The rapid growth of online education, especially since the pandemic, is presenting educators with numerous challenges. Chief among these is concern about academic dishonesty, especially on unproctored online exams. Students cheating on exams is not a new phenomenon. The topic has been discussed and debated within institutions of higher learning, and significant levels of cheating have been reported in the academic literature for over sixty years. Much of this literature, however, has focused on student behavior in a classroom utilizing proctored, in-class exams. Grades on exams usually determine most of a student’s final grade in a course, and GPAs are used by employers and graduate schools to indicate a student’s subject matter mastery. As more conventional colleges and universities expand their online course offerings it is natural to wonder if academic dishonesty is more prevalent in online classes than in face-to-face classes. In particular, are students more likely to cheat when no one is watching (i.e., on unproctored assessment assignments) than they do when someone is watching (i.e., on proctored assessment assignments)? The purpose of this study is to investigate whether students cheat more on unproctored online exams than they do on proctored in-classroom exams, and if so, is there any pattern to their cheating behavior. Our findings are derived from careful empirical analysis of 741 undergraduate students who completed three unproctored online exams, several collaboration-encouraged assignments, and a proctored in-class comprehensive final exam in the same course with the same instructor. Additionally, we collected demographic and human capital data for every student. Using bivariate and regression analysis, we find significant evidence of more cheating on unproctored online exams than on proctored in-class exams even though students were given stern honor code violation warnings. Moreover, we discover that student cheating increased with each unproctored online exam, implying that students learn how to cheat as they become more familiar with taking online assessment assignments. Finally, we find that students with certain demographic and human capital characteristics tend to cheat more than others. This research strongly supports the use of proctoring for all evaluation assignments in online classes to ensure that grades in these classes properly reflect student aptitude as opposed to merely reflecting their ability to cheat.
{"title":"A Robust Examination of Cheating on Unproctored Online Exams","authors":"Richard Fendler, David Beard, Jonathan M. Godbey","doi":"10.34190/ejel.22.5.3173","DOIUrl":"https://doi.org/10.34190/ejel.22.5.3173","url":null,"abstract":"The rapid growth of online education, especially since the pandemic, is presenting educators with numerous challenges. Chief among these is concern about academic dishonesty, especially on unproctored online exams. Students cheating on exams is not a new phenomenon. The topic has been discussed and debated within institutions of higher learning, and significant levels of cheating have been reported in the academic literature for over sixty years. Much of this literature, however, has focused on student behavior in a classroom utilizing proctored, in-class exams. Grades on exams usually determine most of a student’s final grade in a course, and GPAs are used by employers and graduate schools to indicate a student’s subject matter mastery. As more conventional colleges and universities expand their online course offerings it is natural to wonder if academic dishonesty is more prevalent in online classes than in face-to-face classes. In particular, are students more likely to cheat when no one is watching (i.e., on unproctored assessment assignments) than they do when someone is watching (i.e., on proctored assessment assignments)? The purpose of this study is to investigate whether students cheat more on unproctored online exams than they do on proctored in-classroom exams, and if so, is there any pattern to their cheating behavior. Our findings are derived from careful empirical analysis of 741 undergraduate students who completed three unproctored online exams, several collaboration-encouraged assignments, and a proctored in-class comprehensive final exam in the same course with the same instructor. Additionally, we collected demographic and human capital data for every student. Using bivariate and regression analysis, we find significant evidence of more cheating on unproctored online exams than on proctored in-class exams even though students were given stern honor code violation warnings. Moreover, we discover that student cheating increased with each unproctored online exam, implying that students learn how to cheat as they become more familiar with taking online assessment assignments. Finally, we find that students with certain demographic and human capital characteristics tend to cheat more than others. This research strongly supports the use of proctoring for all evaluation assignments in online classes to ensure that grades in these classes properly reflect student aptitude as opposed to merely reflecting their ability to cheat.","PeriodicalId":46105,"journal":{"name":"Electronic Journal of e-Learning","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141001684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tracey A. Anderson, L. Baker-Eveleth, Robert W. Stone
A technological trend influencing society is the provision and adoption of digital books. Digital books are used in education in the form of electronic textbooks (e-textbooks). The research question examined in this manuscript is which students’ characteristics and attitudes influence their adoption or non-adoption of e-textbooks? The study explores these characteristics and attitudes of students who have made the decision to become either an e-textbook user or nonuser. The empirical analysis is conducted using 1191 student responses to a questionnaire distributed in a mid-sized university in the western United States. Among these 1191 responses, 530 of the students had used an e-textbook and 661 had not used an e-textbook. The e-textbook user and nonuser groups are studied in three different ways. The first is by examining the counts and percentages for five respondent characteristics. The second way is through statistical tests (i.e., t-tests and multiple analysis of variance) on these characteristics across the groups. The results from these analyses did not identify any meaningful differences in characteristics across the user and nonuser groups. The third way was a content analysis performed on an open-ended question (i.e., What factors influenced you on whether to use an e-textbook?) on the questionnaire. The student e-textbook attitudes discovered from the content analysis showed that for e-textbook users, the cost or price of an e-textbook had a significant influence on e-textbook adoption. Two other attitudes influencing e-textbook users’ adoption were usability, both positive and negative. The key attitude of nonusers regarding e-textbook adoption is negative e-textbook usability.
影响社会的一种技术趋势是提供和采用数字图书。数字图书以电子教科书（e-textbooks）的形式应用于教育领域。本手稿探讨的研究问题是：学生的哪些特点和态度会影响他们采用或不采用电子教科书？本研究探讨了决定使用或不使用电子教科书的学生的这些特征和态度。实证分析是通过美国西部一所中等规模大学的 1191 份学生问卷进行的。在这 1191 份答卷中，530 名学生使用过电子教科书，661 名学生未使用过电子教科书。我们通过三种不同的方式对电子教科书用户和非用户群体进行了研究。第一种方法是检查五个受访者特征的计数和百分比。第二种方法是对各组的这些特征进行统计检验（即 t 检验和多重方差分析）。这些分析结果并未发现用户组和非用户组在特征方面存在任何有意义的差异。第三种方法是对问卷中的一个开放式问题（即影响您是否使用电子教科书的因素有哪些？从内容分析中发现的学生对电子教科书的态度表明，对于电子教科书用户来说，电子教科书的成本或价格对电子教科书的采用有重要影响。影响电子教科书用户采用电子教科书的另外两种态度是可用性，包括积极和消极两种态度。非用户采用电子教科书的主要态度是消极的电子教科书可用性。
{"title":"Exploring the Characteristics and Attitudes of Electronic Textbook Users and Nonusers","authors":"Tracey A. Anderson, L. Baker-Eveleth, Robert W. Stone","doi":"10.34190/ejel.22.5.3203","DOIUrl":"https://doi.org/10.34190/ejel.22.5.3203","url":null,"abstract":"A technological trend influencing society is the provision and adoption of digital books. Digital books are used in education in the form of electronic textbooks (e-textbooks). The research question examined in this manuscript is which students’ characteristics and attitudes influence their adoption or non-adoption of e-textbooks? The study explores these characteristics and attitudes of students who have made the decision to become either an e-textbook user or nonuser. The empirical analysis is conducted using 1191 student responses to a questionnaire distributed in a mid-sized university in the western United States. Among these 1191 responses, 530 of the students had used an e-textbook and 661 had not used an e-textbook. The e-textbook user and nonuser groups are studied in three different ways. The first is by examining the counts and percentages for five respondent characteristics. The second way is through statistical tests (i.e., t-tests and multiple analysis of variance) on these characteristics across the groups. The results from these analyses did not identify any meaningful differences in characteristics across the user and nonuser groups. The third way was a content analysis performed on an open-ended question (i.e., What factors influenced you on whether to use an e-textbook?) on the questionnaire. The student e-textbook attitudes discovered from the content analysis showed that for e-textbook users, the cost or price of an e-textbook had a significant influence on e-textbook adoption. Two other attitudes influencing e-textbook users’ adoption were usability, both positive and negative. The key attitude of nonusers regarding e-textbook adoption is negative e-textbook usability.","PeriodicalId":46105,"journal":{"name":"Electronic Journal of e-Learning","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141043026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gina Paola Barrera Castro, Andrés Chiappe, Diego Fernando Becerra Rodríguez, Felipe Gonzalo Sepulveda
Personalized learning, a pedagogical approach tailored to individual needs and capacities, has garnered considerable attention in the era of artificial intelligence (AI) and the fourth industrial revolution. This systematic literature review aims to identify key drivers of personalized learning and critically assess the role of AI in reinforcing these drivers. Following PRISMA guidelines, a thorough search was conducted across major peer-reviewed journal databases, resulting in the inclusion of 102 relevant studies published between 2013 and 2022. A combination of qualitative and quantitative analyses, employing categorization and frequency analysis techniques, was performed to discern patterns and insights from the literature. The findings of this review highlight several critical drivers that contribute to the effectiveness of personalized learning, both from a broad view of education and in the specific context of e-learning. Firstly, recognizing and accounting for individual student characteristics is foundational to tailoring educational experiences. Secondly, personalizing content delivery and instructional methods ensures that learning materials resonate with learners' preferences and aptitudes. Thirdly, customizing assessment and feedback mechanisms enables educators to provide timely and relevant guidance to learners. Additionally, tailoring user interfaces and learning environments fosters engagement and accessibility, catering to diverse learning styles and needs. Moreover, the integration of AI presents significant opportunities to enhance personalized learning. AI-driven solutions offer capabilities such as automated learner profiling, adaptive content recommendation, real-time assessment, and the development of intelligent user interfaces, thereby augmenting the personalization of learning experiences. However, the successful adoption of AI in personalized learning requires addressing various challenges, including the need to develop educators' competencies, refine theoretical frameworks, and navigate ethical considerations surrounding data privacy and bias. By providing a comprehensive understanding of the drivers and implications of AI-driven personalized learning, this review offers valuable insights for educators, researchers, and policymakers in the Education 4.0 era. Leveraging the transformative potential of AI while upholding robust pedagogical principles, personalized learning holds the promise of unlocking tailored educational experiences that maximize individual potential and relevance in the digital economy.
{"title":"Harnessing AI for Education 4.0: Drivers of Personalized Learning","authors":"Gina Paola Barrera Castro, Andrés Chiappe, Diego Fernando Becerra Rodríguez, Felipe Gonzalo Sepulveda","doi":"10.34190/ejel.22.5.3467","DOIUrl":"https://doi.org/10.34190/ejel.22.5.3467","url":null,"abstract":"Personalized learning, a pedagogical approach tailored to individual needs and capacities, has garnered considerable attention in the era of artificial intelligence (AI) and the fourth industrial revolution. This systematic literature review aims to identify key drivers of personalized learning and critically assess the role of AI in reinforcing these drivers. Following PRISMA guidelines, a thorough search was conducted across major peer-reviewed journal databases, resulting in the inclusion of 102 relevant studies published between 2013 and 2022. A combination of qualitative and quantitative analyses, employing categorization and frequency analysis techniques, was performed to discern patterns and insights from the literature. The findings of this review highlight several critical drivers that contribute to the effectiveness of personalized learning, both from a broad view of education and in the specific context of e-learning. Firstly, recognizing and accounting for individual student characteristics is foundational to tailoring educational experiences. Secondly, personalizing content delivery and instructional methods ensures that learning materials resonate with learners' preferences and aptitudes. Thirdly, customizing assessment and feedback mechanisms enables educators to provide timely and relevant guidance to learners. Additionally, tailoring user interfaces and learning environments fosters engagement and accessibility, catering to diverse learning styles and needs. Moreover, the integration of AI presents significant opportunities to enhance personalized learning. AI-driven solutions offer capabilities such as automated learner profiling, adaptive content recommendation, real-time assessment, and the development of intelligent user interfaces, thereby augmenting the personalization of learning experiences. However, the successful adoption of AI in personalized learning requires addressing various challenges, including the need to develop educators' competencies, refine theoretical frameworks, and navigate ethical considerations surrounding data privacy and bias. By providing a comprehensive understanding of the drivers and implications of AI-driven personalized learning, this review offers valuable insights for educators, researchers, and policymakers in the Education 4.0 era. Leveraging the transformative potential of AI while upholding robust pedagogical principles, personalized learning holds the promise of unlocking tailored educational experiences that maximize individual potential and relevance in the digital economy.","PeriodicalId":46105,"journal":{"name":"Electronic Journal of e-Learning","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140654136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amal Alrayes, Tara Fryad Henari, Dalal Abdulkarim Ahmed
This paper provides a thorough examination of the role of Artificial Intelligence (AI), particularly ChatGPT and other AI language models, in the realm of education. Drawing insights from existing literature and a novel study on educator perspectives, the paper delves into the potential advantages, ethical dilemmas, and factors shaping educators' attitudes towards AI integration in education. AI language models have the potential to revolutionize educational content creation, personalize learning experiences, and streamline assessment and feedback processes. These capabilities hold the potential to enhance teaching and learning outcomes while catering to the diverse needs of students. However, ethical concerns loom large in the adoption of AI in education. Bias in generated content is a chief concern, as it can perpetuate societal biases and lead to unfair treatment or the dissemination of inaccurate information. The solution lies in rigorous data curation to ensure equitable educational experiences for all students. Moreover, the potential for generating inappropriate or misleading content poses a significant ethical challenge, impacting students' well-being, civic understanding, and social interactions. Safeguards must be implemented to detect and rectify biased or inappropriate content, fostering inclusive and unbiased learning environments. Transparency emerges as a crucial ethical consideration. The opacity of AI models like ChatGPT makes it difficult to comprehend their decision-making processes. Enhancing model interpretability and explainability is vital for accountability and addressing embedded ethical issues. Privacy concerns related to data collection and usage are emphasized in the literature. Clear policies and guidelines must govern data collection, use, and protection, ensuring data is solely employed for educational purposes and maintaining robust data security measures. Our study expands upon these insights by exploring socio-demographic factors, motivations, and social influences affecting educators' AI adoption in higher education. These findings inform institutions on tailoring AI integration strategies, emphasizing responsible usage through training, and assessing the impact on learning outcomes. As educational institutions increasingly embrace AI, including advanced models like GPT-4, a cautious and thoughtful approach is vital. Balancing potential benefits with ethical challenges ensures that AI enhances teaching and learning while upholding fairness, equity, and accountability. In summary, this paper illuminates the potential of AI in education, accentuates ethical concerns, and highlights the significance of understanding educators' perspectives. Collaboration between educators and policymakers is essential to navigate the complexities of AI integration, ensuring that education remains a realm of equitable, efficient, and accountable learning experiences.
{"title":"ChatGPT in Education – Understanding the Bahraini Academics Perspective","authors":"Amal Alrayes, Tara Fryad Henari, Dalal Abdulkarim Ahmed","doi":"10.34190/ejel.22.2.3250","DOIUrl":"https://doi.org/10.34190/ejel.22.2.3250","url":null,"abstract":"This paper provides a thorough examination of the role of Artificial Intelligence (AI), particularly ChatGPT and other AI language models, in the realm of education. Drawing insights from existing literature and a novel study on educator perspectives, the paper delves into the potential advantages, ethical dilemmas, and factors shaping educators' attitudes towards AI integration in education. AI language models have the potential to revolutionize educational content creation, personalize learning experiences, and streamline assessment and feedback processes. These capabilities hold the potential to enhance teaching and learning outcomes while catering to the diverse needs of students. However, ethical concerns loom large in the adoption of AI in education. Bias in generated content is a chief concern, as it can perpetuate societal biases and lead to unfair treatment or the dissemination of inaccurate information. The solution lies in rigorous data curation to ensure equitable educational experiences for all students. Moreover, the potential for generating inappropriate or misleading content poses a significant ethical challenge, impacting students' well-being, civic understanding, and social interactions. Safeguards must be implemented to detect and rectify biased or inappropriate content, fostering inclusive and unbiased learning environments. Transparency emerges as a crucial ethical consideration. The opacity of AI models like ChatGPT makes it difficult to comprehend their decision-making processes. Enhancing model interpretability and explainability is vital for accountability and addressing embedded ethical issues. Privacy concerns related to data collection and usage are emphasized in the literature. Clear policies and guidelines must govern data collection, use, and protection, ensuring data is solely employed for educational purposes and maintaining robust data security measures. Our study expands upon these insights by exploring socio-demographic factors, motivations, and social influences affecting educators' AI adoption in higher education. These findings inform institutions on tailoring AI integration strategies, emphasizing responsible usage through training, and assessing the impact on learning outcomes. As educational institutions increasingly embrace AI, including advanced models like GPT-4, a cautious and thoughtful approach is vital. Balancing potential benefits with ethical challenges ensures that AI enhances teaching and learning while upholding fairness, equity, and accountability. In summary, this paper illuminates the potential of AI in education, accentuates ethical concerns, and highlights the significance of understanding educators' perspectives. Collaboration between educators and policymakers is essential to navigate the complexities of AI integration, ensuring that education remains a realm of equitable, efficient, and accountable learning experiences.","PeriodicalId":46105,"journal":{"name":"Electronic Journal of e-Learning","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140668423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study explores the feasibility of using AI technology, specifically ChatGPT-3, to provide reliable, meaningful, and personalized feedback. Specifically, the study explores the benefits and limitations of using AI-based feedback in language learning; the pedagogical frameworks that underpin the effective use of AI-based feedback; the reliability of ChatGPT-3’s feedback; and the potential implications of AI integration in language instruction. A review of existing literature identifies key themes and findings related to AI-based teaching practices. The study found that social cognitive theory (SCT) supports the potential use of AI chatbots in the learning process as AI can provide students with instant guidance and support that fosters personalized, independent learning experiences. Similarly, Krashen’s second language acquisition theory (SLA) was found to support the hypothesis that AI use can enhance student learning by creating meaningful interaction in the target language wherein learners engage in genuine communication rather than focusing solely on linguistic form. To determine the reliability of AI-generated feedback, an analysis was performed on student writing. First, two rubrics were created by ChatGPT-3; AI then graded the papers, and the results were compared with human graded results using the same rubrics. The study concludes that e-Learning arning certainly has great potential; besides providing timely, personalized learning support, AI feedback can increase student motivation and foster learning independence. Not surprisingly, though, several caveats exist. It was found that ChatGPT-3 is prone to error and hallucination in providing student feedback, especially when presented with longer texts. To avoid this, rubrics must be carefully constructed, and teacher oversight is still very much required. This study will help educators transition to the new era of AI-assisted e-Learning by helping them make informed decisions about how to provide useful AI feedback that is underpinned by sound pedagogical principles.
{"title":"Exploring the Feasibility and Efficacy of ChatGPT3 for Personalized Feedback in Teaching","authors":"Irum Naz, Rodney Robertson","doi":"10.34190/ejel.22.2.3345","DOIUrl":"https://doi.org/10.34190/ejel.22.2.3345","url":null,"abstract":"This study explores the feasibility of using AI technology, specifically ChatGPT-3, to provide reliable, meaningful, and personalized feedback. Specifically, the study explores the benefits and limitations of using AI-based feedback in language learning; the pedagogical frameworks that underpin the effective use of AI-based feedback; the reliability of ChatGPT-3’s feedback; and the potential implications of AI integration in language instruction. A review of existing literature identifies key themes and findings related to AI-based teaching practices. The study found that social cognitive theory (SCT) supports the potential use of AI chatbots in the learning process as AI can provide students with instant guidance and support that fosters personalized, independent learning experiences. Similarly, Krashen’s second language acquisition theory (SLA) was found to support the hypothesis that AI use can enhance student learning by creating meaningful interaction in the target language wherein learners engage in genuine communication rather than focusing solely on linguistic form. To determine the reliability of AI-generated feedback, an analysis was performed on student writing. First, two rubrics were created by ChatGPT-3; AI then graded the papers, and the results were compared with human graded results using the same rubrics. The study concludes that e-Learning arning certainly has great potential; besides providing timely, personalized learning support, AI feedback can increase student motivation and foster learning independence. Not surprisingly, though, several caveats exist. It was found that ChatGPT-3 is prone to error and hallucination in providing student feedback, especially when presented with longer texts. To avoid this, rubrics must be carefully constructed, and teacher oversight is still very much required. This study will help educators transition to the new era of AI-assisted e-Learning by helping them make informed decisions about how to provide useful AI feedback that is underpinned by sound pedagogical principles.","PeriodicalId":46105,"journal":{"name":"Electronic Journal of e-Learning","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140672581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research explores the innovative integration of OpenAI’s GPT-3.5 within a university-level English as a Foreign Language (EFL) writing course, illustrating a novel approach to academic instruction. The course follows the ADDIE instructional design model, encompassing five systematic stages: analysis, design, development, implementation, and evaluation. This model serves as the backbone of the course structure, ensuring a comprehensive educational experience. The incorporation of the Technological Pedagogical Content Knowledge (TPACK) framework in this course facilitates the effective integration of GPT-3.5 by enabling instructors to align advanced AI capabilities with appropriate pedagogical strategies, thereby enhancing the learning experience. TPACK guides educators in applying GPT-3.5’s features in a manner that is contextually relevant and pedagogically sound, ensuring the technology’s use complements the course content. The findings from this research are significant. They reveal that GPT-3.5 addresses three fundamental challenges often encountered in academic writing courses. Firstly, it enhances efficiency by providing immediate feedback and generating content ideas, accelerating the writing process. Secondly, GPT-3.5 ensures cohesive organization within students’ writing, guiding them to structure their thoughts more logically. Lastly, it serves as a reliable substitute for traditional peer reviewers, offering critical and objective feedback that students can use to refine their drafts. As students engage with AI, they enter a dynamic partnership. This collaboration with GPT-3.5 fosters critical thinking and empowers students to develop a distinctive writing voice. Through this interaction, students are not merely passive recipients of knowledge but active participants in a learning process that is augmented by cutting-edge technology. This study not only provides insight into the potential of AI-augmented academic writing but also highlights GPT-3.5’s role in promoting writing proficiency. It demonstrates that the application of AI in education can enhance the learning experience without compromising the individuality of student expression.
{"title":"Enhancing English as a Foreign Language (EFL) Learners’ Writing with ChatGPT: A University-Level Course Design","authors":"Yu-Ching Tseng, Yi-Hsuan Lin","doi":"10.34190/ejel.21.5.3329","DOIUrl":"https://doi.org/10.34190/ejel.21.5.3329","url":null,"abstract":"This research explores the innovative integration of OpenAI’s GPT-3.5 within a university-level English as a Foreign Language (EFL) writing course, illustrating a novel approach to academic instruction. The course follows the ADDIE instructional design model, encompassing five systematic stages: analysis, design, development, implementation, and evaluation. This model serves as the backbone of the course structure, ensuring a comprehensive educational experience. The incorporation of the Technological Pedagogical Content Knowledge (TPACK) framework in this course facilitates the effective integration of GPT-3.5 by enabling instructors to align advanced AI capabilities with appropriate pedagogical strategies, thereby enhancing the learning experience. TPACK guides educators in applying GPT-3.5’s features in a manner that is contextually relevant and pedagogically sound, ensuring the technology’s use complements the course content. The findings from this research are significant. They reveal that GPT-3.5 addresses three fundamental challenges often encountered in academic writing courses. Firstly, it enhances efficiency by providing immediate feedback and generating content ideas, accelerating the writing process. Secondly, GPT-3.5 ensures cohesive organization within students’ writing, guiding them to structure their thoughts more logically. Lastly, it serves as a reliable substitute for traditional peer reviewers, offering critical and objective feedback that students can use to refine their drafts. As students engage with AI, they enter a dynamic partnership. This collaboration with GPT-3.5 fosters critical thinking and empowers students to develop a distinctive writing voice. Through this interaction, students are not merely passive recipients of knowledge but active participants in a learning process that is augmented by cutting-edge technology. This study not only provides insight into the potential of AI-augmented academic writing but also highlights GPT-3.5’s role in promoting writing proficiency. It demonstrates that the application of AI in education can enhance the learning experience without compromising the individuality of student expression.","PeriodicalId":46105,"journal":{"name":"Electronic Journal of e-Learning","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140710829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The study on " Educators’ Academic Insights on Artificial Intelligence – Challenges and Opportunities" was conducted to gain a deeper understanding of the rapidly evolving phenomenon of AI in education. This research serves multiple objectives. Firstly, it aims to foster awareness regarding the integration of AI into teaching and learning practices by providing clear definitions of AI and explaining key AI-related terms. It also seeks to illustrate AI's diverse applications within a broader context, with a special focus on AI-supported research and learning platforms. Additionally, the study delves into the current discourse surrounding chatbots, contributing to address the central research question. Lastly, this initiative aims to provide valuable recommendations for effectively harnessing AI in education, enhancing the teaching and learning experience. The researchers conducted a review of literature concerning artificial intelligence. They adopted a qualitative method, using open-ended questions to collect feedback from educators globally, including those from the University of Technology and Applied Sciences, Al Musannah, and participants in the online discussion forum at Oxford English Learning Exchange.com. The qualitative data was analysed, leading to the identification of key themes and subthemes derived from the responses of research participants. The study's findings incorporated a wide range of concerns expressed by educators, comprising ten key subthemes. These concerns ranged from doubts about AI's ability to replace human educators and fears of its potential to hinder student development to worries about its hyped popularity and its perceived futuristic nature. Educators stressed the importance of effective AI training while emphasizing the need to prioritize human expertise over excessive reliance on AI. They were also acutely aware of both the advantages and disadvantages of AI, viewing it as both a potential boon and a looming threat. Furthermore, educators recognized the potential for enjoyable experiences with AI and acknowledged the pivotal role of users in determining the extent of AI adoption. Content analysis revealed additional apprehensions, such as concerns about job displacement, AI's impact on critical thinking, teacher frustration in assessing AI-assisted student writing, the use of AI-generated content for assessments, potential erosion of human services, stifling of user and learner creativity by AI, the risk of errors in AI-generated information, opportunities for cheating in exams, and concerns about the overreliance on and overrating of AI platforms. Positively, the findings included an array of opportunities that AI platforms offer. Study participants highlighted various aspects of these opportunities that surpassed their concerns and associated risks. The opportunities are categorized into twenty subthemes: enhancing learner motivation, facilitating template creation, utilizing AI as an educational aid, promoti
{"title":"Educators’ Academic Insights on Artificial Intelligence: Challenges and Opportunities","authors":"Jayaron Jose, Blessy Jayaron Jose","doi":"10.34190/ejel.21.5.3272","DOIUrl":"https://doi.org/10.34190/ejel.21.5.3272","url":null,"abstract":"The study on \" Educators’ Academic Insights on Artificial Intelligence – Challenges and Opportunities\" was conducted to gain a deeper understanding of the rapidly evolving phenomenon of AI in education. This research serves multiple objectives. Firstly, it aims to foster awareness regarding the integration of AI into teaching and learning practices by providing clear definitions of AI and explaining key AI-related terms. It also seeks to illustrate AI's diverse applications within a broader context, with a special focus on AI-supported research and learning platforms. Additionally, the study delves into the current discourse surrounding chatbots, contributing to address the central research question. Lastly, this initiative aims to provide valuable recommendations for effectively harnessing AI in education, enhancing the teaching and learning experience. The researchers conducted a review of literature concerning artificial intelligence. They adopted a qualitative method, using open-ended questions to collect feedback from educators globally, including those from the University of Technology and Applied Sciences, Al Musannah, and participants in the online discussion forum at Oxford English Learning Exchange.com. The qualitative data was analysed, leading to the identification of key themes and subthemes derived from the responses of research participants. The study's findings incorporated a wide range of concerns expressed by educators, comprising ten key subthemes. These concerns ranged from doubts about AI's ability to replace human educators and fears of its potential to hinder student development to worries about its hyped popularity and its perceived futuristic nature. Educators stressed the importance of effective AI training while emphasizing the need to prioritize human expertise over excessive reliance on AI. They were also acutely aware of both the advantages and disadvantages of AI, viewing it as both a potential boon and a looming threat. Furthermore, educators recognized the potential for enjoyable experiences with AI and acknowledged the pivotal role of users in determining the extent of AI adoption. Content analysis revealed additional apprehensions, such as concerns about job displacement, AI's impact on critical thinking, teacher frustration in assessing AI-assisted student writing, the use of AI-generated content for assessments, potential erosion of human services, stifling of user and learner creativity by AI, the risk of errors in AI-generated information, opportunities for cheating in exams, and concerns about the overreliance on and overrating of AI platforms. Positively, the findings included an array of opportunities that AI platforms offer. Study participants highlighted various aspects of these opportunities that surpassed their concerns and associated risks. The opportunities are categorized into twenty subthemes: enhancing learner motivation, facilitating template creation, utilizing AI as an educational aid, promoti","PeriodicalId":46105,"journal":{"name":"Electronic Journal of e-Learning","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140725050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Artificial intelligence (AI) in higher education is a complex issue that can be discussed from many different perspectives. There is currently a great need for ethical discussions about the use of AI in universities. For example, educational researchers and teachers are already talking a lot about fairness, accountability, transparency, bias, autonomy, agency and inclusion of AI applications, and discussing these in terms of concrete teaching-learning settings. However, less explored are the implications of AI-enhanced teaching and learning in relation to fundamental educational ideals and goals. The article takes this research desideratum as a starting point by relating the use of AI in universities to Kant's reflections on enlightenment. The aim of this article is to theoretically analyse the compatibility of various AI tools with the ideal of maturity on an educational philosophical level and to formulate recommendations for action based on the results. Through a comprehensive literature review, the article analyses the impact of intelligent tutoring systems, ChatGPT and AI-supported research tools on students’ maturity and discusses both opportunities and challenges for higher education. The theoretical analysis shows that intelligent tutoring systems and ChatGPT threaten student maturity, while AI-supported research tools can have a positive effect. In addition, the analysis provides several recommendations that can help to minimise the risks of AI applications in terms of student maturity. The educational principle of research-based learning is of particular importance in this context, illustrating how students can learn to use AI tools responsibly and maturely. In this sense, the paper presents a theoretical study that fundamentally reflects on the maturity of students in the age of AI and thus both encourages teachers in the field of e-teaching to critically reflect on AI-based tools and provides a basis for further empirical research.
{"title":"Have Courage to Use your Own Mind, with or without AI: The Relevance of Kant's Enlightenment to Higher Education in the Age of Artificial Intelligence","authors":"Alice Watanabe","doi":"10.34190/ejel.21.5.3229","DOIUrl":"https://doi.org/10.34190/ejel.21.5.3229","url":null,"abstract":"Artificial intelligence (AI) in higher education is a complex issue that can be discussed from many different perspectives. There is currently a great need for ethical discussions about the use of AI in universities. For example, educational researchers and teachers are already talking a lot about fairness, accountability, transparency, bias, autonomy, agency and inclusion of AI applications, and discussing these in terms of concrete teaching-learning settings. However, less explored are the implications of AI-enhanced teaching and learning in relation to fundamental educational ideals and goals. The article takes this research desideratum as a starting point by relating the use of AI in universities to Kant's reflections on enlightenment. The aim of this article is to theoretically analyse the compatibility of various AI tools with the ideal of maturity on an educational philosophical level and to formulate recommendations for action based on the results. Through a comprehensive literature review, the article analyses the impact of intelligent tutoring systems, ChatGPT and AI-supported research tools on students’ maturity and discusses both opportunities and challenges for higher education. The theoretical analysis shows that intelligent tutoring systems and ChatGPT threaten student maturity, while AI-supported research tools can have a positive effect. In addition, the analysis provides several recommendations that can help to minimise the risks of AI applications in terms of student maturity. The educational principle of research-based learning is of particular importance in this context, illustrating how students can learn to use AI tools responsibly and maturely. In this sense, the paper presents a theoretical study that fundamentally reflects on the maturity of students in the age of AI and thus both encourages teachers in the field of e-teaching to critically reflect on AI-based tools and provides a basis for further empirical research.","PeriodicalId":46105,"journal":{"name":"Electronic Journal of e-Learning","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140225021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the pervasiveness of laptops in the classroom setting, the effectiveness of laptop-assisted note-taking has not been comprehensively investigated. Many inconsistencies in this area still existed with intense debate towards academic performance, learning strategies, and student satisfaction. To fill this missing gap, this study probed the effect of laptop note-taking on the above constructs. The present study applied the comprehensive review by objectively selecting all relative literature from online database, with a main focus on learning areas and conducting the objective procedure. This study covered the positive, negative, as well as neutral effects of laptop note-taking on learning performance. Reasons behind the negative impact and worries were investigated in caution. Tackling the major concerns of distraction and multitasking, this study argued that these concerns might not be the main cause of low performance, individual’s characteristics and preference for the teaching styles shall be taken into consideration. Based on the above arguments, this study provided educators with multiple suggestions on alternative pedagogical approaches to improve teaching practice and student learning experience. The satisfaction of courses was probed together with the reasons for low satisfaction which promoted relative teaching instruction and teacher training. In this vein, this study contributed to the laptop note-taking areas by comprehensively analyzing the effect of laptop note-taking on learning strategies and satisfaction, which were unfortunately ignored by previous studies. Moreover, the present study enriches the e-learning knowledge and supports its practice by proving the side effects of simply banning laptops in class and suggests educators to integrate laptops into their pedagogical designs as well as learn more technology-based teaching strategies. Future research should reinvestigate the effect of laptop note-taking in class with more caution and endeavor to enhance the effectiveness of laptop note-taking in the class by capturing all possible variables of student learning, especially technology-relative variables.
{"title":"The Effect of Laptop Note-Taking on Students’ Learning Performance, Strategies, and Satisfaction","authors":"Yuxia Shi, Zhonggen Yu","doi":"10.34190/ejel.22.1.3396","DOIUrl":"https://doi.org/10.34190/ejel.22.1.3396","url":null,"abstract":"With the pervasiveness of laptops in the classroom setting, the effectiveness of laptop-assisted note-taking has not been comprehensively investigated. Many inconsistencies in this area still existed with intense debate towards academic performance, learning strategies, and student satisfaction. To fill this missing gap, this study probed the effect of laptop note-taking on the above constructs. The present study applied the comprehensive review by objectively selecting all relative literature from online database, with a main focus on learning areas and conducting the objective procedure. This study covered the positive, negative, as well as neutral effects of laptop note-taking on learning performance. Reasons behind the negative impact and worries were investigated in caution. Tackling the major concerns of distraction and multitasking, this study argued that these concerns might not be the main cause of low performance, individual’s characteristics and preference for the teaching styles shall be taken into consideration. Based on the above arguments, this study provided educators with multiple suggestions on alternative pedagogical approaches to improve teaching practice and student learning experience. The satisfaction of courses was probed together with the reasons for low satisfaction which promoted relative teaching instruction and teacher training. In this vein, this study contributed to the laptop note-taking areas by comprehensively analyzing the effect of laptop note-taking on learning strategies and satisfaction, which were unfortunately ignored by previous studies. Moreover, the present study enriches the e-learning knowledge and supports its practice by proving the side effects of simply banning laptops in class and suggests educators to integrate laptops into their pedagogical designs as well as learn more technology-based teaching strategies. Future research should reinvestigate the effect of laptop note-taking in class with more caution and endeavor to enhance the effectiveness of laptop note-taking in the class by capturing all possible variables of student learning, especially technology-relative variables.","PeriodicalId":46105,"journal":{"name":"Electronic Journal of e-Learning","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140231193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The increasingly neoliberal course of Higher Education is linked to rises in student anxiety around assessment and increased fear of the consequences of failure. Making mistakes is an inevitable part of any learning process (and of life generally) and managing failure in a productive and positive way is crucial for success and wellbeing beyond university. In this article, we argue that academia does not adequately prepare learners for managing mistake-making progressively and that escape rooms can provide a way to facilitate learning through failure. We first present an original model of failure-based learning that explores why being able to make mistakes safely is important for students and why the use of escape rooms in Higher Education presents an excellent opportunity for the application of this model. We then show the relevance of this model by using it to analyse two case studies that explore different ways in which educational escape rooms can be used in Higher Education: either designed to facilitate learning by playing a game; or supporting learning through designing a game. Our model of failure-based learning has three stages, emphasising the importance of preparation, an iterative play cycle of testing, failing, reflecting, and revising, and finishing with a presentation phase. The article concludes by considering the limitations of educational escape rooms in this context and highlighting some practical considerations for the use of these approaches.
{"title":"Escape Rooms as Tools for Learning Through Failure","authors":"Rachelle Emily Rawlinson, Nicola Whitton","doi":"10.34190/ejel.21.7.3182","DOIUrl":"https://doi.org/10.34190/ejel.21.7.3182","url":null,"abstract":"The increasingly neoliberal course of Higher Education is linked to rises in student anxiety around assessment and increased fear of the consequences of failure. Making mistakes is an inevitable part of any learning process (and of life generally) and managing failure in a productive and positive way is crucial for success and wellbeing beyond university. In this article, we argue that academia does not adequately prepare learners for managing mistake-making progressively and that escape rooms can provide a way to facilitate learning through failure. We first present an original model of failure-based learning that explores why being able to make mistakes safely is important for students and why the use of escape rooms in Higher Education presents an excellent opportunity for the application of this model. We then show the relevance of this model by using it to analyse two case studies that explore different ways in which educational escape rooms can be used in Higher Education: either designed to facilitate learning by playing a game; or supporting learning through designing a game. Our model of failure-based learning has three stages, emphasising the importance of preparation, an iterative play cycle of testing, failing, reflecting, and revising, and finishing with a presentation phase. The article concludes by considering the limitations of educational escape rooms in this context and highlighting some practical considerations for the use of these approaches.","PeriodicalId":46105,"journal":{"name":"Electronic Journal of e-Learning","volume":null,"pages":null},"PeriodicalIF":2.2,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140232770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：