Deriving a prototype for the dynamic generation of learning path in an e-learning environment using genetic algorithm

Anitha, D.; Deisy, C.

doi:10.1504/ijil.2013.054812

Cited by 7 publications

(8 citation statements)

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“…The theories that are most often presented in the studies are social cognitive theory (n=9) and meaningful learning (n=8). Mainly, in the studies that were designed with the social cognitive theory perspective, the development of strategies based on input tests (e.g., [40]) and self-report questionnaires to determine learning style (e.g., [41]) was evidenced. The studies that reported their design through meaningful learning, a sequential relationship was found in the development of pathways (e.g., [10]) and sequences of learning (e.g., [42]), indicating that one concept should be learned before another, based on Ausubel's assimilation theory [43].…”

Section: Characteristics Of the Studiesmentioning

confidence: 99%

Designing and Representing Learning Itineraries: A Systematic Review of the Literature

Buitrago¹,

Salinas²,

Boude³

2021

IxD&A

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Designing learning in higher education involves developing teaching-learning strategies adapted to the individual characteristics of students. Therefore, it is necessary to create models that facilitate the creation and dissemination of learning itinerary designs. This review aims to identify characteristics of the models design for learning itineraries in higher education. The methodology used is the systematic review at the level of meta-analysis following the PRISMA protocol. The 28 articles selected for mapping and synthesis allowed the identification of characteristics of the studies, the structure and the pattern language of the designs, the personalization strategies, the elaboration methodology and strategies to evaluate the results of the implementation of learning itineraries. In conclusion, designers and teachers showed a tendency for an own design, therefore it is necessary to extend the existing knowledge about its implications in the educational practice, and the construction of knowledge is directed to the models design.

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Section: Characteristics Of the Studiesmentioning

confidence: 99%

Designing and Representing Learning Itineraries: A Systematic Review of the Literature

Buitrago¹,

Salinas²,

Boude³

2021

IxD&A

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“…But in the real context, where there is a limited number of quality LOs, extensive evolutionary algorithms may not be suitable. There has been an attempt by the author to use genetic algorithms in a research work that addresses Bloom's taxonomy's cognitive and affective domain. AES developed in this work does not adapt to learning style.…”

Section: Literature Reviewmentioning

confidence: 99%

“…There has been an attempt by the author to use genetic algorithms in a research work that addresses Bloom's taxonomy's cognitive and affective domain. AES developed in this work does not adapt to learning style. There is a need of updating this work to accommodate learning style.…”

Section: Literature Reviewmentioning

confidence: 99%

KLSAS—An adaptive dynamic learning environment based on knowledge level and learning style

Dhakshinamoorthy¹,

Dhakshinamoorthy²

2018

Comp Applic In Engineering

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Adaptive learning environment, the extension of E‐learning, has gained its importance in the present education scenario with the growth in information and communication technology. To develop such an environment, suitable learner characteristics have to be selected and learning materials are to be adapted to the selected characteristics with intelligent mechanisms. The effectiveness of the adaptive systems improves with increased number of learner characteristics considered to build the systems. However, the observations from the existing adaptive systems reveal that the development of systems that adapts to more than one learner characteristic is more challenging than systems that adapt to single learner characteristic. This paper proposes an adaptive learning system, KLSAS: Knowledge and Learning Style based Adaptive System, which is based on two significant learner characteristics, knowledge level and learning style. The adaptation model of KLSAS is proposed as a dynamic system which updates itself with learner performance and preferences. The evaluation of the system is done through the test performance of two different set of learners. The results are encouraging and ensuring that the adaptive strategy based on the selected learner characteristics leads to improvement in learning.

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“…The output of the neural network is adjusted to better fit individual students' performance. Anitha and Deisy (2013) propose a method to dynamically generate learning path in an e-learning environment using genetic algorithm. A learning agent is constructed to model how likely a student is to answer a problem correctly and how long time he/she takes (Beck and Woolf, 1998).…”

Section: Related Workmentioning

confidence: 99%

Topic-aware staff learning material generation in complaint management systems

Junmin

Meng

et al. 2018

IJIL

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In this paper, a topic-aware staff learning material generation approach is proposed in complaint management systems. Historical processing logs are extracted to form a complaint space. Complaint processing skills of staff members are assessed in terms of quantity, efficiency and quality on various topics. Similar staff members are clustered according to their behavioural characteristics. A resource recommendation algorithm is proposed to recommend complaint processing records from highly skilled colleagues in the cluster for the staff member to learn. Preliminary experiment results show good performance of the proposed method.

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Deriving a prototype for the dynamic generation of learning path in an e-learning environment using genetic algorithm

Cited by 7 publications

References 0 publications

Designing and Representing Learning Itineraries: A Systematic Review of the Literature

Designing and Representing Learning Itineraries: A Systematic Review of the Literature

KLSAS—An adaptive dynamic learning environment based on knowledge level and learning style

Topic-aware staff learning material generation in complaint management systems

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