Multi-criteria Comparative Evaluation of the E-Learning Systems: A Case Study

Munkhtsetseg, N.; Garmaa, D.; Sambuu, Uyanga

doi:10.1109/u-media.2014.47

Cited by 8 publications

(4 citation statements)

References 1 publication

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Section: Case Study and Discussion Of Resultsmentioning

confidence: 99%

“…Another concept widely discussed in the literature, considered an essential criterion for evaluating an e-learning system, is the ability to personalise (Alptekin and Karsak, 2011;Munkhtsetseg et al, 2014;Shee and Wang, 2008) the system. The personalisation is mainly related to the computational environment (as design interface), content (as recommending specific modules and courses) and instructions received by students.…”

Section: Evolution Of Scientific Interestmentioning

confidence: 99%

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SciBR-M: a method to map the evolution of scientific interest - A case study in educational data mining

Zárate

Rodrigues

Dias³

et al. 2023

LHT

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PurposeThe scientific community shares a heritage of knowledge generated by several different fields of research. Identifying how scientific interest evolves is relevant for recording and understanding research trends and society’s demands.Design/methodology/approachThis article presents SciBR-M, a novel method to identify scientific interest evolution from bibliographic material based on Formal Concept Analysis. The SciBR-M aims to describe the thematic evolution surrounding a field of research. The method begins by hierarchically organising sub-domains within the field of study to identify the themes that are more relevant. After this organisation, we apply a temporal analysis that extracts implication rules with minimal premises and a single conclusion, which are helpful to observe the evolution of scientific interest in a specific field of study. To analyse the results, we consider support, confidence, and lift metrics to evaluate the extracted implications.FindingsThe authors applied the SciBR-M method for the Educational Data Mining (EDM) field considering 23 years since the first publications. In the digital libraries context, SciBR-M allows the integration of the academy, education, and cultural memory, in relation to a study domain.Social implicationsCultural changes lead to the production of new knowledge and to the evolution of scientific interest. This knowledge is part of the scientific heritage of society and should be transmitted in a structured and organised form to future generations of scientists and the general public.Originality/valueThe method, based on Formal Concept Analysis, identifies the evolution of scientific interest to a field of study. SciBR-M hierarchically organises bibliographic material to different time periods and explores this hierarchy from proper implication rules. These rules permit identifying recurring themes, i.e. themes subset that received more attention from the scientific community during a specific period. Analysing these rules, it is possible to identify the temporal evolution of scientific interest in the field of study. This evolution is observed by the emergence, increase or decrease of interest in topics in the domain. The SciBR-M method can be used to register and analyse the scientific, cultural heritage of a field of study. In addition, the authors can use the method to stimulate the process of creating knowledge and innovation and encouraging the emergence of new research.

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Section: Case Study and Discussion Of Resultsmentioning

confidence: 99%

Section: Evolution Of Scientific Interestmentioning

confidence: 99%

SciBR-M: a method to map the evolution of scientific interest - A case study in educational data mining

Zárate

Rodrigues

Dias³

et al. 2023

LHT

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“…The two most important subfactors in content quality are useful content (local weight = 0.396) and up-to-date content (local weight = 0.221), which means that the course materials uploaded in e-learning should be useful, up-to-date, and fit the current needs of learners (Garg & Jain, 2017;Munkhtsetseg et al, 2014;Sadi-Nezhad et al, 2010). Good content quality enhances learner-content interaction while poor content quality raises the dropout rates of e-learning courses (Bhuasiri et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

An information system success model for e-learning postadoption using the fuzzy analytic network process

et al. 2023

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The underutilization of e-learning among university lecturers is an important issue that needs to be resolved. This study aimed to formulate an e-learning postadoption model for Malaysian universities. Data were collected using self-administered questionnaires involving 36 e-learning experts who from lecturers in public and private universities in Malaysia. The data collected was then analyzed using the extent analysis method proposed by Chang ( European Journal of Operational Research, 95 (3), 649–655, 1996 ) to examine the weights and rankings of the factors and subfactors. This study showed that for e-learning postadoption, the most important factor is institution service quality, followed by system quality, content quality, instructors' characteristics, and learners' characteristics. This study extends the information systems success model into the e-learning postadoption context. In particular, this study offered insights concerning the dependencies among the factors in the model within the Malaysian university context. The findings are useful for the long-range strategic management of university administrators, and the model can be adopted as a reference to form a rating system to analyze e-learning postadoption. University administrators can analyze critical factors that increase e-learning’s post adoption and lead to more efficient resource allocation and management of e-learning.

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“…They have flexible learning solutions, having the possibility of choosing the didactic process, without space and time limits, but, above all, they create new knowledge and contents to share on-line. In this context, the learning object is continuously fragmented and rebuilt thanks to the collective and collaborative elaboration with new participative technologies [22] in a highly social and interactive environment.…”

Section: The Evolution Of the E-learning Platformmentioning

confidence: 99%

“The Postman Always Rings Twice”: Evaluating E-Learning Platform a Decade Later

Ballarano¹,

Colace

Santo³

et al. 2016

Int. J. Emerg. Technol. Learn.

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Abstract-E-Learning represents an effective answer to the continuous request for life-long learning. In fact, this approach allows the flexibility and quality requested by such a kind of learning process. In this scenario, a great number of E-Learning platforms have been introduced on the market and their selection is not a trivial task: various features should be taken into account during the evaluation of an ELearning platform. Obviously, the analysis of the technical features of an E-Learning platform is not enough: it is also important to understand how it can be integrated in the didactic context in order to facilitate learning and training and what principles are applied to guide the way the system is used. A decade ago, the authors of this paper introduced a model for describing and characterizing on-line learning platform components. Today, the aim of this paper is to evaluate and upgrade this model. During this period, many things have changed in the Web (the introduction of social networks, new multimedia protocols, etc.): what has changed in the E-Learning platforms? Today how to evaluate the E-Learning platforms? In this paper the evaluation model will be updated and then used to evaluate the most known existing platforms.

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Multi-criteria Comparative Evaluation of the E-Learning Systems: A Case Study

Cited by 8 publications

References 1 publication

SciBR-M: a method to map the evolution of scientific interest - A case study in educational data mining

SciBR-M: a method to map the evolution of scientific interest - A case study in educational data mining

An information system success model for e-learning postadoption using the fuzzy analytic network process

“The Postman Always Rings Twice”: Evaluating E-Learning Platform a Decade Later

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