Using process mining to analyze students' quiz-taking behavior patterns in a learning management system

This paper deals with the possibility of predicting student’s grades based on their usage of Learning Management System (LMS) Moodle. It is important to know what materials would be best suited in LMS as study materials and what materials could be improved or removed based on the student’s usage of the materials and the final grade. In order to do this, the correlations between access to materials and the final grade were observed. These correlations could also be used to predict the grades of the student. Therefore, a model with Petri nets was created that based on the highest correlation would be able to predict what grade the student would get based on his usage of LMS. Obviously, it would not be possible to predict every result with certainty, however, more precise predictions could be obtained with higher correlations.

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“…However, the use of technology is a standard in most universities. Examples include LMS and Virtual Learning Environments (VLE) [9].…”

Section: Related Workmentioning

confidence: 99%

Predicting Student Grades Based on Their Usage of LMS Moodle Using Petri Nets

Balogh

Kuchárik

2019

Applied Sciences

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“…The method worked fine with the social media but cannot be extended to applicant personality and their fitness with a certain organization in e-recruiting process in crowd sourcing market. Focusing on various online materials a process oriented approach proposed in [5] dealt with student behavioral patterns through the mining of their interaction with different quizzes from different course. The drawback of this model was that the time required to complete the mining process was not minimized.…”

Section: Literature Reviewmentioning

confidence: 99%

Research Aligned Analysis on Web Access Behavioral Pattern Mining for User Identification

Gokulapriya¹,

R²

2019

IJEAT

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Human activity understanding includes activity recognition and activity pattern discovery. Monitoring human activity and finding abnormality in their activities used by many field like medical applications, security systems etc. Basically it helps and support in decision making systems. Mining user activity from web logs can helps in finding hidden information about the user access pattern which reveals the web access behaviour of the users. Clustering and Classification techniques are used for web user identification. Clustering is the task of grouping similar patterns for web user identification. Classification is the process of classifying web patterns for user identification. In this paper we have implemented the existing works and discussed the results here to find the limitations. In existing methods, many data mining techniques were introduced for web user behaviour identification. But, the user identification accuracy was not improved and time consumption was not reduced. Our objective is to study the existing work and explore the possibility to improve the identification accuracy and reduce the time consumption using machine learning and deep learning techniques

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“…This score is multiplied by a credibility factor associated to each learner by the system and pedagogical team according to learner' attendance rate and honesty. Appreciation score is transformed to pheromone laid on previous edges that the learner has followed to reach the current resource while respecting the back-propagation principal as follows: (10) Evaporation of pheromones is calculated the same way as formula 9. 11Values of are considered as collective factors, they are used by all learners to construct their own path.…”

Section: Semantic Micro Service Discovery and Selectionmentioning

confidence: 99%

“…To tackle the massification of higher education, the automation of learning process has become crucial; it allows the autonomous and remote learning and the individualization of learning paths. Many research works have been interested in improving educational content presented in e-Learning platforms [1,3,10,15]. Other researches have proposed approaches for learning paths optimization [6,11].…”

Section: Introductionmentioning

confidence: 99%

QoS-based Semantic Micro Services Discovery and Composition using ACO Algorithm

Essayah¹,

Youssfi²,

Bouattane³

et al. 2019

IJACSA

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In this paper, we present a new model of e-Learning platforms based on semantic micro services using discovery, selection and composition methods to generate learning paths. In this model, each semantic micro service represents an elementary educational resource that can be a course, an exercise, a tutorial or an evaluation implementing a precise learning path objective. The semantic micro services are described using ontologies and deployed in multi-instances in a cloud environment according to a load balancing and a fault tolerance system. Learners' requests are sent to a proxy micro service having learning paths abstract structures represented as an oriented graph. Proxy micro service analyses the request to define the learner profile and context in order to provide him with the semantic micro services responsible of the educational resources satisfying his functional and non-functional needs. In this model, to achieve an optimal learning path generation a two steps process is employed, where local optimization uses semantic discovery and selection based on a matchmaking algorithm and a quality of service measurement, and global optimization adopts an ant colony optimization algorithm to select the best resource combination. Our experimental results show that the proposed model can effectively returns optimized learning paths considering individual, collective and pedagogical factors.

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Using process mining to analyze students' quiz-taking behavior patterns in a learning management system

Cited by 133 publications

References 33 publications

Predicting Student Grades Based on Their Usage of LMS Moodle Using Petri Nets

Predicting Student Grades Based on Their Usage of LMS Moodle Using Petri Nets

Research Aligned Analysis on Web Access Behavioral Pattern Mining for User Identification

QoS-based Semantic Micro Services Discovery and Composition using ACO Algorithm

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