Integrated learning pathways in higher education: A framework enhanced with machine learning and semantics

Iatrellis, Omiros; Savvas, Ilias Κ.; Kameas, Achilles; Fitsilis, Panos

doi:10.1007/s10639-020-10105-7

Cited by 26 publications

(17 citation statements)

References 14 publications

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“…Also, the authors in [24] evaluated ML methods to detect and distinguish diverse self-injurious behavior types. Iatrellis et al [25] provided a complete tool for the optimization and calculation of the offered services by the Higher Educational Institutions in combination with the minimization of respective costs that are enhanced with Machine Learning and semantics. Finally, an improved hybrid ontology-based approach for online learning resource recommendations [1] combining collaborative filtering algorithm and sequential pattern mining techniques was proposed by Shang et al [26].…”

Section: Related Literaturementioning

confidence: 99%

[Retracted] Classification and Analysis of College Students’ Skills Using Hybrid AI Models

Tang¹,

Wei²

2022

Journal of Mathematics

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Each individual diversifies in the population with certain characteristics. Thus, diversity is a scientifically proven and widely accepted phenomenon when the human being is a concern. One of the areas where the diversity of human beings is mostly paid attention to is called the learning process, since different forms of responses could be observed. For example, each student perceives, assimilates, and uniquely processes the information when being transmitted to him, which confirms the inherited diversity. In this regard, educational systems are required to deal effectively with students and to apply the principles of personalized learning, which is pertinent to learning processes that meet the individual needs and interests of learners. By doing so, taking into account their unique characteristics, talents, skills, inclinations, and desires are satisfied. This manuscript presents an innovative model to classify college students’ skills. A hybrid artificial intelligence (AI) system that fully automates the process of personalized training is proposed based on individual skills by taking into account the priority of personalized and fully customized learning systems. The process specifically utilizes the Rasch statistical analysis model and an innovative fuzzy Bayesian network. Higher-level reasoning is generated for the automated and personalized learning process in which college students are automatically classified into a certain category based on their skills.

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Section: Related Literaturementioning

confidence: 99%

[Retracted] Classification and Analysis of College Students’ Skills Using Hybrid AI Models

Tang¹,

Wei²

2022

Journal of Mathematics

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“…After identifying the many growth requirements of college instructors' teaching power, we set out to discover an efficient strategy for enhancing college teachers' teaching power in light of the era's evolving educational landscape, and we produced a study report as a result. This paper's goal is to use the study's findings to help colleges and universities better adapt to changing times, bravely improve teachers' teaching power, and dare to promote education and teaching quality improvement [ 5 ]. Through the further excavation of the theory of counselor professionalization construction under the background of big data, new ideas and methods of using big data technology to strengthen the scientific, intelligent, and data-based construction of the counselor team are proposed, which will help enrich the theoretical content of the professionalization construction of college counselors in the new era.…”

Section: Introductionmentioning

confidence: 99%

A Coordinated and Optimized Mechanism of Artificial Intelligence for Student Management by College Counselors Based on Big Data

Yang

Talha

2021

Computational and Mathematical Methods in Medicine

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The purpose of this article is to perform in-depth research and analysis on the artificial intelligence coordination and optimization mechanism of college counseling student management using big data technology. This study places the collaborative ideological and political work of colleges and universities in the context of big data, and by analyzing its basic connotation and changes in the real situation, it explores the development progression of colleges and universities making full use of big data resources to cultivate a collaborative education model, which is conducive to promoting colleges and universities to cultivate a whole staff, whole process, and all-round accurate ideological education and value-led services and to shape excellent young college students with comprehensive growth. The first is to scientifically build a multilevel linked big data management platform for counselor professionalization construction, plan the technical architecture of the organizational platform, build a cloud database of counselor career files, and extract valuable information and data from the organizational activities at the macrolevel and personal activities at the microlevel with counselor professionalization construction activities; the second is to realize the integrated application of information resources for counselor team construction. The second is to realize the integrated application of counselor team construction information resources, visualise and accurately analyze and evaluate the counselor group’s focus on career development and individual counselors’ feedback on career capacity construction, and improve the overall construction, personalized education management level, and self-improvement development ability. Fourth, in the professionalization of counselors, attention should be paid to the scientific selection and prevention of risks of big data application, ensuring the authenticity and reliability of data and leakage prevention and control, etc.

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“…It is important to review the indicators of efficient information behaviour and make efforts to support students' self-management, initiative in learning, and personal productivity. Personalisation affects the quality and cost of education (Iatrellis et al, 2020). Personalisation of information behaviour in the digital learning environment is one of the problems of education (Han and Ellis, 2020).…”

Section: Discussionmentioning

confidence: 99%

A Study of Students’ Preferences in The Information Resources of The Digital Learning Environment

2021

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The digital learning environment comprises various resources - didactically transformed and untransformed information, and mediated communication. Students’ information behaviour combines both actions characteristic of the traditional educational process and specific for the digital environment, based on digital tools and user interactions. Students’ information behaviour in the digital environment is considered as an indicator of their engagement in various educational activities that contribute to the personalisation of learning. The results of a survey on students’ preferences of information resources in the digital environment show that learners use a variety of information sources, but they mainly apply the methods of work in the “traditional” learning paradigm. They insufficiently use the digital environment potential of collaboration, knowledge exchange, and knowledge extraction from authentic sources. Obtained data indicates problems in students’ information culture and shortcomings in the methodological support of students’ autonomous work. Based on the results, recommendations on creating conditions for developing students’ prospective strategies of interaction with digital resources are proposed. These recommendations include a gradual increase of the authentic digital learning resources, an account of students’ information preferences, and a particular attention to the management issues in the digital learning environment.

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Integrated learning pathways in higher education: A framework enhanced with machine learning and semantics

Cited by 26 publications

References 14 publications

[Retracted] Classification and Analysis of College Students’ Skills Using Hybrid AI Models

[Retracted] Classification and Analysis of College Students’ Skills Using Hybrid AI Models

A Coordinated and Optimized Mechanism of Artificial Intelligence for Student Management by College Counselors Based on Big Data

A Study of Students’ Preferences in The Information Resources of The Digital Learning Environment

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