University of Alicante (SPAIN)
About this paper:
Appears in: ICERI2010 Proceedings
Publication year: 2010
Pages: 5596-5600
ISBN: 978-84-614-2439-9
ISSN: 2340-1095
Conference name: 3rd International Conference of Education, Research and Innovation
Dates: 15-17 November, 2010
Location: Madrid, Spain
Online learning represents an environment where the principles of the European Higher Education Area have an immediate, straightforward application, with a huge positive potential in graduate and postgraduate courses. This fact is shown in the feasibility of continuous assessment in this context. However, a topic that deserves especial attention is the distribution of the task delivering in order to implement continuous assessment in a modular subject, characterized by a duration usually shorter but more concentrated than a non-modular one. As some authors suggest, the later the deadlines, the greater the opportunity to learn the most about the topic before submitting the papers; however, the later the teacher’s feedback will arrive, and following the literature, the gradualism in the assimilation of a subject’s knowledge advocated by continuous assessment is somewhat lost. This article analyzes the features that explain the optimal temporal distribution of task delivering, understanding optimal as the one that allows students to maximize their performance. The empirical application is based on a sample of 59 students enrolled on the modular subject Marketing and Market Research in the Master in “Tourism Management” from Postgraduate University Institute (Instituto Universitario de Postgrado, IUP). The results show that a continuous assessment uniformly distributed along the course, i.e. a continuous assessment with evenly spaced deadlines, leads to better performance than concentrating such an evaluation on the second half or even on the last two thirds of the course (this is evidenced by using a pseudo-skewness measure). Nevertheless, the even spaced distribution is still suboptimal, as a slight delay in task delivering can be accepted; in particular, for a thirty-eight-day length, a delay of four days (for all tasks as a whole) can be allowed as the optimum is then arrived at.
Online learning, optimal timing.