Social network analysis to influence career development

Khousa, Eman Abu; Atif, Yacine

doi:10.1007/s12652-017-0457-9

Cited by 14 publications

(4 citation statements)

References 62 publications

(52 reference statements)

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“…Clustering algorithm is the most commonly used technology in big data analysis, which is used to gather similar data points. The main research content of this category is to apply different clustering algorithms to analyze learners' learning ability and professional skills, match industrial needs [6], conduct learning analysis [7], and realize the clustering and visualization of interactive education network resources [12]. Research related to AI technology includes monitoring and early warning of students' online learning performance [14], or evaluating the quality of education management of a discipline to help the discipline achieve intelligent teaching [5,9,20,21].…”

Section: Resultsmentioning

confidence: 99%

Study on Document Measurement and Visual Analysis of Big Data Education Evaluation

Yang¹,

Luo²,

Gao³

2022

Proceedings of the 2022 2nd International Conference on Education, Information Management and Service Science (EIMSS 2022)

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Machine learning, recommendation algorithm and other new big data technology, has become an important tool for learning analysis education evaluation. Educational evaluation is an important means to evaluate the value of educational activities, and big data technology is becoming an important part of educational evaluation. It has become an urgent problem to sort out the status quo of big data education evaluation research and clarify the context of its research development. In this paper, we use bibliometrics to analyze the data of 391 articles indexed by Web of Science in recent ten years, and use Cite Space software to generate visual results. Through the analysis of the results, it is concluded that big data technology is of great significance for educational evaluation.

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Section: Resultsmentioning

confidence: 99%

Study on Document Measurement and Visual Analysis of Big Data Education Evaluation

Yang¹,

Luo²,

Gao³

2022

Proceedings of the 2022 2nd International Conference on Education, Information Management and Service Science (EIMSS 2022)

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“…In previous works, we employed social learning analytics, social network analytics, machine learning, and behavioral change support systems to construct virtual social spaces for learners to connect, practice, and share experiences, as well as to reflect, and develop their career-readiness. In these virtual communities, learners are engaged in a cognitive apprenticeship process designed to meet their individual dispositions, interests and needs [23,24]. In this present work, we extend our previous investigations into metaverse developments through a novel architecture of Metaverse Intensive Learning Experience (MiLEx) platform.…”

Section: Introductionmentioning

confidence: 80%

Envisioning Architecture of Metaverse Intensive Learning Experience (MiLEx): Career Readiness in the 21st Century and Collective Intelligence Development Scenario

AbuKhousa

El-Tahawy²,

Atif

2023

Future Internet

Self Cite

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Th metaverse presents a new opportunity to construct personalized learning paths and to promote practices that scale the development of future skills and collective intelligence. The attitudes, knowledge and skills that are necessary to face the challenges of the 21st century should be developed through iterative cycles of continuous learning, where learners are enabled to experience, reflect, and produce new ideas while participating in a collective creativity process. In this paper, we propose an architecture to develop a metaverse-intensive learning experience (MiLEx) platform with an illustrative scenario that reinforces the development of 21st century career practices and collective intelligence. The learning ecosystem of MiLEx integrates four key elements: (1) key players that define the main actors and their roles in the learning process; (2) a learning context that defines the learning space and the networks of expected interactions among human and non-human objects; (3) experiential learning instances that deliver education via a real-life–virtual merge; and (4) technology support for building practice communities online, developing experiential cycles and transforming knowledge between human and non-human objects within the community. The proposed MiLEx architecture incorporates sets of technological and data components to (1) discover/profile learners and design learner-centric, theoretically grounded and immersive learning experiences; (2) create elements and experiential learning scenarios; (3) analyze learner’s interactive and behavioral patterns; (4) support the emergence of collective intelligence; (5) assess learning outcomes and monitor the learner’s maturity process; and (6) evaluate experienced learning and recommend future experiences. We also present the MiLEx continuum as a cyclic flow of information to promote immersive learning. Finally, we discuss some open issues to increase the learning value and propose some future work suggestions to further shape the transformative potential of metaverse-based learning environments.

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“…Thus, developing these skills in preparation for a career requires a sustained and progressive growth of professional habits. The community of professional practice represents an alternative, informal way to achieve this goal ( Cook et al, 2012 ), a term that is repeated again in the literature because it fosters a new way of learning as students observe and emulate mentors, while engaging in a cycle of “learning to be” in order to master a particular discipline ( Khousa and Atif, 2018 ).…”

Section: Findings and Discussionmentioning

confidence: 99%

Learning analytics for enhanced professional capital development: a systematic review

de La Hoz-Ruiz,

Khalil,

Domingo Segovia

et al. 2024

Front. Psychol.

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Background/MotivationThis article presents a systematic review aimed at examining the utilization of learning analytics (LA) to enhance teachers’ professional capital.AimThe study focuses on three primary research questions: (1) exploring the characteristics and approaches of LA in professional capital, (2) investigating suggestions from LA for assessing and improving professional capital, and (3) examining variables studied in enhancing the most intricate dimension of professional capital using LA.MethodologyTo address the research objectives, a systematic review was conducted focusing on the key concepts “learning analytics” and “professional capital.” Following the procedures outlined encompassed in four stages: identification, screening, inclusion, and adequacy. The PRISMA 2009 protocol guided the systematic review process.Principal findingsThe findings of the study underscore the efficacy of LA as a catalyst for improving professional capital, particularly through collaborative learning and the utilization of tools like forums and online learning platforms. Social capital emerges as a pivotal component in integrating diverse types of professional capital, fostering opportunities for knowledge creation and social networking.Conclusion/SignificanceIn conclusion, the study highlights the paramount significance of addressing teachers’ professional capital development through collaborative approaches and leveraging technology, particularly in primary education. The article concludes by emphasizing the imperative for more research and knowledge dissemination in this field, aiming to ensure equity in learning and address the challenges posed by the COVID−19 pandemic.

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Social network analysis to influence career development

Abstract: approach leads to a persuasive model that supports behavioral changes and developments. The performance results obtained from both analytical and experimental evaluations validate our data-driven strategy for persuasive behavioral change.

Cited by 14 publications

References 62 publications

Study on Document Measurement and Visual Analysis of Big Data Education Evaluation

Study on Document Measurement and Visual Analysis of Big Data Education Evaluation

Envisioning Architecture of Metaverse Intensive Learning Experience (MiLEx): Career Readiness in the 21st Century and Collective Intelligence Development Scenario

Learning analytics for enhanced professional capital development: a systematic review

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