Big data and mobile learning in generating pervasive knowledge

2017 ◽  
Author(s):  
Novan Zulkarnain ◽  
Muhammad Anshari ◽  
Muhammad Nabil Almunawar
Keyword(s):  
Big Data ◽  
Mobile Learning

scholarly journals Active learning analytics in mobile: visions from PhD students

2020 ◽  
Vol 15 (2) ◽  
pp. 145-166
Author(s):  
Eda Atasoy ◽  
Harun Bozna ◽  
Abdulvahap Sönmez ◽  
Ayşe Aydın Akkurt ◽  
Gamze Tuna Büyükköse ◽  
...  
Keyword(s):  
Big Data ◽  
Mobile Learning ◽  
Learning Analytics ◽  
Mobile Technologies ◽  
Content Type ◽  
New Type ◽  
Continuous Feedback ◽  
Age Range ◽  
New Education ◽  
Evaluation And Assessment

PurposeThis study aims to investigate the futuristic visions of PhD students at Distance Education department of Anadolu University on the use of learning analytics (LA) and mobile technologies together.Design/methodology/approachThis qualitative research study, designed in the single cross-section model, aimed to reveal futuristic visions of PhD students on the use of LA in mobile learning. In this respect, SCAMPER method, which is also known as a focused brainstorming technique, was used to collect data.FindingsThe findings of the study revealed that the use of LA in mobile can solve everyday problems ranging from health to education, enable personalized learning for each learner, offer a new type of evaluation and assessment and allow continuous feedback and feedforwards; yet this situation can also arise some ethical concerns since the big data collected can threaten the learners by interfering with their privacy, reaching their subconscious and manipulating them as well as the whole society by wars, mind games, political games, dictation and loss of humanity.Research limitations/implicationsThe research is limited with the views of six participants. Also, the sample of the study is homogeneous in terms of their backgrounds – their age range, their departments as PhD students and their fields of expertise.Practical implicationsThe positive perceptions of PhD students provide a ground for the active use of LA in mobile. Further, big data collected through LA can help educators and system makers to identify patterns which will enable tailored education for all. Also, use of LA in mobile learning may stimulate the development of a new education system including a new type of evaluation and assessment and continuous feedback and feedforwards.Originality/valueThe widespread use of mobile technologies opens new possibilities for LA in the future. The originality of this research comes from its focus on this critical point.

scholarly journals Towards a New Scalable Big Data System Semantic Web Applied on Mobile Learning

2020 ◽  
Vol 14 (01) ◽  
pp. 126 ◽  
Author(s):  
Mouad Banane ◽  
Abdessamad Belangour
Keyword(s):  
Big Data ◽  
Semantic Web ◽  
Mobile Learning ◽  
Data System ◽  
Data Availability ◽  
Process Data ◽  
Data Resource ◽  
Web 3.0 ◽  
High Data ◽  
Adaptation Model

In Web 3.0, semantic data gives machines the ability to understand and process data. Resource Description Framework (RDF) is the liagna franca of Semantic Web. While Big Data handles the problematic of storing and processing massive data, it still does not provide a support for RDF data. In this paper, we present a new Big Data semantic web comprised of a classical Big Data system with a semantic layer. As a proof of concept of our approach, we use Mobile-learning as a case study. The architecture we propose is composed of two main parts: a knowledge server and an adaptation model. The knowledge server allows trainers and business experts to represent their expertise using business rules and ontology to ensure heterogeneous knowledge. Then, in a mobility environment, the knowledge server makes it possible to take into account the constraints of the environment and the user constraints thanks to the RDF exchange format. The adaptation model based on RDF graphs corresponds to combinatorial optimization algorithms, whose objective is to propose to the learner a relevant combination of Learning Object based on its contextual constraints. Our solution guarantees scalability, and high data availability through the use of the principle of replication. The results obtained in the system evaluation experiments, on a large number of servers show the efficiency, scalability, and robustness of our system if the amount of data processed is very large.

Framework to Develop a Learning Analytics System for Smartphone Blended Learning Environment

2016 ◽  
pp. 423-442
Author(s):  
Mazharuddin Syed Ahmed
Keyword(s):  
Big Data ◽  
Learning Environment ◽  
Blended Learning ◽  
Everyday Life ◽  
Mobile Learning ◽  
Learning Analytics ◽  
Educational Tools ◽  
Tools And Techniques

Smartphones today are ubiquitous and universally influence our everyday life. Now, they are also creating a formidable impact as educational tools. The use of the smartphone specifically for mobile learning has exponentially increased the availability of big data in education. This data can be used for conducting Learning Analytics. Learning Analytics is a powerful tool that supports learners, instructors and institutions in better predicting and understanding a learner's performance and needs. The available tools and techniques used for Learning Analytics' are often not well-defined or well represented. This research proposes a four stage Learning Analytics framework which will aid in defining and understanding all the components of Learning Analytics in a smartphone-based blended learning environment. The proposed framework will be a useful guide for setting up Learning Analytics workflow or can be used to improve an existing system.

Harnessing the power of big data analytics in the cloud to support learning analytics in mobile learning environment

2019 ◽  
Vol 92 ◽  
pp. 578-588 ◽  
Author(s):  
Mohammad Shorfuzzaman ◽  
M. Shamim Hossain ◽  
Amril Nazir ◽  
Ghulam Muhammad ◽  
Atif Alamri
Keyword(s):  
Big Data ◽  
Learning Environment ◽  
Mobile Learning ◽  
Learning Analytics ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Mobile Learning Environment
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