In an increasingly data-driven world, large volumes of fine-grained data are infiltrating all aspects of our lives. The world of education is no exception to this phenomenon: in classrooms, we are witnessing an increasing amount of information being collected on learners and teachers. Because educational practitioners have so much contextual and…

Knowing insights as to why learners from diverse social and demographic profile choose to enroll in distance education can be a useful tool for Open and Distance Learning (ODL) Institutions to understand the requirements of their target segment, help in fine-tuning service offerings for attracting potential students and finally retaining them…

Contemporary students have a varied set of needs--the "lifecycle" of a typical student may no longer be 4 years of continuous enrollment between the ages of 18 and 22, and many students bring rich and varied experiences with them to college. As institutions strive to allocate resources in ways that provide the most benefit to student…

Learning analytics research has used both knowledge engineering and machine learning methods to model student behaviors within the context of digital learning environments. In this paper, we compare these two approaches, as well as a hybrid approach combining the two types of methods. We illustrate the strengths of each approach in the context of…

The growing practice of students as partners (SaP) has sparked numerous conversations in higher education about the roles students do and should play in shaping the future. SaP scholars contend that by engaging with students in meaningful partnership, underpinned by values such reciprocity, students can have deeper and more meaningful learning…

Interdisciplinary research from the learning sciences has helped us understand a great deal about the way that humans learn, and as a result we now have an improved understanding about how best to teach and train people. This same body of research must now be used to better inform the development of Artificial Intelligence (AI) technologies for…

Using data to understand learning and improve education has great promise. However, the promise will not be achieved simply by AI and Machine Learning researchers developing innovative models that more accurately predict labeled data. As AI advances, modeling techniques and the models they produce are getting increasingly complex, often involving…

Writing is a challenge and a potential obstacle for students in U.S. 4-year postsecondary institutions lacking prerequisite writing skills. This study aims to address the research question: Is there a relationship between specific features (analytics) in coursework writing and broader success predictors? Knowledge about this relationship could…

Success prediction in Massive Open Online Courses (MOOCs) is now tackled in numerous works, but still needs new case studies to compare the solutions proposed. We study here a specific dataset from a French MOOC provided by the OpenClassrooms company, featuring 12 courses. We exploit various features present in the literature and test several…

Early alert systems (EAS) are an important technological tool to help manage and improve student retention. Data spanning 16,091 students over 156 weeks was collected from a regionally based university in Australia to explore various microeconometric approaches that establish links between EAS and student retention outcomes. Controlling for…

Interest in the use of analytics to support evidence-based decision-making in higher education is relatively a new phenomenon. The available research suggests that analytics can enhance an institution's ability to make evidence-based informed decisions that foster growth and increased productivity. The present study explored how institutions of…

Face-to-face education has changed to blended or distance teaching due to the COVID-19 pandemic. Since education took a digital format, it can be investigated when course materials are accessed relative to online exams: are they opened before exams or during them? Therefore, four subjects were chosen for investigation at the University of…

The purposes of the two studies reported in this research are to adapt and validate the instrument of the Evaluation Framework for Learning Analytics (EFLA) for learners into the Turkish context, and to examine how metacognitive and behavioral factors predict learner performance. Study 1 was conducted with 83 online learners enrolled in a 16-week…

Purpose: Short-term motivation encompasses specific, challenging and attainable goals that develop in the limited timespan. On the other hand, long-term motivation indicates a sort of continuing commitment that is required to complete assigned task. As short-term motivational problems span for a limited period of time, such as a session,…

Automatic machine learning (AutoML) methods automate the time-consuming, feature-engineering process so that researchers produce accurate student models more quickly and easily. In this paper, we compare two AutoML feature engineering methods in the context of the National Assessment of Educational Progress (NAEP) data mining competition. The…