlling for self-selection bias, are there significant differences in academic success (i.e., cumulative GPA; credential attainment and/or four-year transfer) among UCC students who did and did not participate in the FWS program? Research Design The longitudinal data set (fall 2010 through summer 2016) analyzed in this study was built using detailed student-level transcript data records. The full sample included 8,837 students who had filed a Free Application for Federal Student Aid (a necessary step to receive FWS funding), but the primary focus was on the subsample of FWS participants (n = 260). Descriptive analysis was performed to compare the demographic and academic characteristics of FWS participants with nonparticipants. To assuage self-selection bias, propensity score matching (nearest neighbor matching algorithm) was used to match similar students who did and did not participate in FWS. We employed multiple regression and logistic regression techniques on the matched data to investigate whether FWS participation was associated with students’ academic outcomes. Results Relative to their non-FWS peers, FWS participants at this community college were more likely to be female, African American, 24 years of age or older, very low income, and academically underprepared. After successfully matching FWS participants with similar non-FWS participants, results indicated that FWS participation was associated with a higher cumulative GPA and significantly higher odds of credential completion and/or vertical transfer. Conclusions There are important equity implications in our findings; the results suggest that the FWS program can improve educational outcomes for student populations that are often marginalized and underserved by the higher education system. We describe several ways that the FWS program could be redesigned and expanded to better meet the needs of community college students.

scholarly journals Prediction of the Academic Achievement of Pupils Using Data Mining Techniques

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 1355-1364
Author(s):  
Mokhalad Eesee Khudhur ◽  
Mohammed Shihab Ahmed ◽  
Saif Muhannad Maher
Keyword(s):  
Data Mining ◽  
Academic Achievement ◽  
Academic Success ◽  
Student Performance ◽  
Drop Out ◽  
Data Set ◽  
Perceptron Algorithm ◽  
Improve Student Performance ◽  
Using Data ◽  
Logistic Regression Algorithm

Introduction: During this epidemic, a problem in fundamental education affecting all globe is occurring, and we note that education and learning were online and conducted in students. Academic performance of students must be forecast, so that the instructor may better identify the missing pupils and offer teachers a proactive opportunity to develop additional resources for the student to maximize their chances of graduation. Students' academic achievement in higher learning (EH) has been extensively studied in addressing academic inadequacies, rising drop-out rates, graduation delays, and other difficult questions. Simply said, the performance of students refers to the amount to which short and long-term educational objectives are met. Academics nonetheless judge student achievement from different viewpoints, from grades, average grade points (GPAs) to prospective jobs. The literature encompasses numerous computing attempts to improve student performance in schools and colleges, primarily through data mining and analysis learning. However, the efficiency of current smart techniques and models is still unanimous. Method: This study employs multiple methods for machine learning to forecast student progress. With its accurate data sample prediction, five integrated classification algorithms have been created to forecast students' academic success (support vectors, decision-making trees algorithm and perceptron algorithm, logistic regression algorithm and a random forest algorithm). Results: Students' academic achievement has been reviewed and assessed. The performance of five learning machines mentioned in Section 4 is discussed here. First, we displayed the data after pre-processing by simply displaying distributions to form the data packet and then evaluated 5 important learning methods and described the variables in the data set. The entire series of 480 characteristics were examined.

Trends and Influences in the Integration of Students with Disabilities in Australia

1995 ◽  
Vol 19 (2) ◽  
pp. 47-53 ◽  
Author(s):  
Ian Dempsey ◽  
Phil Foreman
Keyword(s):  
Students With Disabilities ◽  
People With Disabilities ◽  
Australian Bureau ◽  
Educational Placement ◽  
National Data ◽  
School Students ◽  
Data Set ◽  
The Past ◽  
Australian Bureau Of Statistics ◽  
Educational Placements

Although support for the integration of students with disabilities has increased in the past 20 years in Australia, it has not been clear to what extent this support has resulted in less restrictive educational placements for these students. This paper reports the results of an analysis of trends in the placement of students with disabilities in Australian schools. The paper also discusses the influence on this educational placement by sex, age and number of disabilities of school students, and their State of residence. This discussion follows the analysis of portions of a national data set compiled by the Australian Bureau of Statistics that related to people with disabilities.

The Scope of Rape Victimization and Perpetration Among National Samples of College Students Across 30 years

2021 ◽  
pp. 088626052110501
Author(s):  
Mary P. Koss ◽  
Kevin M. Swartout ◽  
Elise C. Lopez ◽  
Raina V. Lamade ◽  
Elizabeth J. Anderson ◽  
...  
Keyword(s):  
College Students ◽  
Rape Prevention ◽  
Prevalence Rates ◽  
Rape Victims ◽  
Current Version ◽  
National Data ◽  
Prevention Focus ◽  
Cross Sectional ◽  
Data Set ◽  
Research Questions

Research Questions: Rape prevention practice and policy have roots in data from 1985. This study uses 2015 national data to project recent prevalence, assesses whether rates now differ from those of 30 years ago, and disaggregates 2015 prevalence into rape of alcohol incapacitated victims, rapes combining both alcohol and physical tactics, and violent rape. Methods: Cross-sectional analyses were conducted comparing two national samples. The first was collected in 1984-85 (Koss, Gidycz, & Wisniewski, 1987); the second was collected 30 years later in 2014-2015. Both surveys used in-person administration and measurement by the most current version at the time of the Sexual Experiences Survey (SES). Prevalence rates were compared using Bayesian binomial tests. Results: In 2015, 33.4% (1 in 3) of women reported experiencing rape or attempted rape and 12.7% of men reported perpetration (1 in 8). Using Jeffreys' label for effect size of the Bayes binomial (1961), both results are “decisively” greater than expected given the 1985 benchmarks of 27.9% for victimization and 7.7% for perpetration. Victimization when incapacitated characterized approximately 75% of incidents in 2015 up from 50% in 1985. Cautions apply as cross-sectional data does not establish causality and the recent data set involved the revised SES. Conclusions: Across 30 years, neither containment nor reduction of rape was demonstrated and the increasingly prominent association with alcohol was apparent. Among the men who disclosed raping, 9 of 10 incidents were alcohol-involved. Prevention focus might profitably be directed to constraining alcohol environments and policies that facilitate rape of incapacitated persons and on misconduct responses that are proportional to the harm caused to rape victims, thereby raising the perceived risks of perpetration.

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

Motivation Matters: Three Strategies to Support Motivation and Engagement in Mathematics

2021 ◽  
pp. 105345122199480
Author(s):  
Stephanie Morano ◽  
Andrew M. Markelz ◽  
Kathleen M. Randolph ◽  
Anna Moriah Myers ◽  
Naomi Church
Keyword(s):  
Academic Success ◽  
Students With Disabilities ◽  
Mathematics Classroom ◽  
Self Regulation ◽  
Token Economy ◽  
Self Monitoring ◽  
High Preference ◽  
Motivation And Engagement ◽  
Behavior Specific Praise ◽  
Attentive Behavior

Motivation and engagement in mathematics are important for academic success and are sometimes compromised in students with disabilities who have experienced a history of frustration and failure. This article explains how general and special education teachers can implement three research-supported strategies for boosting motivation and engagement for elementary students with or at risk of emotional and behavioral disorders (EBD) in the mathematics classroom. The strategies include (a) reinforcing engagement and motivation in mathematics using behavior-specific praise and token economy systems; (b) teaching self-monitoring and self-regulation strategies to promote attentive behavior and academic achievement; and (c) using the high-preference strategy to build behavioral momentum and support completion of nonpreferred tasks.

Risk-based capital regulation revisited: evidence from the early 2000s

2015 ◽  
Vol 23 (2) ◽  
pp. 115-134 ◽  
Author(s):  
Thomas L. Hogan ◽  
Neil R. Meredith ◽  
Xuhao (Harry) Pan
Keyword(s):  
Design Methodology ◽  
Banking Regulation ◽  
Bank Performance ◽  
Capital Regulation ◽  
Data Set ◽  
Content Type ◽  
Future Performance ◽  
Capital Ratios ◽  
Using Data ◽  
Capital Regulations

Purpose – The purpose of this study is to replicate Avery and Berger’s (1991) analysis using data from 2001 through 2011. Although risk-based capital (RBC) regulation is a key component of US banking regulation, empirical evidence of the effectiveness of these regulations has been mixed. Among the first studies of RBC regulation, Avery and Berger (1991) provide evidence from data on US banks that new RBC regulations outperformed old capital regulations from 1982 through 1989. Design/methodology/approach – Using data from the Federal Reserve’s Call Reports, the authors compare banks’ capital ratios and RBC ratios to five measures of bank performance: income, standard deviation of income, non-performing loans, loan charge-offs and probability of failure. Findings – Consistent with Avery and Berger (1991), the authors find banks’ risk-weighted assets to be significant predictors of their future performance and that RBC ratios outperform regular capital ratios as predictors of risk. Originality/value – The study improves on Avery and Berger (1991) by using an updated data set from 2001 through 2011. The authors also discuss some potential limitations of this method of analysis.

Disparities Across Time: Exploring Absenteeism Patterns between Cohorts of Students with Disabilities

2020 ◽  
Vol 122 (11) ◽  
pp. 1-32
Author(s):  
Michael A. Gottfried ◽  
Vi-Nhuan Le ◽  
J. Jacob Kirksey
Keyword(s):  
Students With Disabilities ◽  
Social Needs ◽  
Data Sets ◽  
Chronic Absenteeism ◽  
Data Set ◽  
Full Day Kindergarten ◽  
Effective Interventions ◽  
Nationally Representative ◽  
Single Data ◽  
Over Time

Background It is of grave concern that kindergartners are missing more school than students in any other year of elementary school; therefore, documenting which students are absent and for how long is of upmost importance. Yet, doing so for students with disabilities (SWDs) has received little attention. This study addresses this gap by examining two cohorts of SWDs, separated by more than a decade, to document changes in attendance patterns. Research Questions First, for SWDs, has the number of school days missed or chronic absenteeism rates changed over time? Second, how are changes in the number of school days missed and chronic absenteeism rates related to changes in academic emphasis, presence of teacher aides, SWD-specific teacher training, and preschool participation? Subjects This study uses data from the Early Childhood Longitudinal Study (ECLS), a nationally representative data set of children in kindergarten. We rely on both ECLS data sets— the kindergarten classes of 1998–1999 and 2010–2011. Measures were identical in both data sets, making it feasible to compare children across the two cohorts. Given identical measures, we combined the data sets into a single data set with an indicator for being in the older cohort. Research Design This study examined two sets of outcomes: The first was number of days absent, and the second was likelihood of being chronically absent. These outcomes were regressed on a measure for being in the older cohort (our key measure for changes over time) and numerous control variables. The error term was clustered by classroom. Findings We found that SWDs are absent more often now than they were a decade earlier, and this growth in absenteeism was larger than what students without disabilities experienced. Absenteeism among SWDs was higher for those enrolled in full-day kindergarten, although having attended center-based care mitigates this disparity over time. Implications are discussed. Conclusions Our study calls for additional attention and supports to combat the increasing rates of absenteeism for SWDs over time. Understanding contextual shifts and trends in rates of absenteeism for SWDs in kindergarten is pertinent to crafting effective interventions and research geared toward supporting the academic and social needs of these students.

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