This paper studies the use of Reinforcement Learning (RL) policies for optimizing the sequencing of online learning materials to students. Our approach provides an end to end pipeline for automatically deriving and evaluating robust representations of students' interactions and policies for content sequencing in online educational settings. We…

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…

This chapter examines the application of learning analytics techniques within higher education -- learning analytics -- and its application in supporting "student success." Learning analytics focuses on the practice of using data about students to inform interventions aimed at improving outcomes (e.g., retention, graduation, and learning…

Leading by doing takes an unwavering and visible commitment to an intentional, continuous improvement process. Teams at every system level distribute leadership to support effective practice so outcomes improve. Using a practice-based approach to improving mathematics outcomes, the Kentucky Department of Education (KDE) facilitated a continuous…

Many existing reviews of educational technologies focus on the affordances of specific types of technology rather than how different technologies can be designed and used to achieve specific teaching and learning objectives. Furthermore, there appears to be a widely held assumption that the use of educational technology will result in improved…

This article describes Learning Analytics (LA) as a predictive and formative approach that enables the planning of educational scenarios in line with students' needs and languages in order to set a priori and in progress systems of control and inspection of the following: consistency, relevance and effectiveness of training objectives, curriculum…

This study developed a learning system that allows teachers to edit assignments designed to teach students the text structure strategy through the use of four phases: instructing, modeling, practicing, and reflecting. A 7-week instructional experiment was conducted in which 84 12th-grade students learned the text structure strategy using this…

Increasingly learning analytics (LA) has begun utilising staff- and student-facing dashboards capturing visualisations to present data to support student success and improve learning and teaching. The use of LA is complex, multifaceted and raises many issues for consideration, including ethical and legal challenges, competing stakeholder views and…