A context-aware adaptive learning system using agents

Yaghmaie, Mahkameh; Bahreininejad, Ardeshir

doi:10.1016/j.eswa.2010.08.113

Cited by 81 publications

(54 citation statements)

References 20 publications

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“…However, many existing e-learning systems are not developed to support learning adaptivity (Bennett, 2011) and others have supported adaptivity from the instructor"s rather than the learners" perspective (Yaghmaie and Bahreininejad, 2011). The LO-based learning management systems, for example, have adopted a modularity approach.…”

Section: Literature Review Flipped Learning and Learning Adaptivitymentioning

confidence: 99%

Learning Path Adaptivity in Support of Flipped Learning: A Knowledge-Based Approach

Chi¹,

Chen²

2016

Proceedings of the 23rd International Academic Conference, Venice

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Section: Literature Review Flipped Learning and Learning Adaptivitymentioning

confidence: 99%

Learning Path Adaptivity in Support of Flipped Learning: A Knowledge-Based Approach

Chi¹,

Chen²

2016

Proceedings of the 23rd International Academic Conference, Venice

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“…PLE are basically founded on the framework of learning management systems coupled with data mining to perform functions for personalized recommendations. This model is also called adaptive LMS and offers a unique learning experience to the user by providing a customized environment (based on personal interest, personality, prior knowledge, and skills) fitted for achieving the learning goals of the student (Yaghmaie & Bahreininejad, 2011). Sunil and Saini (2013) defined recommender systems (RS) as software that can operate as a personalized information agent.…”

Section: Figure 1: Model Of Traditional Learning Management Systemmentioning

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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“…Several studies have focused on developing an approach, framework, and/or system that uses semantic Web technologies (Yaghmaie and Bahreininejad, 2011;Vesin, et al, 2012;Caravantes and Galán, 2011). For example, Vesin, et al (2012) proposed a new approach for effective personalization that uses semantic Web technologies.…”

Section: Semantic Web Technologiesmentioning

confidence: 99%

“…In some studies, a multi-agent concept was employed to include certain properties of agents, such as autonomy, pre-activity and cooperation (Yaghmaie and Bahreininejad, 2011;Aroyo & Dicheva, 2004). For example, Yaghmaie and Bahreininejad (2011) suggested a framework to build an adaptive learning management system based on multi-agent systems.…”

Section: The Modular Approachmentioning

confidence: 99%

The new trends in adaptive educational hypermedia systems

Somyürek

2015

IRRODL

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This paper aims to give a general review of existing literature on adaptive educational hypermedia systems and to reveal technological trends and approaches within these studies. Fifty-six studies conducted between 2002 and 2012 were examined, to identify prominent themes and approaches.According to the content analysis, the new technological trends and approaches were grouped into seven categories: standardization, semantic web, modular frameworks, data mining, machine learning techniques, social web, and device adaptation. Furthermore, four challenges are suggested as explanation why adaptive systems are still not used on a large scale: inter-operability, open corpus knowledge, usage across a variety of delivery devices, and the design of metaadaptive systems.

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A context-aware adaptive learning system using agents

Cited by 81 publications

References 20 publications

Learning Path Adaptivity in Support of Flipped Learning: A Knowledge-Based Approach

Learning Path Adaptivity in Support of Flipped Learning: A Knowledge-Based Approach

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

The new trends in adaptive educational hypermedia systems

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