31,793 research outputs found
The generation of e-learning exercise problems from subject ontologies
The teaching/ learning of cognitive skills, such as
problem-solving, is an important goal in most forms of
education. In well-structured subject areas certain
exercise problem types may be precisely described by
means of machine-processable knowledge structures
or ontologies. These ontologies can readily be used to
generate individual problem examples for the student,
where each problem consists of a question and its
solution. An example is given from the subject domain
of computer databases
Distribution-based aggregation for relational learning with identifier attributes
Identifier attributesâvery high-dimensional categorical attributes such as particular
product ids or peopleâs namesârarely are incorporated in statistical modeling. However,
they can play an important role in relational modeling: it may be informative to have communicated
with a particular set of people or to have purchased a particular set of products. A
key limitation of existing relational modeling techniques is how they aggregate bags (multisets)
of values from related entities. The aggregations used by existing methods are simple
summaries of the distributions of features of related entities: e.g., MEAN, MODE, SUM,
or COUNT. This paperâs main contribution is the introduction of aggregation operators that
capture more information about the value distributions, by storing meta-data about value
distributions and referencing this meta-data when aggregatingâfor example by computing
class-conditional distributional distances. Such aggregations are particularly important for
aggregating values from high-dimensional categorical attributes, for which the simple aggregates
provide little information. In the first half of the paper we provide general guidelines
for designing aggregation operators, introduce the new aggregators in the context of the
relational learning system ACORA (Automated Construction of Relational Attributes), and
provide theoretical justification.We also conjecture special properties of identifier attributes,
e.g., they proxy for unobserved attributes and for information deeper in the relationship
network. In the second half of the paper we provide extensive empirical evidence that the
distribution-based aggregators indeed do facilitate modeling with high-dimensional categorical
attributes, and in support of the aforementioned conjectures.NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc
Model-driven description and validation of composite learning content
Authoring of learning content for courseware systems is a complex activity requiring the combination of a range of design and validation techniques. We introduce the CAVIAr courseware models allowing for learning content description and validation. Model-based representation and analysis of different concerns such as the subject domain, learning context, resources and instructional design used are key contributors to this integrated solution. Personalised learning is particularly difficult to design as dynamic configurations cannot easily be predicted and tested. A tool-supported technique based on CAVIAr can alleviate this complexity through the validation of a set of pedagogical and non-pedagogical requirements. Courseware validation checks intra- and inter-content relationships and the compliance with requirements and educational theories
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