Knowledge Services for Work-Integrated Learning

Lindstaedt, Stefanie; Scheir, Peter; Lokaiczyk, Robert; Kump, Barbara; Beham, Günter; Pammer, Viktoria

doi:10.1007/978-3-540-87605-2_26

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…Learning activities frequently occur within work processes [5]. The work-integrated learning (WIL) paradigm [14,23] takes these observations seriously and sees learning as a dimension of work. One goal of WIL is to foster learning during work in order to enhance task performance.…”

Section: Introductionmentioning

confidence: 99%

Studying the Factors Influencing Automatic User Task Detection on the Computer Desktop

Rath

Devaurs

Lindstaedt

2010

Sustaining TEL: From Innovation to Learning and Practice

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Supporting learning activities during work has gained momentum for organizations since work-integrated learning (WIL) has been shown to increase productivity of knowledge workers. WIL aims at fostering learning at the workplace, during work, for enhancing task performance. A key challenge for enabling task-specific, contextualized, personalized learning and work support is to automatically detect the user's task. In this paper we utilize our ontology-based user task detection approach for studying the factors influencing task detection performance. We describe three laboratory experiments we have performed in two domains including over 40 users and more than 500 recorded task executions. The insights gained from our evaluation are: (i) the J48 decision tree and Naïve Bayes classifiers perform best, (ii) six features can be isolated, which provide good classification accuracy, (iii) knowledge-intensive tasks can be classified as well as routine tasks and (iv) a classifier trained by experts on standardized tasks can be used to classify users' personal tasks.

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Section: Introductionmentioning

confidence: 99%

Studying the Factors Influencing Automatic User Task Detection on the Computer Desktop

Rath

Devaurs

Lindstaedt

2010

Sustaining TEL: From Innovation to Learning and Practice

Self Cite

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“…Lately, not surprisingly, many educational institutions were applying competences to understand whether a person has a particular level of ability or skill. For example, the prototype proposed by Lindstaedt et al [12] is a great example of competence usage in practice.…”

Section: Competencesmentioning

confidence: 99%

Automatic Competence Leveling of Learning Objects

Kawase

Siehndel

Nunes

et al. 2013

2013 IEEE 13th International Conference on Advanced Learning Technologies

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A competence is the effective performance in a domain at different levels of proficiency. Educational institutions apply competences to understand whether a person has a particular level of ability or skill. Educational resource enriched with competence information allows learners identifying, on a fine-grained level, which resources to study with the aim to reach a specific competence target. However, the process of annotating learning objects with competence levels is a very time consuming task; ideally, this task should be performed by experts on the subjects of the educational resources. Due to this, most educational resources available online do not enclose competence information. In this paper, we present a method to tackle the problem of automatically assigning an educational resource with competence levels. To solve these problems, we exploit information extracted from external repositories available on the Web, which lead us to a domain independent approach. We demonstrate the quality of the proposed methods through an evaluation on real world data with an additional user study. Results show that the automatic competence level assignment achieves 84% precision on ground truth data. The key implications of our approach are: first, it effectively facilitates experts in the arduous task of competence assignment and second, it directly supports learners to retrieve proper leveled material.

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“…Learning at work takes mainly place as a by-product of work processes and practices rather than as a result of explicit learning actions the knowledge worker performs [1]. This concept of seamlessly integrated working and learning is described by [2] as work-integrated learning (WIL) and makes great demands on the designers and developers of systems supporting knowledge workers at their workplaces as compared to traditional learning systems: WIL requires learning support (1) during work task execution and tightly contextualized to the work context (2) within the work environment and (3) utilizes knowledge artefacts available within the organizational memory for learning. Furthermore, knowledge workers need to compensate with information overload and time pressure for finding relevant information to meet their current information needs.…”

Section: Introductionmentioning

confidence: 99%

Towards a user model for personalized recommendations in work-integrated learning: A report on an experimental study with a collaborative tagging system

Schoefegger

Seitlinger

Ley

2010

Procedia Computer Science

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The informal setting of learning at work give rise for unique challenges to the field of technology enhanced learning systems. Personalized recommendations taking into account the current context of the individual knowledge worker are a powerful approach to overcome those challenges and effectively support the knowledge workers to meet their individual information needs. Basis for these recommendations to adopt to the current context of a knowledge worker can be provided by user models which reflects the topics knowledge workers are dealing with and their corresponding knowledge levels, but research has only focused on user modeling in settings with a static underlying domain model so far. We suggest to model the users' context based on the emergent topics they are dealing with and their individual current knowledge levels within these topics by extracting the necessary information from the user's past activities within the system. Based on data from an experiment with students learning a new topic with the help of a collaborative tagging system, we started to evaluate this approach and report on first results.

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Knowledge Services for Work-Integrated Learning

Cited by 9 publications

References 9 publications

Studying the Factors Influencing Automatic User Task Detection on the Computer Desktop

Studying the Factors Influencing Automatic User Task Detection on the Computer Desktop

Automatic Competence Leveling of Learning Objects

Towards a user model for personalized recommendations in work-integrated learning: A report on an experimental study with a collaborative tagging system

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