User Profile Modeling in the context of web-based learning management systems

Kritikou, Yiouli; Demestichas, Panagiotis; Adamopoulou, Evgenia; Demestichas, Konstantinos; Theologou, M.; Paradia, Maria

doi:10.1016/j.jnca.2007.11.006

Cited by 63 publications

(30 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The finding is also consistent with the works of Kritikou and Demestichas (2008);ElementK (2003) and Meerts (2003) who highlighted the need to import content from and supply content to digital libraries and other e-Learning systems. This finding supports the importance of interoperability standards and modular, extendable architectures in a technology environment (Wyles, n.d.).…”

Section: Other Non-functional Characteristics Deemed Importantsupporting

confidence: 89%

Educator Perceptions of Virtual Learning System Quality Characteristics

Padayachee

2017

SACJ

View full text Add to dashboard Cite

In higher education institutions, digital learning environments, referred to as virtual learning systems in this article, have been adopted and are becoming increasingly popular among academics. A virtual learning system (VLS) has a suite of tools with associated functions and non-functional system characteristics. Higher education institutions (HEIs) implement a VLS with the intent to assimilate e-Learning with face-to-face instruction and thereby derive associated benefits from its usage. Currently there is limited information on educators’ perceptions on the usefulness of VLS tool functionality, and the importance assigned to non-functional characteristics. This article adapted the generic framework of the ISO 9126 external software quality model to ascertain the perceptions of educators with regards to VLS functionality and non-functional quality characteristics. A case study research strategy was adopted, using two South African higher education institutions. The main contribution of this article is a proposed model representing VLS quality in use characteristics, measured by the constructs of perceived usefulness and perceived importance. In addition to the theoretical contribution, this article makes a practical contribution by providing educators’ recommendations on the improvement of VLS quality characteristics. This article has potentially useful implications for the use of a VLS by educators and future VLS design iterations.

show abstract

Section: Other Non-functional Characteristics Deemed Importantsupporting

confidence: 89%

Educator Perceptions of Virtual Learning System Quality Characteristics

Padayachee

2017

SACJ

View full text Add to dashboard Cite

show abstract

“…Hence, one of the issues that must be addressed, is the prediction of these learner preferences and the modeling of the learner's profile, in terms of educational level, familiarity with a particular course, overall performance etc. The learner model and the learner preferences can be predicted effectively using Bayesian network probabilistic relationships (Kritikou et al 2008). To learn effectively and manage their way of learning, learners have to be aware of their preferences.…”

Section: Learner Preferencesmentioning

confidence: 99%

Learning content design and learner adaptation for adaptive e-learning environment: a survey

Premlatha

Geetha

2015

Artif Intell Rev

View full text Add to dashboard Cite

This paper presents a survey about learning content designs and various adaptation levels, in order to adapt the learners' necessities in e-learning environment. Normally, learners have different learning styles, cognitive traits, learning goals and varying progress of their learning over period of time, which affects the learner's performance while providing the same bundle of course to all learners. Hence, there is a need to create adaptive e-learning environment to offer appropriate learning content to all individuals. In general, the adaptation can be done based on learners' characteristics. Here, we explore the adaptation that can be done, not only based on learner context parameters but also on the learning content (learning object) and the configuration of e-learning environment. In this paper, we provide a detail review about the various levels of adaptation, learning object design and process for learning content design, learner context parameters, and models/components of e-learning; moreover, we analyze and portray the associations among the components, necessary to achieve the well-defined adaptation in e-learning environment.

show abstract

“…LMS is relatively a new concept which is often confused with other concepts like elearning, digital learning, virtual learning and distance learning (Kritikou et al, 2008). All of these concepts represent modern advancements in the education process which in most cases involve the utilization of ICT tools and technologies (Albirini, 2006).…”

Section: Lms Definition and Benefitsmentioning

confidence: 99%

Instructors' Acceptance of Learning Management Systems: A Theoretical Framework

Al‐Busaidi¹,

Al-Shihi²

2010

CIBIMA

View full text Add to dashboard Cite

In the knowledge age, the use of the information technology (IT) tools including learning management systems (LMS) has became an imperative. The adoption rate of LMS in academic and training institutions is very promising worldwide. Learning Management system includes several tools that provide academic and training institutions efficient and effective means to support distance education and supplement their traditional way of teaching. LMS also provide academic insinuations mechanisms and tools to store, manage, and share its academic resources and knowledge. Instructors' acceptance is essential for the deployment of LMS. The success of LMS in any institution starts by instructors' acceptance, which in turns initiates and promotes learners' utilization of LMS. Consequently, the objective of this paper is to develop a theoretical framework for evaluating instructors' acceptance of LMS based on the Technology Acceptance Model. This framework provides a comprehensive look of the critical factors that influence the instructors' perceived ease of use and perceived usefulness of LMS and consequently the actual use. These critical factors are related to the instructor, organization, and technology. Instructor factors include self efficacy, attitude toward LMS, experience, teaching style and personal innovativeness. Organization factors include motivators, technology alignment, organization support, technical support and training. Technology factors include system quality, information quality and service quality.

show abstract

User Profile Modeling in the context of web-based learning management systems

Cited by 63 publications

References 21 publications

Educator Perceptions of Virtual Learning System Quality Characteristics

Educator Perceptions of Virtual Learning System Quality Characteristics

Learning content design and learner adaptation for adaptive e-learning environment: a survey

Instructors' Acceptance of Learning Management Systems: A Theoretical Framework

Contact Info

Product

Resources

About