To analyze educational outcomes or evaluate operations, academic institutions need to produce high-quality data. Unfortunately, many institutions do not have accurate, timely, and useful data and information despite investments to manage technology. Furthermore, data management issues in education, including access, quality, and ownership, inhibit…

Students, families, colleges, and lawmakers need clearer information on postsecondary outcomes to make informed decisions. By leveraging data available at institutions and federal agencies, a nationwide student-level data network (SLDN) would close information gaps that persist in our higher education landscape to answer critical questions about…

Institutions of higher education are operating in an increasingly complex and competitive environment. This paper identifies contemporary challenges facing institutions of higher education worldwide and explores the potential of Big Data in addressing these challenges. The paper then outlines a number of opportunities and challenges associated…

Learning analytics provide a set of powerful tools to inform and support learners. They enable institutions and individuals to better understand and predict personal learning needs and performance. Universities already collect vast amounts of data about their student populations, but often this is underutilised. The current "state of the…

Predictive Learning Analytics (PLA) aim to improve learning by identifying students at risk of failing their studies. Yet, little is known about how best to integrate and scaffold PLA initiatives into higher education institutions. Towards this end, it becomes essential to capture and analyze the perceptions of relevant educational stakeholders…

For 8 years Washington State has operated a performance funding policy, the Student Achievement Initiative (SAI). The policy allocates appropriations to the state's 34 community and technical colleges based on points earned through student achievement of college-readiness, retention, and completion milestones. Grounded in a conceptual framework of…

This chapter describes how data analysis and data-driven decision making were critical for designing, developing, and assessing a new academic program. The authors--one, the program's founder; the other, an alumna--begin by highlighting some of the elements in the program's incubation and, subsequently, describe some of the components for data…

This paper presents a dialogical tool for the advancement of learning analytics implementation for student retention in Higher Education institutions. The framework was developed as an outcome of a project commissioned and funded by the Australian Government's "Office for Learning and Teaching". The project took a mixed-method approach…

This article provides a critique of current debates about what quality enhancement is for and what it does. It outlines a conceptual framework drawing on different understandings of quality assurance and quality enhancement in higher education, which helps to refine the role of quality enhancement in improving student learning. The paper analyses…

Each edition of the IDRA Newsletter strives to provide many different perspectives on the issues in education topics discussed and to define its significance in the state and national dialogue. This issue focuses on Using Data for Action and includes: (1) Community and School Use of Data for College Readiness and Postsecondary Success (Karmen…

Teaching portfolios have become increasingly important to university teachers. Portfolio requirements for the appointment or promotion of academic staff recognize that the assessment of teaching practice requires more depth and detail than a candidate's academic CV generally affords. The focus of this study is the electronic teaching portfolios,…

Purpose: This paper aims to examine how academics enact trust in e-learning through an inductive identification of perceived risks and enablers involved in e-learning adoption, in the context of higher education institutions (HEIs). Design/methodology/approach: Grounded Theory was the methodology used to systematically analyse data collected in…

This study captured the experience and perceptions of faculty members who have implemented Universal Design (UD) in their courses, to understand the implementation process and outcomes UD had on their students and themselves; as the current research on UD has been long on best practices, and short on empirical evidence. A thorough review of…

BHEF has achieved particular success in operationalizing its National Higher Education and Workforce Initiative (HEWI) through regional initiatives in data science and analytics (DSA). Leveraging its membership of corporate CEOs, university presidents and government agency leaders, BHEF seeded new undergraduate pathways in DSA at Case Western…

A core goal for most learning analytic projects is to move from small-scale research towards broader institutional implementation, but this introduces a new set of challenges because institutions are stable systems, resistant to change. To avoid failure and maximize success, implementation of learning analytics at scale requires explicit and…