Applying Web usage mining for personalizing hyperlinks in Web-based adaptive educational systems

Romero, Cristóbal; Ventura, Sebastián; Zafra, Amelia; Bra, Paul De

doi:10.1016/j.compedu.2009.05.003

Cited by 136 publications

(70 citation statements)

References 29 publications

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“…Therefore, we can find WBES with adaptive techniques [2], some other WBES with intelligent mechanisms [3] and more complex systems that combine both properties (a detailed review of AIWBES was presented by Brusilovsky and Peylo [4]). …”

Section: Introductionmentioning

confidence: 99%

Mining Web-based Educational Systems to Predict Student Learning Achievements

Campo-Ávila¹,

Conejo²,

Triguero³

et al. 2015

IJIMAI

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-Educational Data Mining (EDM) is getting great importance as a new interdisciplinary research field related to some other areas. It is directly connected with Web-based Educational Systems (WBES) and Data Mining (DM, a fundamental part of Knowledge Discovery in Databases).The former defines the context: WBES store and manage huge amounts of data. Such data are increasingly growing and they contain hidden knowledge that could be very useful to the users (both teachers and students). It is desirable to identify such knowledge in the form of models, patterns or any other representation schema that allows a better exploitation of the system. The latter reveals itself as the tool to achieve such discovering. Data mining must afford very complex and different situations to reach quality solutions. Therefore, data mining is a research field where many advances are being done to accommodate and solve emerging problems. For this purpose, many techniques are usually considered.In this paper we study how data mining can be used to induce student models from the data acquired by a specific Web-based tool for adaptive testing, called SIETTE. Concretely we have used top down induction decision trees algorithms to extract the patterns because these models, decision trees, are easily understandable. In addition, the conducted validation processes have assured high quality models.

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Section: Introductionmentioning

confidence: 99%

Mining Web-based Educational Systems to Predict Student Learning Achievements

Campo-Ávila¹,

Conejo²,

Triguero³

et al. 2015

IJIMAI

View full text Add to dashboard Cite

show abstract

“… Hybrid Recommender Systems (Albadvi 2009, Burke 2002b: To overcome the disadvantages of previously mentioned recommender systems sometimes is used hybrid techniques which try to make useful the best of methods used in hybridization. The most spread recommender systems are collaborative and content based systems both have provided good results in different areas as tourism (Sebastia 2009), e-learning (Romero 2009), academic orientation (Castellano 2009b), etc. In (Castellano 2009b) was introduced the use of collaborative filtering for academic orientation, in the case of study of Spanish Academic System, by using marks as ratings of user's profile that are filtered in order to support advisors in their orientation for the students.…”

Section: Fig 1 Recommendation Schemementioning

confidence: 99%

Academic Orientation Supported by Hybrid Intelligent Decision Support System

Castellano¹,

Barranco²,

Martínez³

2011

Efficient Decision Support Systems - Practice and Challenges From Current to Future

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“…Here, by scanning the database once, all the 1-length frequent patterns are sorted and SE item-last position list is constructed in ascending order based on each 1-length frequent pattern"s last position. Romero, Sebastián Ventura, Amelia Zafra, Paul de Bra [15] proposed "Applying Web usage mining for personalizing hyperlinks in Web-based adaptive educational systems". It consists of a specific Web mining tool and a recommender engine integrated into the AHA!…”

Section: Web Usage Miningmentioning

confidence: 99%