Study Behavior in Computing Education—A Systematic Literature Review

As the field of computing education grows and matures, it has become essential to unite computing education and higher education research. Educational research has highlighted that how students study is crucial to their learning progress, and study behaviors have been found to play an important role in students’ academic success. This article presents the main results of a systematic literature review intended to determine what we know about the study behaviors of computing students and the role of educational design in shaping them. A taxonomy of study behaviors was developed and used to clarify and classify the definitions of study behavior, process, strategies, habits, and tactics as well as to identify their relationship to the educational context. The literature search resulted in 107 included papers, which were analyzed according to defined criteria and variables. The review of study behavior terminology found that the same terms are used to describe substantially different study behaviors, and the lack of standard terminology makes it difficult to compare findings from different papers. Furthermore, it was more common for papers to use study behaviors to explain other aspects of students rather than exploring and understanding them. Additionally, the results revealed a tendency to focus on specific educational contexts, predominantly introductory programming courses. Although computing education as a field is well equipped to expand the knowledge about both study behaviors and their connection to the educational context, the lack of common terminology and theories limits the impact. The taxonomy of study behaviors in computing education proposed in this article can contribute to contextualizing the research in such a way that researchers and educators across institutional borders can compare and utilize results. Last, the article outlines some areas for future research and recommendations for practice.

A Systematic Literature Review on Virtual Machine Consolidation

Virtual machine consolidation has been a widely explored topic in recent years due to Cloud Data Centers’ effect on global energy consumption. Thus, academia and companies made efforts to achieve green computing, reducing energy consumption to minimize environmental impact. By consolidating Virtual Machines into a fewer number of Physical Machines, resource provisioning mechanisms can shutdown idle Physical Machines to reduce energy consumption and improve resource utilization. However, there is a tradeoff between reducing energy consumption while assuring the Quality of Service established on the Service Level Agreement. This work introduces a Systematic Literature Review of one year of advances in virtual machine consolidation. It provides a discussion on methods used in each step of the virtual machine consolidation, a classification of papers according to their contribution, and a quantitative and qualitative analysis of datasets, scenarios, and metrics.

A Survey on Concept Drift in Process Mining

Concept drift in process mining (PM) is a challenge as classical methods assume processes are in a steady-state, i.e., events share the same process version. We conducted a systematic literature review on the intersection of these areas, and thus, we review concept drift in PM and bring forward a taxonomy of existing techniques for drift detection and online PM for evolving environments. Existing works depict that (i) PM still primarily focuses on offline analysis, and (ii) the assessment of concept drift techniques in processes is cumbersome due to the lack of common evaluation protocol, datasets, and metrics.