Identifying Learning Styles from Chat Conversation using Ontology-Based Dynamic Bayesian Network Model

Al-Abri, Amal; Al-Khanjari, Zuhoor; Jamoussi, Yassine; Kraïem, Naoufel

doi:10.1109/csit.2018.8486169

Cited by 6 publications

(10 citation statements)

References 27 publications

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“…Collaboration among students is an important element of learning. Collaborative learning experiences emphasize the concept of activities [2]. Performing any learning activity requires working with other learners.…”

Section: Chat Conversations For Collaborative Learning Activities and Personal Izationmentioning

confidence: 99%

“…With the aid of data mining and proper analysis of chat conversation, the valuable hidden information about learners and discussed topics can be attained [1], [2]. Chat mining as an application of text mining deals with the text or messages extracted from chat conversation [12] took place between actors.…”

Section: Mining Chat Conversationmentioning

confidence: 99%

“…Thus, there is a need for the best utilization of this valuable data in an effective way to support the learning process. However, to extract useful information to provide personalized e-learning environment, there is a need to collect these conversations and analyze them [1]. To do so, the mining technique is required.…”

Section: Introductionmentioning

confidence: 99%

“…This involves applying some important tasks to the data to be suitable for information extraction. These tasks are; the preprocessing of the collected data, the term/concept extraction, relationship discovery, representation of extracted concept and relations, and finally collaborative filtering task [2]. The preprocessing task is the cleaning step of the collected data to be more meaningful.…”

Section: Introductionmentioning

confidence: 99%

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Mining the Students’ Chat Conversations in a Personalized e-Learning Environment

Al-Abri

Al-Khanjari

Jamoussi

et al. 2019

Int. J. Emerg. Technol. Learn.

Self Cite

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Providing personalized e-learning environment is normally relying on a domain model representing the knowledge to be acquired by learners and learners’ characteristics to be used in the personalization process. Therefore, constructing the domain model and understanding the characteristics of the learners are very crucial in such an environment. With the inclusion of social collaboration tools for collaborative learning activities, the generated data during conversations enrich with valuable information to be used for personalization. However, when considering chat conversations as a source for constructing the domain model, there is a need to perform a mining technique for chat conversations in order to extract the semantic relations from the user-generated contents hidden inside these conversations. As well as the learner’s characteristics like learning style and knowledge level expressed during conversations. Thus in this paper, we are aiming for the best utilization of chat conversation by proposing a model containing a rule-based technique as a form of mining technique. This mining aims at extracting the semantic relations to build the domain model as an ontology-based depiction. In addition, the mining model is proposed to perform some collaborative filtering techniques to identify the learning styles and knowledge level of the learners.

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Section: Chat Conversations For Collaborative Learning Activities and Personal Izationmentioning

confidence: 99%

Section: Mining Chat Conversationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mining the Students’ Chat Conversations in a Personalized e-Learning Environment

Al-Abri

Al-Khanjari

Jamoussi

et al. 2019

Int. J. Emerg. Technol. Learn.

Self Cite

View full text Add to dashboard Cite

show abstract

“…This information is important to understand the characteristics of the learner. Consequently, provide personalization feature as it is highly demanded in today's educational environments [1], [15]- [17]. Thus, information related to the knowledge on the discussed topic and expressed opinion via like/dislike or textual expression needs to be extracted from social media.…”

Section: Introductionmentioning

confidence: 99%

Towards a Classification View of Personalized e-Learning with Social Collaboration Support

Al-Abri¹,

Jamoussi²,

Al-Khanjari³

et al. 2019

IJACSA

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With the emergence of Web 2.0 technologies, interaction and collaboration support in the educational field have been augmented. These types of support embrace researchers to enrich the e-learning environment with personalized characteristics with the utilization of the collaboration support outputs. Achieving this requires understanding the existing environments and highlights their eminence. As a result, there are many attempts to state the current status of personalized e-learning environment from different perspectives. However, these attempts targeted a specific view and direction which failed to provide us with the general view of the adoption of personalized e-learning environment with the support of social collaboration tools. This paper provides a classified view of the current status of personalized e-learning environments which incorporate social collaboration tools for providing the personalization feature. The classification adopts four different views to carry out the classification; these views are subject, purpose, method, and tool. The findings show that the utilization of the user-generated contents and social interaction functionalities for personalization is tight and not fully consumed. In short, the potential of providing personalized learning with social interaction and collaboration features remains not fully explored.

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An evolving learning style detection approach for online education using bipartite graph embedding

Muhammad,

Qi,

et al. 2024

Applied Soft Computing

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Identifying Learning Styles from Chat Conversation using Ontology-Based Dynamic Bayesian Network Model

Cited by 6 publications

References 27 publications

Mining the Students’ Chat Conversations in a Personalized e-Learning Environment

Mining the Students’ Chat Conversations in a Personalized e-Learning Environment

Towards a Classification View of Personalized e-Learning with Social Collaboration Support

An evolving learning style detection approach for online education using bipartite graph embedding

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