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Miedema, Daphne; Fletcher, George; Aivaloglou, Efthimia – ACM Transactions on Computing Education, 2023
Prior studies in the Computer Science education literature have illustrated that novices make many mistakes in composing SQL queries. Query formulation proves to be difficult for students. Only recently, some headway was made towards understanding why SQL leads to so many mistakes, by uncovering student misconceptions. In this article, we shed new…
Descriptors: Computer Science Education, Novices, Misconceptions, Programming Languages
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
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
Bick, Markus; Pawlowski, Jan M.; Veith, Patrick – 2001
The importance of the Extensible Markup Language (XML) technology family in the field of Computer Assisted Learning (CAL) can not be denied. The Instructional Management Systems Project (IMS), for example, provides a learning resource XML binding specification. Considering this specification and other implementations using XML to represent…
Descriptors: Computer Assisted Instruction, Educational Technology, Foreign Countries, Instructional Design
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