scholarly journals The Transition to STEM Higher Education: Policy Recommendation – Conclusions of the readySTEMgo-Project

2018 ◽  
Vol 8 (2) ◽  
pp. 10
Author(s):  
Greet Langie ◽  
Maarten Pinxten
Keyword(s):  
Higher Education ◽  
At Risk ◽  
Higher Education Policy ◽  
Early Stage ◽  
At Risk Students ◽  
Retention Rates ◽  
First Year ◽  
Policy Recommendations ◽  
Policy Recommendation ◽  
Key Skills

For Europe to remain at the forefront of scientific and technological devel-opment, the current shortage of persons trained in these fields at secondary and higher education has to be overcome. The readySTEMgo project aims to improve the retention rates of higher education STEM programmes by the identification of at-risk students in an early stage. We successfully identified a number of key skills that are essential for first-year achievement in a STEM programme. Additionally, we investigated which intervention tools can support at-risk students and evaluated their effectiveness. Based on the output of this research project four policy recommendations are formulated.

scholarly journals Predictive Analysis of Higher Education Graduation and Retention in Saudi Arabia u sing Multinomial Logistic Regression

2021 ◽  
Vol 3 (6) ◽  
pp. 1-8
Author(s):  
Ahmed Bagabir ◽  
◽  
Mohammad Zaino ◽  
Ahmed Abutaleb ◽  
Ahmed Fagehi ◽  
...  
Keyword(s):  
Higher Education ◽  
Logistic Regression ◽  
Undergraduate Students ◽  
Degree Completion ◽  
Multinomial Logistic Regression ◽  
At Risk Students ◽  
Retention Rates ◽  
First Year ◽  
Important Indicator ◽  
Admissions Policy

It is suggested that this study contributes by establishing a robust methodology for analyzing the longitudinal outcomes of higher education. The current research uses multinomial logistic regression. To the knowledge of the authors, this is the first logistic regression analysis performed at Saudi higher education institutions. The study can help decision-makers take action to improve the academic performance of at-risk students. The analyses are based on enrollment and completion data of 5,203 undergraduate students in the colleges of engineering and medicine. The observation period was extended for ten academic years from 2010 to 2020. Four outcomes were identified for students: (i) degree completion on time, (ii) degree completion with delay, (iii) dropout, and (iv) still enrolled in programs. The objectives are twofold: (i) to study the present situation by measuring graduation and retention rates with benchmarking, and (ii) to determine the effect of twelve continuous and dummy predictors (covariates) on outcomes. The present results show that the pre-admission covariates slightly affect performance in higher education programs. The results indicate that the most important indicator of graduation is the student's achievement in the first year of the program. Finally, it is highly suggested that initiatives be taken to increase graduation and retention rates and to review the admissions policy currently in place.

scholarly journals A controlled intervention to improve freshman retention in a STEM-based physiology major

2020 ◽  
Vol 44 (3) ◽  
pp. 334-343
Author(s):  
Miriam Leary ◽  
Aimee Morewood ◽  
Randy Bryner
Keyword(s):  
At Risk ◽  
Student Perceptions ◽  
Teaching And Learning ◽  
At Risk Students ◽  
Retention Rates ◽  
Control Group ◽  
First Year ◽  
Freshman Retention ◽  
Retention Group ◽  
And Mathematics

Using a Scholarship of Teaching and Learning lens, this study systematically examined if a targeted intervention in at-risk students within a science, technology, engineering, and mathematics (STEM)-based physiology program would elicit positive student perceptions and higher retention rates into the second year. Those students who were considered at risk for attrition (retention; n = 82) were compared against a control group (non-retention; n = 165), and outcomes were evaluated with an End-of-Semester Survey and university enrollment data. Students in the retention group reported more favorable responses to questions pertaining to a first-year seminar course and academic advising. By the start of the following (spring 2019) semester, 48 students transferred out of the program (20%) with little difference between groups (non-retention 19%; retention 22%). At the start of fall 2019 term, 55% of the 2018 freshman class were retained within the program (non-retention 66%; retention 39%), and 85% were retained within the university (non-retention 91%, retention 74%). The intervention was successful in eliciting positive student perceptions of the major, but did not improve retention of at-risk students within the physiology major.

Adopting Learning Analytics in a First-Year Veterinarian Professional Program: What We Could Know in Advance about Student Learning Progress

2021 ◽  
Vol 48 (6) ◽  
pp. 720-728
Author(s):  
Wenting Weng ◽  
Nicola L. Ritter ◽  
Karen Cornell ◽  
Molly Gonzales
Keyword(s):  
At Risk ◽  
Academic Success ◽  
Student Learning ◽  
Learning Analytics ◽  
Prediction Models ◽  
Early Stage ◽  
At Risk Students ◽  
Learning Performance ◽  
First Year ◽  
Actual Performance

Over the past decade, the field of education has seen stark changes in the way that data are collected and leveraged to support high-stakes decision-making. Utilizing big data as a meaningful lens to inform teaching and learning can increase academic success. Data-driven research has been conducted to understand student learning performance, such as predicting at-risk students at an early stage and recommending tailored interventions to support services. However, few studies in veterinary education have adopted Learning Analytics. This article examines the adoption of Learning Analytics by using the retrospective data from the first-year professional Doctor of Veterinary Medicine program. The article gives detailed examples of predicting six courses from week 0 (i.e., before the classes started) to week 14 in the semester of Spring 2018. The weekly models for each course showed the change of prediction results as well as the comparison between the prediction results and students’ actual performance. From the prediction models, at-risk students were successfully identified at the early stage, which would help inform instructors to pay more attention to them at this point.

A FIRST-YEAR STUDENTS’ ADAPTATION TO DIFFICULTIES AT HIGH EDUCATIONAL ESTABLISHMENTS

2021 ◽  
pp. 97-113
Author(s):  
Nailya R. Salikhova ◽  
◽  
Aida R. Fakhrutdinova ◽  
Keyword(s):  
Higher Education ◽  
Personal Growth ◽  
Personal Development ◽  
Early Stage ◽  
Educational Process ◽  
First Year ◽  
First Year Students ◽  
Social Community ◽  
Transition To Higher Education ◽  
Study Participants

Data from an empirical study aimed at identifying the difficulties faced by students in their transition to higher education, the overcoming of which is important for personal development, are presented in the article. The study participants (n=179) were asked to describe the difficulties of transition from school to University in the 1st year based on analysis of their autobiographical memory. The content analysis of texts allowed identifying the main themes and compiling a list of challenges, and then the frequency of occurrence of each of them was determined. According to the results, the most actual difficulties are the different aspects of integrating into the new social community due to sharp changes in the social environment during the transition from school to University. A big challenge is the need for self-organization in educational and everyday matters, planning and organizing your time. The third most frequently mentioned is learning difficulties, especially those related to mastering the material in the new educational environment. Problematic areas of adaptation to higher education that have not been previously reflected in the sources are the establishment of a common life in the dormitory, pressure from parents, the manifestation of their individuality, the increase in the length of classes and the pace of learning, romantic relationships and language barriers. The difficulties of the first examination session are much less frequently mentioned, and are more frequently mentioned when examining the current adaptation process. The results of the study can be used for the development and subsequent implementation of a system of practical measures aimed at helping students to adapt to the new environment and conditions. Such assistance to students in building a new way of life at a university, especially at an early stage of study, is necessary not only to improve the effectiveness of the educational process, but also to facilitate the processes of personal growth and development of students

Redesigning Prediction Algorithms for At-Risk Students in Higher Education

2019 ◽  
pp. 232-250 ◽  
Author(s):  
Dennis Foung
Keyword(s):  
Higher Education ◽  
At Risk ◽  
Logistic Regression ◽  
Academic Writing ◽  
Industry 4.0 ◽  
Classification Tree ◽  
At Risk Students ◽  
Higher Education Administrators ◽  
Writing Courses ◽  
Education Administrators

Use of algorithms and data mining approaches are not new to Industry 4.0. However, these may not be common for students and educators in higher education. This chapter compares various classification techniques: classification tree, logistic regression, and artificial neural networks (ANN). The comparison focuses on each method's accuracy, algorithm, and practicality in higher education. This study made use of a dataset from two academic writing courses in a university in Hong Kong with more than 5,000 records. Results suggest that classification trees and logistic regression can be easily used in the higher education context, but ANN may not be applicable in higher educational settings. The research team suggests that higher education administrators take this research forward and design platforms to realize these classification algorithms to predict at-risk students.

scholarly journals An Early Warning System to Detect At-Risk Students in Online Higher Education

2020 ◽  
Vol 10 (13) ◽  
pp. 4427 ◽  
Author(s):  
David Bañeres ◽  
M. Elena Rodríguez ◽  
Ana Elena Guerrero-Roldán ◽  
Abdulkadir Karadeniz
Keyword(s):  
Higher Education ◽  
At Risk ◽  
Predictive Model ◽  
Early Warning ◽  
Early Warning System ◽  
Time Lag ◽  
At Risk Students ◽  
Warning System ◽  
Online Higher Education ◽  
Depth Analysis

Artificial intelligence has impacted education in recent years. Datafication of education has allowed developing automated methods to detect patterns in extensive collections of educational data to estimate unknown information and behavior about the students. This research has focused on finding accurate predictive models to identify at-risk students. This challenge may reduce the students’ risk of failure or disengage by decreasing the time lag between identification and the real at-risk state. The contribution of this paper is threefold. First, an in-depth analysis of a predictive model to detect at-risk students is performed. This model has been tested using data available in an institutional data mart where curated data from six semesters are available, and a method to obtain the best classifier and training set is proposed. Second, a method to determine a threshold for evaluating the quality of the predictive model is established. Third, an early warning system has been developed and tested in a real educational setting being accurate and useful for its purpose to detect at-risk students in online higher education. The stakeholders (i.e., students and teachers) can analyze the information through different dashboards, and teachers can also send early feedback as an intervention mechanism to mitigate at-risk situations. The system has been evaluated on two undergraduate courses where results shown a high accuracy to correctly detect at-risk students.

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