Indicators for Deducting the Learners' Learning Styles: Case of the Navigation Typology Indicator

Bousbia, Nabila; Labat, Jean-Marc; Balla, Amar

doi:10.1109/icalt.2009.220

Cited by 9 publications

(7 citation statements)

References 6 publications

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“…Using the navigation type indicator learners' navigation behavior is evaluated through educational hypermedia resources. This' indicator with qualitative value classified the learner's navigation behavior in four types (Bousbia et al 2009) i.e. (i) Overviewing-this value is close to the Canter "scanning" value.…”

Section: Learner Behavior Analysismentioning

confidence: 86%

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

Premlatha

Geetha

2015

Artif Intell Rev

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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.

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Section: Learner Behavior Analysismentioning

confidence: 86%

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

Premlatha

Geetha

2015

Artif Intell Rev

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“…Based on Bidel et al (2003), and preliminary tests, we adapted the Canter et al (1985) typology in our learning context to propose a navigation typology with four navigation types (Bousbia et al 2009):…”

Section: Navigation Typologymentioning

confidence: 99%

Learners’ navigation behavior identification based on trace analysis

Bousbia

Labat

Balla

2010

User Model User-Adap Inter

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Identifying learners' behaviors and learning preferences or styles in a Web-based learning environment is crucial for organizing the tracking and specifying how and when assistance is needed. Moreover, it helps online course designers to adapt the learning material in a way that guarantees individualized learning, and helps learners to acquire meta-cognitive knowledge. The goal of this research is to identify learners' behaviors and learning styles automatically during training sessions, based on trace analysis. In this paper, we focus on the identification of learners' behaviors through our system: Indicators for the Deduction of Learning Styles. We shall first present our trace analysis approach. Then, we shall propose a 'navigation type' indicator to analyze learners' behaviors and we shall define a method for calculating it. To this end, we shall build a decision tree based on semantic assumptions and tests. To validate our approach, and improve the proposed calculation method, we shall present and discuss the results of two experiments that we conducted.

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“…In remote learning settings, tutors need to understand students' actions so as to efficiently adjust their tutoring strategy. Some studies addressed this problem by delivering tools that enable tutors to visualize multiple indicators about students' activities, including students' performances (Mazza & Dimitrova, 2007), curriculum and productions (Lekira et al, 2012), or learning styles (Bousbia et al, 2009). All these data reduce the time spent by tutors to analyse and react to students' actions and productions.…”

Section: The Learning Analytics Toolmentioning

confidence: 99%

Lab4CE: a Remote Laboratory for Computer Education

Broisin

Venant

Vidal

2015

Int J Artif Intell Educ

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Remote practical activities have been demonstrated to be efficient when learners come to acquire inquiry skills. In computer science education, virtualization technologies are gaining popularity as this technological advance enables instructors to implement realistic practical learning activities, and learners to engage in authentic and problem-based learning. However, virtualization solutions have not been designed especially for education and do not address any pedagogical concern. Since several large-scale studies showed that instructional supports during practical activities are almost as important as technical features, this article investigates the following research question: how the scaffolding around the lab increases students' engagement in remote practical learning of computer science? To answer this question, we introduce the Lab4CE environment, a remote laboratory for computer education which adopts a distributed, modular and flexible architecture to integrate a set of scaffolding tools and services intended for instructors and learners. An exploratory study conducted with 139 undergraduate students enrolled in the first year of a computer science degree suggests a positive effect of the framework on learners' engagement when they come to practice system administration, and reveals a significant positive correlation between students' activity within the system and students' learning achievement.

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Indicators for Deducting the Learners' Learning Styles: Case of the Navigation Typology Indicator

Cited by 9 publications

References 6 publications

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

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

Learners’ navigation behavior identification based on trace analysis

Lab4CE: a Remote Laboratory for Computer Education

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