Semantic Reranking

Using Data Warehouse and Data Mining Resources for Ongoing Assessment of Distance Learning
Daniela Resende Silva1 E-mail: [e-mail] Marina Teresa Pires Vieira E-mail: [e-mail]

Department of Computer Sciences UFSCar - Federal University of São Carlos Rod. Washington Luís, Km 235 Caixa Postal 676 13565-905 / São Carlos – SP – Brazil Phone/Fax:[phone] Abstract
This paper discusses the use of Data Warehouse and Data Mining resources to aid in the assessment of distance learning of students enrolled in distance courses. Information considered relevant for the assessment of distance learning is presented, as is the modeling of a data warehouse to store this information and the MultiStar environment, which allows for knowledge discovery to be performed in the data warehouse. The work proposed herein presents an approach that differs from the existing ones for the ongoing assessment of distance learning using some of the aspects relating to those utilized in the above cited studies. Section 2 provides a set of information to guide the implementation of ongoing assessment of learning in distance learning environments, while Section 3 briefly discusses the modeling of a data warehouse based on the set of information proposed. Section 4 presents the implementation of this data warehouse using the MultiStar environment, and finally, Section 5 lists our conclusions to this paper.

1. Introduction
A variety of applications have benefited from the use of Data Warehousing technology [1, 2, 3] to support management analyses, which can be obtained through the use of Data Mining [4]. The joint use of Data Warehousing and Data Mining techniques is a trend in KDD – Knowledge Discovery in Data Warehousing applications (referred to herein as KDW – Knowledge Discovery in Data Warehouse), since the data in a warehouse are better prepared for data mining. This paper discusses how the data warehouse and data mining resources can be used for the

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