This article provides an overview of the state of research in bioinformatics education in the years 1998 through 2013. It identifies current curricular approaches for integrating bioinformatics education, concepts and skills being taught, pedagogical approaches and methods of delivery, and educational research and evaluation results.

Purpose The purpose of this paper is to develop a framework for utilizing Six Sigma (SS) principles and Big Data analytics at a US public university for the improvement of student success. This research utilizes findings from the Gallup index to identify performance factors of higher education. The goal is to offer a reimagined SS DMAIC methodology that incorporates Big Data principles. Design/methodology/approach The authors utilize a conceptual research design methodology based upon theory building consisting of discovery, description, explanation of the disciplines of SS and Big Data. Findings The authors have found that the interdisciplinary approach to SS and Big Data may be grounded in a framework that reimagines the define, measure, analyze, improve and control (DMAIC) methodology that incorporates Big Data principles. The authors offer propositions of SS DMAIC to be theory tested in subsequent study and offer the practitioner managing the performance of higher education institutions (HEIs) indicators and examples for managing the student success mission of the organization. Research limitations/implications The study is limited to conceptual research design with regard to the SS and Big Data interdisciplinary research. For performance management, this study is limited to HEIs and non-FERPA student data. Implications of this study include a detailed framework for conducting SS Big Data projects. Practical implications Devising a more effective management approach for higher education needs to be based upon student success and performance indicators that accurately measure and support the higher education mission. A proactive approach should utilize the data rich environment being generated. The individual that is most successful in engaging and managing this effort will have the knowledge and skills that are found in both SS and Big Data. Social implications HEIs have historically been significant contributors to the development of meritocracy in democratic societies. Due to a variety of factors, HEIs, especially publicly funded institutions, have been under stress due to a reduction of public funding, resulting in more limited access to the public in which they serve. Originality/value This paper examines Big Data and SS in interdisciplinary effort, an important contribution to SS but lacking a conceptual foundation in the literature. Higher education, as an industry, lacks penetration and adoption of continuous improvement efforts, despite being under tremendous cost pressures and ripe for disruption.

Objectives. To implement an elective course in pharmacogenomics designed to teach pharmacy students about the fundamentals of pharmacogenomics and the anticipated changes it will bring to the profession. Design. The 8 sessions of the course covered the basics of pharmacogenomics, genomic biotechnology, implementation of pharmacogenetics in pharmacy, information security and privacy, ethical issues related to the use of genomic data, pharmacoepidemiology, and use and promotion of GeneScription, a software program designed to mimic the professional pharmacy environment. Assessment. Student grades were based on completion of a patient education pamphlet, a 2-page paper on pharmacogenomics, and precourse and postcourse survey instruments. In the postcourse survey, all students strongly agreed that genomic data could be used to determine the optimal dose of a drug and genomic data for metabolizing enzymes could be stored in a safe place. Students also were more willing to submit deoxyribonucleic acid (DNA) data for genetic profiling and better understood how DNA analysis is performed after completing the course.Conclusions. An elective course in pharmacogenomics equipped pharmacy students with the basic knowledge necessary to make clinical decisions based on pharmacogenomic data and to teach other healthcare professionals and patients about pharmacogenomics. For personalized medicine to become a reality, all pharmacists and pharmacy students must learn this knowledge and these skills.

This paper pf^esents a number of principles related to the construction and use of enterprise architecture frameworks. These principles are intended to guide the development of a formal foundation for frameworks but also serve as guidance for efforts to enable the interoperability of enterprise models and model components. The principles are drawn from analyses of a number of existing frameworks and from observation of and participation in framework development.

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