Machine learning has achieved state-of-the-art performance in many areas, including image recognition and natural language processing. However, there are still many challenges and mysteries attracting numerous researchers. This dissertation comprises a series of works concerning problems at the intersection of computer science theory, adversarial…

Learning analytics systems are software designed to aggregate student data to be analyzed for the purpose of delivering information to students, with the goal of increasing student success, academic goal completion, and retention. Despite being identified as stakeholders and beneficiaries of learning analytics, student perceptions make up a small…

Design science research (DSR) is one of important research paradigms in information systems (IS) that focus on addressing business problems by building and implementing design artifacts. Recently, predictive analytics has become one major stream of DSR thanks to the improvement of computational power and methods and the increase in available…

Educational Data Mining (EDM) is an emerging field that aims to better understand students' behavior patterns and learning environments by employing statistical and machine learning methods to analyze large repositories of educational data. Analysis of variable data in the early stages of a course might be used to develop a comprehensive…

Learning analytics researchers have been diligently integrating trace data to study Self-Regulated Learning (SRL). Compared to traditionally used survey data, trace data, such as log or clickstream data designed and interpreted to understand a certain SRL construct, are considered to be more effective in capturing dynamic SRL as fine-grained…

This phenomenological study expands upon Bean and Metzner's (1985) A Conceptual Model of Nontraditional Student Attrition framework by introducing a new Academic Variable, Learning Analytics (LA), and identifying two specific Social Integration Variables (Sense of belonging; Microaggressions). LA was not a factor in 1985 when the original model…

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…

The increased reliance on Big Data Analytics (BDA) in society, politics, policy, and industry has catalyzed conversations related to the need for promoting ethical reasoning and decision-making in the mathematical sciences. While the majority of professional data scientists today come from privileged positions in society, those processed by the…

The purpose of this study was to explore and analyze the utilization of learning analytics data produced by a learning management system as an indicator of learners' self-regulation. In the Spring of 2021, 258 learners at a four-year, mid-Atlantic university provided access to their learning management system data. Of those 258 learners, 86…

The main goal of learning analytics and early detection systems is to extract knowledge from student data to understand students' trends of activities towards success and risk and design intervention methods to improve learning performance and experience. However, many factors contribute to the challenge of designing and building effective…

The purpose of learning analytics is to improve and optimize learning using student data (Siemens, 2013). An early alert warning is learning analytics designed to promote student success (Baneres et al., 2019; Foung, 2019; Lawson et al., 2016; Villano et al., 2018). An early alert has an intervention component that includes, at minimum, an email…

This dissertation is presented in multiple article format with an introduction, a literature review, a design case, a mixed methods study, and a conclusion. The literature review outlines important concepts and theories that appear in subsequent manuscripts. The design case describes the seven-year design process that resulted in the online…

A distinctive feature of game-based learning environments is their capacity to create learning experiences that are both effective and engaging. Recent advances in sensor technologies (e.g., facial expression analysis and gaze tracking) and natural language processing have introduced the opportunity to leverage multimodal data streams for learning…

Required gateway courses in higher education present ongoing challenges to student persistence, retention, and degree completion. Adaptive learning technology (ALT) offers personalized support and generates learning analytics that can inform faculty teaching and course design practices. This qualitative case study examined faculty experiences with…

Clustering, a prevalent class of machine learning (ML) algorithms used in data mining and pattern-finding--has increasingly helped engineering education researchers and educators see and understand assessment patterns at scale. However, a challenge remains to make ML-enabled educational inferences that are useful and reliable for research or…