Automatically recognizing the e-learning activities is an important task for improving the online... more Automatically recognizing the e-learning activities is an important task for improving the online learning process. Probabilistic graphical models such as hidden Markov models and conditional random fields have been successfully used in order to identify a Web users activity. For such models, the sequences of observation are crucial for training and inference processes. Despite the efficiency of these probabilistic graphical models in segmenting and labeling stochastic sequences, their performance is adversely affected by the imperfect quality of data used for the construction of sequences of observation. In this paper, a formalism of the possibilistic theory will be used in order to propose a new approach for observation sequences preparation. The eminent contribution of our approach is to evaluate the effect of possibilistic reasoning during the generation of observation sequences on the effectiveness of hidden Markov models and conditional random fields models. Using a dataset co...
One of the issues of e-learning web based application is to understand how the learner interacts ... more One of the issues of e-learning web based application is to understand how the learner interacts with an e-learning application to perform a given task. This study proposes a methodology to analyze learner mouse movement in order to infer the task performed. To do this, a Hidden Markov Model is used for modeling the interaction of the learner with an e-learning application. The obtained results show the ability of our model to analyze the interaction in order to recognize the task performed by the learner.
In this paper we apply the Conditional Random Fields approach for modeling human navigational beh... more In this paper we apply the Conditional Random Fields approach for modeling human navigational behavior based on mouse movements to recognize web user tasks. In fact, inferring activity of web users is an important topic of Human Computer Interaction. To improve the interaction process, many studies have been performed for understanding how users interact with web interfaces in order to perform a given activity. The Experimental evaluation and analysis of the results of the model we present in this paper demonstrate the efficiency of our model in human tasks recognition.
One of the issues of e-learning web based application is to understand how the learner interacts ... more One of the issues of e-learning web based application is to understand how the learner interacts with an e-learning application to perform a given task. This study proposes a methodology to analyze learner mouse movement in order to infer the task performed. To do this, a Hidden Markov Model is used for modeling the interaction of the learner with an e-learning application. The obtained results show the ability of our model to analyze the interaction in order to recognize the task performed by the learner.
Recognition activity of web users based on their navigational
behavior during interaction process... more Recognition activity of web users based on their navigational behavior during interaction process is an important topic of Human Computer Interaction. To improve the interaction process and interface usability, many studies have been performed for understanding how users interact with a web interface in order to perform a given activity. In this paper we apply the Conditional Random Fields approach for modeling human navigational behavior based on mouse movements to recognize web user tasks. Experimental results show the efficiency of the proposed model and confirm the superiority of Conditional Random Fields approach with respect to the Hidden Markov Models approach in human activity recognition.
Hidden Markov Models (HMM) have been widely used in classification tasks. Despite their efficienc... more Hidden Markov Models (HMM) have been widely used in classification tasks. Despite their efficiency in stochastic sequences labeling, they are overwhelmed by imperfect quality of used data in the learning and inference processes. In this paper, we try to evaluate the contribution of possibilistic theory in creating sequences of observations used by HMM models. Experimental results show that observation sequences, obtained by possibilistic reasoning significantly, improve the performance of HMM in the recognition of online e-learning activities.
In this paper we apply the Conditional Random Fields approach for modeling human navigational beh... more In this paper we apply the Conditional Random Fields approach for modeling human navigational behavior based on mouse movements to recognize web user tasks. In fact, inferring activity of web users is an important topic of Human Computer Interaction. To improve the interaction process, many studies have been performed for understanding how users interact with web interfaces in order to perform a given activity. The Experimental evaluation and analysis of the results of the model we present in this paper demonstrate the efficiency of our model in human tasks recognition.
Automatically recognizing the e-learning activities is an important task for improving the online... more Automatically recognizing the e-learning activities is an important task for improving the online learning process. Probabilistic graphical models such as hidden Markov models and conditional random fields have been successfully used in order to identify a Web users activity. For such models, the sequences of observation are crucial for training and inference processes. Despite the efficiency of these probabilistic graphical models in segmenting and labeling stochastic sequences, their performance is adversely affected by the imperfect quality of data used for the construction of sequences of observation. In this paper, a formalism of the possibilistic theory will be used in order to propose a new approach for observation sequences preparation. The eminent contribution of our approach is to evaluate the effect of possibilistic reasoning during the generation of observation sequences on the effectiveness of hidden Markov models and conditional random fields models. Using a dataset co...
One of the issues of e-learning web based application is to understand how the learner interacts ... more One of the issues of e-learning web based application is to understand how the learner interacts with an e-learning application to perform a given task. This study proposes a methodology to analyze learner mouse movement in order to infer the task performed. To do this, a Hidden Markov Model is used for modeling the interaction of the learner with an e-learning application. The obtained results show the ability of our model to analyze the interaction in order to recognize the task performed by the learner.
In this paper we apply the Conditional Random Fields approach for modeling human navigational beh... more In this paper we apply the Conditional Random Fields approach for modeling human navigational behavior based on mouse movements to recognize web user tasks. In fact, inferring activity of web users is an important topic of Human Computer Interaction. To improve the interaction process, many studies have been performed for understanding how users interact with web interfaces in order to perform a given activity. The Experimental evaluation and analysis of the results of the model we present in this paper demonstrate the efficiency of our model in human tasks recognition.
One of the issues of e-learning web based application is to understand how the learner interacts ... more One of the issues of e-learning web based application is to understand how the learner interacts with an e-learning application to perform a given task. This study proposes a methodology to analyze learner mouse movement in order to infer the task performed. To do this, a Hidden Markov Model is used for modeling the interaction of the learner with an e-learning application. The obtained results show the ability of our model to analyze the interaction in order to recognize the task performed by the learner.
Recognition activity of web users based on their navigational
behavior during interaction process... more Recognition activity of web users based on their navigational behavior during interaction process is an important topic of Human Computer Interaction. To improve the interaction process and interface usability, many studies have been performed for understanding how users interact with a web interface in order to perform a given activity. In this paper we apply the Conditional Random Fields approach for modeling human navigational behavior based on mouse movements to recognize web user tasks. Experimental results show the efficiency of the proposed model and confirm the superiority of Conditional Random Fields approach with respect to the Hidden Markov Models approach in human activity recognition.
Hidden Markov Models (HMM) have been widely used in classification tasks. Despite their efficienc... more Hidden Markov Models (HMM) have been widely used in classification tasks. Despite their efficiency in stochastic sequences labeling, they are overwhelmed by imperfect quality of used data in the learning and inference processes. In this paper, we try to evaluate the contribution of possibilistic theory in creating sequences of observations used by HMM models. Experimental results show that observation sequences, obtained by possibilistic reasoning significantly, improve the performance of HMM in the recognition of online e-learning activities.
In this paper we apply the Conditional Random Fields approach for modeling human navigational beh... more In this paper we apply the Conditional Random Fields approach for modeling human navigational behavior based on mouse movements to recognize web user tasks. In fact, inferring activity of web users is an important topic of Human Computer Interaction. To improve the interaction process, many studies have been performed for understanding how users interact with web interfaces in order to perform a given activity. The Experimental evaluation and analysis of the results of the model we present in this paper demonstrate the efficiency of our model in human tasks recognition.
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Papers by Anis Elbahi
behavior during interaction process is an important topic of Human Computer Interaction. To improve the interaction process and interface usability,
many studies have been performed for understanding how users interact with a web interface in order to perform a given activity. In this paper we apply the Conditional Random Fields approach for modeling human navigational behavior based on mouse movements to recognize web user tasks. Experimental results show the efficiency of the proposed
model and confirm the superiority of Conditional Random Fields approach with respect to the Hidden Markov Models approach in human activity recognition.
behavior during interaction process is an important topic of Human Computer Interaction. To improve the interaction process and interface usability,
many studies have been performed for understanding how users interact with a web interface in order to perform a given activity. In this paper we apply the Conditional Random Fields approach for modeling human navigational behavior based on mouse movements to recognize web user tasks. Experimental results show the efficiency of the proposed
model and confirm the superiority of Conditional Random Fields approach with respect to the Hidden Markov Models approach in human activity recognition.