A critical assessment of imbalanced class distribution problem: the case of predicting freshmen student attrition

Predicting student attrition is an intriguing yet challenging problem for any academic institution. Class-imbalanced data is a common in the field of student retention, mainly because a lot of students register but fewer students drop out. Classification techniques for imbalanced dataset can yield deceivingly high prediction accuracy where the overall predictive accuracy is usually driven by the majority class at the expense of having very poor performance on the crucial minority class. In this study, we compared different data balancing techniques to improve the predictive accuracy in minority class while maintaining satisfactory overall classification performance. Specifically, we tested three balancing techniques—oversampling, under-sampling and synthetic minority over-sampling (SMOTE)—along with four popular classification methods—logistic regression, decision trees, neuron networks and support vector machines. We used a large and feature rich institutional student data (between the years 2005 and 2011) to assess the efficacy of both balancing techniques as well as prediction methods. The results indicated that the support vector machine combined with SMOTE data-balancing technique achieved the best classification performance with a 90.24% overall accuracy on the 10-fold holdout sample. All three data-balancing techniques improved the prediction accuracy for the minority class. Applying sensitivity analyses on developed models, we also identified the most important variables for accurate prediction of student attrition. Application of these models has the potential to accurately predict at-risk students and help reduce student dropout rates

Increasing Retention Among First-Year Master\u27s in Counseling Students: Evaluation of a Social Integration Program

Comprised of three individual articles, this article-based dissertation represents different aspects of a study involving a program designed to increase retention among master’s level Counselor Education (CE) students. Chapter One provides an overview of the dissertation’s purpose along with a discussion of how the studies comprising the dissertation extend the current literature on student retention in CE programs. Chapter Two discusses a qualitative study that explores students’ perceptions of a Social Integration Program designed to increase program satisfaction and sense of belonging among first-year students in a Master of Arts in Counseling program. The article in Chapter Two presents findings from focus groups conducted with first-year CE students regarding their experiences in participating in the Social Integration Program. Findings suggest that the activities within the program promoted a sense of connection and satisfaction, and suggest faculty engagement may help to increase student program satisfaction. Chapter Three explores the impact of the Social Integration Program on sense of belonging among first-year CE students through a comparison of two cohorts using a quasi-experimental design. Findings did not support the hypothesis that the program would increase sense of belonging. Methodological limitations of the study that may have contributed to the lack of differences between the cohorts are discussed at the end of Chapter Three. Chapter Four examines the effectiveness of the Social Integration Program in increasing retention rates among first-year CE students. This research was designed to address a gap in the literature regarding programs designed to increase retention rates among this population. Retention rates of students participating in the Social Integration Program were compared to retention rates of students in a control cohort. Findings indicate that the students who participated in the Social Integration program had significantly higher rates of retention from program orientation to fall of their second year of the program compared to the control cohort

Protecting University Students From Bullying And Harassment: A Review Of The Initiatives At Canadian Universities

Students’ bullying and harassment have been shown to be a problem and more schools around the world are starting to address them. Although much of the attention and research has focused on middle-school students, addressing bullying and harassment in universities is important and makes the object of the present research. We provide an overview of how student versus student bullying and harassment are reported, monitored, and dealt with at Canadian educational institutions. Specifically, we identify schools where there is information and policies regarding students’ persecution; we describe how colleges help and what advice they offer; we discuss frameworks used to tackle it; as well, we present other initiatives aiming to prevent it. We also attempt to evaluate measures by linking them with incidence figures. This review may guide future initiatives to tackle intimidation with the ultimate goal of improving the quality of university environment

Evaluation of a Program Designed to Increase Retention in Counselor Education: Reaching Year Two

Student retention is a key issue in maintaining academic programs’ viability. This study evaluated a program designed to increase retention for first year Masters in Counseling students (N = 44). The program consisted of a series of activities developed to increase social integration with both students and faculty. Results of this study indicated that students in the cohort who participated in the program reported higher retention rates than students in the control cohort. Findings suggest that implementing a program designed to increase social integration may be a promising approach to retaining first year students in Counselor Education (CE) programs

Biyo-ekolojik kuramın gözünden Türkiye yükseköğretiminde öğrenciyi okulda tutma

Higher education not only improves individuals academically, socially, and emotionally but also provides any capital for societies and states. In order to serve this purpose, retention of students in higher education is greatly significant. Higher education institutions have complex structures and processes. Thus, their ecosystems are affected by both inner dynamics and outside pressure. However, a gap exists in the literature since student retention studies in the literature focused on narrower perspectives neglecting multi-dimensional situations. Therefore, there is a need for extensive perspectives drawing big and comprehensive picture of student retention in Turkey. The current study aims to investigate student retention in higher education context of Turkey through the lenses of Bronfenbrenner’s Bio-Ecological Theory and to compare and contrast with international literature. Considering layers of theory which are microsystem, mesosystem, exosystem, macrosystem, and chronosystem, retention concept was discussed in the context of core ideas of each layer. Finally, it was concluded that higher education system in Turkey should consider inclusion of multidimensional approaches to create an awareness about student retention.: Yükseköğretim sadece bireylerin akademik, sosyal ve duygusal olarak gelişmelerine değil toplum ve devlet için sermaye üretilmesine de katkı sağlamaktadır. Bu hedef için öğrencilerin yükseköğretim sistemi içinde kalıcı olmaları büyük ölçüde önemlidir. Yükseköğretim kurumları karmaşık yapı ve süreçlere sahiptir. Öyle ki, bu kurumların ekosistemleri hem iç dinamiklerden hem de dış baskılardan etkilenmektedir. Fakat alan yazındaki öğrenciyi okulda tutma çalışmalarının çok boyutlu durumları göz ardı etmesi önemli bir boşluk oluşturmaktadır. Bu yüzden, öğrencinin okulda tutulmasının büyük ve anlaşılır resmini ortaya koyabilecek kapsayıcı bakış açılarına ihtiyaç duyulmaktadır. Bu çalışma, Biyo-Ekolojik Kuramın gözünden Türkiye yükseköğretimindeki öğrenciyi okulda tutmayı incelemeyi ve uluslararası yazınla kıyaslamayı amaçlamaktadır. Kuramın katmanları olan mikrosistem, mezosistem, ekzosistem, makrosistem ve kronosistem bağlamında okulda tutma kavramı tartışılmıştır. Sonuç olarak, okulda tutma ile ilgili farkındalık oluşturmak için Türkiye'deki yükseköğretim sisteminin çok boyutlu yaklaşımları benimsemesi gerektiği önerilmişti

Using Big Data for Predicting Freshmen Retention

Traditional research in student retention is survey-based, relying on data collected from questionnaires, which is not optimal for proactive prediction and real-time decision (student intervention) support. Machine learning approaches have their own limitations. Therefore, in this research, we propose a big data approach to formulating a predictive model. We used commonly available (student demographic and academic) data in academic institutions augmented by derived implicit social networks from students’ university smart card transactions. Furthermore, we applied a sequence learning method to infer students’ campus integration from their purchasing behaviors. Since student retention data is highly imbalanced, we built a new ensemble classifier to predict students at-risk of dropping out. For model evaluation, we use a real-world dataset of smart card transactions from a large educational institution. The experimental results show that the addition of campus integration and social behavior features refined using the ensemble method significantly improve prediction accuracy and recall

Predicting STEM Achievement with Learning Management System Data: Prediction Modeling and a Test of an Early Warning System

ABSTRACT Learning management systems log users' behaviors, which can be used to predict achievement in a course. This paper examines the implications of data representations (e.g., dichotomous vs. count vs. principled, per learning theory) and applies forward selection algorithms to predict achievement in a biology course. Accuracy is compared across models. The paper closes with a description of an ongoing experiment that employs the prediction model, tests how multiple versions of an early alert message impact students' access of learning resources, and compares the influence of messaging approaches related to personalization and feedback

What makes a great MOOC? An interdisciplinary analysis of student retention in online courses

Massive Open Online Courses (MOOCs) have experienced rapid expansion and gained significant popularity among students and educators. Although the broad acceptance of MOOCs, there is still a long way to go in terms of satisfaction of students\u27 needs, as witnessed in the extremely high drop-out rates. Working toward improving MOOCs, we employ the Grounded Theory Method (GTM) in a quantitative study and explore this new phenomenon. In particular, we present a novel analysis using a real-world data set with user-generated online reviews, where we both identify the student, course, platform, and university characteristics that affect student retention and estimate their relative effect. In the conducted analysis, we integrate econometric, text mining, opinion mining, and machine learning techniques, building both explanatory and predictive models, toward a more complete analysis. This study also provides actionable insights for MOOCs and education, in general, and contributes to the related literature discovering new findings

A Descoberta das Causas da Retenção Acadêmica Utilizando Mineração de Dados: Uma Revisão Sistemática da Literatura

Nos últimos anos as instituições de ensino superior têm enfrentado grandes problemas ocasionados pela retenção acadêmica. Diante disso, diversos pesquisadores têm dedicado seus esforços a descoberta das causas e a prevenção contra a retenção acadêmica e a mineração de dados educacionais pode auxiliar nesse processo. Porém, para obter resultados satisfatórios com a mineração de dados aplicada ao contexto da retenção acadêmica é necessário conhecer os melhores recursos de mineração de dados a serem utilizados. Dessa forma, no presente trabalho foi realizada uma revisão sistemática da literatura a fim de identificar as principais técnicas e ferramentas da mineração de dados que podem ser aplicadas nesse processo, além de identificar os principais fatores indutores à retenção acadêmica