The importance of temporality in learning has been long established, but it is only recently that serious attention has begun to be paid to the precise identification, measurement, and analysis of the temporal features of learning. From 2009 to 2016, a series of temporality workshops explored temporal concepts and data types, analysis methods for…

An increasing effort has been put into dealing with the question of time-series analysis regarding institutional efficiency, including in the area of higher education. Universities are important institutions for economies and societies and are expected to provide excellence as well as efficiency in their processes and outputs. This is reflected in…

Increased focus on data-driven decision making requires Extension professionals to excel at data analysis and interpretation. Data parties have become increasingly popular for involving stakeholders in making sense of data. As these parties become more frequently used by Extension professionals, best practices are emerging from lessons learned to…

Two of the most important issues facing those in the field of academic advising are the use of technology and data analytics. There is no question that technology and data will shape the delivery and expectations for academic advising in higher education in the years to come. This chapter explores the intersections between advising, technology,…

All methodologies for detecting plagiarism to date have focused on the final digital "outcome", such as a document or source code. Our novel approach takes the creation process into account using logged events collected by special software or by the macro recorders found in most office applications. We look at an author's interaction…

The field of learning analytics holds considerable promise for higher education, with reports of successful uses now emerging in selected institutions. At the same time, critics have expressed concerns regarding privacy, ethics, and intrusions into teachers' pedagogy. Without attentive planning, higher-education professionals applying learning…

Throughout the different phases of a drug development program, randomized trials are used to establish the tolerability, safety and efficacy of a candidate drug. At each stage one aims to optimize the design of future studies by extrapolation from the available evidence at the time. This includes collected trial data and relevant external data.…

Process mining methodologies are designed to uncover underlying business processes, deviations from them, and in general, usage patterns. One of the key limitations of these methodologies is that they struggle in cases in which there is no structured process, or when a process can be performed in many ways. Learning Management Systems are a…

Quantitative decision making (management science, business statistics) textbooks rarely address data cleansing issues, rather, these textbooks come with neat, clean, well-formatted data sets for the student to perform analysis on. However, with a majority of the data analyst's time spent on gathering, cleaning, and pre-conditioning data, students…

Predicting the students' performance has become a challenging task due to the increasing amount of data in educational systems. In keeping with this, identifying the factors affecting the students' performance in higher education, especially by using predictive data mining techniques, is still in short supply. This field of research is usually…

Data--their collection, analysis and use--have always been part of education, used to inform policy, strategy, operations, resource allocation, and, in the past, teaching and learning. Recently, with the emergence of learning analytics, the collection, measurement, analysis and use of student data have become an increasingly important research…

The paper aims to present application of Educational Data Mining and particularly Case-Based Reasoning (CBR) for students profiling and further to design a personalised intelligent learning system. The main aim here is to develop a recommender system which should help the learners to create learning units (scenarios) that are the most suitable for…

This article is a response to an article written by Wang and Ma "An Examination of Plausible Score Correlation from the Trend in Mathematics and Science Study", published in the Athens Journal of Education. The purpose of this paper is to address issues with Wang's and Ma's suggestion to use analysis method for correlating plausible…

Founded on the need to help university students develop a greater academic metacognitive capacity, student-facing learning analytics are considered useful tools for making students overtly aware of their own learning processes, helping students to develop control over their learning, and subsequently supporting more effective learning. However,…

Longitudinal administrative data generated by public education systems in the United States have become increasingly available and have tremendous potential to inform policy research. While it is tempting to take the accuracy of administrative data for granted, there are reasons why researchers should approach these data with a degree of caution.…