Enhanced Learning by Extending Metadata of Learning Objects with Knowledge Objects

Sabitha, Sai; Mehrotra, Deepti; Bansal, Abhay

doi:10.14257/ijel.2014.3.1.01

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…A set of common attributes can be considered for both LO & KO. Based on the user query with respect to a domain and topic, a set of LO & KO are retrieved from LMS & KMS respectively and can be classified using classification techniques like a decision tree (Sabitha, Mehotra, 2013) or a naïve Bayes algorithm etc., as in Fig. 2.…”

Section: Creation Of Lkomentioning

confidence: 99%

“…An ontology model was proposed to generate LKOs based on instructional theories (Wang, 2008). For the formation of LKO, it is proposed that KO can be classified with LO through classification technique (Sabitha, Mehotra 2013). These LKOs are a self-contained instructional unit and can be delivered to learners (who have the basic prerequisite) to improve their pedagogical learning experience.…”

Section: Convergence Of Lo and Ko As Lkomentioning

confidence: 99%

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Similarity Based Convergence of Learning Knowledge Objects and Delivery Using Agglomerative Clustering

Sabitha¹,

Mehrotra²,

Bansal³

2014

Journal of Information Technology and Application in Education

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E-learning today shows an exponential growth and there is a need to develop more flexible delivery processes, which will add value to the learning experiences of a student during his/her learning process. The atomic unit of any e-learning environment is a Learning Object, a reusable digital entity. These Learning Objects are stored in repositories and managed by Learning Management Systems in order to provide a better coverage of concepts to the learner. A close relative of Learning Object is the Knowledge Object which is an essential unit of a Knowledge Management System. In this research paper, a way is proposed to converge these objects together and most similar Learning Knowledge Objects delivered to a student using hierarchical clustering techniques. The learning experience is more valuable for a student especially for those with higher order thinking skills.

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Section: Creation Of Lkomentioning

confidence: 99%

Section: Convergence Of Lo and Ko As Lkomentioning

confidence: 99%

Similarity Based Convergence of Learning Knowledge Objects and Delivery Using Agglomerative Clustering

Sabitha¹,

Mehrotra²,

Bansal³

2014

Journal of Information Technology and Application in Education

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“…Learning objects standards are used. An example of learning object metadata's standard is the IEEE LOM (Learning Object Metadata) XML scheme [3], which was developed by LTSC which contains only the object meta-data and allows access to learning materials hosted in the connected repositories. The objects stored in these repositories are characterized according to international standards for learning objects meta-data (LOM).…”

Section: State Of the Art And Related Workmentioning

confidence: 99%

Big Data X-Learning Resources Integration and Processing in Cloud Environments

Kong¹

2014

Int. J. Emerg. Technol. Learn.

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The cloud computing platform has good flexibility characteristics, more and more learning systems are migrated to the cloud platform. Firstly, this paper describes different types of educational environments and the data they provide. Then, it proposes a kind of heterogeneous learning resources mining, integration and processing architecture. In order to integrate and process the different types of learning resources in different educational environments, this paper specifically proposes a novel solution and massive storage integration algorithm and conversion algorithm to the heterogeneous learning resources storage and management cloud environments.

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“…An enhanced LO called a Learning Knowledge Object has already been proposed, for improved learning, which uses the K-Nearest Neighbour approach (Sabitha, Mehrotra, & Bansal, 2014a) and an agglomerative clustering technique (Sabitha, Mehrotra, & Bansal, 2014b). The aim of these is to produce more consistent objects with coherent topic coverage that will satisfy various kinds of learners.…”

Section: Introductionmentioning

confidence: 99%

A Data Mining Approach to Improve Re-Accessibility and Delivery of Learning Knowledge Objects

Sabitha¹,

Mehrotra²,

Bansal³

2014

IJELL

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Today Learning Management Systems (LMS) have become an integral part of learning mechanism of both learning institutes and industry. A Learning Object (LO) can be one of the atomic components of LMS. A large amount of research is conducted into identifying benchmarks for creating Learning Objects. Some of the major concerns associated with LO are size, learning outcomes, pedagogical relevance, and amount of information it delivers to learners. With the advent of knowledge enriched learning, there is a need to create Knowledge Objects (KO) as well and combine these with LOs to create Learning Knowledge Objects (LKO), which can be delivered through an LMS, so that a more holistic knowledge bank is provided to the learners. For an effective LMS, creating a high quality LKO using an algorithm that ensures the delivery of appropriate learning material to the learners is the key issue. Smaller and relevant objects can be delivered to the student using data mining approaches, thereby helping advanced learners to improve their higher order thinking skills. Use of hierarchical clustering techniques for identifying LOs based on user needs is already established. In this paper the Shared Density Approach (SDA) is used to get cohesive clusters and handle cluster of different densities. Finding similar learning objects through clustering technique reduces the domain of search. SDA not only helps with delivery of Learning Objects from a relevant cluster, but also helps in finding objects that are closer to one another but belong to a different class. Objects can be delivered based on user learning approaches, thereby have a wider usage and thus improve re-accessibility.

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Enhanced Learning by Extending Metadata of Learning Objects with Knowledge Objects

Abstract: Abstract

Cited by 7 publications

References 5 publications

Similarity Based Convergence of Learning Knowledge Objects and Delivery Using Agglomerative Clustering

Similarity Based Convergence of Learning Knowledge Objects and Delivery Using Agglomerative Clustering

Big Data X-Learning Resources Integration and Processing in Cloud Environments

A Data Mining Approach to Improve Re-Accessibility and Delivery of Learning Knowledge Objects

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