Learning is a multimodal process, and learning analytics (LA) researchers can readily access rich learning process data from multiple modalities, including audio-video recordings or transcripts of in-person interactions; logfiles and messages from online activities; and biometric measurements such as eye-tracking, movement, and galvanic skin…

Learning analytics (LA), like much of machine learning, assumes the training and test datasets come from the same distribution. Therefore, LA models built on past observations are (implicitly) expected to work well for future observations. However, this assumption does not always hold in practice because the dataset may drift. Recently,…

Data are fundamental to Learning Analytics (LA) research and practice. However, the ethical use of data, particularly in terms of respecting learners' privacy rights, is a potential barrier that could hinder the widespread adoption of LA in the education industry. Despite the policies and guidelines of privacy protection being available worldwide,…

The rise of massive open online courses (MOOCs) brings rich opportunities for understanding learners' experiences based on analyzing learner-generated content such as course reviews. Traditionally, the unstructured textual data is analyzed qualitatively via manual coding, thus failing to offer a timely understanding of the learner's experiences.…

Learning analytics offer unprecedented opportunities for tracking and storing learning behaviors, thereby providing chances for optimizing learner engagement and success. The limited adoption of learning analytics by educational institutions hinders efforts to optimize learning processes through organizational and educational interventions,…

This article investigates the state of AI regulations from diverse geopolitical contexts including the European Union, the United States, China, and several African nations, and their implications for learning analytics (LA) and AI research. We used a comparative analysis approach of 11 AI regulatory documents and applied the OECD framework to…

A learner can autonomously acquire knowledge by experiencing the world, without necessarily being explicitly taught. The contents and ways of this type of real-world learning are grounded on his/her surroundings and are self-determined by computing real-world information. However, conventional studies have not modeled, observed, or understood a…

Mind mapping is a powerful technique that is often used for teaching declarative knowledge, but seldom implemented to record procedural knowledge. The present study focused on the latter. During a 12-week public presentation course, self-developed mind mapping software was utilized as a learning tool and an instrument to collect and analyze user…

Learning analytics (LA) adoption is a challenging task for higher education institutions (HEIs) since it involves different aspects of the academic environment, such as information technology infrastructure, human resource management, ethics, and pedagogical issues. Therefore, it is necessary to provide institutions with supporting instruments to…

The study of learning behaviors with multi features is of great significance for interactive cooperation. The data prediction and decision are to realize the comprehensive analysis and value mining. In this study, hierarchical learning behavior based on feature cluster is proposed. Based on the massive data in interactive learning environment, the…

Forum discussions in Massive Open Online Courses (MOOCs) play a crucial role in promoting learning engagement and academic achievement. In particular, discussion topics significantly influence learners' emotional and cognitive engagement. However, the complex interrelationships among these factors remain underexplored. This study introduces an…

Despite the increasing interest in data analytics applications within postgraduate education research, there remains a significant gap in research dedicated to exploring mixed methods research for such investigations. This study undertook to bridge this gap by exploring the application and use of mixed methods research to examine data analytics…

With technological advances, institutional stakeholders are considering evidence-based developments such as Curriculum Analytics (CA) to reflect on curriculum and its impact on student learning, dropouts, program quality, and overall educational effectiveness. However, little is known about the CA state of the art in Higher Education Institutions…

Despite the increasing availability of data used to inform educational policies and practices, concerns persist regarding its quality and accessibility. This study surveys quality education data from Brazil, Colombia, and Peru and evaluates their alignment with the FAIR principles and availability to support academic analytics (AA) and learning…

Recently, there has been a surge in developing curricula and tools that integrate computing (C) into Science, Technology, Engineering, and Math (STEM) programs. These environments foster authentic problem-solving while facilitating students' concurrent learning of STEM+C content. In our study, we analyzed students' behaviors as they worked in…