The use of learning management systems: A Longitudinal Case Study

In this article the use of Learning Management Systems (LMS) at the School of Engineering, University of Borås, in the year 2004 and the academic year 2009-2010 is investigated. The tools in the LMS were classified into four groups (tools for distribution, tools for communication, tools for interaction and tools for course administration) and the pattern of use was analyzed. The preliminary interpretation of the results was discussed with a group of teachers from the School of Engineering with long experience of using LMS. High expectations about LMS as a tool to facilitate flexible education, student centered methods and the creation of an effective learning environment is abundant in the literature. This study, however, shows that in most of the surveyed courses the available LMS is predominantly used to distribute documents to students. The authors argue that a more elaborate use of LMS and a transformation of pedagogical practices towards social constructivist, learner centered procedures should be treated as an integrated process of professional development.

Learning Curves of Temporary Workers in Manual Order Picking Activities

Person-to-stock order picking is highly flexible and requires minimal investment costs in comparison to automated picking solutions. For these reasons, tradi-tional picking is widespread in distribution and production logistics. Due to its typically large proportion of manual activities, picking causes the highest operative personnel costs of all intralogistics process. The required personnel capacity in picking varies short- and mid-term due to capacity requirement fluctuations. These dynamics are often balanced by employing minimal permanent staff and using seasonal help when needed. The resulting high personnel fluctuation necessitates the frequent training of new pickers, which, in combination with in-creasingly complex work contents, highlights the im-portance of learning processes in picking. In industrial settings, learning is often quantified based on diminishing processing time and cost requirements with increasing experience. The best-known industrial learning curve models include those from Wright, de Jong, Baloff and Crossman, which are typically applied to the learning effects of an entire work crew rather than of individuals. These models have been validated in largely static work environments with homogeneous work contents. Little is known of learning effects in picking systems. Here, work contents are heterogeneous and individual work strategies vary among employees. A mix of temporary and steady employees with varying degrees of experience necessitates the observation of individual learning curves. In this paper, the individual picking performance development of temporary employees is analyzed and compared to that of steady employees in the same working environment.