Assessment of learning outcomes in computing studies

The assessment of learning outcomes is a key concept in the European Credit Transfer and Accumulation System (ECTS) since credits are awarded when the assessment shows the competences which were aimed at have been developed at an appropriate level. This paper describes a study which was first part of the project of the Bologna Experts Team-Spain and then developed as an independent study. It was carried out with the overall goal to gain experience in the assessment of learning outcomes. More specifically it aimed at 1) designing procedures for the assessment of learning outcomes related to these compulsory generic competences; 2) testing some basic psychometric features that an assessment device with some consequences for the subjects being evaluated needs to prove; 3) testing different procedures of standard setting, and 4) using assessment results as orienting feedback to students and their tutors. The process of development of tests to carry out the assessment of learning outcomes related to these competences, as well as some basic features regarding their reliability and validity is described and first results on the comparison of results achieved at two academic levels, will also be described at a later stage.

Virtual Learning Environments In Faculties Of Education In Spain: Changes On Student Attitude

This paper presents some results of a R+D project entitled “e-Learning system for Practical Training of University students in Education Faculties (ForELearn)”, developed in Spain by the Universidad de Granada and the Universidad Politécnica de Madrid and funded by the Spanish Ministry of Education. In a first phase, through the use of AulaWeb Learning Management System, a set of adaptations and improvements of this software application have been done for the design and development of an experimental course of Practicum supervision. Next, the implementation of this course by means of a group of face to face and online seminars provides experimental data for the analysis and discussion about the point of view of users (preservice teachers) that have tracked their practice supervision with AulaWeb.

Statistical Machine Learning for Automatic Assessment of Physical Activity Intensity Using Multi-axial Accelerometry and Heart Rate

This work explores the automatic recognition of physical activity intensity patterns from multi-axial accelerometry and heart rate signals. Data collection was carried out in free-living conditions and in three controlled gymnasium circuits, for a total amount of 179.80 h of data divided into: sedentary situations (65.5%), light-to-moderate activity (17.6%) and vigorous exercise (16.9%). The proposed machine learning algorithms comprise the following steps: time-domain feature definition, standardization and PCA projection, unsupervised clustering (by k-means and GMM) and a HMM to account for long-term temporal trends. Performance was evaluated by 30 runs of a 10-fold cross-validation. Both k-means and GMM-based approaches yielded high overall accuracy (86.97% and 85.03%, respectively) and, given the imbalance of the dataset, meritorious F-measures (up to 77.88%) for non-sedentary cases. Classification errors tended to be concentrated around transients, what constrains their practical impact. Hence, we consider our proposal to be suitable for 24 h-based monitoring of physical activity in ambulatory scenarios and a first step towards intensity-specific energy expenditure estimators

The Threads of Biosystems Engineering

The core concepts, or threads, of Biosystems Engineering (BSEN) are variously understood by those within the discipline, but have never been unequivocally defined due to its early stage of development. This makes communication and teaching difficult compared to other well established engineering subjects. Biosystems Engineering is a field of Engineering which int egrates engineering science and design with applied biological, environmental and agricultural sciences. It represents an evolution of the Agricultural Engineering discipline applied to all living organisms not including biomedical applications. The basic key element for the emerging EU Biosystems Engineering program of studies is to ensure that it offers essential minimum fundamental engine ering knowledge and competences . A core curriculum developed by Erasmus Thematic Networks is used as benchmark for Agr icultural and Biosystems Engineering studies in Europe. The common basis of the core curriculum for the discipline across the Atlantic , including a minimum of competences comprising the Biosystems Engineering core competencies, has been defined by an Atlan tis project , but this needs to be taken further by defining the threads linking courses together. This paper presents a structured approach to define the Threads of BSEN . The definition of the mid-level competences and the associated learning outcomes has been one of the objectives of the Atlantis programme TABE.NET. The mid-level competences and learning outcomes for each of six specializations of BSEN are defined while the domain-specific knowledge to be acquired for each outcome is proposed. Once the proposed definitions are adopted, these threads will be available for global development of the BSEN.

A comparative analysis of ABE Bachelor of Science programs in European and US Universities.

European Universities are involved in series of great changes regarding teaching and education organization during the last few years. The origin of these changes is the creation of the so-called European Higher Education Area (EHEA), which main target is to harmonize the different University studies throughout Europe. As a consequence, most of the programs of studies in all degrees are suffering changes in order to converge to common structures. Taking advantage of the actual process, some European universities are moving from traditional Agricultural Engineering programs to a more wide discipline named recently as Biosystems Engineering, which is a science- based engineering discipline that integrates engineering science and design with applied biological, environmental and agricultural sciences, broadening in this way the area of application of Engineering sciences not strictly to agricultural sciences, but to the biologic al sciences in general, including the agricultural sciences. This paper presents a comparative study of different Bachelor of Science degrees offered by American and European Universities in the field of Agricultural/Biosystems Engineering. To carry out the analysis 40 programs accredited by ABET in American Universities and 50 European programs. Among other questions, the total number of credits, the number of semesters, the kind of modules and the distribution of subjects in groups (Basic Sciences, Engineering Fundamentals, Agricultural/Biological Sciences, Humanities & Economic Sciences, Applied Agricultural/Biological Engineering and electives) are discussed in the paper. The information provided can be an useful starting point in future definitions of new or renewed degrees with the aim of advancing in internationalization of the programs and helping student’s mobility.

Blended learning in the design of collective protections in building works

The design of collective protections of construction works is a topic that is integrated within one of the MBTI subjects. This paper presents an experience for teaching the design of collective protection in building works. Teaching has been done in a bended way. This subject is taught as part of the content of a wider subject included in the Master "Building Technological Innovation" (MBTI) of the Universidad Politécnica de Madrid (UPM). This Master's degree is a postgraduate of research nature. Graduating in MBTI implies that students are in a position to begin their PhD thesis. At this stage, often students are not able to follow the rigour established by the schedule of classes. For this reason it is interesting that part of the teaching is developed in a non-classroom based way.

Modeling sub-threshold slope and DIBL mismatch of sub-22nm FinFet

A great challenge for future information technologies is building reliable systems on top of unreliable components. Parameters of modern and future technology devices are affected by severe levels of process variability and devices will degrade and even fail during the normal lifeDme of the chip due to aging mechanisms. These extreme levels of variability are caused by the high device miniaturizaDon and the random placement of individual atoms. Variability is considered a "red brick" by the InternaDonal Technology Roadmap for Semiconductors. The session is devoted to this topic presenDng research experiences from the Spanish Network on Variability called VARIABLES. In this session a talk entlited "Modeling sub-threshold slope and DIBL mismatch of sub-22nm FinFet" was presented.

Learning Analytics and Interactions in Virtual Learning Environments. A Comparative Study of Typologies and their Relationship with Academic Performance

Analysis of learning data (learning analytics) is a new research field with high growth potential. The main objective of Learning analytics is the analysis of data (interactions being the basic data unit) generated in virtual learning environments, in order to maximize the outcomes of the learning process; however, a consensus has not been reached yet on which interactions must be measured and what is their influence on learning outcomes. This research is grounded on the study of e-learning interaction typologies and their relationship with students? academic performance, by means of a comparative study between different interaction typologies (based on the agents involved, frequency of use and participation mode). The main conclusions are a) that classifications based on agents offer a better explanation of academic performance; and b) that each of the three typologies are able to explain academic performance in terms of some of their components (student-teacher and student-student interactions, evaluating students interactions and active interactions, respectively), with the other components being nonrelevant.

Predicting academic performance with learning analytics in virtual learning environments: A comparative study of three interaction classifications

Learning analytics is the analysis of static and dynamic data extracted from virtual learning environments, in order to understand and optimize the learning process. Generally, this dynamic data is generated by the interactions which take place in the virtual learning environment. At the present time, many implementations for grouping of data have been proposed, but there is no consensus yet on which interactions and groups must be measured and analyzed. There is also no agreement on what is the influence of these interactions, if any, on learning outcomes, academic performance or student success. This study presents three different extant interaction typologies in e-learning and analyzes the relation of their components with students? academic performance. The three different classifications are based on the agents involved in the learning process, the frequency of use and the participation mode, respectively. The main findings from the research are: a) that agent-based classifications offer a better explanation of student academic performance; b) that at least one component in each typology predicts academic performance; and c) that student-teacher and student-student, evaluating students, and active interactions, respectively, have a significant impact on academic performance, while the other interaction types are not significantly related to academic performance.