Semantic Web mining for Personalized Public Services

Markellos, Konstantinos; Markellou, Penelope; Panayiotaki, Angeliki; Tsakalidis, Athanasios K.

doi:10.4018/9781599040271.ch001

Cited by 11 publications

(13 citation statements)

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“…The support of the rule is the percentage of transactions that contains both antecedent and consequence in all transactions in the database. Association rule mining has been applied to web-based education systems for: building recommender agents that could recommend on-line learning activities or shortcuts (Zaïane, 2002); diagnosing student learning problems and offer students advice (Hwang, Hsiao, & Tseng, 2003); guiding the learner's activities automatically and recommending learning materials (Lu, 2004); determining which learning materials are the most suitable to be recommended to the user (Markellou, Mousourouli, Spiros, & Tsakalidis, 2005); identifying attributes characterizing patterns of performance disparity between various groups of students (Minaei-Bidgoli, Tan, & Punch, 2004); discovering interesting relationships from student's usage information in order to provide feedback to course author (Romero et al, 2004); finding out relationships in learners' behaviour patterns (Yu, Own, & Lin, 2001); finding students' mistakes that often accompany each other (Merceron & Yacef, 2004); guiding the search for best fitting transfer models of student learning (Freyberger, Heffernan, & Ruiz, 2004); and optimizing the content of the elearning portal by determining what most interests the user (Ramli, 2005).…”

Section: Association Rule Miningmentioning

confidence: 99%

Data mining in course management systems: Moodle case study and tutorial

Romero

Ventura

García

2008

Computers & Education

797

334

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Section: Association Rule Miningmentioning

confidence: 99%

Data mining in course management systems: Moodle case study and tutorial

Romero

Ventura

García

2008

Computers & Education

797

334

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“…Contrast rules help one to identify attributes characterizing patterns of performance disparity between various groups of students. Markellou, Mousourouli, Spiros, and Tsakalidis (2005) propose an ontology-based framework and discover association rules, using the Apriori algorithm. The role of ontology is to determine which learning materials are more suitable to be recommended to the user.…”

Section: Rule Discovery In Learning Management Systemsmentioning

confidence: 99%

Evolutionary algorithms for subgroup discovery in e-learning: A practical application using Moodle data

Romero

González

Ventura

et al. 2009

Expert Systems with Applications

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This work describes the application of subgroup discovery using evolutionary algorithms to the usage data of the Moodle course management system, a case study of the University of Cordoba, Spain. The objective is to obtain rules which describe relationships between the student's usage of the different activities and modules provided by this e-learning system and the final marks obtained in the courses. We use an evolutionary algorithm for the induction of fuzzy rules in canonical form and disjunctive normal form. The results obtained by different algorithms for subgroup discovery are compared, showing the suitability of the evolutionary subgroup discovery to this problem.

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“…Markellou et al proposed a Recommendation System using Semantic Web Mining Technologies for personalized e-Learning (Markellou et al 2005). This framework combines content-based filtering and collaborative filtering recommendation strategies to produce a recommendation set, which consists of links to pages that the student may want to visit.…”

Section: Overview Of Tel Recommendation Systems Based On the Proposedmentioning

confidence: 99%

Recommendation Systems for Personalized Technology-Enhanced Learning

Khribi¹,

Jemni²,

Nasraoui

2014

Ubiquitous Learning Environments and Technologies

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From e-commerce to e-learning, recommendation systems have given birth to an important and thriving research niche and have been deployed in a variety of application areas over the last decade. In particular, in the technologyenhanced learning (TEL) field, recommendation systems have attracted increasing interest, especially with the rise of educational data mining and big data learning analytics. Generally, TEL recommendation systems are used to support learners in locating relevant educational content according to their profiles. These systems may involve several phases, such as data acquisition and preparation, modeling, and recommendation computation, phases, which together, can describe a TEL recommendation system and distinguish it from others. However, such a description needs to be expanded and generalized in order to cover most of the TEL recommendation systems, especially, in the context of anywhere and anytime learning based on various Web-based learning environments, including Learning Object Repository (LOR), Open Courseware (OCW), Open Educational Resources (OER), Learning Management Systems (LMS), Massive Open Online Courses (MOOC), Educational Widgets, Educational Mobile applications, etc. In this chapter, we provide a generic meta-level framework for a common description of TEL recommendation systems. Then, we present an analysis of several existing TEL recommendation systems with respect to our defined framework.

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Semantic Web mining for Personalized Public Services

Cited by 11 publications

References 0 publications

Data mining in course management systems: Moodle case study and tutorial

Data mining in course management systems: Moodle case study and tutorial

Evolutionary algorithms for subgroup discovery in e-learning: A practical application using Moodle data

Recommendation Systems for Personalized Technology-Enhanced Learning

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