This article presents the design of a prototype adaptive Massive Open Online Course (MOOC) aimed at promoting algorithmic thinking among first-year engineering students at Universidad Técnica Federico Santa María. The instructional design was based on Merrill’s Principles of Instruction and Bloom’s Taxonomy to create a matrix that linked expected learning outcomes, content, activities, resources, and assessments. The resulting prototype was an adaptive module developed in Moodle, featuring personalized learning pathways based on the matrix and the results of a Computational Thinking Diagnostic Test. As a novel contribution, the study proposes a replicable method for implementing adaptive learning using only Moodle’s built-in features without the need for external tools. Additionally, the instructional design process and implementation described in this work provide empirical evidence to inform the field of personalized learning in higher education, particularly in contexts where students have heterogeneous prior knowledge in large-scale programming courses.

The 2019 global health crisis forced higher education institutions to transition to remote emergency teaching. This article presents the experience of Universidad Técnica Federico Santa María, in Chile, when adapting a massive Introduction to Programming class to a virtual modality. A novel formative model was developed, based on flipped-classroom principles for remote settings. This model allowed to adapt the instruction to the varying personal circumstances and connectivity conditions of the participants, while supporting the delivery of the original learning objectives for the course. Following the ideas from constructive alignment, the contents and learning objectives were grouped into Online Learning Units, each developed within a week. By doing so, it was possible to orchestrate both synchronous and asynchronous activities. After one year of the implementation of this model, students evaluated the course as a positive and fun experience, that gave them confidence regarding their programming skills. As future work, we believe it is possible to apply this structure to hybrid learning environments, as it allows for students to work in and outside the classroom while keeping a common pace.

Apache Giraph is a powerful tool for processing very large graphs in distributed environments. One of its main features is a novel programming model that facilitates the design and execution of graph-oriented algorithms in distributed environments. Despite its benefits, using Giraph is a complex and challenging process. Moreover, the Giraph data model (based on labeled directed graphs) makes its use difficult in application domains where nodes and edges contain metadata. This article presents an extension of the Giraph API to provide support for Property Graphs, that is, graphs whose nodes and edges could have properties. In particular, we define a formal method to transform a Property Graph into a Giraph graph, we describe an API for manipulating and querying Property Graphs in Giraph, and we present experimental results that show the applicability and efficiency of our extension.