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 lack of clarity about the definition of Computational Thinking (CT) undermines its assessment and the formulation of effective learning strategies for its development. We propose an assessment tool in Spanish, designed by carefully selecting Bebras tasks, to measure four specific CT skills in first-year university students with no prior programming experience. The test was validated with a sample of 980 students from a Latin American university. We found a high, positive correlation between test results and Math test results from the National University Selection System, and a medium, positive correlation between test results and grades from the midterm exam in a Programming course. An acceptable level of internal consistency was found (Cronbach's alpha=0.70). We examined the validity of the test using Classical Test Theory. One question showed poor discrimination potential. Its elimination from the test increased the internal consistency, so we propose to replace it.

There is increasing interest in computer science and computing bachelor programs due to the growing importance of technology in the globalized world. Thus, as higher education institutions strive to serve a diverse student demographic, it is salient to gauge their programming abilities to improve guidance on learning processes regarding their initial knowledge state. Despite the availability of certain instruments to measure student programming skills, these are traditionally aimed at younger populations and do not accurately discriminate the different levels of ability among university students. This article introduces a translation into Spanish and validation of an existing English-language aptitude test for computing jobs that can be used to measure the programming abilities of students with no prior experience in the field. Following a cyclic research methodology, two iterations were carried out in this article. First, the aforementioned test was translated and validated via expert judgment and focus groups, in which certain items were removed subsequent to a quantitative analysis. The resultant instrument underwent a second validation using a larger population of students. Analysis conducted after the second iteration showed this instrument to deliver good internal consistency, good difficulty and discrimination indices, and a moderate correlation with the grades of the midterm exam of a programming course undertaken by first year engineering students. This work contributes to both increasing the number of tests available in the Spanish language with which to assess programming abilities, as well as to the broader literature regarding test adaptation, translation and validation.

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.

Chile is a medium-sized developing country, with approximately 3,500,000 school-aged children. Computational thinking (CT) has been recognized as a tool for developing XXI century skills, but Chile has a large population of in-service teachers that do not know what CT is, limiting its application in classrooms. In response, the government has enacted a training program for in-service primary school teachers, focusing on changing their perceptions about CT, and showing them how it can be used in classroom. This training program was designed for grades 1-6 and piloted in the capital region during 2018. The program initially focused on coding activities, but was then redesigned to show how CT can be used to help meet the learning objectives of different subjects in the curriculum. The program was then scaled up nationwide. In this experience report, we discuss the outcomes of this program. The training program scaled as intended and we identified positive changes in teacher perceptions about CT, especially among female participants. We conclude with the lessons we learned from this experience.

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.