scholarly journals Student Ability Best Predicts Final Grade in a College Algebra Course

2018 ◽  
Vol 5 (3) ◽  
Author(s):  
Kyle Anthony O'Connell ◽  
Elijah Wostl ◽  
Matt Crosslin ◽  
T. Lisa Berry ◽  
James P. Grover
Keyword(s):  
Student Success ◽  
Student Performance ◽  
Research University ◽  
Principal Component ◽  
College Level ◽  
Past Performance ◽  
Grade Point ◽  
Final Grade ◽  
Course Success ◽  
Targeted Interventions

Historical student data can help elucidate the factors that promote student success in mathematics courses. Herein we use both multiple regression and principal component analyses to explore ten years of historical data from over 20,000 students in an introductory college-level Algebra course in an urban American research university with a diverse student population in order to understand the relationship between course success and student performance in previous courses, student demographic background, and time spent on coursework. We find that indicators of students’ past performance and experience, including grade-point-average and the number of accumulated credit hours, best predict student success in this course. We also find that overall final grades are representative of the entire course and are not unduly weighted by any one topic. Furthermore, the amount of time spent working on assignments led to improved grade outcomes. With these baseline data, our team plans to design targeted interventions that can increase rates of student success in future courses.

Predicting Community College and University Student Success

2016 ◽  
Vol 18 (4) ◽  
pp. 431-456 ◽  
Author(s):  
Monica L. Heller ◽  
Jerrell C. Cassady
Keyword(s):  
Community College ◽  
Academic Success ◽  
Student Success ◽  
Student Performance ◽  
Learning Strategies ◽  
Prediction Models ◽  
Performance Outcomes ◽  
College Level ◽  
Grade Point ◽  
Predicting Student Performance

The current study explored the differential influences that behavioral learning strategies (i.e., cognitive–metacognitive, resource management), motivational profiles, and academic anxiety appraisals have on college-level learners in two unique learning contexts. Using multivariate analysis of variance and discriminant analysis, the study first compared these variables across learners from a community college and traditional 4-year university located within the same regional area. The study also employed a series of multiple regression analyses to investigate the influence of these variables in predicting student performance outcomes (i.e., grade point average). The results illustrate that prior research on those factors most salient within student academic success prediction models within a social cognitive framework function as expected for the university population. However, the community college learner experience deviates significantly from this standard model. For the community college learner, it is the environmental factor that appears to be the most significant to predicting student success. These findings highlight those factors most influential in academic performance outcomes among diverse student populations.

scholarly journals Modeling of student academic achievement in engineering education using cognitive and non-cognitive factors

2019 ◽  
Vol 11 (2) ◽  
pp. 178-198 ◽  
Author(s):  
Bothaina A. Al-Sheeb ◽  
A.M. Hamouda ◽  
Galal M. Abdella
Keyword(s):  
Student Success ◽  
Student Performance ◽  
Success Factors ◽  
Comprehensive Approach ◽  
First Year ◽  
Valuable Insight ◽  
Data Set ◽  
Grade Point ◽  
Content Type ◽  
Second Year

Purpose The retention and success of engineering undergraduates are increasing concern for higher-education institutions. The study of success determinants are initial steps in any remedial initiative targeted to enhance student success and prevent any immature withdrawals. This study provides a comprehensive approach toward the prediction of student academic performance through the lens of the knowledge, attitudes and behavioral skills (KAB) model. The purpose of this paper is to aim to improve the modeling accuracy of students’ performance by introducing two methodologies based on variable selection and dimensionality reduction. Design/methodology/approach The performance of the proposed methodologies was evaluated using a real data set of ten critical-to-success factors on both attitude and skill-related behaviors of 320 first-year students. The study used two models. In the first model, exploratory factor analysis is used. The second model uses regression model selection. Ridge regression is used as a second step in each model. The efficiency of each model is discussed in the Results section of this paper. Findings The two methods were powerful in providing small mean-squared errors and hence, in improving the prediction of student performance. The results show that the quality of both methods is sensitive to the size of the reduced model and to the magnitude of the penalization parameter. Research limitations/implications First, the survey could have been conducted in two parts; students needed more time than expected to complete it. Second, if the study is to be carried out for second-year students, grades of general engineering courses can be included in the model for better estimation of students’ grade point averages. Third, the study only applies to first-year and second-year students because factors covered are those that are essential for students’ survival through the first few years of study. Practical implications The study proposes that vulnerable students could be identified as early as possible in the academic year. These students could be encouraged to engage more in their learning process. Carrying out such measurement at the beginning of the college year can provide professional and college administration with valuable insight on students perception of their own skills and attitudes toward engineering. Originality/value This study employs the KAB model as a comprehensive approach to the study of success predictors. The implementation of two new methodologies to improve the prediction accuracy of student success.

Assessing the Validity of College Success Indicators for the At-Risk Student: Toward Developing a Best-Practice Model

2017 ◽  
Vol 21 (2) ◽  
pp. 166-183 ◽  
Author(s):  
Leslie Tucker ◽  
Oscar McKnight
Keyword(s):  
At Risk ◽  
Academic Success ◽  
Student Success ◽  
Best Practice ◽  
Research University ◽  
At Risk Students ◽  
American College Test ◽  
Retention Data ◽  
Grade Point ◽  
Success Indicators

This study assessed the feasibility of using precollege success indicators to identify at-risk students at a large 4-year public research university in the Midwest. Retention data from students who participated in an established student success program were examined. The findings affirm that the initial admissions assessment identifying at-risk students is a feasible predictor of academic success, including high school (HS) grade point average (GPA) could predict student success over and above the variance accounted for by American College Test alone; the semester in which students are admitted is a predictor of success; first-semester college GPA can predict academic success over and above chance; there is a significant positive relationship between cognitive ability (i.e., American College Test × HS GPA) and SUCCESS; HS GPA could be used as the single best predictor of student success; and using all three variables to identify student success appears warranted. A PASS model is offered to assist in the development of interventions and success programs.

scholarly journals Enhancing data pipelines for forecasting student performance: integrating feature selection with cross-validation

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Roberto Bertolini ◽  
Stephen J. Finch ◽  
Ross H. Nehm
Keyword(s):  
Feature Selection ◽  
Student Performance ◽  
Cross Validation ◽  
Model Development ◽  
Area Under The Curve ◽  
Performance Outcomes ◽  
Grade Point ◽  
Targeted Interventions ◽  
Attribute Evaluation ◽  
Feature Selection Techniques

AbstractEducators seek to harness knowledge from educational corpora to improve student performance outcomes. Although prior studies have compared the efficacy of data mining methods (DMMs) in pipelines for forecasting student success, less work has focused on identifying a set of relevant features prior to model development and quantifying the stability of feature selection techniques. Pinpointing a subset of pertinent features can (1) reduce the number of variables that need to be managed by stakeholders, (2) make “black-box” algorithms more interpretable, and (3) provide greater guidance for faculty to implement targeted interventions. To that end, we introduce a methodology integrating feature selection with cross-validation and rank each feature on subsets of the training corpus. This modified pipeline was applied to forecast the performance of 3225 students in a baccalaureate science course using a set of 57 features, four DMMs, and four filter feature selection techniques. Correlation Attribute Evaluation (CAE) and Fisher’s Scoring Algorithm (FSA) achieved significantly higher Area Under the Curve (AUC) values for logistic regression (LR) and elastic net regression (GLMNET), compared to when this pipeline step was omitted. Relief Attribute Evaluation (RAE) was highly unstable and produced models with the poorest prediction performance. Borda’s method identified grade point average, number of credits taken, and performance on concept inventory assessments as the primary factors impacting predictions of student performance. We discuss the benefits of this approach when developing data pipelines for predictive modeling in undergraduate settings that are more interpretable and actionable for faculty and stakeholders.

Exploring the Value of Emotional Intelligence: A Means to Improve Academic Performance

NASPA Journal ◽  
2007 ◽  
Vol 44 (3) ◽  
Author(s):  
Audrey J. Jaeger ◽  
M. Kevin Eagan
Keyword(s):  
Higher Education ◽  
Emotional Intelligence ◽  
Academic Performance ◽  
Student Affairs ◽  
Student Performance ◽  
Research University ◽  
Student Affairs Professionals ◽  
Grade Point ◽  
Noncognitive Variables

The academic model of success in higher education often neglects the role of noncognitive variables, including Emotional Intelligence (EI). As higher education educators turn their attention to learning, scholars are focusing on the role of EI and other noncognitive variables in enhancing learning. Although learning takes place both inside and outside the classroom, this specific study addresses learning as it relates to academic performance. To explore the role of noncognitive factors in predicting academic performance, this study utilizes an initial sample of 864 first-year students at a large research university. The research addresses the value of EI in predicting academic performance as measured by cumulative grade point average (GPA). The role student affairs professionals play in the noncognitive development of students, specifically EI, could enhance student performance inside and outside the classroom. Implications for educators, including student affairs professionals, are addressed.

Does More Accurate Knowledge of Course Grade Impact Teaching Evaluation?

2017 ◽  
Vol 12 (2) ◽  
pp. 224-240 ◽  
Author(s):  
Donghun Cho ◽  
Joonmo Cho
Keyword(s):  
Student Evaluation ◽  
Teacher Evaluations ◽  
Accurate Information ◽  
Past Performance ◽  
Grade Point ◽  
Actual Grade ◽  
Evaluation Of Teaching ◽  
Performance Bias ◽  
Role Of Information

Students’ different standards may yield different kinds of bias, such as self-directed (higher than their past performance) bias and peer-directed (higher than their classmates) bias. Utilizing data obtained from a natural experiment where some students were able to see their grades prior to teacher evaluations, and to investigate possible sources of bias, we empirically analyzed the role of information (such as the actual grade students received in their current course and their previous grade point average), and the average grade of the course, on the student evaluation of teaching. Because bias is sensitive to the accuracy of grade information, the randomized data examined in this paper are a valuable source for estimating both self-directed and peer-directed bias. We identify the existence of the two kinds of biases and demonstrate that the influence of peer-directed bias tends to increase after the accurate information on the course grade is revealed.

How High School Coursework Predicts Introductory College-Level Course Success

2018 ◽  
Vol 46 (2) ◽  
pp. 176-196 ◽  
Author(s):  
Chenoa S. Woods ◽  
Toby Park ◽  
Shouping Hu ◽  
Tamara Betrand Jones
Keyword(s):  
High School ◽  
College Level ◽  
Course Success ◽  
Introductory College

scholarly journals Grit in Online Education

2019 ◽  
pp. 1-11
Author(s):  
Nicole Buzzetto-Hollywood ◽  
Kathy Quinn ◽  
Wendy Wang ◽  
Austin Hill
Keyword(s):  
Student Achievement ◽  
Online Education ◽  
Student Success ◽  
Student Performance ◽  
Online Courses ◽  
Self Regulation ◽  
P Value ◽  
Learning Success ◽  
Promising Area ◽  
Learning Efficacy

Aim: This study sought to explore the role of the elusive non-cognitive skill set known as grit, or the resolve and determination to achieve goals regardless of impediments, on student success in online education. It represents an area of exploration where there is a dearth in the available literature and reports the results of a study conducted at a Mid-Atlantic minority-serving university that examined the relationship between grit and student performance in fully online courses. Methodology: Students were administered the standard 12-Question Grit Scale with the addition of a series of validated questions that sought to measure perceived self-learning efficacy. Additionally, student performances in online courses were recorded and correlations conducted. Basic statistical analyses such as mean, mode, standard deviation, variance, and confidence interval were calculated. Two hypotheses were introduced as part of this study and tested with Anovas and crosstabulations. Results: This study found that higher grit scores correlated progressively to both self-discipline and self-efficacy but that a positive relationship to student achievement in fully online courses as measured with a p value of greater than .05 could not be confirmed. Conclusion: As online education continues to grow, providing opportunities to foster and strengthen student success in online courses and programs is increasingly important. E-learning success requires that students exhibit strong self-regulation, self-discipline, resilience, dutifulness, conscientiousness, and low impulsivity all of which are attributes of grit. As such, grit is presented as a promising area of exploration for increasing student achievement in online education.

A Study of Right Shoulder Injury in Collegiate and Professional Orchestral Cellists: An Investigation Using Questionnaires and Physical Assessment

2012 ◽  
Vol 27 (2) ◽  
pp. 65-73 ◽  
Author(s):  
Dale Rickert ◽  
Margaret Barrett ◽  
Mark Halaki ◽  
Tim Driscoll ◽  
Bronwen Ackermann
Keyword(s):  
Rotator Cuff ◽  
Future Research ◽  
College Level ◽  
Shoulder Injuries ◽  
Shoulder Injury ◽  
Injury Rates ◽  
Targeted Interventions ◽  
Physical Testing ◽  
Shoulder Strength ◽  
Two Populations

PURPOSE: Cellists sustain high levels of playing-related injury and are particularly susceptible to right shoulder pain, yet no studies have attempted to propose a mechanism for disease or establish possible causal factors. The aim of this study was to investigate shoulder injury levels and causes in two populations: professional orchestral cellists and college-level student cellists. METHODS: A questionnaire and physical testing protocol was applied to both groups of participants, eliciting information on lifestyle, playing habits, and self-reported injury rates as well as physical data on shoulder strength, range of motion, and signs of injury. RESULTS: Right shoulder injuries are common among both student (20%) and professional (42%) cellists and seem to be associated with measures indicating potential lack of strength in the scapular stabilisers as well as potential degenerative changes in the rotator cuff. Significant differences were found in the lifestyle and playing habits of the two groups. There were increased signs of pain and stiffness in the professionals and evidence of decreased muscular support in the students. Male cellists showed less scapular stability; female cellists, however, generally had higher levels of pain. CONCLUSIONS: These results indicate that injuries at the shoulder, potentially involving impingement-type pathologies, are a common cause of pain in cellists. Based on this study, future research for cello players could focus on targeted interventions, such as exercises for the scapular stabilisers and muscles of the rotator cuff.

Pedagogical Values in Online and Blended Learning Environments in Higher Education

2021 ◽  
pp. 1381-1403
Author(s):  
Sophia Palahicky ◽  
Donna DesBiens ◽  
Ken Jeffery ◽  
Keith Stuart Webster
Keyword(s):  
Higher Education ◽  
Student Success ◽  
Blended Learning ◽  
Student Performance ◽  
Learning Environments ◽  
Teaching Practice ◽  
Secondary Educators ◽  
Post Secondary ◽  
Successful Teaching

Pedagogical values directly affect student performance and, therefore, are essential to successful teaching practice. It is absolutely critical that post-secondary educators examine and reflect on their pedagogical values because these principles pave the path for student success. This chapter describes four pedagogical values that are critical to student success within the context of online and blended learning environments in higher education: 1) value of care; 2) value of diversity; 3) value of community; and 4) value of justice.

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