Workflow approach to design automatic tutor in e-learning environment

Boumiza, Salma; Souilem, Dalila; Bekiarski, Alexander

doi:10.1109/codit.2016.7593571

Cited by 10 publications

(2 citation statements)

References 2 publications

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“…The greater the use of the different systems of the university, the more the model will learn from students to personalize education [19,50]. This learning capacity of the model is achieved through a series of data mining algorithms that count the results of the students [51]. The time they have invested into getting them, the mistakes they have made and their personal performance are compared with the rest of the students in a digital environment.…”

Section: Integration Of a Big Data Model In Adaptive Learningmentioning

confidence: 99%

Data Analysis as a Tool for the Application of Adaptive Learning in a University Environment

et al. 2020

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Currently, data are a very valuable resource for organizations. Through analysis, it is possible to profile people or obtain knowledge about an event or environment and make decisions that help improve their quality of life. This concept takes on greater value in the current pandemic, due to coronavirus disease 2019 (COVID-19), that affects society. This emergency has changed the way people live. As a result, the majority of activities are carried out using the internet, virtually or online. Education is not far behind and has seen the web as the most successful option to continue with its activities. The use of any computer application generates a large volume of data that can be analyzed by a big data architecture in order to obtain knowledge from its students and use it to improve educational processes. The big data, when included as a tool for adaptive learning, allow the analysis of a large volume of data to offer an educational model based on personalized education. In this work, the analysis of educational data through a big data architecture is proposed to generate learning based on meeting the needs of students.

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Section: Integration Of a Big Data Model In Adaptive Learningmentioning

confidence: 99%

Data Analysis as a Tool for the Application of Adaptive Learning in a University Environment

et al. 2020

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“…In MindReader, all learners have a profile which includes background, past lessons, tests and tutoring activities, known problem areas, and their peer groups. MindReader generates tutorials based on students' profile and level of programming competence expected along the lines of the systems such as [31] keeping in mind that for computation thinking classes, the challenges primarily involve the difficulties in learning the syntax and understanding the semantics and use of constructs such as loops, conditional statements, and simple algorithms [32]. For the purpose of both grading and tutoring, MindReader assembles the statement structures written by the student into possible CDGs using the concept structures in the Concept Database according to the rules in Concept Construction rule base with the aim of matching the CDG with one of the known templates in the Algorithm Templates.…”

Section: Assessment and Tutoring Using Mindreadermentioning

confidence: 99%

Smart Assessment of and Tutoring for Computational Thinking MOOC Assignments using MindReader

Jamil¹

2017

Preprint

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One of the major hurdles toward automatic semantic understanding of computer programs is the lack of knowledge about what constitutes functional equivalence of code segments. We postulate that a sound knowledgebase can be used to deductively understand code segments in a hierarchical fashion by first de-constructing a code and then reconstructing it from elementary knowledge and equivalence rules of elementary code segments. The approach can also be engineered to produce computable programs from conceptual and abstract algorithms as an inverse function. In this paper, we introduce the core idea behind the MindReader online assessment system that is able to understand a wide variety of elementary algorithms students learn in their entry level programming classes such as Java, C++ and Python. The MindReader system is able to assess student assignments and guide them how to develop correct and better code in real time without human assistance.

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Development of model for automatic tutor in e-learning environment based on student reactions extraction using facial recognition

Boumiza

Bekiarski

Souilem

et al. 2017

2017 15th International Conference on Electrical Machines, Drives and Power Systems (ELMA)

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Workflow approach to design automatic tutor in e-learning environment

Cited by 10 publications

References 2 publications

Data Analysis as a Tool for the Application of Adaptive Learning in a University Environment

Data Analysis as a Tool for the Application of Adaptive Learning in a University Environment

Smart Assessment of and Tutoring for Computational Thinking MOOC Assignments using MindReader

Development of model for automatic tutor in e-learning environment based on student reactions extraction using facial recognition

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