A Knowledge Map Tool for Supporting Learning in Information Science

Helmut Vieritz (), Hans-Christian Schmitz, Effie Lai-Chon Law, Maren Scheffel, Daniel Schilberg and Sabina Jeschke
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Helmut Vieritz: RWTH Aachen University, IMA/ZLW & IfU
Hans-Christian Schmitz: Fraunhofer Institute of Applied Information Technology FIT
Effie Lai-Chon Law: University of Leicester, Department of Computer Science
Maren Scheffel: Fraunhofer Institute of Applied Information Technology FIT
Daniel Schilberg: RWTH Aachen University, IMA/ZLW & IfU
Sabina Jeschke: RWTH Aachen University, IMA/ZLW & IfU

A chapter in Automation, Communication and Cybernetics in Science and Engineering 2013/2014, 2014, pp 513-525 from Springer

Abstract: Abstract Large classes at universities (> 1600 students) create their own challenges for teaching and learning. Audience feedback is lacking and fine tuning of lectures, courses and exam preparation to address individual needs is very difficult to achieve. At RWTH Aachen University, a course concept and a knowledge map learning tool aimed to support individual students to prepare for exams in information science through theme-based exercises were developed and evaluated. The tool was grounded in the notion of self-regulated learning with the goal of enabling students to learn independently.

Keywords: Knowledge Map; Large Classes; Self-Regulated Learning; Higher Education; Information Science (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/978-3-319-08816-7_40

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