Classification Networks: A Knowledge Representation Scheme for Curriculum Prescription
نویسندگان
چکیده
This paper presents a classification scheme that articulates the categorisation of subject matter. Two basic classificatory devices are introduced: (a) a feature representing the resulting category characterised by a property that the feature denotes, (b) a dimension representing a perspective that partitions the Universe of Discourse (UoD) or a category. A classification based on a dimension can be further classified into the categories it has formed. Dimensions can be juxtaposed to form a classification based on multiple perspectives. The classificatory devices of features and dimensions, which form a classification network, support a wide range of subject organisation types, viz. (a) conceptual organisation, (b) procedural organisation and (c) theoretical organisation. The purpose of the approach is to support the clarity, simplicity and maintainability of large scale general ITS development. Some simple course organisation examples using classification networks are also presented.
منابع مشابه
On the Representation of Bloom's Revised Taxonomy in Interchange Coursebooks
This study intends to evaluate Interchange series (2005), which are still fundamental coursebooks in the EFL curriculum settings, in terms of learning objectives in Bloom’s Revised Taxonomy (2001) to see which levels of Bloom's Revised Taxonomy were more emphasized in these coursebooks. For this purpose, the contents of Interchange textbooks were codified based on a coding scheme designed by th...
متن کاملNeuron Mathematical Model Representation of Neural Tensor Network for RDF Knowledge Base Completion
In this paper, a state-of-the-art neuron mathematical model of neural tensor network (NTN) is proposed to RDF knowledge base completion problem. One of the difficulties with the parameter of the network is that representation of its neuron mathematical model is not possible. For this reason, a new representation of this network is suggested that solves this difficulty. In the representation, th...
متن کاملDeep Unsupervised Domain Adaptation for Image Classification via Low Rank Representation Learning
Domain adaptation is a powerful technique given a wide amount of labeled data from similar attributes in different domains. In real-world applications, there is a huge number of data but almost more of them are unlabeled. It is effective in image classification where it is expensive and time-consuming to obtain adequate label data. We propose a novel method named DALRRL, which consists of deep ...
متن کاملA comprehensive model of hidden curriculum management in medical education
Introduction: Hidden curriculum plays a main role in professionallearning, formation of professional identity, socialization,moral development and learning values, attitudes, beliefs, andknowledge in learners, so it needs to be managed. Althoughthe majority of the theorists believe in the existence of a hiddencurriculum and its greater effect and sustainability com...
متن کاملA New Algorithm for Optimization of Fuzzy Decision Tree in Data Mining
Decision-tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. The resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. The most comprehensible decision trees have been designed for perfect symbolic data. Classical crisp decision trees (DT) are widely applied to classification t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 1993