A working successor of learning management systems: SLOODLE

Yasar, Ozge; Adıgüzel, Tufan

doi:10.1016/j.sbspro.2010.03.928

Cited by 33 publications

(22 citation statements)

References 4 publications

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“…It may also serve as a data bank of examinations and other evaluation tools. LMSs can also be used to generate various records and reports that may aid the schools or organizations to become more effective in delivering of learning process (Yasar & Adiguzel, 2010). With the advent of advanced network technology, the power of Web 2.0 may now be integrated into LMS to promote active interaction not only between students and teachers but also between and among students themselves as well.…”

Section: Literature Reviewmentioning

confidence: 99%

Learning Management System with Prediction Model and Course-content Recommendation Module

Evale

2017

JITE:Research

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Aim/Purpose: This study is an attempt to enhance the existing learning management systems today through the integration of technology, particularly with educational data mining and recommendation systems. Background: It utilized five-year historical data to find patterns for predicting student performance in Java Programming to generate appropriate course-content recommendations for the students based on their predicted performance. Methodology : The author used two models for the system development: these are the Fayyad knowledge discovery in databases (KDD) process model for the data mining phase and the evolutionary prototyping for system development. WEKKA and SPSS were used to find meaningful patterns in the historical data, while Ruby on Rails platform was used to develop the software. Contribution: The contribution of this study is the development of an LMS architecture that can be used to augment the capabilities of the existing systems by integrating a data mining technique for modelling the leaners profile; developing of an algorithm for generating predictions; and making the most appropriate recommendations for the learners based on prior knowledge and learning styles. Findings: The result shows that J48 was the best data mining algorithm to be implemented for finding patterns in the data sets used in this study. Attributes such as age, gender, class schedule, and grades in other programming subjects were found relevant in predicting student performance in Java. Recommendations for Practitioners : It is recommended that collaboration between the academe and IT industry be strengthened to develop a more advanced LMS which could enhance classroom teaching and improve the learning process. Recommendation for Researchers: Combination of multiple algorithm in classifying data set is recommended to further improve the algorithm and rule sets of prediction. Inclusion of intrinsic attributes as part of data set aside from personal and academic records is also recommended. Impact on Society : This LMS can be used to produce independent learners. Future Research: Study about the impact of implementing this LMS in classroom environment will be conducted on the second phase.

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Section: Literature Reviewmentioning

confidence: 99%

Learning Management System with Prediction Model and Course-content Recommendation Module

Evale

2017

JITE:Research

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“…VLE has many functions to support learning [2]. VLE integrates Learning Management System (LMS) with student interface both second life [2], and OpenSim [3,4] via Sloodle. The use of 3D in VLE provides many advantages, especially the interaction between students and teachers through avatars, as well as collaboration among students [5].…”

Section: Introductionmentioning

confidence: 99%

“…VLE has facilities to support the learning process, such as presentation, quizhud, and others. The advantages of VLE are [2]: 1. Web-intercom: a chat-room that brings MOODLE chat-room and Second one together; 2.…”

Section: Introductionmentioning

confidence: 99%

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Implementation of 3D virtual learning environment to improve students’ cognitive achievement

Rasim

Langi

Rosmansyah

et al. 2018

J. Phys.: Conf. Ser.

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“…Reference [11] state that a wellorganized and content-rich design can assist studentcontent interaction. Reference [12] state that lack of student -content interaction is a major weakness in current learning management systems. Reference [13] explain that since learning management systems initially emerged as document repositories, interactive learning support is not being reached as expected as it was not utilized up to full potential.…”

Section: Introductionmentioning

confidence: 99%

What Factors Impact Student – Content Interaction in Fully Online Courses

Nandi¹,

Hamilton²,

Harland³

2015

IJMECS

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Abstract-The rapid advancement of fully online courses has made education accessible from -anywhere‖ and -anytime‖. One of the major success factors of online courses is effective student -content interaction -which defines how students interact with the content in the fully online learning environment. However, there appears to have been little research published about the relationship of content to course design and consequent outcomes for students. In this article, we report on our research on the investigation of factors that affect student -content interaction in fully online computing courses. We have conducted our research through surveys and used a grounded theoretic approach for data analysis. Our results identify the factors that are perceived by students as critical in ensuring effective student -content interaction in fully online computing courses.

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A working successor of learning management systems: SLOODLE

Cited by 33 publications

References 4 publications

Learning Management System with Prediction Model and Course-content Recommendation Module

Learning Management System with Prediction Model and Course-content Recommendation Module

Implementation of 3D virtual learning environment to improve students’ cognitive achievement

What Factors Impact Student – Content Interaction in Fully Online Courses

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