E-learning and educational data mining in cloud computing: an overview

Fernández, Alberto; Peralta, Daniel; Benítez, José Manuel; Herrera, Francisco

doi:10.1504/ijlt.2014.062447

Cited by 35 publications

(23 citation statements)

References 54 publications

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“…This may help improve the quality educational data analysis on a large-scale [14] for proactive and knowledge-driven decision-making [15]. Furthermore, EDM is also possible to improve the learning process and guide students' learning [16]. Table I .…”

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confidence: 99%

The Acceptance of Educational Data Mining Technology among Students in Public Institutions of Higher Learning in Malaysia

Wook¹,

Yusof²,

Nazri³

2015

IJFCC

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Abstract-Accepting the emergence of data mining technology by students is crucial to the successful implementation of the technology in education institutions. Although previous studies have empirically show the result of acceptance or adoption of data mining technology in numerous fields, however, they are focused at organisational-level. Hence, there is a need to explain what are the determinants could influence the acceptance of data mining technology at individual-level since they are the most affected by the technology. Therefore, this study adapts selected constructs in the Technology Acceptance Model 3 (TAM3) to conceptualise the research problem, namely in terms of perceived usefulness, perceived ease of use, relevance for analysing, anxiety of educational data mining technology, self-efficacy and facilitating conditions. To examine the model, this study surveyed 158 students from four public Institutions of Higher Learning in Malaysia. Pearson product-moment correlation coefficient is utilised to analyses the relationship between the constructs. The findings have revealed that most of the constructs have a high level of correlation.Index Terms-Acceptance, data mining, educational data mining, institutions of higher learning (IHLs), technology acceptance model 3 (TAM3). I. INTRODUCTIONData mining (DM) technology has become new paradigms to enhance the scope, quality, efficiency and achievements of educational system [1]. In particular, users increasingly benefit from the technology that provides special data interpretation and processing techniques [2]. With such capabilities, DM is now considered one of promising technologies for education [3] which commonly known as educational data mining (EDM). Earlier educational system faced a number of challenges; lack of significant knowledge for evaluating, planning, monitoring and marketing [4]. Hence, EDM has gained significant interest as suitable and realistic next-generation computer-based educational systems (CBES), which offers a potential solution to these challenges [5].In spite of the rapidly growing popularity of DM in the educational environment, most of studies have emphasize more on the technical aspect of DM application Manuscript received October 10, 2014; revised January 13, 2015. Muslihah Wook is with the Faculty of Defence Science and Technology, National Defence University of Malaysia, Sungai Besi Camp, 57000 Kuala Lumpur, Malaysia (e-mail: muslihah@upnm.edu.my).Zawiyah M. Yusof and Mohd Zakree Ahmad Nazri are with Faculty of Information Science and Technology, National University of Malaysia, 43600 Bangi, Selangor, Malaysia (e-mail: zmy@ftsm.ukm.my; mzan@ftsm.ukm.my).algorithms-ignoring the users' perception of the technology [6]- [8]. Moreover, there has been a lack of study analysing how users use the technology [9]. Therefore, it is important to identify user behaviour prior to implement the EDM technology, as this could help minimising underutilisation or eventual abandonment. II. EDUCATIONAL DATA MINING (EDM)EDM has been defined as "t...

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The Acceptance of Educational Data Mining Technology among Students in Public Institutions of Higher Learning in Malaysia

Wook¹,

Yusof²,

Nazri³

2015

IJFCC

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“…However, some issues are still experienced by traditional and legacy e-learning platforms, such as small scalability, especially at the infrastructural level, poor efficiency in the dynamic resource usage and workload scheduling, software licenses and hardware costs, maintenance time and expenditures, etc. This is why some cloud-based distance learning platforms are emerging [24], and the cloud, be it public, private or hybrid, is becoming a cornerstone of any modern VLE.…”

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confidence: 99%

A Virtual Learning Architecture Enhanced by Fog Computing and Big Data Streams

Pecori

2018

Future Internet

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Abstract:In recent years, virtual learning environments are gaining more and more momentum, considering both the technologies deployed in their support and the sheer number of terminals directly or indirectly interacting with them. This essentially means that every day, more and more smart devices play an active role in this exemplary Web of Things scenario. This digital revolution, affecting education, appears clearly intertwined with the earliest forecasts of the Internet of Things, envisioning around 50 billions heterogeneous devices and gadgets to be active by 2020, considering also the deployment of the fog computing paradigm, which moves part of the computational power to the edge of the network. Moreover, these interconnected objects are expected to produce more and more significant streams of data, themselves generated at unprecedented rates, sometimes to be analyzed almost in real time. Concerning educational environments, this translates to a new type of big data stream, which can be labeled as educational big data streams. Here, pieces of information coming from different sources (such as communications between students and instructors, as well as students' tests, etc.) require accurate analysis and mining techniques in order to retrieve fruitful and well-timed insights from them. This article presents an overview of the current state of the art of virtual learning environments and their limitations; then, it explains the main ideas behind the paradigms of big data streams and of fog computing, in order to introduce an e-learning architecture integrating both of them. Such an action aims to enhance the ability of virtual learning environments to be closer to the needs of all the actors in an educational scenario, as demonstrated by a preliminary implementation of the envisioned architecture. We believe that the proposed big stream and fog-based educational framework may pave the way towards a better understanding of students' educational behaviors and foster new research directions in the field.

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“…After this training, primary and secondary schools in the country gradually began to practice cloud-based teaching [5]. Universities and companies began to establish cloud computing cooperation research projects for college teachers and business employees, and then for primary and secondary school teachers.…”

Section: State Of Artmentioning

confidence: 99%

Exploration of An Open Online Learning Platform Based on Google Cloud Computing

Zhao¹,

Yang²,

Ma³

2017

Int. J. Emerg. Technol. Learn.

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Abstract-With the progress of society, traditional online learning platforms have demonstrated the uneven distribution of information resources, and teacher-student communication exhibits a certain delay. At present, cloud computing, which is a new product of information technology, has been favored in many areas because of its superior feedback mechanism and storage space. Therefore, to improve the integration of online learning information resources and facilitate interaction between teachers and students, we designed our own online learning system based on the Google cloud computing platform. We used Google's cloud computing platform and the Google App Engine to develop a unified and open online learning platform that is capable of storing large amounts of data, integrating considerable amounts of learning resources, and storing them on cloud. Through a test, we determined that the designed online learning platform for sharing information resources and integrating teacherstudent exchanges is highly beneficial. The platform helps the classroom learning atmosphere become active, and has a positive effect on teaching methods. The proposed platform can promote further development of online learning.

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E-learning and educational data mining in cloud computing: an overview

Cited by 35 publications

References 54 publications

The Acceptance of Educational Data Mining Technology among Students in Public Institutions of Higher Learning in Malaysia

The Acceptance of Educational Data Mining Technology among Students in Public Institutions of Higher Learning in Malaysia

A Virtual Learning Architecture Enhanced by Fog Computing and Big Data Streams

Exploration of An Open Online Learning Platform Based on Google Cloud Computing

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