Published: Dec 22, 2023
Converted to Gold OA:
DOI: 10.4018/IJAET.335085
Volume 15
Catherine A. Cherrstrom
This study examined the motives and drives of adult students during online degree completion, including the commonalities among and differences between Hispanic and non-Hispanic adult students....
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This study examined the motives and drives of adult students during online degree completion, including the commonalities among and differences between Hispanic and non-Hispanic adult students. Participants included 364 adult students at a Hispanic-serving institution (HSI) who completed a series of reflective writing assignments, generating over 15,000 pages of data. After linguistic and statistical data analysis, results identified the drives and motives common to all adult students and significant differences between Hispanic and non-Hispanic adult students. Some merit little or no action, while three offer opportunities—achievement and affiliation as drives, the states of acquire and lack, and allure as motive. These results offer implications for Hispanic and non-Hispanic adult students and those in higher education who serve or teach such students—academic advisors, instructional designers, instructors, and program administrators—and inform how technology can support such efforts.
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Add to Your Personal Library: Article Published: Feb 14, 2024
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DOI: 10.4018/IJAET.338219
Volume 15
Andrew Schenck
Power distance (PD), a cultural value denoting acceptance of asymmetrical power relationships, influences the force of rhetoric used by a writer to address their reader. However, AI technologies...
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Power distance (PD), a cultural value denoting acceptance of asymmetrical power relationships, influences the force of rhetoric used by a writer to address their reader. However, AI technologies such as ChatGPT lack an explicit awareness of PD, which could affect the quality of AI-generated persuasive texts used for language learning. To investigate this issue, 200 persuasive essays written by ChatGPT were compared to 200 essays written by L1-English university learners. Three elements of formulaic language related to PD were examined: stances, modals, and pronoun deixis. Differences in stances (z = -3.411; p = .001) and modals (z = -2.100; p = .036) were both significant according to the Wilcoxon signed ranks formula, whereas differences in pronoun deixis were nearly significant (z = -1.917; p = .055). Overall, language of ChatGPT appears generic and incomplete, suggesting that consistent and uniform expressions are being borrowed from an LLM training corpus to mimic aspects of PD. Limitations of AI highlight a need for pedagogical emphasis of culturally imbued discourse.
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MLA
Schenck, Andrew. "ChatGPT Is Powerful, but Does It Have Power Distance?: A Study of Culturally Imbued Discourse in AI-Generated Essays." IJAET vol.15, no.1 2024: pp.1-17. http://doi.org/10.4018/IJAET.338219
APA
Schenck, A. (2024). ChatGPT Is Powerful, but Does It Have Power Distance?: A Study of Culturally Imbued Discourse in AI-Generated Essays. International Journal of Adult Education and Technology (IJAET), 15(1), 1-17. http://doi.org/10.4018/IJAET.338219
Chicago
Schenck, Andrew. "ChatGPT Is Powerful, but Does It Have Power Distance?: A Study of Culturally Imbued Discourse in AI-Generated Essays," International Journal of Adult Education and Technology (IJAET) 15, no.1: 1-17. http://doi.org/10.4018/IJAET.338219
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Published: Feb 14, 2024
Converted to Gold OA:
DOI: 10.4018/IJAET.338318
Volume 15
Wonkyung Choi, Jun Jo, Geraldine Torrisi-Steele
Despite best efforts, the student experience remains poorly understood. One under-explored approach to understanding the student experience is the use of big data analytics. The reported study is a...
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Despite best efforts, the student experience remains poorly understood. One under-explored approach to understanding the student experience is the use of big data analytics. The reported study is a work in progress aimed at exploring the value of big data methods for understanding the student experience. A big data analysis of an open dataset of student comments is being undertaken. The first and simplest use of big data analytics is for the identification of high frequency keyword groups, which, without big data analytics, would be extremely time consuming. However, the lack of context surrounding keyword groups severely limited the ability to draw meaningful conclusions and highlighted the need for human intervention in the analysis process. Future work includes sentiment analysis. This initial work is an impetus for further exploration of big data analytics methods in qualitative contexts, especially in dynamic contexts where rapid data analysis can form a basis for timely interventions.
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MLA
Choi, Wonkyung, et al. "Understanding the University Student Experience Through Big Data Analytics." IJAET vol.15, no.1 2024: pp.1-20. http://doi.org/10.4018/IJAET.338318
APA
Choi, W., Jo, J., & Torrisi-Steele, G. (2024). Understanding the University Student Experience Through Big Data Analytics. International Journal of Adult Education and Technology (IJAET), 15(1), 1-20. http://doi.org/10.4018/IJAET.338318
Chicago
Choi, Wonkyung, Jun Jo, and Geraldine Torrisi-Steele. "Understanding the University Student Experience Through Big Data Analytics," International Journal of Adult Education and Technology (IJAET) 15, no.1: 1-20. http://doi.org/10.4018/IJAET.338318
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Published: Mar 19, 2024
Converted to Gold OA:
DOI: 10.4018/IJAET.340933
Volume 15
Runtong He, Wei Xu, De Dong, Zhonggen Yu
With the enhancement of internet medium and development of science and technology, interactive technologies have gradually emerged in the field of language education. However, previous empirical...
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With the enhancement of internet medium and development of science and technology, interactive technologies have gradually emerged in the field of language education. However, previous empirical studies and meta-analyses have focused on the impact of specific types of technologies. Therefore, this study aims to comprehensively synthesize the effect of overall interactive technologies on language education through meta-analysis. The results revealed that interactive technologies could exert a substantial influence on learning attitudes, self-efficacy, listening, reading, writing skills, vocabulary and grammar level, and overall learning outcomes. Additionally, numerous types of interactive technologies have demonstrated potential in language education, such as mobile apps, multimedia, technology-assisted tools, etc. In future research, educators should judiciously apply interactive technologies to different language educational processes according to their unique features, and researchers should expand their survey scope to ensure data validity.
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MLA
He, Runtong, et al. "A Meta-Analysis of the Effect of Interactive Technologies on Language Education." IJAET vol.15, no.1 2024: pp.1-28. http://doi.org/10.4018/IJAET.340933
APA
He, R., Xu, W., Dong, D., & Yu, Z. (2024). A Meta-Analysis of the Effect of Interactive Technologies on Language Education. International Journal of Adult Education and Technology (IJAET), 15(1), 1-28. http://doi.org/10.4018/IJAET.340933
Chicago
He, Runtong, et al. "A Meta-Analysis of the Effect of Interactive Technologies on Language Education," International Journal of Adult Education and Technology (IJAET) 15, no.1: 1-28. http://doi.org/10.4018/IJAET.340933
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Published: May 7, 2024
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DOI: 10.4018/IJAET.343313
Volume 15
Jamal Eddine Barhone, Mohamed Erradi, Mohamed Khaldi
This paper reports on the experimentation of a collaborative learning approach in university training for a Master's degree in “Pedagogical and Multimedia Engineering”. It is carried out with...
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This paper reports on the experimentation of a collaborative learning approach in university training for a Master's degree in “Pedagogical and Multimedia Engineering”. It is carried out with learner-teachers training in Instructional Design. The approach adopted focuses on the complexity of the collaborative situation, and is based on three andragogical models: shared cognition, self-directed learning and transformative learning. The aim of this study is twofold. On the one hand, to assess the relevance of a totally collaborative approach that fully integrates students into their learning. On the other hand, to design a collaborative model in the form of a grid that can be used to assess the collaborative potential of a learning situation. The indicators evoked by the collaborative groups in relation to each component of the model they developed reflect their level of appropriation of the model, and the impact of the approach on the development of skills for analyzing and designing learning situations.
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Barhone, Jamal Eddine, et al. "An Innovative Collaborative Approach to University Training for Learner-Teachers." IJAET vol.15, no.1 2024: pp.1-14. http://doi.org/10.4018/IJAET.343313
APA
Barhone, J. E., Erradi, M., & Khaldi, M. (2024). An Innovative Collaborative Approach to University Training for Learner-Teachers. International Journal of Adult Education and Technology (IJAET), 15(1), 1-14. http://doi.org/10.4018/IJAET.343313
Chicago
Barhone, Jamal Eddine, Mohamed Erradi, and Mohamed Khaldi. "An Innovative Collaborative Approach to University Training for Learner-Teachers," International Journal of Adult Education and Technology (IJAET) 15, no.1: 1-14. http://doi.org/10.4018/IJAET.343313
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Published: May 7, 2024
Converted to Gold OA:
DOI: 10.4018/IJAET.345921
Volume 15
Valerie A. Storey, Amiee Wagner
This conceptual article provides a comprehensive overview of the current status of Artificial Intelligence (AI) integration and its influence on adult education. It discusses generative AI...
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This conceptual article provides a comprehensive overview of the current status of Artificial Intelligence (AI) integration and its influence on adult education. It discusses generative AI technologies and their potential applications in adult education settings, examines the opportunities and ethical challenges associated with integrating AI, and provides insights into emerging trends. The article consists of five sections. The introduction provides a rationale as to why AI should be integrated into adult education. Second, it describes evolving AI technologies such as Large Language Models (LLM) for personalized learning, Machine Learning Algorithms for adaptive learning systems, Virtual Reality (VR) and Augmented Reality (AR) for immersive learning experiences, Chatbots and virtual assistants for learner support and guidance, and Data Learning Analytics (DLA) for tracking learner progress and performance into adult education. Section three explores the ethical implications of AI in adult education, including academic honesty and integrity, data privacy, and algorithmic bias. In section four, emerging trends and future directions are discussed. The final section considers policy implications and makes recommendations for adult educators working to develop AI-enriched adult education.
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MLA
Storey, Valerie A., and Amiee Wagner. "Integrating Artificial Intelligence (AI) Into Adult Education: Opportunities, Challenges, and Future Directions." IJAET vol.15, no.1 2024: pp.1-15. http://doi.org/10.4018/IJAET.345921
APA
Storey, V. A. & Wagner, A. (2024). Integrating Artificial Intelligence (AI) Into Adult Education: Opportunities, Challenges, and Future Directions. International Journal of Adult Education and Technology (IJAET), 15(1), 1-15. http://doi.org/10.4018/IJAET.345921
Chicago
Storey, Valerie A., and Amiee Wagner. "Integrating Artificial Intelligence (AI) Into Adult Education: Opportunities, Challenges, and Future Directions," International Journal of Adult Education and Technology (IJAET) 15, no.1: 1-15. http://doi.org/10.4018/IJAET.345921
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