Context

There are two main contexts to consider in this project; the Graduate Research Education (GRE) environment and the Fourth Industrial Revolution (4IR). These contexts will be explored through the lens of SocioTechnical Systems (STS) theories.


Graduate Research Education (GRE) environment.

Graduate Research is our highest level of education, developing PhDs and advanced researchers for both society and the Academy. In most universities, the Graduate Research Education (GRE) cohort is the smallest by numbers but often with an impact that outweighs its size. The GRE environment is changing in response to 4IR, but there is much work to be done to keep pace. 

Sociotechnical systems are these days an everyday part of higher education environments.

By understanding how technologies that intersect with human organisations affect and change the STS that emerge, it is likely we can better manage GREs to equip students for ethically responsible leadership in 4IR. Using next-generation STS approaches (Norman & Stappers, 2015; Pasmore et al., 2019) this research seeks to develop new organisational models for technology implementation in GRE management.

Examples of STS in GRE include technological tools designed to assist students in managing their candidature. For instance, candidature management dashboards, communication platforms for peer-peer learning, and digital collaboration tools. These STS are understudied and poorly represented in the literature (Gouseti, 2017). Student interaction with candidature management technologies forms part of their experience and serve purposes beyond the designed intent of each platform, such as improved literacy of human-machine systems leading to “higher-level capabilities and identity development” (Gouseti, 2017). Without a systematic consideration of the impact of technologies used for GRE management, we may be missing opportunities to enhance development of 4IR graduate qualities.

Dashboards can be used to track candidature progress.

The Fourth Industrial Revolution (4IR).

The fourth industrial revolution (4IR) is driven by technologies such as: artificial intelligence (AI), big data analytics, deep neural networking, cloud computing, 3D printing, blockchain, quantum computing, and advanced automation (Schwab, 2017).  Notably, it is how these technologies interact and enhance each other that has driven the exponential changes of 4IR. 

As with previous industrial revolutions, the skills most sought after by employers in 4IR are changing dramatically (Leopold, Ratcheva, & Zahidi, 2018), with a key industry landscape being dubbed Industry 4.0 (Drath & Horch, 2014).  Along with adjustments for Industry 4.0, the ubiquity of new technologies in our daily lives, such as facial recognition, the Internet of Things (IoT), and mobile supercomputing, signals substantial changes in our social systems.  Since the term Industry 4.0 was coined in 2014 (Drath & Horch) and the Fourth Industrial Revolution was declared in 2015 (Schwab) there has been extensive discussion on both of these topics with exact definitions shifting as one would expect of emerging fields. My research takes the view that Industry 4.0 is a subset of 4IR but recognises that some may dispute this view.

Examples from the four industrial revolutions. Exact dates of each revolution are disputed and more likely overlap than have distinct boundary markers.


Socio-Technical Systems (STS).

As we pass into 4IR with its signature fusion of cyber-physical systems, there has been a renewed interest in the field of STS as a way to navigate the enormous changes we are facing (Pasmore, Winby, Mohrman, & Vanasse, 2019). Sociotechnical systems (STS) theory examines how human and technology agents interact with each other and with societal and organisational structures (Walker, Stanton, Salmon, & Jenkins, 2008). The way each of these aspects – technologies, humans, organisational structures – interact with each other can be described by looking at their agency in the system.

The field of STS was initially developed in response to social upheavals caused by the mechanisation of coal extraction (Emery & Trist, 1965; Trist & Bamforth, 1951).  This led to the development of the Tavistock Institute in London (Baxter & Sommerville, 2011).

STS diagram showing the social and technical co-construction
of a management information system. Bostrom & Heinen, 1977

In 4IR, an understanding of how we create, impact, and are impacted by our sociotechnical systems has become more relevant than at any other time in history. The field of cyber-physical systems – think smart networked systems that integrate with humans – is enjoying vibrant discussion right now (Kant, 2016) and presents perhaps a more social trending face of STS theories.  Other examples of 4IR-STS are the way we and our societies interact with the Internet of Things (IoT), facial recognition technologies, and biometric innovations.

An everyday example of a cyber-physical system is wearable tech for fitness and health.

Why are STS important in GRE?

The industrial and social changes that are occurring in 4IR, and how universities should plan for these shifts, has been critically discussed across the literature (i.e. Aoun, 2017; Davidson, 2017; Gleason, 2018; Molla & Cuthbert, 2019; Penprase, 2018; Seldon, 2018).  The research proposed here takes the view that there are two distinct, yet highly interrelated challenges for graduate researchers to successfully transition to 4IR; the changing nature of work as characterised by Industry 4.0; and, the significant ethical consequences of 4IR technologies implemented into our social systems. 

The use of STS theory in higher education policy and planning, employs systems approaches to develop more effective educational environments that better prepare graduates for changing workforces (Richey et al., 2014) and is highly applicable to 4IR which is fundamentally a systems-based revolution. The focus is on using STS theory to understand how technologies can be best used to achieve this goal. The question of what STS are in GRE can be broken into three sub categories.

1. Candidature management.

This category includes technologies that help both the university and the students track their candidature and improve learning outcomes. Examples include candidature management platforms as well as the application of data analytics platforms and machine learning to identify candidates at risk that may require intervention.  There are notable ethical and privacy concerns to be considered in this category.

2. Skills enhancement.

This category includes technologies that help students acquire skills they need for Industry 4.0 such as digital literacy, technology design, and problem solving. Examples include online modules, virtual learning environments, and other self-directed learning tools. 

3. Development of qualities.

This category includes technologies that help students acquire 4IR qualities such as self-reflection, ethical digital citizenship, leadership, and collaboration. Examples include digital wellness apps, digital ethics courses, and collaboration platforms.

Developing deeper understandings of how STS in graduate research education management can better equip students for 4IR is a primary goal of this research.  Creating organisational models for STS implementation to achieve that goal, is a primary objective


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