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

Õpilaste kaasamine STEM-haridusse

In this manuscript we focus on how to develop STEM learning environments, and how STEM can be implemented in K-12 schools. We focus on the following question: “How can we support students in building a deep, integrated knowledge of STEM so that they have the practical knowledge and problem solving skills necessary to live in and improve the world?” We also discuss criteria for evaluating STEM learning environments and the challenges teachers face in implementing STEM. We define STEM as the integration of science, engineering, technology, and mathematics to focus on solving pressing individual and societal problems. Engaging students in STEM also means engaging learners in the design process. Design is integral to student thinking in the STEM world. The design process is very non-linear and iterative in its nature but requires clearly articulating and identifying the design problem, researching what is known about the problem, generating potential solutions, developing prototype designs (artifacts) that demonstrate solutions, and sharing and receiving feedback. With the integration of design, STEM education has the potential to support students in learning big ideas in science and engineering, as well as important scientific and engineering practices, and support students in developing important motivational outcomes such as ownership, agency and efficacy. Moreover, students who engage in STEM learning environments will also develop 21st century capabilities such as problem solving, communication, and collaboration skills

Simulation-Based Electronic Health Record Usability Evaluation: A Proof of Concept

Poor usability of Electronic Health Records (EHR) solutions is directly associated with physician burnout. While the survey and observational methods have been utilized widely in the usability evaluation of EHRs, it does not seem to be helping with the continuous improvement of EHR design and user satisfaction. We address this gap by presenting a discrete event simulation-based model that can add objectivity to the extant EHR usability methods. Evaluating EHR usability from the perspective of operations and workflow can help vendors design and develop better systems. This short paper presents a proof-of-concept simulation model with assumed task-time distributions. Our main research question is how we can use simulation techniques to objectively evaluate EHR usability? The simulation model results in terms of resource (clinician) utilization metrics can serve as a proxy to evaluate the efficiency component of the EHR usability at the departmental leve

Abrupt Shift or Caught Off Guard: A Systematic Review of K-12 Engineering and STEM Education’s Response to the COVID-19 Pandemic

In the past hundred years, there have been a number of pandemics that have affected the entire world, including the 1918 H1N1 influenza pandemic, the 1957 H2N2 influenza pandemic, and the 2009 H1N1 influenza pandemic. While responses to the most recent H1N1 influenza pandemic remained local, the COVID-19 pandemic, on the other hand, resulted in long-term school closures all around the world, prompting a sudden shift to distant education by compelling K-12 educators and students to do so. The purpose of this study is to find out how K-12 education studies reacted to the sudden shift in supporting engineering and STEM (science, technology, engineering, and mathematics) education during the COVID-19 pandemic. To accomplish this goal, we conducted two separate searches in different databases and reviewed 25 articles. These articles were classified into four categories: (1) adaptation to online learning and the effects of a sudden shift, (2) implementing new strategies and tools, (3) STEM education in informal learning environments, and (4) teacher professional development. Our analysis indicated that engineering and STEM education research primarily focused on higher education during the COVID-19 pandemic. The limited number of studies examining K-12 engineering and STEM first investigated the adaptation to online learning by utilizing various resources that elementary and secondary teachers could easily access. Blended learning, flipped learning, and maker pedagogy were encouraged in K-12 engineering and STEM studies. Movies were the most commonly used tool in K-12 engineering and STEM studies. It is encouraging that studies also examined informal learning contexts (outreach initiatives, museums) and inequities in STEM and engineering education. However, the small number of studies in each category reminds us that there is still a lot of work to be done in terms of the future of K-12 engineering education, especially considering that distant education may become a permanent part of K-12 education

Predicting the Outcome of a Football Game: A Comparative Analysis of Single and Ensemble Analytics Methods

As analytical tools and techniques advance, increasingly large numbers of researchers apply these techniques on a variety of different sports. With nearly 4 billion followers, it is estimated that association football, or soccer, is the most popular sports for fans across the world by a large margin. The objective of this study is to develop a model to predict the outcomes of soccer (or association football) games (win-loss-draw), and determine factors that influence game outcomes. We used 10 years of comprehensive game-level data spanning the years 2007-2017 in the Turkish Super League, and tested a variety of classifiers to identify the most promising methods for outcome predictions

Õpilaste kaasamine STEM-haridusse

Artiklis käsitletakse STEM-õpikeskkonna arendamise võimalusi ning STEMi rakendamist põhi- ja keskkooliastmes, keskendudes järgmisele küsimusele: kuidas aidata õpilastel omandada põhjalikke ja integreeritud STEM-valdkonna teadmisi, et neil oleks praktilised teadmised ja probleemilahendusoskused, mis aitaks neil maailmas hakkama saada ja seda paremaks muuta? Lisaks tutvustatakse STEMõppeks sobiva keskkonna hindamise kriteeriume ning käsitletakse probleeme, millega õpetajatel tuleb STEM-ainete õpetamisel kokku puutuda. Meie määratluse järgi on STEM loodusteaduste, tehnoloogia, inseneriteaduse ja matemaatika ühendamine eesmärgiga lahendada pakilisi isiklikke ja ühiskondlikke probleeme. Õpilaste kaasamine STEM-valdkonda tähendab nende kaasamist disainiprotsessi. STEM-maailmas on disain õpilaste mõttemaailma lahutamatu osa. Disainiprotsess on mittelineaarne ja oma olemuselt korduv, kuid nõuab disainiprobleemi kindlaksmääramist ja selget sõnastamist, probleemi kohta juba teada oleva teabe uurimist, võimalike lahenduste pakkumist, prototüüpide (tehisesemete) väljatöötamist, et lahendusi demonstreerida, ning tagasiside jagamist ja saamist. Disainile keskenduva STEM-hariduse kaudu on võimalik toetada õpilasi suurte loodus- ja inseneriteaduslike ideede ning oluliste praktiliste loodus- ja inseneriteaduslike teadmiste omandamisel. Samuti võimaldab STEM-haridus motiveerida õpilasi, et neil tekiks omanikutunne ning vajadus oma ideid tutvustada ja tulemuslikult tegutseda. Enamgi veel, STEM-õpikeskkonda kaasatud õpilased saavad arendada selliseid 21. sajandil vajalikke oskusi nagu probleemilahendus- ja suhtlemisoskus ning koostöövõime.  Full tex

Data Quality in Very Large, Multiple-Source, Secondary Datasets for Data Mining Applications

The data mining research community is increasingly addressing data quality issues, including problems of dirty data. Hand, Blunt, Kelly and Adams (2000) have identified high-level and low-level quality issues in data mining. Kim, Choi, Hong, Kim and Lee (2003) have compiled a useful, complete taxonomy of dirty data that provides a starting point for research in effective techniques and fast algorithms for preprocessing data, and ways to approach the problems of dirty data. In this study we create a classification scheme for data errors by transforming their general taxonomy to apply to very large multiple-source secondary datasets. These types of datasets are increasingly being compiled by organizations for use in their data mining applications. We contribute this classification scheme to the body of research addressing quality issues in the very large multiple-source secondary datasets that are being built through today’s global organizations’ massive data collection from the Internet