2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD) 2021

DOI: 10.1109/cscwd49262.2021.9437622

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TCO : a Teacher Context Ontology

Nader N. Nashed

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Marie‐Hélène Abel

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Introduction2

Methods1

B Integration With Tco Ontology1

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Cited by 5 publications

(11 citation statements)

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“…The approach of this model is based on two previous works: a mood-monitoring platform (Moodflow@doubleYou) [3] and teacher-context ontology (TCO) [4]. Moodflow@doubleYou platform allows the user's mood data collection and detection while the model integration with TCO offers better representation of the collected data.…”

Section: Methodsmentioning

confidence: 99%

“…In TCO, a person can be a teacher or a learner that interacts with resources. These activities and interactions, which are part of the integration between TCO and MEMORAe (MCC) [4], results in a person's mood changement. As a result of this integration, the teacher full context is represented by describing both work and living conditions/environment from TCO, in addition to its associated mood.…”

Section: B Integration With Tco Ontologymentioning

confidence: 99%

“…The authors gratefully acknowledge the contributions of Bernard Blancan, an independent researcher and the inventor of doubleYou technology 4 , for his work on realizing the experiment that is illustrated in this paper.…”

Section: Acknowledgmentmentioning

confidence: 99%

“…In this paper, we propose an integration method between MoodFlow@doubleYou user's mood data [3] and the teacher context ontology (TCO) [4]. The proposed integration introduces a realistic description of the contextual variation in teacher's mood.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Mood detection ontology integration with teacher context

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et al. 2021

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)

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Recommender systems in education improve the teacher's working process by providing relevant resources to aid his course design in addition to learning new teaching methodologies. However, these systems have limited adaptability according to a global evaluation of teacher's activities. This approach of user profiling is convenient, but not adequate for teacher's context description. In our approach, it is assumed that the utilization of teacher's emotions has an inevitable role to accomplish a full contextual description for teacher. Teacher context ontology (TCO) provides a representation for the teacher's living and working contexts along with the main educational concepts. In this paper, we introduce a conceptual integration approach between Moodflow@doubleYou emotional data as a concept and TCO ontology. Furthermore, we intend to prove the importance of integrating such concept for sufficient teacher's context description. The impact of utilization emotional data in educational recommender systems is discussed. Finally, this paper represents the conducted experiments' results which show the advantage of such integration.

“…The approach of this model is based on two previous works: a mood-monitoring platform (Moodflow@doubleYou) [3] and teacher-context ontology (TCO) [4]. Moodflow@doubleYou platform allows the user's mood data collection and detection while the model integration with TCO offers better representation of the collected data.…”

Section: Methodsmentioning

confidence: 99%

“…In TCO, a person can be a teacher or a learner that interacts with resources. These activities and interactions, which are part of the integration between TCO and MEMORAe (MCC) [4], results in a person's mood changement. As a result of this integration, the teacher full context is represented by describing both work and living conditions/environment from TCO, in addition to its associated mood.…”

Section: B Integration With Tco Ontologymentioning

confidence: 99%

“…The authors gratefully acknowledge the contributions of Bernard Blancan, an independent researcher and the inventor of doubleYou technology 4 , for his work on realizing the experiment that is illustrated in this paper.…”

Section: Acknowledgmentmentioning

confidence: 99%

“…In this paper, we propose an integration method between MoodFlow@doubleYou user's mood data [3] and the teacher context ontology (TCO) [4]. The proposed integration introduces a realistic description of the contextual variation in teacher's mood.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mood detection ontology integration with teacher context

¹

,

²

,

³

et al. 2021

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)

Self Cite

View full text Add to dashboard Cite

Recommender systems in education improve the teacher's working process by providing relevant resources to aid his course design in addition to learning new teaching methodologies. However, these systems have limited adaptability according to a global evaluation of teacher's activities. This approach of user profiling is convenient, but not adequate for teacher's context description. In our approach, it is assumed that the utilization of teacher's emotions has an inevitable role to accomplish a full contextual description for teacher. Teacher context ontology (TCO) provides a representation for the teacher's living and working contexts along with the main educational concepts. In this paper, we introduce a conceptual integration approach between Moodflow@doubleYou emotional data as a concept and TCO ontology. Furthermore, we intend to prove the importance of integrating such concept for sufficient teacher's context description. The impact of utilization emotional data in educational recommender systems is discussed. Finally, this paper represents the conducted experiments' results which show the advantage of such integration.

“…An ontology defines types, properties, and relationships of different entities in a common domain context. Teacher-context ontology (TCO) introduces a comprehensive description of the coexistence of one teacher in multiple contexts [2]. This research proposes an automatic ontology-based data integration methodology for multiple diverse heterogeneous data sources to represent the teacher context.…”

Section: Introductionmentioning

confidence: 99%

Ontology-based teacher-context data integration

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2022

2022 IEEE/SICE International Symposium on System Integration (SII)

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Towards Personalized Educational Resources Recommendations for Teachers

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2022

Progresses in Artificial Intelligence &Amp; Robotics: Algorithms &Amp; Applications

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Recommender systems experience the challenging and complex process of finding educational resources recommendations for teachers which requires a careful awareness of the teacher's context. These systems should take into consideration the contextual coexistence of teachers in multiple environments or contexts such as social, working, and sentimental contexts. The collected data for each of these contexts is assembled from different sources and systems with different data architectures. Accordingly, this paper provides a multi-step proposition towards a personalized educational resource recommendations for teachers. Furthermore, the paper discusses the data sources from which the contextual data is collected, in addition to the purposed methodology for integrating and mapping the data into the ontology. At the end, the paper illustrates the importance and the role of the teacher analytics and the sentimental data in this proposition. The proposed approach results in a complete representation of the teacher's context and hence, the personalized recommendations.

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