Supervised classification on navigational behaviours in web-based learning systems to identify learning styles

Bousbia, Nabila; Labat, Jean-Marc; Balla, Amar

doi:10.1504/ijlt.2011.040148

Cited by 5 publications

(9 citation statements)

References 16 publications

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“…They showed that experimental results are superior to the commonly used artificial neural network classification methods. Moreover, Issam et al [17] used supervised learning to detect the image segmentation. They used SVM to probe each region of the image to determine if it contains microcalcifications.…”

Section: Related Workmentioning

confidence: 99%

Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM

et al. 2019

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Microcalcification is the most important landmark information for early breast cancer. At present, morphological artificial observation is the main method for clinical diagnosis of such diseases, but it is easy to cause misdiagnosis and missed diagnosis. The present study proposes an algorithm for detecting microcalcification on mammography for early breast cancer. Firstly, the contrast characteristics of mammograms are enhanced by Contourlet transformation and morphology (CTM). Secondly, split the ROI by the improved K-means algorithm. Thirdly, calculate grayscale feature, shape feature, and Histogram of Oriented Gradient (HOG) for the ROI region. The Adaptive support vector machine (ASVM) is used as a tool to classify the rough calcification point and the false calcification point. Under the guidance of a professional doctor, 280 normal images and 120 calcification images were selected for experimentation, of which 210 normal images and 90 images with calcification images were used for training classification. The remaining 100 are used to test the algorithm. It is found that the accuracy of the automatic classification results of the Adaptive support vector machine (ASVM) algorithm reaches 94%, and the experimental results are superior to similar algorithms. The algorithm overcomes various difficulties in microcalcification detection and has great clinical application value.

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Section: Related Workmentioning

confidence: 99%

Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM

et al. 2019

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“…In their experiment, participants who learned using an environment adapted to their cognitive style performed better than those in the control group. Learners' profile can be initialized through a questionnaire [12] or through an automatic detection of learning styles from learners' traces [13].…”

Section: Aspects Of Adaptationmentioning

confidence: 99%

“…The model used by Natkin et al [13] is also compatible with MAGAM. Their adaptation is based on the Five Factor Model [23], composed of five dimensions expressed in values from -1 to 1 for both users and activities.…”

Section: A Generic Modelmentioning

confidence: 99%

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MAGAM: A Multi-Aspect Generic Adaptation Model for Learning Environments

Monterrat

Yessad

Bouchet

et al. 2017

Data Driven Approaches in Digital Education

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Adaptation in learning environments can be performed according to various aspects, such as didactics, pedagogy or game mechanics. While most current approaches propose to adapt according to a single aspect, this paper proposes a Multi-Aspect Generic Adaptation Model (MAGAM). Based on the Q-matrix, this model aims at taking into account heterogeneous data to select adapted activities. It has been implemented and used into an experiment which allowed the adaptation of learning activities for 97 students based on both knowledge and gaming profiles. This experiment has shown the usefulness of MAGAM to combine various aspects of adaptation in ecological conditions.

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“…We propose adaptation rules depending on the navigation type. We therefore exploit the state of the art on adaptation based on learning styles because our previous results show that there is a correlation between the navigation type indicator and learning styles [3].…”

Section: Introductionmentioning

confidence: 99%

Adaptation Based on Navigation Type and Learning Style

Bousbia

Gheffar

Balla

2015

Lecture Notes in Computer Science

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Abstract. In this paper, we present an adaptation approach of e-learning content based on the navigation type indicator describing the learner's behavior while browsing an e-learning course. This adaptation approach benefits from the found correlation between this indicator and learning styles, particularly Sequential/Global and Active/Reflective styles. Many studies use leaning styles for adaptation based on educational rules. Thus, we propose for each value of the navigation type indicator, to provide the learner with the appropriate adaptation to the learning style correlated with the indicator value.

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Supervised classification on navigational behaviours in web-based learning systems to identify learning styles

Cited by 5 publications

References 16 publications

Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM

Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM

MAGAM: A Multi-Aspect Generic Adaptation Model for Learning Environments

Adaptation Based on Navigation Type and Learning Style

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