Item-based approach for online exam performance and its application

Ketui, Nongnuch; Homjun, Kanitha; Poonyasiri, Kanyawat; Deepinjai, Jirasak; Luekhong, Prasert

doi:10.1109/ecticon.2016.7561297

Cited by 5 publications

(12 citation statements)

References 7 publications

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“…Multiple assessments with formulas are proposed by [11] in order to create serial examinations and also for evaluating continuously. Their method is based on multiple criteria.…”

Section: Related Workmentioning

confidence: 99%

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Design and Implementation of a Computer Aided Intelligent Examination System

Alghamdi

Alanezi

Khan

2020

Int. J. Emerg. Technol. Learn.

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Developing a computer based system for examinations are the substitute for the current examination system based on paper. In recent days, the e-learning has become more popular because of its adaptability, integrity and user friendliness. In terms of the paper based examinations, major challenge is the proctoring techniques used. In this paper, a novel method to avoid the presence of a proctor throughout the examination is proposed by an intelligent based examination system. This method is proposed to improve the e-learning using intelligent question bank and examination system. The system is designed with different complexity levels among the questions and also it acts as a tool to assessing the understanding of student from the teaching materials. This system can be timesaving and more efficient with an adequate level of security. The proposed methodology can be classified in to two main phases such as the design of question bank along with its database, design of the Artificial Intelligence (AI) based system for examination and its evaluation in order to improve the e-learning. Future works in this system can be the addition of theory-based questions and the integration of biometric based systems for enhancing the level of security.

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“…Multiple assessments with formulas are proposed by [11] in order to create serial examinations and also for evaluating continuously. Their method is based on multiple criteria.…”

Section: Related Workmentioning

confidence: 99%

“…Nowadays, E-Learning techniques have rapidly developed and there are many types of E-Learning based methodologies [1][2][3][4]23] and Learning Management methods [7,[9][10][11][12] such as the traditional-based, online-based, and the workplace etc. [3][4][5][6]24] as for education, through which everyone can perform the examinations.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Computer Aided Intelligent Examination System

Alghamdi

Alanezi

Khan

2020

Int. J. Emerg. Technol. Learn.

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“…We systematically developed the inclusion and exclusion criteria (Section No. Year Studies Total 1 2016 [20], [21], [11], [22], [23], [24], [12], [15] 8 2 2017 [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35] 11…”

Section: Quality Assessmentmentioning

confidence: 99%

“…The distribution of selected studies on the basis of type is shown in Figure 3 where 28% of selected studies are from reputed journals while remaining 72% studies are from No. Scientific Database Studies Total 1 IEEE [49], [36], [20], [13], [21], [25], [50], [11], [37], [22], [38], 22 [61], [51], [62], [52], [26], [27], [23], [24], [53], [54], [39] 2 ELSIVER [65], [15], [66], [59] 4…”

Section: Quality Assessmentmentioning

confidence: 99%

A Systematic Review of Online Exams Solutions in E-learning: Techniques, Tools and Global Adoption

Muzaffar¹,

Tahir²,

Anwar³

et al. 2020

Preprint

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E-learning in higher education is exponentially increased during the past decade due to its inevitable benefits in critical situations like natural disasters, wars, and pandemic like COVID 2019. The reliable, fair, and seamless execution of online exams in E-learning is highly significant. Particularly, online exams are conducted on E-learning platforms without the physical presence of students and instructors at the same place. This poses several issues like integrity and security during online exams. To address such issues, researchers frequently proposed different techniques and tools. However, a study summarizing and analyzing latest developments, particularly in the area of online examination, is hard to find in the literature. In this article, a Systematic Literature Review (SLR) of online examination is performed to select and analyze 53 studies published during the last five years (i.e. Jan 2016 to July 2020). Subsequently, five leading online exams features targeted in the selected studies are identified. Moreover, underlying development approaches for the implementation of online exams solutions are explored. Furthermore, 16 important techniques / algorithms and 11 datasets are presented. In addition to this, 21 online exams tools proposed in the selected studies are identified. Additionally, 25 leading existing tools used in the selected studies are also presented. Finally, the par-

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“…For the past couple of years, different data mining implementations have been applied in a wide variety of smart applications such as intelligent traffic systems (ITS) [1]- [4]; digital health [5]- [8]; disaster preparedness [9], [10]; and business economics [11], [12]. This type of technology can also be applied in education for improving learning such as distance learning [13] or predicting student achievement [14]. The rationale of data mining is to recognize data pattern and discover new and practical insights [15], known as Educational data mining (EDM).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Selection of Incoming Engineering Freshmen in Higher Education Institution

Sahagun¹

2022

IJCDS

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The Accrediting Agency of Chartered Colleges and Universities of the Philippines recommends through the university testing unit, a system to interpret and analyse entrance test results that may help direct and guide students in choosing a Baccalaureate degree to take in the college. While the present system of manually evaluating each of the freshman applicants is used, there is a need to adopt technological tools for faster and accurate analysis. Thus, the study presents machine learning methods of classifying freshmen applicants if they are qualified or not in the college of engineering and architecture. Specifically, determining if a freshmen applicant may succeed in the five engineering program at university. The study used classifiers such as Decision Tree, K-Nearest Neighbor (KNN), Decision Tree, and Support Vector Machine (SVM). A cross-validation of ten-fold model was used better accuracy of classifiers. The predicted models performed well, however, the Decision Tree classifier outputs a higher average accuracy and F1measure. The result shows that the classifier accurately classifies qualified and non-qualified engineering freshmen for program acceptance.

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Item-based approach for online exam performance and its application

Cited by 5 publications

References 7 publications

Design and Implementation of a Computer Aided Intelligent Examination System

Design and Implementation of a Computer Aided Intelligent Examination System

A Systematic Review of Online Exams Solutions in E-learning: Techniques, Tools and Global Adoption

Machine Learning Based Selection of Incoming Engineering Freshmen in Higher Education Institution

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