Effectiveness of Data Mining Approaches to E-Learning System: A Survey

Ashraf, Aysha; Khan, Muhammad Gufran

doi:10.24081/nijesr.2016.1.0010

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…In [62], a customization of the multi-specialist learning framework depends on a learning style model that requires a psychological science poll to decide the understudy's learning style. An ANN model, called a GRU network was utilized to predict student's performance [117]. A parallel expandable neural network (xNN) describes the prediction results of the students' learning outcomes [64].…”

Section: Exam Revisionmentioning

confidence: 99%

Feature Evaluation of Emerging E-Learning Systems Using Machine Learning: An Extensive Survey

et al. 2021

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As of late, with the progression of AI and man-made brainpower, there has been a developing spotlight on versatile e-learning. As all ways to deal with e-learning lose their allure and the level of online courses builds, they move towards more customized versatile learning so as to collaborate with students and achieve better learning results. The schools focus on the examination, mindfulness, and arranging techniques that infuse innovation into the vision and educational program. E-learning issues are a standard examination issue for us all. The motivation behind this research analysis is to separate the potential outcomes of assessing e-learning models utilizing AI strategies such as Supervised, Semi Supervised, Reinforced Learning advances by investigating upsides and downsides of various methods organization. The literature review methodology is to review the cross sectional impacts of e-learning and Machine learning algorithms from existing literatures from the year 1993 to 2020 and to assess the essentialness of elearning features to optimize the e-learning models with available Machine learning techniques from peerinspected journals, capable destinations, and books. Second, it legitimizes the chances of e-learning structures introduction, and changes demonstrated through AI and Machine Learning algorithms. This examination assists in providing helpful new highlights to analysts, researchers and academicians. It gives an exhaustive structure of existing e-learning frameworks for the most recent innovations identified with learning framework capacities and learning tasks to envision ML research openings in appropriate spaces. The survey paper identifies and demonstrates the important role of different types of e-learning features such as Individual pertinent feature, Course pertinent feature, Context pertinent feature and Technology pertinent feature in framework performance tuning. The performance of Machine Learning algorithms to optimize the features of E-Learning models were reviewed in previous literatures and Support Vector Machine technique was found to be the one of the best to predict the input and output parameters of elearning models and it is found that Fuzzy C Means, Deep Learning algorithms are producing better results for Big Data sets.

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Section: Exam Revisionmentioning

confidence: 99%

Feature Evaluation of Emerging E-Learning Systems Using Machine Learning: An Extensive Survey

et al. 2021

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“…Education is moving into the online environment, and e-learning in combination with artificial intelligence is a very acute topic today (Ashraf and Khan 2017;Colchester et al 2017). From the educational point of view, knowledge evaluation is still a problematic part of e-learning.…”

Section: Introductionmentioning

confidence: 99%

Automatic question generation based on sentence structure analysis using machine learning approach

Blšták¹,

Rozinajová²

2021

Nat. Lang. Eng.

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Automatic question generation is one of the most challenging tasks of Natural Language Processing. It requires “bidirectional” language processing: first, the system has to understand the input text (Natural Language Understanding), and it then has to generate questions also in the form of text (Natural Language Generation). In this article, we introduce our framework for generating the factual questions from unstructured text in the English language. It uses a combination of traditional linguistic approaches based on sentence patterns with several machine learning methods. We first obtain lexical, syntactic and semantic information from an input text, and we then construct a hierarchical set of patterns for each sentence. The set of features is extracted from the patterns, and it is then used for automated learning of new transformation rules. Our learning process is totally data-driven because the transformation rules are obtained from a set of initial sentence–question pairs. The advantages of this approach lie in a simple expansion of new transformation rules which allows us to generate various types of questions and also in the continuous improvement of the system by reinforcement learning. The framework also includes a question evaluation module which estimates the quality of generated questions. It serves as a filter for selecting the best questions and eliminating incorrect ones or duplicates. We have performed several experiments to evaluate the correctness of generated questions, and we have also compared our system with several state-of-the-art systems. Our results indicate that the quality of generated questions outperforms the state-of-the-art systems and our questions are also comparable to questions created by humans. We have also created and published an interface with all created data sets and evaluated questions, so it is possible to follow up on our work.

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A Model for Enhancing User Experience in an E-learning System: A Review on Student Behavior and Content Formatting

Udugahapattuwa,

Fernando

2023

2023 7th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI)

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Effectiveness of Data Mining Approaches to E-Learning System: A Survey

Cited by 3 publications

References 21 publications

Feature Evaluation of Emerging E-Learning Systems Using Machine Learning: An Extensive Survey

Feature Evaluation of Emerging E-Learning Systems Using Machine Learning: An Extensive Survey

Automatic question generation based on sentence structure analysis using machine learning approach

A Model for Enhancing User Experience in an E-learning System: A Review on Student Behavior and Content Formatting

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