This journal aims to: provide a vehicle for scholarly presentation and exchange of information between professionals, researchers and practitioners in the technology-enhanced education field; contribute to the advancement of scientific knowledge regarding the use of technology and computers in higher education; and inform readers about the latest developments in the application of information technologies (ITs) in higher education learning, training, research and management.

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The Globethics Library contains vol. 13(2016) to current.

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  • Towards teaching-sensitive technology: a hermeneutic analysis of higher education teaching

    Maria Hvid Stenalt; Helle Mathiasen (SpringerOpen, 2024-03-01)
    Abstract Integrating digital technologies to benefit teaching and learning has long been driving higher education. The uptake of technology has been supported by teacher training focused on developing teachers’ capabilities to design for learning. However, in this paper, we raise the point of moving towards teaching-sensitive technology as a clear alternative to current strategies focusing on teachers’ mental processes. To develop this point, the paper offers a qualitative study that explores teaching to identify critical features of technology supporting teachers’ work. Analysing teaching from a hermeneutic perspective, we arrive at six fundamental dynamics within which teachers operate. Based on the factors identified, we present three principles to guide future design of technologies for teaching and two approaches to designing technology sensitive to teachers’ values.
  • Students’ complex trajectories: exploring degree change and time to degree

    João Pedro Pêgo; Vera Lucia Miguéis; Alfredo Soeiro (SpringerOpen, 2024-01-01)
    Abstract The complex trajectories of higher education students are deviations from the regular path due to delays in completing a degree, dropping out, taking breaks, or changing programmes. In this study, we investigated degree changing as a cause of complex student trajectories. We characterised cohorts of students who graduated with a complex trajectory and identified the characteristics that influenced the time to graduation. To support this predictive task, we employed machine learning techniques such as neural networks, support vector machines, and random forests. In addition, we used interpretable techniques such as decision trees to derive managerial insights that could prove useful to decision-makers. We validated the proposed methodology taking the University of Porto (Portugal) as case study. The results show that the time to degree (TTD) of students with and without complex trajectories was different. Moreover, the proposed models effectively predicted TTD, outperforming two benchmark models. The random forest model proved to be the best predictor. Finally, this study shows that the factors that best predict TTD are the median TTD and the admission regime of the programme of destination of transfer students, followed by the admission average of the previous programme. By identifying students who take longer to complete their studies, targeted interventions such as counselling and tutoring can be promoted, potentially improving completion rates and educational outcomes without having to use as many resources.
  • Generative AI and re-weaving a pedagogical horizon of social possibility

    Richard Hall (SpringerOpen, 2024-02-01)
    Abstract This article situates the potential for intellectual work to be renewed through an enriched engagement with the relationship between indigenous protocols and artificial intelligence (AI). It situates this through a dialectical storytelling of the contradictions that emerge from the relationships between humans and capitalist technologies, played out within higher education. It argues that these have ramifications for our conceptions of AI, and its ways of knowing, doing and being within wider ecosystems. In thinking about how technology reinforces social production inside capitalist institutions like universities, the article seeks to refocus our storytelling around mass intellectuality and generative possibilities for transcending alienating social relations. In so doing, the focus shifts to the potential for weaving new protocols, from existing material and historical experiences of technology, which unfold structurally, culturally and practically within communities. At the heart of this lies the question, what does it mean to live? In a world described against polycrisis, is it possible to tell new social science fictions, as departures towards a new mode of higher learning and intellectual work that seeks to negate, abolish and transcend the world as-is?
  • Would ChatGPT-facilitated programming mode impact college students’ programming behaviors, performances, and perceptions? An empirical study

    Dan Sun; Azzeddine Boudouaia; Chengcong Zhu; Yan Li (SpringerOpen, 2024-02-01)
    Abstract ChatGPT, an AI-based chatbot with automatic code generation abilities, has shown its promise in improving the quality of programming education by providing learners with opportunities to better understand the principles of programming. However, limited empirical studies have explored the impact of ChatGPT on learners’ programming processes. This study employed a quasi-experimental design to explore the possible impact of ChatGPT-facilitated programming mode on college students’ programming behaviors, performances, and perceptions. 82 college students were randomly divided into two classes. One class employed ChatGPT-facilitated programming (CFP) practice and the other class utilized self-directed programming (SDP) mode. Mixed methods were utilized to collect multidimensional data. Data analysis uncovered some intriguing results. Firstly, students in the CFP mode had more frequent behaviors of debugging and receiving error messages, as well as pasting console messages on the website and reading feedback. At the same time, students in the CFP mode had more frequent behaviors of copying and pasting codes from ChatGPT and debugging, as well as pasting codes to ChatGPT and reading feedback from ChatGPT. Secondly, CFP practice would improve college students’ programming performance, while the results indicated that there was no statistically significant difference between the students in CFP mode and the SDP mode. Thirdly, student interviews revealed three highly concerned themes from students' user experience about ChatGPT: the services offered by ChatGPT, the stages of ChatGPT usage, and experience with ChatGPT. Finally, college students’ perceptions toward ChatGPT significantly changed after CFP practice, including its perceived usefulness, perceived ease of use, and intention to use. Based on these findings, the study proposes implications for future instructional design and the development of AI-powered tools like ChatGPT.
  • What rationale would work? Unfolding the role of learners’ attitudes and motivation in predicting learning engagement and perceived learning outcomes in MOOCs

    Xiaomei Wei; Nadira Saab; Wilfried Admiraal (SpringerOpen, 2024-01-01)
    Abstract The aim of this study is to gain insight into the interplay between attitudes, motivation, learning engagement, and perceived learning outcomes in massive open online courses (MOOCs). An online survey was administered to 232 MOOC learners. This study provided comprehensive explanations for individual differences in learning engagement and perceived learning outcomes in MOOCs with a modified model of the expectancy-value theory of achievement motivation. The structural equation modeling revealed that attitudes served as a precursor of participation in MOOCs that significantly influenced self-efficacy, intrinsic value, and task effort cost; self-efficacy and intrinsic value were positively associated with both learning engagement and perceived learning outcomes, while attitudes toward MOOC learning was positively related to perceived learning outcomes only. Furthermore, the mediation analyses highlighted that intrinsic value was a powerful mediator, which positively influenced the effects of attitudes and self-efficacy on learning engagement and perceived learning outcomes. The moderation analyses discovered that task effort cost moderated the effects of attitudes on learning engagement and perceived learning outcomes. Curriculum designers and instructors could benefit from this study to understand what rationales drive individuals to be engaged in MOOC learning and to reach greater perceived learning outcomes in MOOCs.
  • Predictors of blended learning adoption in higher education institutions in Oman: theory of planned behavior

    Faten Hamad; Ahmed Shehata; Noura Al Hosni (SpringerOpen, 2024-02-01)
    Abstract The shift toward electronic learning due to the COVID-19 pandemic has created many opportunities to shape Oman’s learning styles. This study explores the factors that affect students’ acceptance of blended learning (BL) in higher education institutions in developing countries, focusing on Oman. The study examines the impact of demographic and social factors, attitude, subjective norms, perceived behavioral control, self-efficacy, beliefs, behavioral intention, and actual use of BL among students. The Theory of Planned Behavior (TPB) was used as a theoretical framework to understand the decision-making processes surrounding BL adoption. Hypotheses are formulated and tested using statistical analysis of survey results. The questionnaire was distributed to students from Sultan Qaboos University in Oman. The data collected were analyzed using inferential predictive modeling methods such as multiple regression analysis and Pearson correlation. The findings indicate that students have a positive attitude toward BL and are likely to choose it in the future. The study also reveals that demographic characteristics and various dimensions, such as attitude, subjective norms, perceived behavioral control, self-efficacy, beliefs, behavioral intention, and actual usage, influence students’ acceptance and utilization of BL. The results contribute to the existing literature and provide insights into the factors that affect BL adoption in developing countries.
  • Brain-based CALL in flipped higher education GE courses held through LMS: Boosting vocabulary learning and reading comprehension

    Nasrin Abdolmaleki; Zari Saeedi (SpringerOpen, 2024-02-01)
    Abstract The thriving technology penetration in all aspects of today’s life and deficiency of traditional pedagogies necessitate wise adoption of modern approaches in the educational context. As a few studies concerned the simultaneous application of classical educational theories with modern technological pedagogy, the present researchers launched General English (GE) courses enjoying the consolidation of Brain-Based Computer-Assisted-Language-Learning (BBCALL) and Flipped-Model (FM) with the aid of the Learning Management System (LMS) for fourteen 150-min sessions to explore their impact on vocabulary learning and reading comprehension (RC). In this pre/post-test experimental study, conducted in coeducational GE courses of a state university, 61 homogenous non-English major bachelors, selected via the convenience-sampling technique and screened by standard RC and GE-VOC tests, participated. Articulate Storyline software was used to develop intentional instructional content according to 12 BBL principles. BBCALL was the common aspect and in-class content attainment of non-flipped versus in-class content engagement and formative quizzes of flipped courses were the distinguishing features of the applied treatments. The statistical analyses of this action research demonstrated significantly meaningful outperformance of flipped BBCALL participants in vocabulary learning ( $$sig=0.001)$$ s i g = 0.001 ) and RC ( $$sig=0.033)$$ s i g = 0.033 ) . To enhance results interpretation precision, gender was considered in groups’ differences. Although females in flipped course meaningfully outperformed on RC, male partakers of flipped course experienced the most meaningful improvement in VOC learning. Additionally, low-proficient learners benefited the most from such a self-paced and learner-centered education. The findings suggest that flexible instructional materials and effective tech integration could facilitate the improvement of higher-order thinking, creative problem-solving, and scaffolding.
  • Empowering ChatGPT with guidance mechanism in blended learning: effect of self-regulated learning, higher-order thinking skills, and knowledge construction

    Hsin-Yu Lee; Pei-Hua Chen; Wei-Sheng Wang; Yueh-Min Huang; Ting-Ting Wu (SpringerOpen, 2024-03-01)
    Abstract In the evolving landscape of higher education, challenges such as the COVID-19 pandemic have underscored the necessity for innovative teaching methodologies. These challenges have catalyzed the integration of technology into education, particularly in blended learning environments, to bolster self-regulated learning (SRL) and higher-order thinking skills (HOTS). However, increased autonomy in blended learning can lead to learning disruptions if issues are not promptly addressed. In this context, OpenAI's ChatGPT, known for its extensive knowledge base and immediate feedback capability, emerges as a significant educational resource. Nonetheless, there are concerns that students might become excessively dependent on such tools, potentially hindering their development of HOTS. To address these concerns, this study introduces the Guidance-based ChatGPT-assisted Learning Aid (GCLA). This approach modifies the use of ChatGPT in educational settings by encouraging students to attempt problem-solving independently before seeking ChatGPT assistance. When engaged, the GCLA provides guidance through hints rather than direct answers, fostering an environment conducive to the development of SRL and HOTS. A randomized controlled trial (RCT) was employed to examine the impact of the GCLA compared to traditional ChatGPT use in a foundational chemistry course within a blended learning setting. This study involved 61 undergraduate students from a university in Taiwan. The findings reveal that the GCLA enhances SRL, HOTS, and knowledge construction compared to traditional ChatGPT use. These results directly align with the research objective to improve learning outcomes through providing guidance rather than answers by ChatGPT. In conclusion, the introduction of the GCLA has not only facilitated more effective learning experiences in blended learning environments but also ensured that students engage more actively in their educational journey. The implications of this study highlight the potential of ChatGPT-based tools in enhancing the quality of higher education, particularly in fostering essential skills such as self-regulation and HOTS. Furthermore, this research offers insights regarding the more effective use of ChatGPT in education.
  • From the main track to the winding path: considering the diversity of trajectories at university

    Helena Troiano; John Brennan; Jean-François Giret (SpringerOpen, 2024-02-01)
  • Development guidelines for individual digital study assistants in higher education

    Claudia M. König; Christin Karrenbauer; Michael H. Breitner (SpringerOpen, 2024-01-01)
    Abstract Increasing student numbers, heterogeneity and individual biographies lead to a growing need for personalized support. To meet these challenges, an Individual Digital Study Assistant (IDSA) provides features to help students improve their self-regulation and organizational skills to achieve individual study goals. Based on qualitative expert interviews, a quantitative student survey, and current literature we derived requirements for an IDSA. Based on them, we designed, developed, and implemented a first IDSA prototype for higher education institutions (HEI). We continuously evaluated the prototype within different workshops and analyzed the usage data to improve it further in three enhanced prototypes. Based on this iterative process, we derived guidelines for an IDSA design and development. Accordingly, the framework, project management, content, team selection, team development, team communication, marketing, and student habits are important to consider. The guidelines advance the knowledge base of IDSA in HEI and guide and support practitioners in the design, development, and implementation of IDSA in HEI.
  • Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students

    Muhammad Abbas; Farooq Ahmed Jam; Tariq Iqbal Khan (SpringerOpen, 2024-02-01)
    Abstract While the discussion on generative artificial intelligence, such as ChatGPT, is making waves in academia and the popular press, there is a need for more insight into the use of ChatGPT among students and the potential harmful or beneficial consequences associated with its usage. Using samples from two studies, the current research examined the causes and consequences of ChatGPT usage among university students. Study 1 developed and validated an eight-item scale to measure ChatGPT usage by conducting a survey among university students (N = 165). Study 2 used a three-wave time-lagged design to collect data from university students (N = 494) to further validate the scale and test the study’s hypotheses. Study 2 also examined the effects of academic workload, academic time pressure, sensitivity to rewards, and sensitivity to quality on ChatGPT usage. Study 2 further examined the effects of ChatGPT usage on students’ levels of procrastination, memory loss, and academic performance. Study 1 provided evidence for the validity and reliability of the ChatGPT usage scale. Furthermore, study 2 revealed that when students faced higher academic workload and time pressure, they were more likely to use ChatGPT. In contrast, students who were sensitive to rewards were less likely to use ChatGPT. Not surprisingly, use of ChatGPT was likely to develop tendencies for procrastination and memory loss and dampen the students’ academic performance. Finally, academic workload, time pressure, and sensitivity to rewards had indirect effects on students’ outcomes through ChatGPT usage.
  • Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern education

    Yoshija Walter (SpringerOpen, 2024-02-01)
    Abstract The present discussion examines the transformative impact of Artificial Intelligence (AI) in educational settings, focusing on the necessity for AI literacy, prompt engineering proficiency, and enhanced critical thinking skills. The introduction of AI into education marks a significant departure from conventional teaching methods, offering personalized learning and support for diverse educational requirements, including students with special needs. However, this integration presents challenges, including the need for comprehensive educator training and curriculum adaptation to align with societal structures. AI literacy is identified as crucial, encompassing an understanding of AI technologies and their broader societal impacts. Prompt engineering is highlighted as a key skill for eliciting specific responses from AI systems, thereby enriching educational experiences and promoting critical thinking. There is detailed analysis of strategies for embedding these skills within educational curricula and pedagogical practices. This is discussed through a case-study based on a Swiss university and a narrative literature review, followed by practical suggestions of how to implement AI in the classroom.
  • Investigating relationships between community of inquiry perceptions and attitudes towards reading circles in Chinese blended EFL learning

    Yilian Teng; Zhuhui Yin; Xia Wang; Hanyu Yang (SpringerOpen, 2024-01-01)
    Abstract Little research has been conducted to investigate relationships between students’ community of inquiry (CoI) perceptions and their attitudes towards reading circles (ARC). To bridge the gap this quantitative research reports a cohort of Chinese students’ perceptions of CoI and its prediction for their attitudes towards reading circles (RC) in English as a Foreign Language learning. The researchers administered and collected 202 valid questionnaires. They analyzed the correlations between CoI and ARC dimensions, did regression analyses and came up with insightful findings. It’s found that, concerning CoI, students have more positive perceptions of teaching and cognitive presences than of social presence. Meanwhile teaching presence, cognitive presence, social presence of CoI and usefulness, affect, and behavior of ARC are closely correlated. CoI teaching presence and cognitive presence are significant predictors for usefulness and behavior of ARC, while the combination of the three CoI presences predicts affect of ARC. Investigating the relationships between a longstanding CoI framework and a fledging ARC scale provides great implications for Chinese blended EFL teaching, especially in reading courses.
  • A meta systematic review of artificial intelligence in higher education: a call for increased ethics, collaboration, and rigour

    Melissa Bond; Hassan Khosravi; Maarten De Laat; Nina Bergdahl; Violeta Negrea; Emily Oxley; Phuong Pham; Sin Wang Chong; George Siemens (SpringerOpen, 2024-01-01)
    Abstract Although the field of Artificial Intelligence in Education (AIEd) has a substantial history as a research domain, never before has the rapid evolution of AI applications in education sparked such prominent public discourse. Given the already rapidly growing AIEd literature base in higher education, now is the time to ensure that the field has a solid research and conceptual grounding. This review of reviews is the first comprehensive meta review to explore the scope and nature of AIEd in higher education (AIHEd) research, by synthesising secondary research (e.g., systematic reviews), indexed in the Web of Science, Scopus, ERIC, EBSCOHost, IEEE Xplore, ScienceDirect and ACM Digital Library, or captured through snowballing in OpenAlex, ResearchGate and Google Scholar. Reviews were included if they synthesised applications of AI solely in formal higher or continuing education, were published in English between 2018 and July 2023, were journal articles or full conference papers, and if they had a method section 66 publications were included for data extraction and synthesis in EPPI Reviewer, which were predominantly systematic reviews (66.7%), published by authors from North America (27.3%), conducted in teams (89.4%) in mostly domestic-only collaborations (71.2%). Findings show that these reviews mostly focused on AIHEd generally (47.0%) or Profiling and Prediction (28.8%) as thematic foci, however key findings indicated a predominance of the use of Adaptive Systems and Personalisation in higher education. Research gaps identified suggest a need for greater ethical, methodological, and contextual considerations within future research, alongside interdisciplinary approaches to AIHEd application. Suggestions are provided to guide future primary and secondary research.
  • Face-to-face vs. blended learning in higher education: a quantitative analysis of biological science student outcomes

    Claire V. Harper; Lucy M. McCormick; Linda Marron (SpringerOpen, 2024-01-01)
    Abstract The COVID-19 pandemic caused a rapid seismic shift to online delivery in otherwise face-to-face higher education settings worldwide. This quantitative research study sought to investigate the effect of different delivery styles and assessment types on student outcomes. Specifically, grades achieved by first year undergraduate Biological Science students at a UK Higher Education institution were compared from seven modules across two different academic years, namely 2018–2019 and 2020–2021. The academic year 2018–2019 was delivered in the traditional face-to-face manner whereas the 2020–2021 method of delivery was via blended learning. The results showed that four of the seven modules were negatively affected by the transition from face-to-face to blended delivery (p < 0.05, T-test). One module was unaffected (p > 0.05, T-test) and the remaining two modules were positively affected (p < 0.05, T-test). However, the percentage of students requiring reassessments increased with blended learning delivery although this was not significant (p < 0.05, T-test). In summary, the majority of individual module marks decreased with blended learning compared to face-to-face delivery, with an associated increase in required reassessments. Although there are positive benefits to incorporating an element of online learning for students, it is important to utilise this information in future module delivery planning to support the varying student cohorts of the future.
  • Students' digital technology attitude, literacy and self-efficacy and their effect on online learning engagement

    Seyum Getenet; Robert Cantle; Petrea Redmond; Peter Albion (SpringerOpen, 2024-01-01)
    Abstract This study utilised students' online engagement, digital technology attitude, digital literacy, and self-efficacy theories to develop and test a model connecting these factors within a regional university in Australia. A field survey collected data from 110 first-year students. AMOS 28 was employed for measurement and structural model path analysis. The study initially examined the impact of students' attitudes and digital literacy on their self-efficacy. Subsequently, the effects of self-efficacy on five dimensions of online engagement were assessed: social, collaborative, cognitive, behavioural, and emotional. The findings indicated that positive student attitudes and digital literacy significantly contributed to self-efficacy, which, in turn, positively affected the engagement dimensions. This suggests that when designing and facilitating online, blended, or technology-enhanced courses in higher education, educators should pay attention to various elements of engagement. The study highlights the importance of considering students' attitudes and digital literacy in fostering self-efficacy and enhancing online learning engagements. Further research and implications for future studies are also recommended.
  • Integrating chatbots in education: insights from the Chatbot-Human Interaction Satisfaction Model (CHISM)

    Jose Belda-Medina; Vendula Kokošková (SpringerOpen, 2023-12-01)
    Abstract Recent advances in Artificial Intelligence (AI) have paved the way for the integration of text-based and voice-enabled chatbots as adaptive virtual tutors in education. Despite the increasing use of AI-powered chatbots in language learning, there is a lack of studies exploring the attitudes and perceptions of teachers and students towards these intelligent tutors. This study aims to compare several linguistic and technological aspects of four App-Integrated Chatbots (AICs) and to examine the perceptions among English as a Foreign Language (EFL) teacher candidates. In this mixed-methods research based on convenience sampling, 237 college students from Spain (n = 155) and the Czech Republic (n = 82) interacted with four AICs over a month, and evaluated them following a rubric based on the Chatbot-Human Interaction Satisfaction Model. This scale was specifically designed to assess different linguistic and technological features of AICs such as response interval, semantic coherence, sentence length, and user interface. Quantitative and qualitative data were gathered through a pre-post-survey, based on the CHISM model and student assessment reports. Quantitative data were analyzed using SPSS statistics software, while qualitative data were examined using QDA Miner software, focusing on identifying recurring themes through frequency analysis. The findings indicated a moderate level of satisfaction with AICs, suggesting that enhancements in areas such as better adapting to learner needs, integrating interactive multimedia, and improving speech technologies are necessary for a more human-like user interaction.
  • Students’ perceptions of using ChatGPT in a physics class as a virtual tutor

    Lu Ding; Tong Li; Shiyan Jiang; Albert Gapud (SpringerOpen, 2023-12-01)
    Abstract The latest development of Generative Artificial Intelligence (GenAI), particularly ChatGPT, has drawn the attention of educational researchers and practitioners. We have witnessed many innovative uses of ChatGPT in STEM classrooms. However, studies regarding students’ perceptions of ChatGPT as a virtual tutoring tool in STEM education are rare. The current study investigated undergraduate students’ perceptions of using ChatGPT in a physics class as an assistant tool for addressing physics questions. Specifically, the study examined the accuracy of ChatGPT in answering physics questions, the relationship between students’ ChatGPT trust levels and answer accuracy, and the influence of trust on students’ perceptions of ChatGPT. Our finding indicates that despite the inaccuracy of GenAI in question answering, most students trust its ability to provide correct answers. Trust in GenAI is also associated with students’ perceptions of GenAI. In addition, this study sheds light on students’ misconceptions toward GenAI and provides suggestions for future considerations in AI literacy teaching and research.
  • Flipped classroom in higher education: a systematic literature review and research challenges

    Maria Ijaz Baig; Elaheh Yadegaridehkordi (SpringerOpen, 2023-11-01)
    Abstract Flipped learning has garnered substantial attention as a potential means to enhance student engagement, improve learning outcomes, and adapt to the evolving educational landscape. However, despite the growing interest and potential benefits of flipped learning, several challenges and areas of concern persist. This systematic literature review critically examines the implementation of the flipped classroom in higher education by focusing on the role of technologies and tools, pedagogical activities and courses, and existing challenges. Using a systematic approach, a total of 30 research articles published between 2014 and 2023 were chosen for the review. This study identified video creation tools, learning management systems (LMS), content repositories, collaborative platforms, podcasts, and online assessment tools as technologies that play a central role in the flipped classroom. Moreover, this study identifies specific pedagogical activities within different courses that contribute to the effectiveness of flipped learning in higher education. The implementation challenges that teachers and students may face in the flipped classroom were presented, and potential strategies to alleviate these challenges were provided. This study will contribute to a more comprehensive understanding of flipped learning's benefits, technologies and tools, challenges, and potential to improve higher education.
  • Beyond emergency remote teaching: did the pandemic lead to lasting change in university courses?

    J. Broadbent; R. Ajjawi; M. Bearman; D. Boud; P. Dawson (SpringerOpen, 2023-11-01)
    Abstract The COVID-19 pandemic significantly disrupted traditional methods of teaching and learning within higher education. But what remained when the pandemic passed? While the majority of the literature explores the shifts during the pandemic, with much speculation about post-pandemic futures, a clear understanding of lasting implications remains elusive. To illuminate this knowledge gap, our study contrasts pedagogical practices in matched courses from the pre-pandemic year (2019) to the post-pandemic phase (2022/2023). We also investigate the factors influencing these changes and the perceptions of academics on these shifts. Data were gathered from academics in a large comprehensive Australian university of varying disciplines through a mixed-methods approach, collecting 67 survey responses and conducting 21 interviews. Findings indicate a notable increase in online learning activities, authentic and scaffolded assessments, and online unsupervised exams post-pandemic. These changes were primarily driven by university-guided adaptations, time and workload pressures, continued COVID-19 challenges, local leadership, an individual desire to innovate, and concerns about academic integrity. While most changes were seen as favourable by academics, perceptions were less positive concerning online examinations. These findings illuminate the enduring effects of the pandemic on higher education, suggesting longer-term implications than previous studies conducted during the acute phase of the pandemic.

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