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DO CO-REQUISITE REMEDIAL MATH CLASSES WORK? A STUDY OF REMEDIATION AT MTC.
Get PDFAs post-secondary institutions look to reform remediation in mathematics, the concept of co-requisite remediation has been widely discussed. This is especially important for community colleges and other open access institutions who receive a large percentage of students that are not yet ready for the college level material. This study observed two years of students at Marion Technical College who received remediation in one of two styles: pre-requisite and co-requisite remediation. This study looked at a STEM course and Non-STEM course. Using logistic regression the study sought to predict the success of students using the remediation style received among other demographic and academic variables. Results were mostly inconclusive, however there is evidence that students who received co-requisite remediation performed as well or better than students who received pre-requisite remediation, supporting theories that it is more efficient for students to receive co-requisite remediation in a college course rather than work on pre-requisite courses that are not at the college level before enrolling in a college course
Effects of Emotional Words on Image Repair: A Crisis Communication Response Analysis
No full textDo certain words or phrases that prompt emotional reaction help an organization restore its image in the aftermath of a crisis? Some industry practitioners believe an emotional message can more effectively rebuild trust than simply stating what the organization is doing to correct the problem. An experiment was conducted to assess the post-crisis effects of reading a less emotional message than a more emotional one offered by the company at the center of the crisis
Strategies/ Use of Phonics in the Classroom and at Home
No full textThe purpose of this study is to determine and find strategies that children, parents, and teachers can use to help the child improve their phonics skills, which will enable them to read at a higher level. This study will investigate different strategies/ tools that parents, teachers, and students can use to help enhance their phonics knowledge and will be available for them to use outside of the classroom as well
Do Manipulatives Help Improve Students Phonics and Math Skills ?
No full textThe purpose of this study is to experiment to see to see if implementing hands on activities increases engagement in kindergarten students. Is there a difference when using hands on activities and when not? I want to look at this topic to see if student engagement increases with the use of these hands-on materials . Manipulates used will range counters, alphabet tiles,Unifix cubes, and counting bears
Boosting Patient Satisfaction Using the 6 C\u27s
No full textImproving patient experience and satisfaction in hospitals is something that is constantly changing or improving. Today in hospitals patient’s satisfaction is becoming the main priority and focus. This is because hospitals may not get paid by insurance companies if the patient they treated returns within 30 days seeking more medical attention for what they just sought medical attention for. Hospital’s staff should by focusing on the 6 C’s, which are the main aspect that hospitals staff need to focus on. This is the communication that they have with their patients. The second is the quality of the care that they provide which goes with the level of competence they have. Finally, the other remaining 6 C’s need to be executed into hospitals patient experience/satisfaction
Evidence-Based Practice in Nursing: Care and Insertion of Nasogastric Tube
No full textEvidence-based practice in nursing provides patient care that is based on current evidence, includes the perspective of the patient, and considers the clinical expertise of the nurse. This poster will discuss the scholarly endeavor to locate, review, and summarize the evidence regarding the care and insertion of nasogastric tubes and compare the findings with contemporary practice
The Effects of Volunteering Through the Years
No full textAn abundance of research completed enables one to look at the effects of volunteering, to see what possible benefits and drawbacks can result from the experience, and to grasp how applicable these qualities can be in the workforce and later years of life. “The Effects of Volunteering through the Years” poster presentation is a visual representing the main findings related to volunteering and provides an overview of the research discussed in a research paper of the same name created by the presenter
November and December 2019, Student Life Calendar
Get PDFStudent Life Calendarhttps://digitalcommons.shawnee.edu/event_calendars/1056/thumbnail.jp
An Exposition of the Hierarchical Linear Model on a Nested Data-Set with a perspective for Nested Effect discovery and modeling; A new metric for group (Nested) effect size is proposed.
Get PDFThe goal of this paper is to broaden general knowledge on nested data analysis, its problems of dependent data, the unit of analysis problem, and non-random one time only sampling, and look as a novice at how Hierarchical Linear Modelling, HLM, deals with this, and what the advantages of HLM, and shortfalls might be. The paper is a learning curve for the author, with observations along the way noted. That learning experience is shared, so other researchers will have a better understanding of errors arising from off-the-cuff interpretations on nested data, and have a broader view of the structural implications of nested data, shoring up a more competent overview of and adding more informed use of the statistical software that models nested data.
Nested Data is ubiquitous. Nested data analysis presents serious challenges to traditional statistical methods, and has been inadequately dealt with in many historical studies. Hierarchical Linear Modelling, HLM specifically addresses Nested data, mapping Nested effects with what are called Deviances, allowing for inference to wider populations when sample sizes are adequate, and carries an easily adaptable methodology for sub-modelling effects with more explanatory variables.
ANY data can be in some sense Nested, that is organized as sets of individual data points collected under groups. Data points inside a nest (group) often-times are not independent, the values among the set are related, and as such violate a critical assumption in statistical tests, that of independence. Determining and ascribing relations to the group level or individual level begins to get fuzzy, as a bias in a set of individuals can be mistaken for a group effect, and a biasing group effect can confound the individual effect. Like it or not, researchers will often be presented with Nested Data Sets. Most of the time, nested data is not collections of samples randomly selected into groups. Groups are presented with a pre auto-selected membership. A class of students is not ordinarily an independent sample of students. Education researchers will benefit from a familiarity of statistical property issues inherent in nested data. A basic understanding of sound modelling of nested data sets will at a minimum, steer the researcher clear of pitfalls such as the ecological fallacy, and atomistic fallacy, (explained herein), and with a modest learning curve, provide the researcher with a significantly better toolbox , Hierarchical Linear Modelling, HLM, for modelling nested data. Conventional statistical techniques, Ordinary Least Squares Regression and ANOVA, only rigorously apply to independent data-sets, and when independence of data is violated, inflated Type 1 , and other errors result, and regression coefficients are mis-represented, if the researcher disregards or is oblivious to the dependence of data.
Statistics in general is of such scope, that many researchers have not been introduced, or gained an appreciation of nested structure analysis difficulties, and HLM style methods readily available which are flexible, highly useful, and minimize errors in nested data analysis.
An actual data-set is examined with HLM and compared with traditional ANOVA, ANCOVA regressions. HLM showed no substantial improvements on analysis of this study data-set compared to ANOVA,ANCOVA results. HLM did however perform as a quality check on the traditional analysis. Regression coefficients generated both ways were very nearly the same, adding confidence to results obtained. No improvement on variance reduction was demonstrated by HLM on this data. The largest nest effect, Minority Status compared HLM with ANCOVA for the effect on Math Gain by Prescore. HLM Deviances were calculated arithmetically for School, Minority Status, and Sex. HLM is nevertheless recommended for its’ explicit and extendable equations value. This author ran r, but would recommend HLM specific software.
HLM is explained in a basic way, and the value of HLM Deviances is highlighted and related to the Linear Transform demonstrated in this paper which maps the Consensus (Total level 1 ) Line into a Group Line. Understanding the relations between the regression line types affords some reverse engineering. Note, that if (generally a good idea) it is desired to run HLM, a simple quality check is to subtract Consensus Slope and Intercepts from a Group line, and one should be able to obtain an estimate the HLM Deviances for that group line. By understanding how the Deviances relate simple 1st order regression lines, you don’t need to run the software to estimate the Group Deviances! The Deviances, slope and intercept, taken together quantify a Group Effect, which is a change on a Main Effect. A Group Effect is a general line shift. By specifying two X coordinates on the Line, pre and post shift, the line change is an AREA.
A proposed new group effect metric is given, Nested Shift Line Effect…NSLE . NSLE is a measure of the line shift area. Where sample sizes are sufficient, given variances relative to effect size, this new scale-free metric NSLE can rank Nesting effects, inside the study, and can be employed on Meta-Studies. NSLE also has value as a single measure diagnostic. By comparing similar magnitude NSLE’s, ( of the same and also opposite polarity ) one can evaluate set and subset influences for common cause variables. A favorable attribute of NSLE, is that HLM is not required for its calculation. One needs only to regress a given group line, and regress the Total (Consensus) Line, fix particular X coordinates and measure the area difference between them. This can all be calculated using only particular Y values. The HLM software is important however for the sub-modeling a group effect. Calculation of NSLE Standard Error is initiated and requires better definition. Were NSLE to be adopted by other researchers, a fixed agreed upon definition of NSLE standard error is essential. This task of selecting a standard definition for NSLE Standard Error is perhaps worthwhile for future researchers to consider. NSLE did identify a school and minority effect, also picked up by ANCOVA and noted them as of similar size, and when investigated, minority ratios in schools were responsible for NSLE on school effect, high, and low, correctly identifying the schools involved, demonstrating usefulness as a diagnostic or exploratory indicator
Evaluating Developmental Math Courses at Shawnee State University in Predicting Success in Gateway Courses
Get PDFHigh school students across the country continue to enroll in universities without the proper mathematical skills and knowledge to be successful in college level mathematics courses. Universities are faced with a challenging question: what do we do with students who are not prepared to take our mathematics courses? For most universities the answer is by placing them in developmental mathematics. The aim is for the students to build a foundation of mathematics that will allow them to succeed in their future college level gateway mathematic courses. However, recent studies are beginning to show that developmental math, while expensive and resource exhausting, may not be achieving what it intends to achieve. \u27Evaluating Developmental Math Courses at Shawnee State University in Predicting Success in Gateway Courses\u27 is a study that takes a close look at how Shawnee State University\u27s developmental math paths impact students in their gateway math courses. The aim is to determine whether the performance of a student in a developmental math course is predictive of a student\u27s success in the successive college level gateway course. The study also briefly models whether performance in a developmental math and gateway math is predictive of earning a degree at Shawnee State University within a specific time period. Shawnee State University offers five different paths from developmental math to gateway math. The researchers of this study used direct logistic regression to test whether developmental math (among other independent variables) could predict success in gateway math. The researchers first defined success in the gateway course as a student earning a C or higher and ran the five models on this definition. The researchers then defined success in a gateway course as students earning a D-or higher and reran all of the logistic regression models. In total, ten logistic regression models were ran to decide if performance in a developmental math course was predictive of success in a college level gateway course. Of the 10 models, 7 brought back developmental grades as a significant predictor of success, or more importantly, predictors of nonsuccess. This study also ran a logistic regression model with earning a degree or not predicted from seven independent variables. The intent was to decide if the developmental and gateway math grades were significant predictors of earning a degree. The model returned gateway grades as a statistically significant predictor, but not developmental grades. The results imply much of what recent studies in the area conclude. That is, a student\u27s success in developmental courses will predict their success in later courses. However, more students are failing or choosing to not move on then succeeding or persevering through the gateway courses. Seventy percent of students who took a developmental math course from 2011 to 2018 did not take a successive gateway math course. Thus indicating developmental courses are not achieving their intended purpose. Perhaps the fact that developmental performance is not a significant predictor of earning a degree is also valuable information. Why place a student in a developmental course if there is no significant data predicting that they will earn a degree? While gateway performance is a significant predictor, it makes more sense to spend resources to help students pass these courses as it may more likely predict whether they complete their degree. The bottom line is that, like Shawnee State University, other universities need to look at their developmental math programs critically and decide, is the time and money expended on these courses truly benefitting the respective students and respective university