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.

Given the importance of the introductory programming course (CS1), the Computer Science Education community has devoted a significant effort to generate empirical evidence and propose tools, techniques, and curricular approaches to support students. For instance, Parsons Problems have emerged as one of the preferred scaffolding strategies to help CS1 students get a grasp on programming before being exposed to open write-code assignments. However, prior literature has not given enough attention to those students who fail CS1 and must retake the course. In this paper, we report the results of an exploratory case study aimed at understanding the effectiveness of Parsons Problems as an active scaffolding strategy delivered to CS1 retakers at the University of Chile. In this version of the course, although students were already introduced to computational thinking, program design, and coding (as they were taking CS1 for a second time), it was the first time they were exposed to Parsons Problems. We conducted a follow-up assessment of the students' perceptions throughout the course, employing a combination of focus groups, semi-structured interviews, and end-course surveys. Our results suggest that Parsons Problems are effective for learners beyond the ''novice programmer'' stage (such as the experience of CS1 retakers), highlighting good practices to support the studied group in terms of engagement, performance, and overall student experience.

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.

One of the weakest points of scientific research is the loss of data. A tiny fraction of the information generated onsite is published or released to public knowledge, and many useful studies end up stored in papers or emails without being utilized. Live ANDES is a mobile-cloud shared workspace designed to address this problem, promoting citizen science, data collection and analysis for wildlife conservation. It works by gathering geo-localized data provided by the scientific community, amateur naturalists, park rangers and people at large through web and mobile applications. Live ANDES offers filters, visualization and download options to work with existing data. Researchers can use this new information to identify species, ranges of distribution, and detect key habitat factors and potential threats to their conservation. Live ANDES is implemented using the Backend as a Service pattern on Microsoft Azure to manage the processing of the large amounts of data generated from sightings. It includes an API for mobile and desktop clients hosted in an Azure Virtual Machine, cloud storage and connection with external services to complement the existing information about recorded sightings. This paper discusses Live ANDES software design, architecture and a study case, in order to demonstrate an actual application of data management in the cloud and its impact on conservation.