11 resultados para Academic study

em Universidad Politécnica de Madrid


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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.

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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.

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Initially, service sector was defined as complementary to manufacturing sector. This situation has changed in recent times; services growth has resulted in a dominance of employment and economic activity in most developed nations and is becoming a key process for the competitiveness of their industrial sectors. New services related to commodities have become a strategy to differentiate their value proposition (Robinson et al., 2002). The service sector's importance is evident when evaluating its share in the gross domestic product. According to the World Bank (2011), in 2009, 74.8% of GDP in the euro area and 77.5% in United States were attributed to services. Globalization and use of information and communication technology has accelerated dissemination of knowledge and increasing customer expectations about services available worldwide. Innovation becomes essential to ensure that service organizations respond with appropriate products and services for each market segment. Customized and placed on time-tomarket new services require a more developed innovation process. Service innovation and new service development process are cited as one of the priorities for academic research in the following years (Karniouchina et al., 2005) This paper has the following objectives: -To present a model for the analysis of innovation process through the service value network, -To verify its applicability through an empirical research, and -To identify the path and mode of innovation for a group of studied organizations and to compare it with previous studies.

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The purpose of this paper is to present a program written in Matlab-Octave for the simulation of the time evolution of student curricula, i.e, how students pass their subjects along time until graduation. The program computes, from the simulations, the academic performance rates for the subjects of the study plan for each semester as well as the overall rates, which are a) the efficiency rate defined as the ratio of the number of students passing the exam to the number of students who registered for it and b) the success rate, defined as the ratio of the number of students passing the exam to the number of students who not only registered for it but also actually took it. Additionally, we compute the rates for the bachelor academic degree which are established for Spain by the National Quality Evaluation and Accreditation Agency (ANECA) and which are the graduation rate (measured as the percentage of students who finish as scheduled in the plan or taking an extra year) and the efficiency rate (measured as the percentage of credits which a student who graduated has really taken). The simulation is done in terms of the probabilities of passing all the subjects in their study plan. The application of the simulator to Polytech students in Madrid, where requirements for passing are specially stiff in first and second year subjects, is particularly relevant to analyze student cohorts and the probabilities of students finishing in the minimum of four years, or taking and extra year or two extra years, and so forth. It is a very useful tool when designing new study plans. The calculation of the probability distribution of the random variable "number of semesters a student has taken to complete the curricula and graduate" is difficult or even unfeasible to obtain analytically, and this is even truer when we incorporate uncertainty in parameter estimation. This is why we apply Monte Carlo simulation which not only provides illustration of the stochastic process but also a method for computation. The stochastic simulator is proving to be a useful tool for identification of the subjects most critical in the distribution of the number of semesters for curriculum vitae (CV) completion and subsequently for a decision making process in terms of CV planning and passing standards in the University. Simulations are performed through a graphical interface where also the results are presented in appropriate figures. The Project has been funded by the Call for Innovation in Education Projects of Universidad Politécnica de Madrid (UPM) through a Project of its school Escuela Técnica Superior de Ingenieros Industriales ETSII during the period September 2010-September 2011.

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In university studies, it is not unusual for students to drop some of the subjects they have enrolled in for the academic year. They start by not attending lectures, sometimes due to neglect or carelessness, or because they find the subject too difficult, this means that they lose the continuity in the topics that the professor follows. If they try to attend again they discover that they hardly understand anything and become discouraged and so decide to give up attending lectures and study on their own. However some fail to turn up to do their final exams and the failure rate of those who actually do the exams is high. The problem is that this is not only the case with one specific subject, but it is often the same with many subjects. The result is that students arent’s productive enough, wasting time and also prolonging their years of study which entails a great cost for families. Degree courses structured to be conducted and completed in three academic courses, it may in fact take up to an average of six or more academic courses. In this paper, we have studied this problem, which apart from the waste of money and time, produces frustration in the student, who finds that he has not been able to achieve what he had proposed at the beginning of the course. It is quite common, to find students who do not even pass nor 50% of the subjects they had enrolled in for the academic year. If this happens repeatedly to a student, it can be the point when he considers dropping out altogether. This is also a concern for the universities, especially in the early courses. In our experience as professors, we have found that students, who attend lectures regularly and follow the explanations, approach the final exams with confidence and rarely fail the subject. In this proposal we present some techniques and methods carried out to solve in possible, the problem of lack of attendance to lectures. This involves "rewarding students for their assistance and participation in lectures". Rewarding assistance with a "prize" that counts for the final mark on the subject and involving more participation in the development of lectures. We believe that we have to teach students to use the lectures as part of their learning in a non-passive way. We consider the professor's work as fundamental in terms of how to convey the usefulness of these topics explained and the applications that they will have for their professional life in the future. In this way the student see for himself the use and importance of what he is learning. When his participation is required, he will feel more involved and confident participating in the educational system. Finally we present statistical results of studies carried out on different degrees and on different subjects over two consecutive years. In the first year we assessed only the final exams without considering the students attendance, or participation. In the second year, we have applied the techniques and methods proposed here. In addition we have compared the two ways of assessing subjects.

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Reproducible research in scientic work ows is often addressed by tracking the provenance of the produced results. While this approach allows inspecting intermediate and nal results, improves understanding, and permits replaying a work ow execution, it does not ensure that the computational environment is available for subsequent executions to reproduce the experiment. In this work, we propose describing the resources involved in the execution of an experiment using a set of semantic vocabularies, so as to conserve the computational environment. We dene a process for documenting the work ow application, management system, and their dependencies based on 4 domain ontologies. We then conduct an experimental evaluation sing a real work ow application on an academic and a public Cloud platform. Results show that our approach can reproduce an equivalent execution environment of a predened virtual machine image on both computing platforms.

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The present work is focused on studying two issues: the “teamwork” generic competence and the “academic motivation”. Currently the professional profile of engineers has a strong component of teamwork. On the other hand, motivational profile of students determines their tendencies when they come to work in team, as well as their performance at work. In this context we suggest four hypotheses: (H1) students improve their teamwork capacity by specific training and carrying out a set of activities integrated into an active learning process; (H2) students with higher mastery motivation have better attitude towards team working; (H3) students with higher mastery motivation obtain better results in academic performance; and (H4) students show different motivation profiles in different circumstances: type of courses, teaching methodologies, different times of the learning process. This study was carried out with computer science engineering students from two Spanish universities. The first results point to an improvement in teamwork competence of students if they have previously received specific training in facets of that competence. Other results indicate that there is a correlation between the motivational profiles of students and their perception about teamwork competence. Finally, and contrary to the initial hypothesis, these profiles appear to not influence significantly the academic performance of students.

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The purpose of this study is to work out how a clear and motivated task goal set for the students can develop several skills that are not only useful in their specific academic contexts but also serve to reinforce links and cooperation with the labor market. The following research on skills was taken during one academic year. The students collected advertisements likely to be selected in a near future by themselves as possibilities to apply for a job. The advertisements selected were 120, and all of them were published on the internet either in jobs links or located by the students themselves in the web-practices of their choice. All the advertisements chosen by the students provided us with a skill list focused on architectural profiles. To conclude, academic research skills versus future motivation jobs positions are fruitful paths to conduct successful students´response at job interviews.

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The present work is aimed at discussing several issues related to the teamwork generic competence, motivational profiles and academic performance. In particular, we study the improvement of teamwork attitude, the predominant types of motivation in different contexts and some correlations among these three components of the learning process. The above-mentioned aspects are of great importance. Currently, the professional profile of engineers has a strong teamwork component and the motivational profile of students determines both their tendencies when they come to work as part of a team, as well as their performance at work. Taking these issues into consideration, we suggest four hypotheses: (H1) students improve their teamwork capacity through specific training and carrying out of a set of activities integrated into an active learning process; (H2) students with higher mastery motivation have a better attitude towards teamwork; (H3) students with different types of motivations reach different levels of academic performance; and (H4) students show different motivation profiles in different circumstances: type of courses, teaching methodologies, different times of the learning process. This study was carried out with Computer Science Engineering students from two Spanish universities. The first results point to an improvement in teamwork competence of students if they have previously received specific training in facets of that competence. Other results indicate that there is a correlation between the motivational profiles of students and their perception of teamwork competence. Finally, results point to a clear relationship between some kind of motivation and academic performance. In particular, four kinds of motivation are analyzed and students are classified into two groups according to them. After analyzing several marks obtained in compulsory courses, we perceive that those students that show higher motivation for avoiding failure obtain, in general, worse academic performance.

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This study examines the relationships between multiple intelligences, academic achievement and motor performance in a group of secondary school children. Four hundred and eighty schoolchildren participated in this study (171 female and 309 male) with an average age of 13.33 years (SD: 1.41). The Revised self-efficacy Inventory for Multiple Intelligences (IAIM-R) and the motor test Sportcomp were applied, and the average results of the academic year they had made were obtained. The analysis of the results showed how female scored significantly higher on the Linguistic, Spatial and Interpersonal intelligences, and older pupils scored significantly higher on the linguistic and naturalistic intelligences. It was the logical-mathematical intelligence which showed significant relationships with academic performance and it was the intelligence that better predicted this achievement. It was the bodily-kinesthetic intelligence that was significantly related to motor competence and the best intelligence that predicted its achievement. Finally, indicate that schoolchildren with better scores in the motor test were those who scored higher in both academic achievement and all the multiple intelligences, with the exception of musical intelligence.

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Reproducible research in scientific workflows is often addressed by tracking the provenance of the produced results. While this approach allows inspecting intermediate and final results, improves understanding, and permits replaying a workflow execution, it does not ensure that the computational environment is available for subsequent executions to reproduce the experiment. In this work, we propose describing the resources involved in the execution of an experiment using a set of semantic vocabularies, so as to conserve the computational environment. We define a process for documenting the workflow application, management system, and their dependencies based on 4 domain ontologies. We then conduct an experimental evaluation using a real workflow application on an academic and a public Cloud platform. Results show that our approach can reproduce an equivalent execution environment of a predefined virtual machine image on both computing platforms.