Semantic Reranking

Only available on StudyMode
  • Download(s) : 80
  • Published : February 20, 2013
Open Document
Text Preview
Using Data Warehouse and Data Mining Resources for Ongoing Assessment of Distance Learning Daniela Resende Silva1 E-mail: Marina Teresa Pires Vieira 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:(55 16) 260-8232 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 assessment of distance learning and proposes the MultiStar environment for KDW to support this assessment. Several studies focus on supporting student assessment, among them those of [5, 6] and [7]. Some studies apply data mining resources to Web log information [8, 9, 10 11].

2. Ongoing Assessment of Distance Learning
The teaching-learning process naturally produces information about the status of a student’s activities in a course. The study of this information and the decisions based on this study characterize the ongoing assessment of the learner. In most computational environments for distance learning involving some kind of student assessment, this is done by collecting the student’s interactions with the environment (the student’s actions). Analyzing the student’s history of interactions can reveal how the manner in which he conducts his studies influences the extent to which he profits from the course. Today there is a wide range of environments available for distance courses. To identify how these environments assess the student’s assimilation, a survey was made of the ones most frequently cited in the literature, as documented by


MPhil scholarship-CAPES/Brazil

0-473-08801-0/01 $20.00 © 2002 IEEE


[12]. Five mechanisms to support the ongoing assessment of distance learning were identified through this survey: − tracking of the student’s actions; − − − − redirectioning through evaluation; records of messages from lists; records of messages from forums; records of messages from chats.

the criterion used to decide whether or not the student has carried them out.

3. Ongoing Assessment of Distance Learning using Data Warehouse Resources The relevant information for ongoing assessment of distance learning can be stored in a data warehouse to support management decisions. This study explores the use of a data warehouse with these characteristics for the application of data mining techniques, allowing for patterns of student behavior to be...
tracking img