The use of Learning Management Systems (LMS) has had rapid growth over the last decades. Great efforts have been recently made to assess online students’ performance level, due to the COVID-19 pandemic. Faculty members with limited experience in the use of LMS such as Moodle, Edmodo, MOOC, Blackboard and Google Classroom face challenges creating online tests. This paper presents a descriptive and comparative study of the existing plugins used to import questions into Moodle, classifying them according to the necessary computing resources. Each of the classifications were compared and ranked, and features such as the support for gamification and the option to create parameterised questions are explored. Parameterised questions can generate a large number of different questions, which is very useful for large classes and avoids fraudulent behaviour. The paper outlines an open-source plugin developed by the authors: FastTest PlugIn, recently approved by Moodle. FastTest PlugIn is a promising alternative to mitigate the detected limitations in analysed plugins. FastTest PlugIn was validated in seminars with 230 faculty members, obtaining positive results about expectations and potential recommendations. The features of the main alternative plugins are discussed and compared, describing the potential advantages of FastTest PlugIn.

A Higher Education degree is composed by courses which can be organized in areas or modules. Over last years, time invested by students to complete Higher Education degrees has increased. This increment can be caused by the existence of bottlenecks in the courses of academic programs. We aim to carry out an analysis of students' performance to detect courses which represent bottlenecks in the process of completing a degree, because of many student failing compulsory courses. Students' performance can be stored in data sets. Unfortunately, analysis of large data set can lead to scalability problems not being comprehensive applying manual analysis methods because of its extension. We applied Process Mining techniques to overtake these scalability problems. Process Mining is a set of Sequence Analysis techniques to analyze event logs. In this paper, we conducted an analysis of a data set which includes the performance of 612 students applying Process Mining tools. We obtained frequencies of students according time invested to complete a course. We compared these frequencies to detect possible bottlenecks. Finally, some requirements to consider courses as bottlenecks are proposed.

The acquisition of foreign language competencies has become one of the main concerns of current ICT educational policies. Mobile smart devices allow teachers to provide students with personalized learning environments in line with their needs. However, most of the available apps, especially in the area of foreign language learning, still focus on form-based learning supporting mainly one-way interaction. In this chapter, the authors designed a learning system based on a dynamic, asynchronous and constructive learning approach. The chapter illustrates how the system helped students to get involved in their learning process by creating, sharing, and assessing their own learning resources and how teachers could benefit from students' logs to retrieve indicators for assessment processes. Finally, two algorithms that guide students' learning processes are compared: the first algorithm is based on community-driven behaviour, the second one on students' individual behaviour. Results show that both algorithms provide similar outcomes.

CARMENES is the new high-resolution high-stability spectrograph built for the 3.5m telescope at the Calar Alto Observatory (CAHA, Almería, Spain) by a consortium formed by German and Spanish institutions. This instrument is composed by two separated spectrographs: VIS channel (550-1050 nm) and NIR channel (950- 1700 nm). The NIR-channel spectrograph's responsible institution is the Instituto de Astrofísica de Andalucía, IAA-CSIC. The contouring conditions have led CARMENES-NIR to be a schedule-driven project with a extremely tight plan. The operation start-up was mandatory to be before the end of 2015. This plays in contradiction to the very complex, calm-requiring tasks and development phases faced during the AIV, which has been fully designed and implemented at IAA through a very ambitious, zero-contingency plan. As a large cryogenic instrument, this plan includes necessarily a certain number cryo-vacuum cycles, this factor being the most important for the overall AIV duration. Indeed, each cryo-vacuum cycle of the NIR channel runs during 3 weeks. This plan has therefore been driven to minimize the amount of cryo-vacuum cycles. Such huge effort has led the AIV at system level at IAA lab to be executed in 9 months from start to end -an astonishingly short duration for a large cryogenic, complex instrument like CARMENES NIR- which has been fully compliant with the final deadline of the installation of the NIR channel at CAHA 3.5m telescope. The detailed description of this planning, as well as the way how it was actually performed, is the main aim of the present paper.