Learning Analytics Data Flow and Visualizing for Ubiquitous Learning Logs in LMS and Learning Analytics Dashboard

Author(s):  
Songran Liu ◽  
Kousuke Mouri ◽  
Hiroaki Ogata
2020 ◽  
Vol 18 (3) ◽  
pp. 78-98
Author(s):  
Mohammad Nehal Hasnine ◽  
Hiroaki Ogata ◽  
Gökhan Akçapınar ◽  
Kousuke Mouri ◽  
Keiichi Kaneko

In ubiquitous learning, authentic experiences are captured and later reused as those are rich resources for foreign vocabulary development. This article presents an experiential theory-oriented approach to the design of learning analytics support for sharing and reusing authentic experiences. In this regard, first, a conceptual framework to support vocabulary learning using learners' authentic experiences is proposed. Next, learning experiences are captured using a context-aware ubiquitous learning system. Finally, grounded in the theoretical framework, the development of a web-based tool called learn from others (LFO) panel is presented. The LFO panel analyzes various learning logs (authentic, partially-authentic, and words) using the profiling method while determining the top-five learning partners inside a seamless learning analytics platform. This article contributes to the research in the area of theory-oriented design of learning analytics for vocabulary learning through authentic activities and focuses on closing the loops of experiential learning using learning analytics cycles.


2020 ◽  
Vol 51 (4) ◽  
pp. 1078-1100 ◽  
Author(s):  
Gerti Pishtari ◽  
María J. Rodríguez‐Triana ◽  
Edna M. Sarmiento‐Márquez ◽  
Mar Pérez‐Sanagustín ◽  
Adolfo Ruiz‐Calleja ◽  
...  

Author(s):  
Samina Kausar ◽  
Solomon Sunday Oyelere ◽  
Yass Khudheir Salal ◽  
Sadiq Hussain ◽  
Mehmet Akif Cifci ◽  
...  

Recent progress in technology has altered the learning behaviors of students; besides giving a new impulse which reshapes the education itself. It can easily be said that the improvements in technologies empower students to learn more efficiently, effectively and contentedly. Smart Learning (SL) despite not being a new concept describing learning methods in the digital age- has caught attention of researchers. Smart Learning Analytics (SLA) provides students of all ages with research-proven frameworks, helping students to benefit from all kinds of resources and intelligent tools. It aims to stimulate students to have a deep comprehension of the context and leads to higher levels of achievements. The transformation of education to smart learning will be realized by reengineering the fundamental structures and operations of conventional educational systems. Accordingly, students can learn the proper information yet to support to learn real-world context, more and more factors are needed to be taken into account. Learning has shifted from web-based dumb materials to context-aware smart ubiquitous learning. In the study, a SLA dataset was explored and advanced ensemble techniques were applied for the classification task. Bagging Tree and Stacking Classifiers have outperformed other classical techniques with an accuracy of 79% and 78% respectively.


E-books have been introduced to educational institutions in many countries. The use of e-books in traditional classrooms enables the recording of learning logs. Recently, researchers have begun to carry out learning analytics on the learning logs of e-books. However, there has been limited attention devoted to understanding the types of learning strategies that students employ when they read e-books. In this paper, using e-book learning logs, we examine the learning strategies that students employed when reading e-books. In this paper, we will introduce how to identify learning strategies from e-book learning logs with two case studies. One is “Identifying Learning Strategies Using Clustering” and the other is “Examining Learning Strategies Using Sequential Analysis.”


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