Lindstaedt Stefanie , Kump Barbara, Beham Günter, Pammer-Schindler Viktoria, Ley Tobias, de Hoog R., Dotan A.
2010
We present a work-integrated learning (WIL) concept which aims atempowering employees to learn while performing their work tasks. Withinthree usage scenarios we introduce the APOSDLE environment whichembodies the WIL concept and helps knowledge workers move fluidly alongthe whole spectrum of WIL activities. By doing so, they are experiencingvarying degrees of learning guidance: from building awareness, over exposingknowledge structures and contextualizing cooperation, to triggering reflectionand systematic competence development. Four key APOSDLE components areresponsible for providing this variety of learning guidance. The challenge intheir design lies in offering learning guidance without being domain-specificand without relying on manually created learning content. Our three monthsummative workplace evaluation within three application organizationssuggests that learners prefer awarenss building functionalities and descriptivelearning guidance and reveals that they benefited from it.
Beham Günter, Kump Barbara, Lindstaedt Stefanie , Ley Tobias
2010
According to studies into learning at work, interpersonal help seeking is the most important strategy of how people acquireknowledge at their workplaces. Finding knowledgeable persons, however, can often be difficult for several reasons. Expertfinding systems can support the process of identifying knowledgeable colleagues thus facilitating communication andcollaboration within an organization. In order to provide the expert finding functionality, an underlying user model is needed thatrepresents the characteristics of each individual user. In our article we discuss requirements for user models for the workintegratedlearning (WIL) situation. Then, we present the APOSDLE People Recommender Service which is based on anunderlying domain model, and on the APOSDLE User Model. We describe the APOSDLE People Recommender Service on thebasis of the Intuitive Domain Model of expert finding systems, and explain how this service can support interpersonal helpseeking at workplaces.
Ley Tobias, Seitlinger Paul
2010
Researching the emergence of semantics in social systems needs totake into account how users process information in their cognitive system. Wereport results of an experimental study in which we examined the interactionbetween individual expertise and the basic level advantage in collaborative tagging.The basic level advantage describes availability in memory of certain preferredlevels of taxonomic abstraction when categorizing objects and has beenshown to vary with level of expertise. In the study, groups of students taggedinternet resources for a 10-week period. We measured the availability of tags inmemory with an association test and a relevance rating and found a basic leveladvantage for tags from more general as opposed to specific levels of the taxonomy.An interaction with expertise also emerged. Contrary to our expectations,groups that spent less time to develop a shared understanding shifted tomore specific levels as compared to groups that spent more time on a topic. Weattribute this to impaired collaboration in the groups. We discuss implicationsfor personalized tag and resource recommendations.
Ley Tobias, Kump Barbara, Gerdenitsch C.
2010
Adaptive scaffolding has been proposed as an efficient means for supporting self-directed learning both in educational as well as in adaptive learning systems research. However, the effects of adaptation on self-directed learning and the differential contributions of different adaptation models have not been systematically examined. In this paper, we examine whether personalized scaffolding in the learning process improves learning. We conducted a controlled lab study in which 29 students had to solve several tasks and learn with the help of an adaptive learning system in a within-subjects control condition design. In the learning process, participants obtained recommendations for learning goals from the system in three conditions: fixed scaffolding where learning goals were generated from the domain model, personalized scaffolding where these recommendations were ranked according to the user model, and random suggestions of learning goals (control condition). Students in the two experimental conditions clearly outperformed students in the control condition and felt better supported by the system. Additionally, students who received personalized scaffolding selected fewer learning goals than participants from the other groups.