Special Issues

Call for Proposals for 2024 Special Issues

The Journal of University Teaching and Learning Practice invites proposals for special issues on contemporary themes associated with effective and innovative teaching and learning practice in the higher education environment. The expectation is that the special issue would be of interest to an international audience. To propose a special issue for 2022 or 2023 please complete the Special Issue Proposal and return to the Senior Editor, Special Issues Dr Jo-Anne Kelder by email jo.kelder@utas.edu.au. The Journal periodically has specific calls for Special Issues, but also accepts unsolicited proposals.

Collaborative expressions of interest

At the Journal of University Teaching and Learning Practice, we understand the role that we can have in supporting early career researchers and academics to launch their international networks in the absence of international conferences. The Journal of University Teaching and Learning Practice would like to support this transition during COVID-19. For academics seeking to form new international research relationships, please fill in the expression of interest form. We will aim to pair you with potential collaborators. At this stage, we can only pair English projects. Please note our support in forming collaborative teams does not guarantee publication in our Journal, and all final submissions will undergo rigorous peer review processes.

Current Calls for Papers

The following are Special Issues open for submissions, with a link to take you to the specific Call for Papers

Forthcoming Special Issues

The following are Special Issues closed for submissions that are preparing for future publication

Are the Technology Acceptance Models still fit for purpose?

Guest Editors

Background

The Technology Acceptance Model (TAM) developed by Davis in the 1980s (Davis, 1989), has been a widely used theoretical framework to explain how and why individuals adopt or reject technology. It draws heavily on the Theory of Reasoned Action (TRA) (Ajzen and Fishbein 1975) and its fundamental characteristics and emphasis are a focus on individuals and, in particular, on the factors that affect an individual’s adoption of a technology based on its perceived ease of use and the perceived usefulness of the technology to model intention to use, and then actual use. There have been a number of updates to the model, notably TAM2 (Venkatesh and Davis, 2000) and TAM3 (Venkatech and Bala, 2005), both of which extended the framework with additional constructs.

The main alternative to TAM is the Unified Theory of Acceptance and Use of Technology (UTAUT and UTAUT2: Venkatesh et al, 2003). The UTAUT models are a synthesis of eight acceptance models, whilst drawing significantly on TAM they also incorporate the Theory of Planned Behaviour, the Motivational Model, Innovation Diffusion Theory and Social Cognitive Theory. Another alternative is the Technology Readiness Index (TRI: Parasuraman, 2000), which focuses on users' beliefs regarding technology as opposed to TAM and UTAUT which focus upon acceptance.

In higher education research, all of these models have been, and still are, commonly employed in research, but TAM still remains a widely employed theoretical framework, recent studies utilising TAM and/or UTAUT include: Huang and Lucas (2021), Koulouri and Macredie and Olikitan (2021), Liu et al (2023) and Balogun et al (2023). Granić and Marangunić (2019) in their systematic review of the use of TAM in an educational context found that the use of TAM in exploring technology use and integration had grown since 2013. They found that TAM was utilised in a variety of different forms from the original model through to its extended model and modified versions and that there was still great potential for its use. One of the most interesting elements of their study highlighted the location of study, with a prevalence for the use of TAM in Asian contexts.

Researchers do consider that TAM has limitations, amongst those identified include Bagozzi (2007) who noted that the model conflates intention and use and that the model emphasises that use is the ultimate goal of an individual. However, he notes that decision-makers take action to achieve goals rather than just to use technology. Others, such as Lee et al (2003) noted that TAM studies tend to use self-reporting mechanisms to measure technology use rather than measuring actual use. There is also a recognition that although TAM can highlight cultural variations in response to technology where comparative studies are utilised, there are questions about the acceptance of the models used within different cultural contexts which TAM does not easily lend itself to analysing, although there have been adaptations of TAM for use in different cultural contexts. So, despite its continued application in research and that this work has applied the model in some very different contexts compared to how it was originally used, some researchers and practitioners have questioned its continued relevance and fit for purpose in the current technological landscape, particularly in relation to Higher Education. Recently major technological developments within society have disrupted HE and this has been significantly different to previous technologies such as the Web or eLearning. These new developments lead us to consider that there should be a re-examination of TAM and its ability to help us understand the integration and adoption of technology within learning and teaching. Notable amongst these developments has been the advent of easy-to-use and freely available Generative AI applications, in particular ChatGPT. The extensive use of ChatGPT is requiring changes from educators in terms of how they respond to this technology, it already looks likely that assessment practices will have to make significant adaptations to respond to this technology, but it is very likely that most other learning and research activities will be impacted by the technology in some way or another. Most significantly, though, this is a student-driven technology adoption and not a teacher-initiated one, this is unprecedented.

Alongside these, other parallel developments have changed the educational landscape. Issues such as EDI (Equality, Diversity and Inclusion), culture, and an increasing emphasis on the student experience and mental health all now play a more prominent role in HE and so also influence technology adoption and application. These factors impacting technology adoption are also more societal, contextual and external to the educators and technology adoption is increasingly responsive rather than proactive for many educators. TAM with its focus on individual-level factors may not have the ability to deal with these new developments. In particular, these have significantly changed the L&T environment within which educators are operating and rather than teacher-driven or institutional-driven changes, these are externally driven. So, is TAM still appropriate, does it still have the depth and breadth of constructs, factors and an appropriate focus to help us understand, examine and evaluate the adoption and application of technology in the current HE context?

This special edition proposes to examine the question “Are the Technology Acceptance Models still fit for purpose?”

We invite researchers and practitioners to submit their original research papers on the Technology Acceptance Model and/or alternative technology adoption approaches. We welcome both theoretical and empirical contributions that expand the understanding of the TAM and its applications. Potential topics of interest include, but are not limited to:

  • New developments in the TAM framework and its extensions
  • Empirical studies testing the TAM and its extensions in different contexts
  • The comparison of TAM’s use between education and different contexts – does the context matter?
  • The use of TAM to design and evaluate technology-based interventions in education
  • The relationship between TAM and constructs such as digital literacy, digital competence, and digital divides

Types of publications accepted into this Special Issue

The types of publications that are eligible for acceptance into this Special Issue include:

  • Research papers
  • Review articles (e.g., systematic review or meta-analysis)
  • Case studies and evidence-based good practice examples

Developing a high-quality proposal

We recommend the creation of a single document (Word document preferably) that contains the following:

  • Proposed article title
  • Proposed authors names and affiliations
  • A clear evidence-based rationale for the line of inquiry proposed
  • Research question(s)
  • Proposed method (for both theoretical and empirical manuscripts)
  • Practice-based implications of the proposed research

The word limit for the proposal is 250 words (not including references) and is designed to give the Editorial Team a sense of the rigour of the manuscript proposed and the possible implications of such research. The Editorial Team may return with an invitation to combine similar manuscripts. Acceptance of proposals does not guarantee acceptance of final manuscripts.

Timeline

  • Proposals due: 30 June 2023
  • Acceptance notifications: 30 July 2023
  • Full articles due: 30 December 2023
  • Final revised papers due: 30 March 2024
  • Final publication: 30 March 2024

For further information, or to submit an abstract, please email: Dr Mike O'Dea. You can download a .pdf version of the Call for Expressions of Interest by clicking here

References

Ajzen, I. & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychological bulletin, 82(2), 261-277. Bagozzi, R. P. (2007). The legacy of the technology acceptance model and a proposal for a paradigm shift. Journal of the Association for information systems, 8(4), 244-254. Balogun, N. A., Adeleke, F. A., Abdulrahaman, M. D., Shehu, Y. I., & Adedoyin, A. (2023). Undergraduate students' perception on e-learning systems during COVID-19 pandemic in Nigeria. Heliyon, 9(3). Camilleri, M. A., & Camilleri, A. C. (2022, June). Utilitarian and intrinsic motivations to use mobile learning technologies: An extended technology acceptance model. In Proceedings of the 8th International Conference on e-Society, e-Learning and e-Technologies (pp. 76-81). Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly,13(3), 319-340. Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572-2593. Huang, P., & Lucas, H. C. (2021). Early Exploration of MOOCs in the US Higher Education: An Absorptive Capacity Perspective. ACM Transactions on Management Information Systems, 12(3), 1-28. Koulouri, T., Macredie, R. D., & Olakitan, D. (2022). Chatbots to Support Young Adults’ Mental Health: An Exploratory Study of Acceptability. ACM Transactions on Interactive Intelligent Systems (TiiS), 12(2), 1-39. Lampo, A. (2022, September). How is Technology Accepted? Fundamental Works in User Technology Acceptance from Diffusion of Innovations to UTAUT-2. In Proceedings of the 8th International Conference on Industrial and Business Engineering (pp. 260-266). Lee, Y., Kozar, K. A., & Larsen, K. R. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for information systems, 12(1), 752-780. Liu, Q., Gladman, T., Grove, C., Eberhard, S., Geertshuis, S., Ali, A., Blyth, P., & Grainger, R. (2023). Capturing the invisible: Non-institutional technologies in undergraduate learning within three New Zealand universities. Internet & Higher Education, 58, 100910 Maita, I., Indrajit, R. E., & Irmayani, A. (2018, April). User behavior analysis in academic information system using unified theory of acceptance and use of technology (UTAUT). In Proceedings of the 2018 1st International Conference on Internet and e-Business (pp. 223-228). Mathieson, K., Peacock, E., & Chin, W. W. (2001). Extending the technology acceptance model: the influence of perceived user resources. ACM SIGMIS Database: the DATABASE for Advances in Information Systems, 32(3), 86-112. Meiryani, M., Putri Hendratno, S., Juwita, A., & Dafi Putra, I. (2021, July). The Impacts of Information Technology on Accounting Systems. In Proceedings of the 9th International Conference on Computer and Communications Management (pp. 1-8). Ng, C., & Siew Hoong Lee, A. (2021, January). Factors influencing the acceptance of blockchain technology: real estate industry. In 2021 The 4th International Conference on Software Engineering and Information Management (pp. 63-67). Tsai, C. M., & Chang, C. H. (2022, October). The Effect of Privacy Concerns on the Acceptance of the Global Navigation Satellite System (GNSS): The Application of Technology Acceptance Model (TAM). In Proceedings of the 2022 6th International Conference on E-Business and Internet (pp. 119-124). Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27 (3),425-478.

Enhancing Student Engagement using Artificial Intelligence (AI) and chatbots like ChatGPT

Guest Editors

Background

Chatbots are intelligent agent technologies automatic software tools that interact with human beings on a specific topic or in a particular domain in a natural, conversational manner, and mainly using text information (Smutny and Schreiberova, 2020). Chatbot technology has the potential to provide quick and personalized services to everyone (Adamopoulou and Moussiades, 2020a), and is powered by AI technologies such as deep learning, NLP, and machine learning algorithms. Their ubiquitous presence on the Internet continues to grow faster than ever (Dale, 2016), with up to one third of online interactions involving a chatbot tool. In fact, according to Moran (2023), chatbots handle 68.9% of chats from start to finish. The use of chatbots has been seen in a wide variety of sectors, including marketing, customer service, technical support, education, and training. Chatbots have the potential to support sustained learning and improve students’ learning efforts (Okonkwo and Ade-Ibijola, 2021; Kuhail et al., 2022). They can engage learners, personalise learning activities, streamline the enrolment process, increase tutor/lecturer operational efficiency, and increase student engagement (Okonkwo and Ade-Ibijola, 2021). According to Yin et al. (2021), the use of chatbot-based micro-learning systems has a positive effect on students' learning motivation and performance.

The text communication technology and natural language processing (NLP) has expanded in recent years resulting in people preferring it over other forms of direct contact (Qaffas, 2019). Recent research points out potential benefits with respect to the users’ motivation (Hasler, Tuchman and Friedman, 2013; Hill, Ford and Farreras, 2015). Several studies have investigated the role of Chatbots in fostering learner autonomy (Shawar and Atwell, 2007; Abbas et al., 2022), intrinsic motivation (Jia and Chen, 2008), and an inquiry-orientated frame of mind (Goda et al., 2014). Chatbots can support sustained learning (Fryer et al., 2017) and improve students’ learning efforts (e.g., Fryer et al., 2017). Chatbots have the potential to enhance the digital learning infrastructure and improve student motivation (Hasler et al., 2013; Hill et al., 2015). They provide a friendly and interactive environment for students to access information, ask questions, and receive answers. This can help to increase student engagement and improve their understanding of university regulations and processes.

Recently, the debate about the impact of ChatGPT in education and its use as chatbots for enhancing student engagement is gaining attention in recent years (EdTech, 2022). ChatGPT is a large language model developed by OpenAI that applies deep learning techniques to generate natural language responses (OpenAI, 2023). The adoption of this technology has provided chatbots with more advanced capabilities, allowing them to offer personalised, context-aware responses in real-time (Adamopoulou and Moussiades, 2020b). The use of ChatGPT has the potential to improve the efficiency of chatbots in education by enabling them to understand and interpret more complex student queries. This can lead to increased engagement and better student outcomes (Zhai, 2022). Furthermore, the integration of ChatGPT in chatbots for education can also offer a more interactive and personalized learning experience for students. Chatbots powered by ChatGPT can provide real-time feedback, assessment, and learning support, which can improve student motivation, autonomy, and performance. Additionally, the technology can offer personalised learning activities, streamlining enrolment processes, and increasing tutor/lecturer operational efficiency, which could further support student engagement (Kasneci et al., 2023).

The purpose of this special issue is to bring together experts in the field of higher education and technology to explore the use of chatbots for teaching and learning. The aim is to answer questions including, but not limited to:

  • How a friendly interactive environment within Chatbot has been built and implemented to enhance universities or a particular module’s digital learning infrastructure to improve student motivation?
  • To what extent have the learners assimilated new concepts and/or better understood university’s regulations and processes while interacting with the Chatbot’s functions?
  • Considering learners as research partners when investigating the efficiency of the conversation flows and the contents included within a Chatbot.
  • How can current and emerging LLMs (e.g., ChatGPT and Google BARD) be leveraged to develop more intelligent and personalised chatbots for enhancing student engagement and improving their understanding of university regulations and processes?
  • The design and implementation of chatbots for teaching and learning in higher education
  • The impact of chatbots on student motivation and understanding of university regulations and processes
  • Student voice and student experience
  • The role of learners as research partners in investigating the efficiency of chatbots
  • Case studies and evaluations of chatbots in higher education
  • Exploring ethical considerations related to the use of large language models in education, including issues related to privacy, data security, and potential biases in natural language processing algorithms
  • Future directions and potential limitations of chatbots in higher education

Types of publications accepted into this Special Issue

The types of publications that are eligible for acceptance into this Special Issue include:

  • Research papers
  • Review articles (e.g., systematic review or meta-analysis
  • Case studies and evidence-based good practice examples

Developing a high-quality proposal

We recommend the creation of a single document (Word document preferably) that contains the following:

  • Proposed article title
  • Proposed authors names and affiliations
  • A clear evidence-based rationale for the line of inquiry proposed
  • Research question(s)
  • Proposed method (for both theoretical and empirical manuscripts)
  • Practice-based implications of the proposed research

The word limit for the proposal is 250 words (not including references) and is designed to give the Editorial Team a sense of the rigour of the manuscript proposed and the possible implications of such research. The Editorial Team may return with an invitation to combine similar manuscripts. Acceptance of proposals does not guarantee acceptance of final manuscripts.

Timeline

  • Proposals due: 30 May 2023
  • Acceptance notifications: 30 June 2023
  • Full articles due: 30 December 2023
  • Final revised papers due: 30 March 2024
  • Final publication: 30 March 2024

For further information, or to submit an abstract, please email: Dr Mario Kremantzis. You can download a .pdf version of the Call for Expressions of Interest by clicking here

References

Abbas, N., Whitfield, J., Atwell, E., Bowman, H., Pickard, T. and Walker, A. (2022) ‘Online chat and chatbots to enhance mature student engagement in higher education’, International Journal of Lifelong Education, 41(3), pp. 308–326. Adamopoulou, E. and Moussiades, L. (2020a) ‘An overview of chatbot technology’, in Artificial Intelligence Applications and Innovations: 16th IFIP WG 12.5 International Conference, AIAI 2020, Neos Marmaras, Greece, June 5–7, 2020, Proceedings, Part II 16. Springer, pp. 373–383. Adamopoulou, E. and Moussiades, L. (2020b) ‘Chatbots: History, technology, and applications’, Machine Learning with Applications, 2, p. 100006. Dale, R. (2016) ‘The return of the chatbots’, Natural Language Engineering, 22(5), pp. 811–817. EdTech (2022) ChatGPT, Chatbots and Artificial Intelligence in Education - Ditch That Textbook. Available at: https://ditchthattextbook.com/ai/ (Accessed: 21 February 2023). Fryer, L.K., Ainley, M., Thompson, A., Gibson, A. and Sherlock, Z. (2017) ‘Stimulating and sustaining interest in a language course: An experimental comparison of Chatbot and Human task partners’, Computers in Human Behavior, 75, pp. 461–468. Goda, Y., Yamada, M., Matsukawa, H., Hata, K. and Yasunami, S. (2014) ‘Conversation with a chatbot before an online EFL group discussion and the effects on critical thinking’, The Journal of Information and Systems in Education, 13(1), pp. 1–7. Hasler, B.S., Tuchman, P. and Friedman, D. (2013) ‘Virtual research assistants: Replacing human interviewers by automated avatars in virtual worlds’, Computers in Human Behavior, 29(4), pp. 1608–1616. Hill, J., Ford, W.R. and Farreras, I.G. (2015) ‘Real conversations with artificial intelligence: A comparison between human–human online conversations and human–chatbot conversations’, Computers in human behavior, 49, pp. 245–250. Jia, J. and Chen, W. (2008) ‘Motivate the learners to practice English through playing with Chatbot CSIEC’, in Technologies for E-Learning and Digital Entertainment: Third International Conference, Edutainment 2008 Nanjing, China, June 25-27, 2008 Proceedings 3. Springer, pp. 180–191. Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S. and Hüllermeier, E. (2023) ‘ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education’. Kuhail, M.A., Alturki, N., Alramlawi, S. and Alhejori, K. (2022) ‘Interacting with educational chatbots: A systematic review’, Education and Information Technologies, pp. 1–46. Moran, M. (2023) 25+ Top Chatbot Statistics For 2023: Usage, Demographics, Trends. Available at: https://startupbonsai.com/chatbot-statistics (Accessed: 8 February 2023). Okonkwo, C.W. and Ade-Ibijola, A. (2021) ‘Chatbots applications in education: A systematic review’, Computers and Education: Artificial Intelligence, 2, p. 100033. OpenAI (2023) ChatGPT: Optimizing Language Models for Dialogue. Available at: https://openai.com/blog/chatgpt/ (Accessed: 21 February 2023). Qaffas, A.A. (2019) ‘Improvement of Chatbots semantics using wit. ai and word sequence kernel: Education Chatbot as a case study’, International journal of modern education and computer science, 11(3), p. 16. Shawar, B.A. and Atwell, E. (2007) ‘Chatbots: are they really useful?’, Journal for Language Technology and Computational Linguistics, 22(1), pp. 29–49. Smutny, P. and Schreiberova, P. (2020) ‘Chatbots for learning: A review of educational chatbots for the Facebook Messenger’, Computers & Education, 151, p. 103862. Yin, J., Goh, T.-T., Yang, B. and Xiaobin, Y. (2021) ‘Conversation technology with micro-learning: The impact of chatbot-based learning on students’ learning motivation and performance’, Journal of Educational Computing Research, 59(1), pp. 154–177. Zhai, X. (2022) ‘ChatGPT user experience: Implications for education’, Available at SSRN 4312418 [Preprint].

SPECIAL ISSUE 2023 Internationalisation of Higher Education at Home: Implications for an Evolving World

Guest Editors

Timeline

  • Proposals due: 1 November 2022
  • Acceptance notifications: 10 December 2022
  • Full articles due: 10 May 2023
  • Final revised papers due: 30 September 2023
  • Final publication: 20 November 2023

For further information, or to submit an abstract, please email: Dr Dongmei Li. You can download a .pdf version of the Call for Expressions of Interest by clicking here

SPECIAL ISSUE 2024 Intensive modes of learning and teaching in higher education

Guest Editors

Timeline

  • Proposals due: 21 November 2022
  • Acceptance notifications: 30 January 2023
  • Full articles due: 24 April 2023
  • Final revised papers due: 28 August 2023
  • Final publication: 20 January 2024

For further information, or to submit an abstract, please email: Dr Gayani Samarawickrema. You can download a .pdf version of the Call for Expressions of Interest by clicking here

SPECIAL ISSUE 2023 Sustainability in Learning and Teaching During and Beyond the COVID-19 Pandemic

Guest Editors

Timeline

  • Proposals due: 30 October 2022
  • Acceptance notifications: 15 November 2022
  • Full articles due: 27 February 2023
  • Final revised papers due: 15 September 2023
  • Final publication: 15 October 2023

For further information, or to submit an abstract, please email: Dr Kim Beasy. You can download a .pdf version of the Call for Expressions of Interest by clicking here