Educational data mining: A survey from 1995 to 2005

Romero, Cristóbal; Ventura, Sebastián

doi:10.1016/j.eswa.2006.04.005

Cited by 1,180 publications

(679 citation statements)

References 29 publications

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“…Researchers analyze this data to identify different aspects of learning behaviors, such as patterns of navigation, time spans, and sequences of learning (Romero & Ventura, 2007). The log files of online learning environments might also hold information about students' profile (e.g., age, gender, achievements), so it becomes possible to correlate between their behaviors and their personal profile.…”

Section: Log File Analysis For Understanding Learning Behaviorsmentioning

confidence: 99%

“…While they are learning in online environments, students actually leave a continuous trace of their activity in the form of log file records, which document every action taken with reference to three parameters: what was the action taken, who took it, and when. Extracting these actions from the log files and analyzing them can yield meaningful information and new insights about how students learn in online environments and about their learning styles and needs (Pahl, 2004;Romero & Ventura, 2007).…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Online Learning Behaviors in School vs. at Home in Terms of Age and Gender Based on Log File Analysis

Ben-Zadok¹,

Leiba

Nachmias

2010

IJELL

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The research objectives of this study are: (a) to compare learning behaviors in an online science learning environment -in school vs. at home; and (b) to explore the existence of some behavioral differences, in school and at home, in terms of age and gender. The actions of 1,179 elementary school students in an online science learning environment were documented in a log file and statistically analyzed. Results suggest that students who learn at home tend to spend more time learning; they learn at a slower pace and score higher on a test than students who learn in school. However, no significant differences were found between home and school in terms of the amount of completed activities, the rate of simulation use, and the sequence of learning. Comparison between age groups indicates that younger students tend to learn for more time, at a slower pace, and complete fewer activities than older students. Comparison between genders, on the other hand, reflects similar learning behaviors for boys and girls. The results also suggest that neither age nor gender affect the differences found between school and home behaviors. Our conclusions indicate that extending teaching time to the home by means of online learning environments is possible and worthwhile, regardless of age or gender. Nevertheless, it should be noticed that in some cases more learning time is necessary for young students in online assignments.

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Section: Log File Analysis For Understanding Learning Behaviorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of Online Learning Behaviors in School vs. at Home in Terms of Age and Gender Based on Log File Analysis

Ben-Zadok¹,

Leiba

Nachmias

2010

IJELL

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“…The methods, their descriptions and a few examples are shown in Table 1. Baker & Yacef (2009) study the proportion of works employing each group of methods during the period from 1995 to 2005 (using data extracted from Romero & Ventura, 2007) and from 2008 to 2009 (using data from Baker et al, 2008, andBarnes, Desmarais, Romero, &Ventura, 2009). Papers from the first period mainly involved relationship mining methods (43%) or prediction methods (28%).…”

Section: Common Methods In Edm and Lamentioning

confidence: 99%

Educational Data Mining and Learning Analytics: differences, similarities, and time evolution

Calvet

Juan

2015

RUSC. Univ. and Know. Soc.

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“…The objective of data mining is to discover new, interesting and useful knowledge using a variety of techniques such as prediction, classification, clustering, association rule mining and sequential pattern discovery. Currently, there is an increasing interest in data mining and educational systems, making educational data mining a new and growing research community [20] [21]. The data mining approach to personalization uses all the available information about users/students on the web site (in the web course) in order to learn user models and to use these models for personalization.…”

Section: Introductionmentioning

confidence: 99%

Personalized Links Recommendation Based on Data Mining in Adaptive Educational Hypermedia Systems

Romero

Ventura

Delgado

et al. 2007

Creating New Learning Experiences on a Global Scale

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Abstract. In this paper, we describe a personalized recommender system that uses web mining techniques for recommending a student which (next) links to visit within an adaptable educational hypermedia system. We present a specific mining tool and a recommender engine that we have integrated in the AHA! system in order to help the teacher to carry out the whole web mining process. We report on several experiments with real data in order to show the suitability of using both clustering and sequential pattern mining algorithms together for discovering personalized recommendation links.

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Educational data mining: A survey from 1995 to 2005

Cited by 1,180 publications

References 29 publications

Comparison of Online Learning Behaviors in School vs. at Home in Terms of Age and Gender Based on Log File Analysis

Comparison of Online Learning Behaviors in School vs. at Home in Terms of Age and Gender Based on Log File Analysis

Educational Data Mining and Learning Analytics: differences, similarities, and time evolution

Personalized Links Recommendation Based on Data Mining in Adaptive Educational Hypermedia Systems

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