Knowledge Level Assessment in e-Learning Systems Using Machine Learning and User Activity Analysis

Ghatasheh, Nazeeh

doi:10.14569/ijacsa.2015.060415

Cited by 26 publications

(19 citation statements)

References 31 publications

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“…Recently, a lot of studies on eye movements [15] [16] [17] and machine learning techniques [18] [19] have been done to detect different kinds of learner's behaviors during learning. However, detection of learners' concentration with a feedback to an instructor has not been given much attention in distance learning environments.…”

Section: Related Workmentioning

confidence: 99%

Detection of Learner’s Concentration in Distance Learning System with Multiple Biological Information

Daniel¹,

Kamioka²

2017

JCC

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The trend of distance learning education has increased year by year because of the rapid advancement of information and communication technologies. Distance learning system can be regarded as one of ubiquitous computing applications since the learners can study anywhere even in mobile environments. However, the instructor cannot know if the learners comprehend the lecture or not since each learner is physically isolated. Therefore, a framework which detects the learners' concentration condition is required. If a distance learning system obtains the information that many learners are not concentrated on the class due to the incomprehensible lecture style, the instructor can perceive it through the system and change the presentation strategy. This is a contextaware technology which is widely used for ubiquitous computing services. In this paper, an efficient distance learning system, which accurately detects learners' concentration condition during a class, is proposed. The proposed system uses multiple biological information which are learners' eye movement metrics, i.e. fixation counts, fixation rate, fixation duration and average saccade length obtained by an eye tracking system. The learners' concentration condition is classified by using machine learning techniques. The proposed system has performed the detection accuracy of 90.7% when Multilayer Perceptron is used as a classifier. In addition, the effectiveness of the proposed eye metrics has been confirmed. Furthermore, it has been clarified that the fixation duration is the most important eye metric among the four metrics based on the investigation of evaluation experiment.

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Section: Related Workmentioning

confidence: 99%

Detection of Learner’s Concentration in Distance Learning System with Multiple Biological Information

Daniel¹,

Kamioka²

2017

JCC

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“…Carmona et al [8] demonstrate elearning student preferences in order to make decision for matching some features of the LMS with the student's elearning methodology. Ghatasheh [9] presents an innovative design of an eLearning organization, where user activities and machine learning are taken into consideration. The paper focuses on the evaluation of online students' behavior.…”

Section: E-learning Datasetmentioning

confidence: 99%

Proposed classification for eLearning data analytics with MOA

Jittawiriyanukoon¹

2019

IJECE

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<span>Elearning education has developed a crucial factor in the educational organization. With the situation of declining student size, elearning has to offer more cross-departmental and multi-disciplinary courses for individual needs to go over “one-size-fits-all” traditional model. Elearning data analytics which has not been professionally classified cannot produce reliable results. Classifications for elearning data help comfort the accuracy of outcomes and reducible pre-processing time. This research proposes a practical model for individual learning and personality. The proposed model based on data from the LMS classifies both the student preferences and personalities. The model helps design future curricula to suit student personalities, which intangibly assists them to be efficient in the study practice. Performance of the proposed classification is evaluated by using MOA software. It outperforms and improves the accuracy of complex elearning datasets. Besides, the results indicate an achievement in the students' study time after applying the association rule model on the elearning.</span>

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“…Recent technologies will significantly boost the capacities, reliability, robustness, and speed of networks, so that the transmission of multimedia-rich learning materials will be much faster than today. NazeehGhatasheh: [23] This research introduced a number of enhancements to dynamic E-Learning systems in terms of knowledge transmission and evaluation. It proposed an abstract framework of futuristic clearning systems, with more focus on the need of machine learning [1] see the E-learning implementation as a continuous and iterative process.…”

Section: Related Workmentioning

confidence: 99%

“…José Paulo Leal: [23] This chapter presents a comparative study on LMS interoperability. Given size of this category we focused on a couple of representative systems -Model and Blackboard -since combined they represent a Significant market share and cover both the commercial and open source development models.…”

Section: Related Workmentioning

confidence: 99%

A Survey on E-Learning System with Data Mining

Aechham¹,

Govindarajulu²

2017

IJDMTA

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E-learning process has been widely used in university campus and educational institutions are playing vital role to enhance the skill set of students. Modern E-learning done by many electronic devices, such as smartphones, Tabs, and so on, on existing E-learning tools is insufficient to achieve the purpose of online training of education. This paper presents a survey of online e-Learning authoring tools for creating and integrating reusable e-learning tool for generation and enhancing existing learning resources with them. The work concentrates on evaluation of the existing e-learning tools a, and authoring tools that have shown good performance in the past for online learners. This survey work takes more than 20 online tools that deal with the educational sector mechanism, for the purpose of observations, and the outcome were analyzed. The findings of this paper are the main reason for developing a new tool, and it shows that educators can enhance existing learning resources by adding assessment resources, if suitable authoring tools are provided. Finally, the different factors that assure the reusability of the created new e-learning tool has been analysed in this paper.E-learning environment is a guide for both students and tutorial management system. The useful on the e-learning system for apart from students and distance learning students. The purpose of using e-learning environment for online education system, developed in data mining for more number of clustering servers and resource chain has been good.

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Knowledge Level Assessment in e-Learning Systems Using Machine Learning and User Activity Analysis

Cited by 26 publications

References 31 publications

Detection of Learner’s Concentration in Distance Learning System with Multiple Biological Information

Detection of Learner’s Concentration in Distance Learning System with Multiple Biological Information

Proposed classification for eLearning data analytics with MOA

A Survey on E-Learning System with Data Mining

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