Research In Learning Technology Research Articles

Exploring 30 years of research in learning technology: an analysis of the RLT journal

Exploring 30 years of research in learning technology: an analysis of the RLT journal

Pedagogical agents communicating and scaffolding students' learning: High school teachers' and students' perspectives

Pedagogical agents (PAs) communicate verbally and non-verbally with students in digital and virtual reality/augmented reality learning environments. PAs have been shown to be beneficial for learning, and generative artificial intelligence, such as large language models, can improve PAs' communication abilities significantly. K-12 education is underrepresented in learning technology research and teachers' and students' insights have not been considered when developing PA communication. The current study addresses this research gap by conducting and analyzing semi-structured, in-depth interviews with eleven high school teachers and sixteen high school students about their expectations for PAs' communication capabilities. The interviewees identified relational and task-related communication capabilities that a PA should perform to communicate effectively with students and scaffold their learning. PA communication that is simultaneously affirmative and relational can induce immediacy, foster the relationship and engagement with a PA, and support students' learning management. Additionally, the teachers and students described the activities and technological aspects that should be considered when designing conversational PAs. The study showed that teachers and students applied human-to-human communication scripts when outlining their desired PA communication characteristics. The study offers novel insights and recommendations to researchers and developers on the communicational, pedagogical, and technological aspects that must be considered when designing communicative PAs that scaffold students’ learning, and discusses the contributions on human–machine communication in education.

MDT: semi-supervised medical image segmentation with mixup-decoupling training.

Objective. In the field of medicine, semi-supervised segmentation algorithms hold crucial research significance while also facing substantial challenges, primarily due to the extreme scarcity of expert-level annotated medical image data. However, many existing semi-supervised methods still process labeled and unlabeled data in inconsistent ways, which can lead to knowledge learned from labeled data being discarded to some extent. This not only lacks a variety of perturbations to explore potential robust information in unlabeled data but also ignores the confirmation bias and class imbalance issues in pseudo-labeling methods.Approach. To solve these problems, this paper proposes a semi-supervised medical image segmentation method 'mixup-decoupling training (MDT)' that combines the idea of consistency and pseudo-labeling. Firstly, MDT introduces a new perturbation strategy 'mixup-decoupling' to fully regularize training data. It not only mixes labeled and unlabeled data at the data level but also performs decoupling operations between the output predictions of mixed target data and labeled data at the feature level to obtain strong version predictions of unlabeled data. Then it establishes a dual learning paradigm based on consistency and pseudo-labeling. Secondly, MDT employs a novel categorical entropy filtering approach to pick high-confidence pseudo-labels for unlabeled data, facilitating more refined supervision.Main results. This paper compares MDT with other advanced semi-supervised methods on 2D and 3D datasets separately. A large number of experimental results show that MDT achieves competitive segmentation performance and outperforms other state-of-the-art semi-supervised segmentation methods.Significance. This paper proposes a semi-supervised medical image segmentation method MDT, which greatly reduces the demand for manually labeled data and eases the difficulty of data annotation to a great extent. In addition, MDT not only outperforms many advanced semi-supervised image segmentation methods in quantitative and qualitative experimental results, but also provides a new and developable idea for semi-supervised learning and computer-aided diagnosis technology research.

Research in Learning Technology: making friends and influencing people

The first issue of Research in Learning Technology (RLT) was published in 1993. Over 30 years, the journal has comprised an informal research and development facility for new ideas and practices in technology enhanced learning. This paper takes nine published articles from RLT: the three most downloaded in the period January 2021 – March 2023 (but published at any time); the three most downloaded articles published from January 2021 to March 2023; and the three most cited articles published from January 2018 to March 2023. The aim is to identify different areas of current interest and influence, different areas of practice, and different scholarly approaches. The authors are the journal’s current editorial team. This paper identifies diversity of technology enhanced learning-related subject matter and different approaches, too, but with ongoing interest in efficacy and in the ‘how’ of technology enhanced learning: how technology can be applied to truly enhance learning, comprising an approachable community, generating influence.

Research on Methods and Applications Related to Question-and-Answer Dialogue Systems

In the face of more and more network data information, search engines have gradually become the main retrieval method to obtain relevant information knowledge. However, in today's increasingly explosive development of information on the Internet, by contrast, traditional search engines have problems such as semantic understanding and complicated answers. Therefore, question answering systems are more important. The automatic question answering system generally adopts natural language processing related technologies. When users ask questions, the system automatically judges and gives answers. It involves computer linguistics, machine learning, artificial intelligence and other popular technology research. According to different classification criteria, the automatic question answering system is roughly divided into open field automatic question answering system and stereotyped automatic question answering system.. This thesis investigates methods and applications related to question-and-answer dialogue systems. On the methodological side, we introduce commonly used datasets and the principles and techniques of text, speech and visual question and answer systems, and analyse in detail the excellent example ChatGPT. In terms of applications, we present the application of Q&A dialogue systems in search engines, smart campuses. There is some reference value.

Self‐regulated learning with computer‐based learning environments

AbstractAlong with the increased interest in self‐regulated learning research, there has been a high engrossment in computer‐based learning environments that facilitate instruction and promote learning. These two crucial pedagogical approaches have been identified as being valuable to educators and learners. They merit refreshed considerations in light of new advanced theories, research, and practices in digital technologies and self‐regulated learning. This chapter introduces this volume on contemporary self‐regulated learning research with computer‐based learning environments that facilitate teaching and learning in postsecondary education. Contributors to this volume are internationally recognized scholars in self‐regulation and interactive learning technology research. They give an overview of the current state of knowledge in their specific disciplines as they integrate theoretical approaches to self‐regulated learning with contemporary technology issues. The authors provide research‐based analyses of the topic with specific examples of how practitioners can apply new knowledge and offer concrete recommendations for action and future directions.

콘텐츠 생성기, 매체풍부성 및 콘텐츠 주제가 학습성과에 미치는 영향

[Purpose] With the advent of ESG management, the demand for ESG management education for attitudinal change and enhancing self-efficacy is rapidly increasing. Yet the difficulties to meet the growing demand on ESG management instructors has prompted the development of alternative method of delivering learning contents using generative AI technology. However, the studies to examine the performance of the AI generated contents lecturers has been very few. Hence, the this study aims to experimentally examine the learing effect of AI generated ESG management education. [Research methodology] The experiment was conducted by introducing the learning content to the Chinese employees and subsequently conducting an online survey. We used a mixed subjects design of 2(Content Generators: Artificial Intelligence, Human) × 2(Media Richness: Audio, Video) × 3(Content Theme: Explanatory-concept of ESG management-, Sensitive-importance of ESG management-, Volitional-how to do-). [Research Findings] The results suggest that the interaction between content generator, information delivery method, and content’s topic have an effect on the learner's cognitive change. In particular, it was found that the use of virtual-human instructors powered by generative AI and emotional themes had greater effect on learning performance in the ESG management education for Chinese emplyoees context. [Implications] This study empirically contributes to the AI generated learning technology research. Existing generative AI technology is expected to contribute as a guideline to its successful establishment in global management.

Mixed reality results in vocational education: a case study with HoloLens 2

Pedagogy in vocational education is challenged by the increasing availability of mixed reality (MR) technologies. Wearable technologies such as the Microsoft HoloLens are being embedded in nursing education programmes in Aotearoa New Zealand and Australia and explored in other subject areas such as construction, architecture, and engineering. Empirical research undertaken with staff and students in a nursing programme reinforces and expands on previous research findings about enhanced student motivation, learning and organisational change implications, highlighted in the 2020 special issue of Research in Learning Technology that provided a ‘state of the art’ review of mobile mixed reality (MMR) in education as at 2019. This new research also identifies a number of technical or design constraints that need to be overcome to optimise MR for vocational and higher education use. Importantly, it highlights the need for significant improvement in integration of MR platforms into the learner journey so that use of the technology is not confined to professional users and early adopter student cohorts. Finally, the authors note the portability that MR headsets offer with the potential to deliver training across multiple campus and work-based learning environments to a consistent standard.

Open Educational Resources in Higher Education

Open educational resources (OER) are openly licensed educational materials that can be used in different educational settings to help make education available to all. However, proper deployment of OER still faces many barriers, including the lack of know-how among educators and helpful supporting tools. Many educators do not have the necessary knowledge to efficiently find, utilize, edit, or distribute high quality OER. Adding to this challenge is the lack of technical tools to support them when producing OER or converting their existing educational materials to OER. In this paper, the authors introduce two practical approaches applied at the Learning Technologies Research Group at RWTH Aachen University to enhance the utilization of OER in higher education. The first one addresses the qualification of educators to the concept of OER, and the second one focuses on providing technical support to educators when producing OER.

멀티 세그멘테이션과 태깅을 이용한 딥러닝 기반 송변전설비 학습 방법 연구

Recently, with the development of deep learning technology, failure analysis and failure diagnosis research using image analysis of objects have been actively conducted. In particular, research on algorithm and system development for diagnosing facilities using drone photographed images is being applied to the industrial field. The results are reaching the level of commercialization. In the electricity field, drone images have been used in the field of power facility diagnosis since two to three years ago. There are not many abnormal learning data to determine whether transmission facilities are abnormal, so full-scale use in the actual field is limited. Therefore, this study proposes a method of securing more learning data by utilizing images of limited failure data. In addition, the obtained data is used for learning to present deep learning methods and research results for developing a more accurate transmission facility diagnosis system. As a result of this study, it was confirmed that the average precision was improved by about twice from 39.2% to 81.1% by applying the learning model technology. This improved method of artificial intelligence learning technology is expected to prevent power transmission failure in advance, avoid power outage costs caused by failure, and reduce maintenance costs through inspection automation.

Improving Learning Environments: Avoiding Stupidity Perspective

Research in learning technologies is often focused on optimizing some aspects of human learning. However, the usefulness of practical learning environments is heavily influenced by their weakest aspects, and, unfortunately, there are many things that can go wrong in the learning process. In this article, we argue that in many circumstances, it is more useful to focus on avoiding stupidity rather than seeking optimality. To make this perspective specific and actionable, we propose a definition of stupidity, a taxonomy of undesirable behaviors of learning environments, and an overview of data-driven techniques for finding defects. The provided overview is directly applicable in the development of learning environments and also provides inspiration for novel research directions and novel applications of existing techniques.

Pembelajaran Berbasis Multimedia Interaktif Mahasiswa Di Perguruan Tinggi

Education and Learning Journal (ELJour) is a Journal published by the departement of Education, Faculty of Islamic Religion, Universitas Muslim Indonesia. ELJour is an open-access journal which has been established for the dissemination of state-of-the-art knowledge in the field of education, learning and other matters relating to education such as: Islamic Education, Curriculum and teaching methodology, Contemporary issues in education and learning, Development in education and learning, Education and learning technology, Education and learning research, Educational Psychology, Innovations in education and learning, Management Education, Special Education, and, vocational education.

Enhancing Health Care Education and Practice Post COVID

Enhancing Health Care Education and Practice Post COVID

Perception on the Implementation of Flexible Learning in the Time of Covid 19

The rapid spread of Corona Virus 2019 known as COVID 19 has brought disturbance to a large number of people’s lives and economic stability. It has greatly challenged the people’s holistic way of life with uncertainties considering the significant public health risks COVID-19 poses. This pandemic has affected the global educational systems which led to delve into advanced teaching-learning tools or modalities that would help bring about the transition from the usual face-to-face mode to flexible learning. The objective of this study was to find out the perception of the respondents on the implementation of Flexible Learning in the delivery of nursing education programs as the basis for the recommended course of actions in the schools of nursing. This is a descriptive-quantitative study, which utilized a self-rated standardized questionnaire—The flexibility questionnaire developed by Bergamin et al., (2012). The questionnaires were purposively administered to a total of sixty-six (66) respondents composed of the deans, coordinators/program heads, and faculty members of selected nursing schools in Metro Manila. The results had shown that the respondents agreed to all indicators mentioned in the survey tool. It has therefore shown in this study that respondents desired an approach to flexible learning in which teaching and learning could exercise regulation and jurisdiction.ReferencesAlly, M. (2004). Foundations of educational theory for online learning. In T. Anderson (Ed.), The theory and practice of online learning. 15–44. Athabasca University Press.Arbaugh, J. B. (2000). Virtual classroom characteristics and student satisfaction with Internet-based MBA courses. Journal of Management Education, 24(1), 32–54. doi:10.1177/105256290002400104.Barnard, L., Lan, W. Y., To, Y. M., Paton, V. O., & Lai, S.-L. (2009). Measuring self-regulation in online and blended learning environments. The Internet and Higher Education, 12(1), 1–6. doi:10.1016/j.iheduc.2008.10.005.Bergamin, P.B., Werlen, E.,Siegenthaler, E., & Zizka, S. (2012). The relationship between flexible and self-regulated learning in open and distance universities. International Review of Research in Open and Distance Learning. 13(2), 101-103.Daniel, J. (2020). Education and the Covid 19 pandemic. Viewpoints/Controversies. https://doi.org/10.1007/s11125-020-09464-3.Ghebreyesus, T. (2020). Addressing mental health needs: an integral part of COVID 19 response. World Psychiatry. 19(2), 129-30.Hart, I. (2000). Learning and the “F” word. Educational Media International, 37(2), 98-101. doi:10.1080/095239800410388.Lewis, R. & Spencer, D. (1986). What is Open Learning?. Council for Educational Technology.Moran, L., & Myringer, B. (1999). Flexible learning and university change. In K. Harry (Ed.), Higher education through open and distance learning: World review of distance education and open learning.157–171. Routledge.Palmer, S.R. (2011). The lived experience of flexible education –theory, policy and practice. Journal of University Teaching and Learning Practice. 8(3), 16.Sadler‐Smith, E., & Smith, J. P. (2004). Strategies for accommodating individuals’ styles andpreferences in flexible learning programmes. British Journal of Educational Technology, 35(4), 395–412. doi:10.1111/j.0007-1013.2004.00399.Shurville, S., O’Grady, T. and Mayall, P. (2008). Educational and institutional flexibility of Australian Educational Software. Campus-Wide Information Systems, Emerald Group Publishing Limited. 25(2), 74-84.Tucker, R. & Morris, G. (2012). By Design: Negotiating Flexible Learning in the Built Environment. Discipline Research in Learning Technology, Co-Action Publishing. 20(1).United Nations. (2020). Policy brief: COVID 19 and the need for action on mental health.

Educational Technology Research: Contexts, Complexity and Challenges

This paper explores the development of educational technology research over the last 50 years. This is done by considering what has influenced this development and what are current trends. The issue is further explored by considering what influence these trends have had on the development of distance learning pedagogy, especially for the education of adults. Technology Enhanced Learning or educational technology research (TEL) is a relatively young area of research. The work of educational technologists and others involved in education research has indeed been interdisciplinary. So, part of the exploration of trends in this paper has been directed towards understanding the impact of other areas of research on progress in educational technology research. A central focus of the paper is the understanding of how the “TEL Complex” (Scanlon et al. 2013) encapsulates some of these influences both from interdisciplinary perspectives and from an understanding of the complexity which has to be considered as part of the situatedness of the developments. The paper discusses some key contemporary trends in educational technology research. These are identified as personalisation, social learning, learning design, machine learning, and data driven improvement. These are worthy of exploration, in part, because each has a basis in early work in the field and therefore illustrate some continuing concerns. The paper concludes by acknowledging the broader influences on the development of educational technology and the complexity of the challenges facing the field and its practical applications.

Multimodal data capabilities for learning: What can multimodal data tell us about learning?

AbstractMost research on learning technology uses clickstreams and questionnaires as their primary source of quantitative data. This study presents the outcomes of a systematic literature review of empirical evidence on the capabilities of multimodal data (MMD) for human learning. This paper provides an overview of what and how MMD have been used to inform learning and in what contexts. A search resulted in 42 papers that were included in the analysis. The results of the review depict the capabilities of MMD for learning and the ongoing advances and implications that emerge from the employment of MMD to capture and improve learning. In particular, we identified the six main objectives (ie, behavioral trajectories, learning outcome, learning‐task performance, teacher support, engagement and student feedback) that the MMLA research has been focusing on. We also summarize the implications derived from the reviewed articles and frame them within six thematic areas. Finally, this review stresses that future research should consider developing a framework that would enable MMD capacities to be aligned with the research and learning design (LD). These MMD capacities could also be utilized on furthering theory and practice. Our findings set a baseline to support the adoption and democratization of MMD within future learning technology research and development. Practitioner NotesWhat is already known about this topic Capturing and measuring learners’ engagement and behavior using MMD has been explored in recent years and exhibits great potential. There are documented challenges and opportunities associated with capturing, processing, analyzing and interpreting MMD to support human learning. MMD can provide insights into predicting learning engagement and performance as well as into supporting the process. What this paper adds Provides a systematic literature review (SLR) of empirical evidence on MMD for human learning. Summarizes the insights MMD can give us about the learning outcomes and process. Identifies challenges and opportunities of MMD to support human learning. Implications for practice and/or policy Learning analytics researchers will be able to use the SLR as a guide for future research. Learning analytics practitioners will be able to use the SLR as a summary of the current state of the field.

Special collection on mobile mixed reality 2019 update

This special collection of Research in Learning Technology explores the development of the state of the art of Mobile Mixed Reality (MMR) in education. The special collection was established in 2018 to provide research-informed exploration of this emergent and rapidly developing arena of educational technology through the lens of Scholarship Of Technology Enhanced Learning (SOTEL). The special collection update for 2019 includes four articles that cover self-efficacy and motivation of MMR users, analysis of student experiences of MMR, and a selection of case studies on designing and implementing MMR in educational contexts. The range of articles illustrates the further development of MMR as a platform for designing authentic learning environments in both formal and informal learning situations. The articles also highlight attempts to address the issue identified in the 2018 collection of a general lack of engagement with new learning theories and models in the use of MMR to design transformative learning experiences. This editorial is part of the special collection ‘Mobile Mixed Reality-Enhanced Learning’ edited by Thomas Cochrane, Vickel Narayan, James Birt, Helen Farley and Fiona Smart. Read all articles from this collection here.

Big Educational Data & Analytics: Survey, Architecture and Challenges

The proliferation of mobile devices and the rapid development of information and communication technologies (ICT) have seen increasingly large volume and variety of data being generated at an unprecedented pace. Big data have started to demonstrate significant values in higher education. This paper gives several contributions to the state-of-the-art for Big data in higher education and learning technologies research. Currently, there is no comprehensive survey or literature review for Big educational data. Most literature reviews from a few authors have focused on one of these fields: educational mining, learning analytics with discussions on one or two aspects such as Big data technologies without educational focus, social media data in education, etc. Most of these literature reviews are short and insufficient to provide more inclusive reviews for Big educational data. In this paper, we present a comprehensive literature review of the current and emerging paradigms for Big educational data. The survey is presented in five parts: (1) The first part presents an overview and classification of Big education research to show the full landscape in this field, which also gives a concise summary of the overall scope of this paper; (2) The second part presents a discussion for the various data sources from education platforms or systems including learning management systems (LMS), massive open online courses (MOOC), learning object repository (LOR), OpenCourseWare (OCW), open educational resources (OER), social media, linked data and mobile learning contributing to Big education data; (3) The third part presents the data collection, data mining and databases in Big education data; (4) The fourth part presents the technological aspects including Big data platforms and architectures such as Hadoop, Spark, Samza and Big data tools for Big education data; and (5) The fifth part presents different approaches of data analytics for Big education data. This part provides a more inclusive discussion on data analytics which is beyond traditional forms of learning analysis in higher education. This includes predictive analytics, learning analytics including collaborative, behavior, personal learnings and assessment, followed by recommendation systems, graph analytics, visual analytics, immersive learning and analytics, etc. The final part of the paper discusses social (e.g. privacy and ethical issues) and technological challenges for Big data in education. This part also illustrates the technological challenges faced by giving an example for utilizing graph-based analytics for a cross-institution learning analytics scenario.

Mobile Video Production

Smartphone ownership has become ubiquitous (International Telecommunication Union, 2017), but also fragmented by the variety of manufacturers and models available. In particular there are significant differences between the two major smartphone platforms (iOS and Android). This workshop is designed to provide participants with a cross-platform toolkit for implementing BYOD user-generated mobile video content in higher education contexts (Cochrane & Sinfield, 2019). Grounded on the concepts of learner-generated contexts, heutagogy or self-determined learning (Blaschke & Hase, 2019), and authentic learning (Burden & Kearney, 2016), the workshop facilitators will outline a framework for user-generated mobile video production (Sinfield, 2018). The workshop will illustrate the framework by examples of BYOD student projects, and will introduce participants to a hands-on exploration of an ecology of resources for mobile collaborative video production and sharing via digital social media platforms.
 
 Schedule (100 mins)
 
 Introductions (5 min) 
 Participant survey (5 min)
 
 
 Overview of collaborative mobile video production technologies (10 min)
 
 Hardware
 Student project examples 
 
 Mobile video activities (20 min) 
 
 
 
 Adobe Premier Clip App
 LG360 Cam’s
 
 Support
 Streetview
 YouTube360
 
 
 Collaborative Video project development (45 min) 
 Participants choose a topic to work on as a mobile video production team, for example:
 
 
 
 Autumn (Albert Park)
 Construction
 Transportation
 Time Lapse
 
 Sharing and review of participant projects (Upload to YouTube and share) (10 min)
 Reflections – sharing of project URLs and reflections via Twitter and the #SOTELNZ hashtag (5 min)
 END
 
 References
 
 Blaschke, L. M., & Hase, S. (2019). Heutagogy and digital media networks: Setting students on the path to lifelong learning. Pacific Journal of Technology Enhanced Learning, 1(1), 1-14. doi:https://doi.org/10.24135/pjtel.v1i1.1
 Burden, K., & Kearney, M. (2016). Conceptualising Authentic Mobile Learning. In D. Churchill, J. Lu, K. F. T. Chiu, & B. Fox (Eds.), Mobile Learning Design: Theories and Application (pp. 27-42). Singapore: Springer Singapore.
 Cochrane, T., & Sinfield, D. (2019, 4 July). Mobile Video Production Workshop: Exploring a BYOD development framework and toolkit. Paper presented at the HERDSA Conference 2019: Next Generation, Higher Education: Challenges, Changes and Opportunities, University of Auckland.
 International Telecommunication Union. (2017). ICT facts and figures 20172017(April). Retrieved from https://www.itu.int/en/ITU-D/Statistics/Documents/facts/ICTFactsFigures2017.pdf
 Sinfield, D. (2018). The Boundaries of Education: Using mobile devices for connecting people to places. Research in Learning Technology, 26(Special collection on Mobile Mixed Reality). doi:https://doi.org/10.25304/rlt.v26.2121

A Model for Developing a SOTEL Research Cluster

This case study critically reflects upon the development of a scholarship of technology enhanced learning (SOTEL) research cluster in clinical sciences higher education. The research cluster has grown from an initial community of practice established in 2015 in the context of Paramedicine education (Cochrane, Cook, Aiello, Harrison, & Aguayo, 2016), to a collaborative transdisciplinary research cluster that now encompasses: the School of Clinical Sciences, Journalism, the Centre for Teaching And Learning, the AppLAB, and international research partners (Cochrane, 2019; Cochrane et al., 2018). The MESH360 research cluster (initially standing for the Multiple Environment Simulation VR Hub, but now covering the growing body of immersive reality enhanced learning projects) focuses upon the common domain of the exploration of immersive reality to enhance higher education to develop student creativity, critical thinking, and problem-solving capabilities.
 
 The research cluster is built upon the shared ontology, epistemology, and research methodology of the wider SOTEL research cluster hub (https://sotel.nz/about-the-cluster/). We established an ecology of resources to support the research cluster (Cochrane & Narayan, 2018), and encourage open educational practice via social media, publishing in open access channels, and regular project showcases. Outcomes from the MESH360 research cluster include: innovative curriculum design, journal articles, conference proceedings, 2 Vice Chancellors teaching innovation awards, a Prime Minister’s research scholarship, and award of a variety of internal project funding. The activity of the research cluster is curated in a ResearchGate Project at https://www.researchgate.net/project/MESH360 and on social media via the #MESH360 hashtag. While the activity of the MESH360 has been predominantly within the Faculty of Health and Environmental Sciences, we are seeing wider impact into Schools within the other Faculties at the university, and potential national and international collaborations.
 
 The SOTEL model includes the following main elements:
 
 An online hub - The SOTEL Research Cluster https://sotel.nz/
 An annual Symposium showcasing SOTEL in practice https://sotel.nz
 The Pacific Journal of Educational Technology (PJTEL)
 The CMALT cMOOC and the MOSOMELT cMOOC
 A weekly webinar series
 Brokering international TEL networks such as the ASCILITE Mobile Learning Special Interest Group
 A series of TEL workshops and showcases
 
 
 The presentation will outline the above elements of the SOTEL Research Cluster. We believe the MESH360 research cluster model can be applied to a wide variety of higher education domains.
 References
 
 Cochrane, T. (2019). How AUT is Designing Authentic Student Learning Experiences with Immersive Reality. Paper presented at the 2nd New Zealand Digital Campus and Blended Learning Transformation From K6 to Higher education: Immersive AR/VR, blended learning innovations and next generation learning spaces, Stamford Plaza, Auckland, New Zealand. https://tinyurl.com/SOTELprojects
 Cochrane, T., Cook, S., Aiello, S., Harrison, D., & Aguayo, C. (2016, 28-30 November). Designing Virtual Reality Environments for Paramedic Education: MESH360. Paper presented at the Show Me The Learning. Proceedings ASCILITE 2016 Adelaide, University of South Australia, Adelaide, Australia.
 Cochrane, T., & Narayan, V. (2018, 25-29 June, 2018). The Scholarship of Technology Enhanced Learning: Reimagining SOTL for the Social Network Age. Paper presented at the EdMedia: World Conference on Educational Media and Technology 2018, Amsterdam, Netherlands.
 Cochrane, T., Stretton, T., Aiello, S., Britnell, S., Cook, S., & Narayan, V. (2018). Authentic Interprofessional Health Education Scenarios using Mobile VR. Research in Learning Technology, 26, 2130. doi:http://dx.doi.org/10.25304/rlt.v26.2130