A scheme for extracting information from collaborative social interaction tools for personalized educational environments

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

doi:10.1109/iccni.2017.8123795

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…Therefore, a pre-processing technique is needed where raw data is transformed into a usable format, mainly by cleaning, assigning attributes, and integrating data. Subsequently, we have applied various data mining and text mining techniques to examine the data sets in order to create the domain model and gain insights about the learner's knowledge level and preferences [42]. To facilitate the text mining process, GATE [43] has been selected.…”

Section: Stagementioning

confidence: 99%

PerLCol: A Framework for Personalized e-Learning with Social Collaboration Support

Abri¹,

Jamoussi²,

Al-Khanjari³

et al. 2020

IJCDS

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The emergence of Web 2.0 technologies like social media has enhanced the interaction and social collaboration support in the educational field. On the one hand, the integration of such sources equips learners with channels to share information, resources, ideas, as well as expressing opinions and comments during the interactions. On the other hand, such integration raises some issues related to personalization by filtering the collaboration support and provide a tailored pedagogical intervention to provide adequate hints and feedback as a means of adaptivity. Existing learning management systems provide restricted collaboration features and are not tailored to students' characteristics like knowledge level and individual traits. In order to overcome these issues, we propose a framework to upgrade the virtual learning environment to provide flexible collaboration and be adaptive to learners' needs. The framework integrates social media tools for seamless collaboration. In addition, it utilizes the generated content during collaboration to identify discussed concepts and learners' characteristics to provide a personalized learning package accordingly. Descriptions of the notions required to develop the framework and an overview of its components and functionality are provided in this paper.

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Section: Stagementioning

confidence: 99%

PerLCol: A Framework for Personalized e-Learning with Social Collaboration Support

Abri¹,

Jamoussi²,

Al-Khanjari³

et al. 2020

IJCDS

Self Cite

View full text Add to dashboard Cite

show abstract

“…These relations can be extracted using the rule-based technique. The extraction rules specify a pattern by capturing the relations that identify specific lexical elements in a text such as keywords [3]. The extraction and description of relations from textual data like chat messages require applying some text mining and algorithms [20].…”

Section: Semantic Relationsmentioning

confidence: 99%

“…The domain ontology is commonly used as a source of knowledge for information retrieval from semi or unstructured text [3] like chat conversations. Ontology is a formal modeling pattern showing how different concepts are related within the same domain [4].…”

Section: Introductionmentioning

confidence: 99%

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

Al-Abri

Al-Khanjari

Jamoussi

et al. 2019

Int. J. Emerg. Technol. Learn.

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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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“… Adaptation: adaptation techniques and parameters enlighten the path to follow and the aspects of learners to take under considerations when providing personalization [30]. In the area of collaboration support, the user-generated content is utilized to extract information related to the parameters used for personalization as well as discovering and representing the domain under discussion [7]. Measuring to what extent the system is adaptive to the learners' needs is very important as discussed in the classification framework.…”

Section: Generated Facetsmentioning

confidence: 99%

“…With the emerging technology of web services like web 2.0 tools and enormous adoption of social collaboration tools for learning [6], the possibility of developing an e-learning platform for description, discovery, interaction, collaboration and interoperability of distributed, heterogeneous applications as services has been amplified enormously [7]- [10].…”

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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A scheme for extracting information from collaborative social interaction tools for personalized educational environments

Cited by 6 publications

References 18 publications

PerLCol: A Framework for Personalized e-Learning with Social Collaboration Support

PerLCol: A Framework for Personalized e-Learning with Social Collaboration Support

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

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

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