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Eric Schätz; Alke Martens – International Association for Development of the Information Society, 2023
Due the wideness of the term Physical Computing, there is a need for a better structure of this topic. This paper is about an approach of structuring this field by finding attributes of different physical computing devices which can be used in class. Those attributes are meant to enforce teachers as well as researchers to analyze different devices…
Descriptors: Computer Use, Handheld Devices, Telecommunications, Teaching Methods
Podworny, Susanne; Hüsing, Sven; Schulte, Carsten – Statistics Education Research Journal, 2022
Data science surrounds us in contexts as diverse as climate change, air pollution, route-finding, genomics, market manipulation, and movie recommendations. To open the "data-science-black-box" for lower secondary school students, we developed a data science teaching unit focusing on the analysis of environmental data, which we embedded…
Descriptors: Statistics Education, Programming, Programming Languages, Data Analysis
Krüger, Ralph – Interpreter and Translator Trainer, 2022
This paper intends to illustrate the didactic potential of Python-based Jupyter notebooks in teaching translation technology, machine translation in particular, to translation students. It discusses the basic makeup of Jupyter notebooks and shows how these notebooks can be set up for students who have had little to no prior exposure to the Python…
Descriptors: Translation, Second Language Learning, Second Language Instruction, Natural Language Processing
Amelung, M.; Krieger, K.; Rosner, D. – IEEE Transactions on Learning Technologies, 2011
Assessment is an essential element in learning processes. It is therefore not unsurprising that almost all learning management systems (LMSs) offer support for assessment, e.g., for the creation, execution, and evaluation of multiple choice tests. We have designed and implemented generic support for assessment that is based on assignments that…
Descriptors: Learning Processes, Programming Languages, Assignments, Programming
Knauf, Rainer; Sakurai, Yoshitaka; Tsuruta, Setsuo; Jantke, Klaus P. – Journal of Educational Computing Research, 2010
University education often suffers from a lack of an explicit and adaptable didactic design. Students complain about the insufficient adaptability to the learners' needs. Learning content and services need to reach their audience according to their different prerequisites, needs, and different learning styles and conditions. A way to overcome such…
Descriptors: Prerequisites, College Instruction, Educational Experiments, Cognitive Style
Schwieren, Joachim; Vossen, Gottfried; Westerkamp, Peter – Electronic Journal of e-Learning, 2006
e-Learning has become a major field of interest in recent years, and multiple approaches and solutions have been developed. A typical form of e-learning application comprises exercise submission and assessment systems that allow students to work on assignments whenever and where they want (i.e., dislocated, asynchronous work). In basic computer…
Descriptors: Computer Software, Programming, Electronic Learning, Higher Education
Pawlowski, Jan M.; Bick, Markus – Educational Technology & Society, 2006
The DIN Didactical Object Model extends the approaches of existing Educational Modeling Languages introducing specifications for contexts and experiences. In this paper, we show how the Didactical Object Model can be used for sharing didactical expertise. Educational Modeling Languages change the design paradigm from content orientation towards…
Descriptors: Knowledge Management, Teaching Methods, Expertise, Foreign Countries
Brase, Jan; Painter, Mark – Educational Technology & Society, 2004
Learning Objects Metadata (LOM) aims at describing educational resources in order to allow better reusability and retrieval. In this article we show how additional inference rules allows us to derive additional metadata from existing ones. Additionally, using these rules as integrity constraints helps us to define the constraints on LOM elements,…
Descriptors: Inferences, Metadata, Information Retrieval, Standards