Data Provenance in E-Learning

Topics: Data, Data management, E-learning Pages: 27 (7663 words) Published: August 24, 2013

M.S.A. Ahsan 100022U
A. Burusothman 100063U
S. Paraneetharan 100369M
B. Sanjith 100484K
K. Sureshkumar 100527X

1. Introduction

We live in an information age, where the volume of data processed by humans and organizations increases exponentially by grid middleware and availability of huge storage capacity. So, Data management comprises all the disciplines related to managing data as a valuable resource. The openness of the Web and the ease to combine linked data from different sources creates new challenges. Systems that consume linked data must evaluate quality and trustworthiness of the data. A common approach for data quality assessment is the analysis of provenance information.[1] Data provenance, one kind of Meta data, relate to the transformational workflows of a data products (files, tables and virtual collections) starting from its original sources. Meta Data refers to “Data about Data”. The workflows can generate huge amount of data with rich Meta data in order to understand and reuse the data. Data provenance techniques used in e-science projects, e learning environment, etc.

E-learning can be difficult to understand because different authors use the term differently. E-learning is a new education idea by using the Internet technology, it delivers the digital content, provides a learner-orient environment for the teachers and students. This definition extends the environment on the Internet. We mean that the Internet provides a learning environment for the students and teachers. This environment is learner-oriented, so we can throw out the thoughts of traditionally teacher-centre’s instruction in classroom.

2. E- Learning in Detail

2. 1 ‘E’ side of E-Learning

As it apparently seems, the word can be thought of having two different sides. ‘E’ side and ‘Learning’ side are the elements which construct this norm. ‘E’ side has more impact in the idea of E-Learning. Just for a look, it might give the explanation as ‘electronic’. But in this phenomena, ideas are broad considering different aspects of electronic technologies. Normally in an e-learning environment, Store, access and use of information occurs seamlessly. This needs to be addressed indicating different technologies/products. That might include operating systems (Windows, Mac OS, etc) , standalone applications (word processor, excel , etc) and any other web applications. In fact, this different products/technologies collaboratively build the norm of virtual learning environment. So, rather than just think of ‘e’ as electronics, above given factors should come into the mind to fix the ‘e’ side with learning aspects.

Science of e-learning involves investigation about how people learn in e-learning environments. This subsequently results in three elements such as 1) evidence 2) theory 3) applications. And now, there is a question of what the e-learning is. It is approached to answer the question by considering what, how, why of e-learning keywords. Definition derived from this, may rise doubt whether e-learning would fulfill the conventional learning. But the fact is always there that as long as same instructional methods are used to convey the contents, medium doesn't come into any concern. When presenting multimedia materials in e-learning environments, there are concerns of how it is to be presented as there are lots of methods like words, pictures, narrations and more. The article goes through nine effects: modality effect, contiguity effect, multimedia effect, personalization effect, coherence effect, redundancy effect, pretraining effect, signaling effect, and pacing effect. Each of which explains an efficient way of presenting materials within their context.

When the theory for science of e-learning is considered, it depicts that the process of meaningful learning from multimedia involves five cognitive processes: selecting words, selecting...

References: [4] Yogesh L.Simmhan. Beth Plale, Dennis Gannon. “A Survey of Data Provenance in e-Science” Newsletter ACM SIGMOD record 34.3 (2005):31-36
[5] Susan B
[14] Len Seligman, Shaun Brady, Barbara Blaustein, Paula Mutchler, Adriane Chapman, Charles Worrell, The MITRE Corporation, USA; “DATA PROVENANCE AND FINANCIAL SYSTEMIC RISK (Case Study)” The MITRE Corporation (2012)
[15] James P
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