Using data mining on student behavior and cognitive style data for improving e-learning systems: a case study

Jovanović, Miloš; Vukičević, Milan; Milovanović, Miloš; Minović, Miroslav

doi:10.1080/18756891.2012.696923

Cited by 71 publications

(48 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Similarly, mining data obtained from recording patterns of classroom or e-learning activity of students (using e.g., Moodle) may lead to successful clustering of students based on their cognitive styles (Jovanovic et al, 2012) or to building customised content recommendations for them (Khribi et al, 2009). In a sense, the wealth of data obtained from the users of an e-learning system constitutes, by itself, feedback (Khribi et al, 2009).…”

Section: No Room For Hand-waving: Make the Criteria Quantitativementioning

confidence: 99%

Features of an e-learning environment which promote critical and creative thinking: choice, feedback, anonymity, and assessment

Jasieński

2014

IJCEELL

View full text Add to dashboard Cite

I discuss features that are important for creative and critical thinking which should be recreated in e-learning applications. Anonymity maximises chances for development of creativity and for objective and accurate assessment. I also describe a 'quadruple anonymity' system implemented at Nowy Sacz Business School -National-Louis University in Poland, the goal of which is to improve objectivity of thesis evaluation by referees. E-learning environment is ideal for implementing functionalities which make choice, feedback, and controlled anonymity easily available to the users to be effective, feedback should be appropriately timed, incremental, impartial, and impersonal. Evaluation of student or employee performance or of proposed ideas or solutions should rely on explicitly stated quantitative criteria, developed along well thought-through measurement scales and utilising proper descriptive statistics and visualisation methods. A description of the nominal group heuristic method is provided as an example of a heuristic method which relies on creativity, anonymity and unbiased evaluation.

show abstract

Section: No Room For Hand-waving: Make the Criteria Quantitativementioning

confidence: 99%

Features of an e-learning environment which promote critical and creative thinking: choice, feedback, anonymity, and assessment

Jasieński

2014

IJCEELL

View full text Add to dashboard Cite

show abstract

“…In [47] Jovanovica et al applied classification models for predicting students' performance, and cluster models for grouping students based on their cognitive styles in Moodle. They developed a Moodle module that allows automatic extraction of data needed for educational data mining analysis and deploys models developed in this study.…”

Section: Edm Applications For Predicting and Evaluatingmentioning

confidence: 99%

“…Some reviewed studies have discussed some of these results that contribute to a better adaptation and personalization of CBLE: improving adaptation based on cognitive styles in [47], classifying the learners' activities according to their influence on the performance in [48], identifying the required skills for a learning resource in [31].…”

Section: Edm Applications For Predicting and Evaluatingmentioning

confidence: 99%

Which Contribution Does EDM Provide to Computer-Based Learning Environments?

Bousbia

Belamri

2013

Educational Data Mining

View full text Add to dashboard Cite

Educational Data Mining is a new growing research area that can be defined as the application of data mining techniques on raw data from educational systems in order to respond to the educational questions and problems, and also to discover the information hidden after this data. Over the last few years, the popularity of this field enhanced a large number of research studies that is difficult to surround and to identify the contribution of data mining techniques in educational systems. In fact, exploit and understand the raw data collected from educational systems can be ''a gold mine'' to help the designers and the users of these systems improving their performance and extracting useful information on the behaviors of students in the learning process. The use of data mining techniques in e-learning systems could be very interesting to resolve learning problems. Researchers' ambition is to respond to questions like: What can predict learners' success? Which scenario sequence is more efficient for a specific student? What are the student actions that indicate the learning progress? What are the characteristics of a learning environment allowing a better learning? etc. The current feedback allows detecting the usefulness of applying EDM on visualizing and describing the learning raw data. The predictions take also an interest, particularly the prediction of performance and learners' behaviors. The aim of this chapter is to establish a bibliographic review of the various studies made in the field of educational data mining (EDM) to identify the different aspects studied: the analyzed data, the objectives of these studies, the used techniques and the contribution of the application of these techniques in the field of computer based learning. The goal is not only to list the existing work but also to facilitate the use and the understanding of data mining techniques to help the educational field specialists to give their

show abstract

“…Artificial neural network techniques are widely utilized as powerful tools in dealing with modeling nonlinear behaviors in a large number of areas including traditional and online education 8,19,28,29,35,36,48 . They enable the construction of models that efficiently describe real world systems.…”

Section: Training the Modelmentioning

confidence: 99%

A Decision Support System for Course Offering in Online Higher Education Institutes

Kardan¹,

Sadeghi²

2013

IJCIS

View full text Add to dashboard Cite

Prior to every academic semester, every department's administrator is required to offer the best overall set of courses to meet student requirements, instructor needs and department regulations. The key contributions of this research is firstly, determining the potential factors that influence student behavior on the online courses they choose, secondly, modeling the course offering problem and fitting a function to a training set of data using neural network approach, thirdly, design and implementation of a decision support system to help the department's administrator to simulate student behavior in course selection process and support his/her decisions on the courses to be offered, and lastly, employing the proposed decision support system to perform what-if analysis and goal seeking behavior. The samples of the experiments came from 298 online graduate courses in 14 academic terms from 2005 to 2011. The results revealed high prediction accuracy on the experimental data. The performance of the introduced decision support system was also compared with three well-known regression techniques, "support vector regression", "K-nearest neighborhood", "decision tree" and a traditional approach. The finding exposed that the suggested decision support system outperformed the others significantly.

show abstract

Using data mining on student behavior and cognitive style data for improving e-learning systems: a case study

Cited by 71 publications

References 48 publications

Features of an e-learning environment which promote critical and creative thinking: choice, feedback, anonymity, and assessment

Features of an e-learning environment which promote critical and creative thinking: choice, feedback, anonymity, and assessment

Which Contribution Does EDM Provide to Computer-Based Learning Environments?

A Decision Support System for Course Offering in Online Higher Education Institutes

Contact Info

Product

Resources

About