Call for Papers TLT Special Issue on Designing Technologies to Support Professional and Workplace Learning for Situated Practice

Call for Papers

TLT Special Issue on Designing Technologies to Support Professional and Workplace Learning for Situated Practice

Abstract Submission (Optional): 15 April 2021
Full Manuscripts Due: 15 June 2021

In an era of global, organizational, and technological change, all of which are transforming the world of work, professional and workplace learning are critical for both employability and organizational competitiveness. Such learning is therefore needed on a greater scale than ever before, and the only way to provide that scale is through the integration of technology and learning. At the same time, if it is to serve the goal of boosting productivity, professional and workplace learning needs to be based around and integrated with work. Yet most advances in learning technologies have been made within K–12 and higher education settings, and in the area of formal learning environments in general. Technologies developed within formal education settings are nevertheless also increasingly being appropriated for use with/by professional learners in contexts other than those for which they were originally designed.

This special issue of the IEEE Transactions on Learning Technologies (TLT)? on “Designing Technologies to Support Professional and Workplace Learning for Situated Practice” aims to showcase the latest developments and innovations in, and advance the scientific discourse on issues specific to, designing technologies that support learning for work. In setting out to achieve this aim, we place focus on learning that is intended to improve work practice, and that is situated within work activities and contexts.

Background and Context

Global change impacts professional learning

Work is becoming increasingly specialized, which means professionals with specific expertise have to work in collaborative and networked ways; increasingly global, making work more distributed across sites; and increasingly time- and place-independent, as people work and connect via digital technologies [1]. Globalization, automation, digitalization, and advances in medical and material sciences are important drivers of change. At the time of writing, the global COVID-19 pandemic is ongoing and has triggered one of the biggest and most agile sets of changes to work practices and working environments in generations, possibly ever. In April 2020, more than two-thirds of the world’s working population was in lockdown in an attempt to stop the spread of this disease. Many organizations asked their employees to work from home where possible. Although long-term effects cannot be predicted with certainty, plausible effects include an acceleration of digitalization and automation of workplaces alongside economic disruption characterized by a rapid decline in the gross domestic product (GDP), business closures, uncertainty across whole sectors (e.g., hospitality and tourism) with possible re-regionalization of production. Globally, millions of people face job losses and will have to re-train as they change jobs or careers. Even those who retain their positions will likely have to adapt their work practice within work environments that are transformed.

Professional and workplace learning will be even more critical

Such learning increases the responsiveness and adaptability of both individuals and organizations. Continuous professional learning is important to help people prepare for ongoing changes in work and for new work roles and this has become accepted as a crucial component to help people re-skill as jobs are lost, and to upskill knowledge within specific professional domains as practices transform [1]. In summary, professional learning is understood as a critical component to ensure people continue to be employable, and for organizations to remain competitive.

Professional learning and technologies research needs to be ramped up

Despite this, most advances in the research and development of learning technologies are focused on formal learning, such as school, university, or professional training outside the workplace [2]. Some of the most visible examples include massive open online courses (MOOCs) and other forms of open education. MOOCs are formal educational environments that have been used to support professional learning [3], [4]. However, although open education can support professional learning, they often are based on formal learning paradigms, rather than supporting situated learning of professional practice. Overall, while digitalization has been a triggered substantial change in many areas of life, it has not significantly changed the ways professionals learn [1]; and in the context of the ongoing COVID-19 pandemic, there is a critical need to focus on the research and development of technologies to support situated learning of professional practice.

This challenge of building imaginaries describing how technologies could support and transform professional learning has been critiqued [5]. [6]. One critique concluded that, because technology-enhanced professional learning sites at the intersection of work psychology, learning science, and computer science, concepts from each of the three domains have to be brought together to find solutions; these concepts have been superficially pieced together, and not (yet) transformed into an unorthodox and imaginative conceptualization of technology-enhanced professional learning [6]. Examples of research that connect self-tracking and quantified self, individual and collaborative computer-mediated reflection, professional learning analytics, and (work) behavior change, include Rivera-Pelayo et al. [7], who investigated how manually tracked and shared mood serves as a simple, agile fast tool to support group awareness. This tool serves as an entry point for work-related reflection, and hence as a trigger for work behavior change. Another example is the work of Ruiz-Calleja et al. [8], who carried out research on a learning analytics infrastructure for professional learning that addresses the need to integrate data and analytics services across a heterogeneous IT landscape. Fessl, Bratic, and Pammer [9] examined the effectiveness of automatic prompts for reflection that connect between work analytics and learning. Other examples of research and development of technology tools for professional learning that bring together diverse domains include work that connects augmented reality training for work practice, work process assistance, and reflection by Büttner, Prilla, and Röcker [10] and Limbu et al. [11]. These studies investigate training effectiveness and user satisfaction as professionals engage in augmented reality training, a form of immersive learning that takes into consideration the reality of the workplace.

Littlejohn and Pammer-Schindler [1] have argued for the need to integrate perspectives, theories, and methods from psychology, learning science, and computer science in ways that enable the research and development of effective technology tools for professional learning. The adaptation of theories and methods from the learning and computer sciences alone are not sufficient because of the qualitative difference between learning for work and learning in formal education. In formal education, the motivation to learn usually is to gain specific knowledge and qualifications to progress. Professional learning often is motivated by getting a job done better and improving professional practice. Therefore, in professional contexts, “learning” tends to be relevant only in relation to professional practice, in the sense that it changes and improves (some aspects of) practice. In workplace environments, professional learning has a lower priority compared with work performance. This can be traced to a fundamental view of the organization as a social construct with shared goals [12], which tends to focus on services, product production, and so on. This results in a tight integration of working and learning, of individual and organizational aspects of learning (such as human resource management, personnel development, knowledge management, or continuous process improvement), and of learning and knowledge creation. Another important and relevant factor is that it can be difficult to find the time [13] or space [14] to learn in a busy workplace. Finally, it can be challenging for learners to transfer knowledge from an educational context to a workplace context, or from one workplace context to another [15] and learners may not have the capability to do this [16], even though it is critical for effective learning. In summary, professional learning is characterized by a distinct socio-technical system of technology-enhanced, professional learning, in which professional learners live, work, and learn; these systems operate differently compared with the socio-technical systems that define formal education [6].

Role of the Special Issue

As a starting point in bringing together the relevant, but fragmented, areas of research that underpin professional learning and workplace learning, we are calling for papers that contribute to the domain of technology-enhanced, professional learning by:

  1. Identifying characteristics of professional learning that are due to the social context in which professional learning is embedded and that are relevant for technology design;
  2. Interrogating technology practices specific to these characteristics of professional learning, and using the results to inform design—examples are issues of setting aside time and space for learning, privacy issues or issues related to existing power hierarchies, and the potential non-sharedness of learning as a goal of organizational relevance, to name a few;
  3. Evidencing the ways in which emerging, novel technologies might improve professional learning in a variety of work contexts. Such papers could both be based on experimental studies on emerging, novel technologies that consider salient aspects of professional learning in the experiment design as well as based on field studies investigating the design, development, and application of the technologies in professional learning settings. Special emphasis could be placed on reducing risks introduced by modern technologies, such as increased surveillance in the workplace.

We look forward to receiving papers detailing relevant studies and hope the special issue seeds discussion and debate, raising important questions to help advance the field.

Suggested Topics

Topics of interest for the special issue thus include, but are not limited to, the design and development of technological solutions and applications aimed at:

  • Remote or distributed work-based learning
  • Workplace and professional learning that helps ensure resilience to continuity crises (e.g., pandemics, natural disasters)
  • Supporting and assessing the transfer of learning to, and between, on-the-job situations
  • Learning-as-knowledge-creation in the workplace and in professional settings
  • Learning in complex professional domains and/or in domains with low uptake of learning technologies
  • Coaching and mentoring in the workplace (both human and intelligent agent-based)
  • The development of “soft” skills in the workplace and professions
  • Supporting the links between individual learning, organizational learning, and capability building
  • Learning through reflection on workplace and professional practice, including collaborative or shared reflection
  • Assessment and credentialing of the workplace and professional learning
  • The modeling, development, and management of workplace and professional competencies (i.e., competency-based learning and assessment)
  • Computer-supported collaborative workplace and professional learning

Also of interest are investigations of specific technologies as applied to workplace and professional learning, such as:

  • Adaptive and personalized learning systems, including learner models for enabling those systems
  • Authoring and instructional design tools/platforms
  • Games and gamification
  • Modeling, simulation, and digital twin technologies and applications
  • Reusable learning objects and learning designs
  • Semantic Web services, applications, and ontologies
  • Social networking and knowledge-sharing infrastructures
  • Virtual reality (VR), augmented reality (AR), mixed reality (MR), and other extended reality (XR) technologies
  • Wearable devices and interfaces
  • Learning analytics and data mining technologies/applications

Note: TLT is somewhat unique among educational technology journals in that it is both a computer science journal and an education journal. In order to be considered for publication in TLT, papers must make substantive technical and/or design-knowledge contributions to the development of learning technologies as well as show how the technologies can be used to support learning. Papers that are concerned primarily with the evaluation of existing learning technologies and their applications are suitable for TLT only if the technologies themselves are novel, or if significant technical and/or design insights are offered.

Submission and Review Process

Abstracts may be submitted to the guest editors via email at; this is not mandatory but will enable the editors to offer early feedback on the paper’s suitability with respect to the aims and scope of the special issue.

Full manuscripts should be prepared in accordance with the IEEE Transactions on Learning Technologies guidelines and submitted via the journal’s ScholarOne Manuscripts portal?, being sure to select the relevant special issue name during the submission process. Manuscripts must not have been published or currently be under consideration for publication elsewhere. Only full manuscripts intended for review, not abstracts, should be submitted via the ScholarOne portal, and conversely, full manuscripts cannot be accepted via email.

Each full manuscript that passes an initial prescreening will be subjected to rigorous peer review in accordance with TLT’s editorial policies and procedures. It is anticipated that 7 or 8 articles (plus a guest editorial) will ultimately be published in the special issue.

Important Dates

  • Abstract submission (optional): 15 April 2021
  • Feedback from guest editors to authors on abstracts: 22 April 2021
  • Full manuscripts due: 15 June 2021
  • Completion of first review round: End of September 2021
  • Revised manuscripts due: End of November 2021
  • Final decision notification: End of January 2022
  • Publication materials due: End of March 2022
  • Publication of special issue: Summer 2022

Guest Editors

  • Viktoria Pammer-Schindler – Graz University of Technology, Austria
  • Allison Littlejohn – University College London, U.K.
  • Tobias Ley – Tallinn University, Estonia
  • Joachim Kimmerle – IWM-KMRC Tuebingen, Germany
  • Mark J. W. Lee – Charles Sturt University, Australia

Please contact with any questions, comments, or concerns.


  1. A. Littlejohn and V. Pammer-Schindler, “Technologies for professional learning,” in Handbook of Research Approaches on Workplace Learning, D. Gijbels and C. Hartelis, Eds., Berlin, Germany: Springer-Verlag, in press.
  2. I. Roll and R. Wylie, “Evolution and revolution in artificial intelligence in education,” Int. J. Artif. Intell. Educ., vol. 26, no. 2, pp. 582–599, Jun. 2016. doi: 10.1007/s40593-016-0110-3?.
  3. A. Littlejohn and C. Milligan, “Designing MOOCs for professional learners: Tools and patterns to encourage self-regulated learning,” eLearn. Papers, vol. 42, Jun. 2015, Art. no. 4.
  4. C. Milligan and A. Littlejohn, “Why study on a MOOC? The motives of students and professionals,” Int. Rev. Res. Open Distrib. Learn., vol. 18, no. 2, pp. 92–102, 2017. doi: 10.19173/irrodl.v18i2.3033?.
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  6. A. Littlejohn and A. Margaryan, Eds., Technology-Enhanced Professional Learning: Processes, Practices, and Tools, Evanston, IL, USA: Routledge, 2013. doi: 10.4324/9780203745052?.
  7. V. Rivera-Pelayo, A. Fessl, L. Müller, and V. Pammer, “Introducing mood self-tracking at work: Empirical insights from call centers,” ACM Trans. Comput.-Hum. Interact., vol. 24, no. 1, Feb. 2017, Art. no. 3. doi: 10.1145/3014058?.
  8. A. Ruiz-Calleja, S. Dennerlein, D. Kowald, D. Theiler, Dieter, E. Lex, and T. Ley, “An infrastructure for workplace learning analytics: Tracing knowledge creation with the social semantic server,” J. Learn. Analytics, vol. 6, no. 2, pp. 120–139, Aug. 2019. doi: 10.18608/jla.2019.62.9?.
  9. A. Fessl, G. Wesiak, V. Rivera-Pelayo, S. Feyertag, and V. Pammer, “In-app reflection guidance: Lessons Learned across four field trials at the workplace,” IEEE Trans. Learn. Technol., vol. 10, no. 4, pp. 488–501, Oct.–Dec. 2017. doi: 10.1109/TLT.2017.2708097?.
  10. S. Büttner, M. Prilla, and C. Röcker, “Augmented reality training for industrial assembly work—are projection-based AR assistive systems an appropriate tool for assembly training?,” in Proc. 38th Annu. ACM Conf. Human Factors in Computing Systems (CHI’20), Honolulu, HI, USA, Apr. 2020. doi: 10.1145/3313831.3376720?.
  11. B. H. Limbu, H. Jarodzka, R. Klemke, F. Wild, and M. Specht, “From AR to expertise: A user study of an augmented reality training to support expertise development,” J. Universal Comput. Sci., vol. 24, no. 2, 2019. doi: 10.3217/jucs-024-02-0108?.
  12. C. Argyris and D. A. Schön, Organizational Learning II: Theory, Method, and Practice. Reading, MA, USA: Addison Wesley, 1996.
  13. Renner et al., “Computer-supported reflective learning: How apps can foster reflection at work,” Behav. & Inform. Technol., vol. 39, no. 2, pp. 167–187, Feb. 2020. doi: 10.1080/0144929X.2019.1595726?.
  14. A. Fessl, M. Bratic, and V. Pammer, “Continuous learning with a quiz for stroke nurses,” Int. J. Technol.-Enhanced Learn., vol. 6, no. 3, pp. 265–275, 2014. doi: 10.1504/IJTEL.2014.068362?.
  15. A. Littlejohn, K. Charitonos, and H. Kaatrakoski, “The role of professional learning in addressing global challenges: Tensions and innovations associated with AMR,” Frontiers Educ., vol. 4, 2019, Art. no. 112. doi: 10.3389/feduc.2019.00112?.
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