Two process tracing studies investigated how the information acquisition process in a binary choice task is influenced by the overall level of attractiveness of alternatives, by the magnitude of differences in attractiveness of alternatives, and by the dominance of one alternative. All three factors influenced the subjects' information selection process regarding the multiattribute choice alternatives. Subjects selected more information when the attractiveness difference was small and when one of the alternatives was not dominant. Moreover, they considered more information when the choice alternatives were both unattractive. These findings were obtained when information was presented about the alternatives both numerically and nonnumerically. The experimental results were explained within a sequential sampling strategy framework.8 1991 Academic press, IK.Process tracing methods are useful for determining important factors that influence the utilization of information in a choice situation. One well-established finding of process tracing studies is that an increase in the number of choice alternatives leads to a more noncompensatory integration of information about the alternatives (Onken, Hastie, BE Revelle,We are grateful to Jerome Busemeyer, Douglas Wedell, and Elke Weber for valuable comments on an earlier version of this manuscript. Maria Bannert, Nancy Linn, and Thomas Schneyer helped in conducting the experiments. In addition, we thank the referees for their helpful suggestions.

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To find a balance between learning analytics research and individual privacy, learning analytics initiatives need to appropriately address ethical, privacy, and data protection issues. A range of general guidelines, model codes, and principles for handling ethical issues and for appropriate data and privacy protection are available, which may serve the consideration of these topics in a learning analytics context. The importance and significance of data security and protection are also reflected in national and international laws and directives, where data protection is usually considered as a fundamental right. Existing guidelines, approaches, and regulations served as a basis for elaborating a comprehensive privacy and data protection framework for the LEA's BOX project. It comprises a set of eight principles to derive implications for ensuring ethical treatment of personal data in a learning analytics platform and its services. The privacy and data protection policy set out in the framework is translated into the learning analytics technologies and tools that were developed in the project and may be used as best practice for other learning analytics projects.

Purpose-The purpose of this paper is to suggest a way to support work-integrated learning for knowledge work, which poses a great challenge for current research and practice. Design/methodology/approach-The authors first suggest a workplace learning context model, which has been derived by analyzing knowledge work and the knowledge sources used by knowledge workers. The authors then focus on the part of the context that specifies competencies by applying the competence performance approach, a formal framework developed in cognitive psychology. From the formal framework, a methodology is then derived of how to model competence and performance in the workplace. The methodology is tested in a case study for the learning domain of requirements engineering. Findings-The Workplace Learning Context Model specifies an integrative view on knowledge workers' work environment by connecting learning, work and knowledge spaces. The competence performance approach suggests that human competencies be formalized with a strong connection to workplace performance (i.e. the tasks performed by the knowledge worker). As a result, competency diagnosis and competency gap analysis can be embedded into the normal working tasks and learning interventions can be offered accordingly. The results of the case study indicate that experts were generally in moderate to high agreement when assigning competencies to tasks. Research limitations/implications-The model needs to be evaluated with regard to the learning outcomes in order to test whether the learning interventions offered benefit the user. Also, the validity and efficiency of competency diagnosis need to be compared to other standard practices in competency management. Practical implications-Use of competence performance structures within organizational settings has the potential to more closely relate the diagnosis of competency needs to actual work tasks, and to embed it into work processes. Originality/value-The paper connects the latest research in cognitive psychology and in the behavioural sciences with a formal approach that makes it appropriate for integration into technology-enhanced learning environments.

The ELeGI project focuses on integrating technology-enhanced learning methodologies into a pedagogy-driven and service-oriented architecture based on Grid technology. It aims at a system that is capable of realising personalised, adaptive, and experiential learning. This requires to have available a framework that, on the one hand, allows for representing existing domain knowledge, and, on the other hand, provides a representation of the learner's current state of knowledge. It is shown that a competence-based extension of Knowledge Space Theory provides a representation of the conceptual organisation of a domain that not only allows for an adaptive assessment of an individual's knowledge, but also for generating personalised learning paths. The discussion of this framework emphasizes its application within an open distributed service model, and in the context of Virtual Scientific Experiments.