Decision Making Using Statistical Methods for Opening a Training at Continuing Education Centers Under Smart Campus Applications

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dc.authorscopusid58654445900
dc.authorscopusid57202924825
dc.authorscopusid58654280800
dc.contributor.authorAkca B.
dc.contributor.authorDogan O.
dc.contributor.authorHiziroglu K.
dc.date.accessioned2024-03-09T19:39:55Z
dc.date.available2024-03-09T19:39:55Z
dc.date.issued2023
dc.departmentİzmir Bakırçay Üniversitesien_US
dc.description2nd International Conference on Electronic Governance with Emerging Technologies, EGETC 2023 -- 11 September 2023 through 12 September 2023 -- -- 302019en_US
dc.description.abstractContinuing education centers play a crucial role in meeting the ever-growing demand for lifelong learning, providing opportunities for individuals to expand their knowledge and skills throughout their lives. Moreover, for educators, these centers offer a platform to share their expertise and contribute to the personal and professional development of learners. By teaching in continuing education programs, educators can not only make a positive impact on others but also generate additional income and broaden their career prospects. This study encompasses statistical analyses highlighting the importance of data-driven decision making in continuous education centers. Correlation and regression analyses were conducted on the data to examine the relationships between the number of participants in training programs and variables such as duration, fee, and popularity. According to the correlation analysis, weak positive relationships were observed between the number of participants and duration, fee, and popularity. The regression analysis aimed to determine how the number of participants is associated with factors: duration, fee, and popularity. The obtained coefficients represent the impact of these factors on the number of participants. Using the prediction formula derived from the regression analysis, the number of participants can be predicted based on specific values of duration, fee, and popularity. During the decision-making process, the expected income from the predicted number of participants is calculated to make a decision on whether to offer the training program. If the predicted income meets the instructor’s expectations, the program can be opened. However, if the income expectation is not met, it may be more suitable not to proceed with offering the training. By making data-driven decisions, continuous education centers can effectively plan and guide their activities. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.identifier.doi10.1007/978-3-031-43940-7_4
dc.identifier.endpage46en_US
dc.identifier.isbn9783031439391
dc.identifier.issn1865-0929
dc.identifier.scopus2-s2.0-85174445900en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage38en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-031-43940-7_4
dc.identifier.urihttps://hdl.handle.net/20.500.14034/1550
dc.identifier.volume1888 CCISen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofCommunications in Computer and Information Scienceen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectContinuous education; Smart campus applications; Statistical decision-makingen_US
dc.subjectDecision making; Continuing education; Continuous educations; Data driven decision; Decisions makings; Growing demand; Life long learning; Personal development; Smart campus application; Statistical decision-making; Training program; Regression analysisen_US
dc.titleDecision Making Using Statistical Methods for Opening a Training at Continuing Education Centers Under Smart Campus Applicationsen_US
dc.typeConference Objecten_US

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