Mining interesting contrast rules for a web-based educational system

Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions

“…• building recommender agents for on-line learning activities or shortcuts (Zaiane 2002), • automatically leading the learner's activities and intelligently recommend on-line learning activities or shortcuts in the course web site to the learners (Lu 2004), • identifying attributes of performance inconsistency between various groups of learners (Minaei-Bidgoli et al 2004), • discovering interesting learner's usage information in order to provide feedback to course author (Romero et al 2004), • finding out the relation among the learning materials from a large amount of material data (Yu et al 2001), • finding learners' mistakes that are often occur together (Merceron and Yacef 2004), • optimizing the content of an e-learning portal by determining the content of most interest to the learner (Ramli 2005), • deriving useful patterns to help educators and instructors evaluating and interpreting on-line course activities (Zaiane 2002), and • personalizing e-learning based on comprehensive usage profiles and a domain ontology (Markellou et al 2005).…”

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

“…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).…”

E-Learning Platform Usage Analysis

“…Most of the subjective approaches involve user participation in order to express, in accordance with his/her previous knowledge, which rules are of interest. One technique proposes the division of the discovered rules into three categories (Minaei-Bidgoli, Tan, & Punch, 2004 …”