Learning Analytics (LA) is the collection and analysis of data about learners and their environments. University faculty are among some of the most important stakeholders in the successful implementation of LA initiatives, whose participation can influence the success or failure of innovative educational changes. However, limited research exists…

As the engineering education system continuously evolves to meet the demands of modern industry and society, there is a need for a methodology that would manage and resolve the complexities inherent in engineering educational systems. Model-based Systems Engineering (MBSE) is a structured approach to system design that utilizes models across all…

The Society for Learning Analytics Research (SoLAR) defines learning analytics as "the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs" (SoLAR, n.d.). To fully realize the potential of learning…

The mastery of pedagogical analytical and digital literacy (PADL) emerges as a foundation, allowing higher education institutions (HEIs) faculty to navigate the challenges of the digital realm and modify teaching methods to meet the various learning needs of the students. This study aimed to explore the need for analytical literacy for faculty in…

Learning Analytics (LA) has had a growing interest by academics, researchers, and administrators motivated by the use of data to identify and intervene with students at risk of underperformance or discontinuation. Typically, faculty leadership and advisors use data sources hosted on different institutional databases to advise their students for…

Higher education institutions (HEIs) implementing learning analytics (LA) use student data to improve the learning experience. The problem for LA implementation originates from individuals responsible for analytic programs from different institutional departments and the lack of a framework for communication and productive dialogue about usages of…

The purpose of this study is to present results from a multiple-case study on factors leading to successful analytic technology implementation within higher education. This study focuses on factors and characteristics leading to successful analytic implementation at the institutional level and does not discuss specific analytic tools. In the past…

Sources of information are growing as a result of the changing technology landscape in our learning environments. This presents an opportunity to leverage information in new ways to benefit students and faculty by illuminating different aspects of the practice of teaching. For this information to have an impact, it is first necessary to understand…

The purpose of this study is to determine what demographics and learning analytics can be used as predictors to identify at-risk students in higher education. These indicators can be used to develop policies and instructional design techniques that faculty can use to intervene with at-risk students. This quantitative study uses geographic…

The purpose of this study was to examine the predictive power of Academic and Learning Analytics models on the persistence, retention, and graduation rates for students enrolled in higher education institutions in the United States. Specifically, this study is concerned with the relationships between the present usage of Academic and Learning…

The goal of learning analytics is to optimize learning and the environments in which it occurs. Since 2011, when learning analytics was defined as a separate and distinct area of academic inquiry, the literature has identified a need for research that presents evidence of effective learning analytics, as well as, learning analytics research that…