Learning analytics is pivotal for supporting learning, with recent research shifting from concepts to reviews. However, understanding factors influencing learning outcomes in higher education through analytics systems remains underexplored. This study aims to fill this gap, providing a holistic view of factors affecting learning outcomes in higher education through a systematic literature review. The goal is to unravel the intricate connections among factors, learning environments, research scopes, and theory. The search yielded 1,662 results, with 27 studies selected. “Engagement” emerged as the most prevalent factor, followed by “platform behavior,” while “demographic features” and “self-assessment” were least prevalent. Most research focused on learning outcomes as final grades, lacking a consistent theoretical definition. Studies were mainly conducted in virtual and blended learning environments, spanning multiple courses or focusing on a single course. Despite extensive research on learning behavior and engagement in learning management systems, prevalence does not necessarily indicate a comprehensive understanding of factors influencing learning outcomes. This implies a potential gap in information gathering or analysis for deeper insights. Moreover, the lack of a consistent theoretical framework and the restriction of analyses to final or partial grades in virtual or blended learning environments suggest areas for improvement in learning analytics research. The study proposes directions for enhancing research in this field to unlock its full potential.

Software maintenance is a complex process that requires the understanding and comprehension of software project details. It involves the understanding of the evolution of the software project, hundreds of software components and the relationships among software items in the form of inheritance, interface implementation, coupling and cohesion. Consequently, the aim of evolutionary visual software analytics is to support software project managers and developers during software maintenance. It takes into account the mining of evolutionary data, the subsequent analysis of the results produced by the mining process for producing evolution facts, the use of visualizations supported by interaction techniques and the active participation of users. Hence, this paper proposes an evolutionary visual software analytics tool for the exploration and comparison of project structural, interface implementation and class hierarchy data, and the correlation of structural data with metrics, as well as socio-technical relationships. Its main contribution is a tool that automatically retrieves evolutionary software facts and represent them using a scalable visualization design.

This paper discusses a proposal for the visualization of software evolution, with a focus on combining insight on changes that affect software metrics at project and class level, the project structure, the class hierarchy and the viewing of source code correlated to indirect class coupling. The proposed visualization supports several tasks: the comparison of structural information, including class hierarchies, across several revisions; uncovering collaboration patterns between developers; and determining which classes have been added or deleted to the project during the creation of a given revision. We propose and discuss several design elements supporting these tasks, including interaction patterns and linked views.