Tag Archives: Cybernetics

Carrying Von Bertalanffy’s baton: Systems, complexity and network science, Part 1

Title: Carrying Von Bertalanffy’s baton: Systems, complexity and network science, Part 1

Author: Dr. Burgert Senekal, University of the Free State.

Ensovoort, volume 42 (2021), number 5: 3


Ludwig von Bertalanffy formulated his General Systems Theory (GST) shortly after World War II. He envisaged an umbrella science that would unite the scientific endeavour by identifying universal laws that could facilitate interdisciplinary research. In addition, he argued that computers allowed scientists to conduct research in a new way, and he advocated that the connections between entities are key to understanding how the universe functions. Although his GST only gained limited acceptance in the scientific community at the time, in recent years, GST has become an important component of complexity theory. In addition, Von Bertalanffy included network and graph theory under GST, and these theoretical avenues have developed into what is today known as network science, which is one of the most important components of complexity theory. This article shows how network science realised Von Bertalanffy’s vision in three important ways: by foregrounding connections, by facilitating interdisciplinary research and by using computers to study phenomena in news ways. Parts 2 and 3 of this three-part series of articles discuss key theoretical insights shared between Von Bertalanffy and network science.

Keywords: Von Bertalanffy, General Systems Theory, systems science, complexity, network science

1. Introduction

Ludwig von Bertalanffy (1901-1972) proposed General Systems Theory (GST) through a series of publications (e.g. 1940, 1950a, 1950b, 1952, 1956, 1968 and 1972). His views have had a noticeable influence on science, across continents and disciplinary boundaries (Hammond, 2019). Among other insights, he recognized the potential of computer-driven research, argued that the workings of entities could not be understood in isolation, and suggested that systems theory could provide an approach to integrating science through interdisciplinary research. Although his GST did not succeed in these goals at the time, network science has realized this vision in the 21st century.

Network science has become one of the most important approaches within systems theory, and Barabási (2011:15) claims, for example, that network science has hijacked the theory of complexity. Von Bertalanffy (1972:416, 1968:21,90) also included graph theory (one of the components of network science) under the umbrella of GST (see also Geurts, 1974:215), although in Von Bertalanffy’s time, graph theory was still in a developmental stage. Barabási (2016:7) argues that network science has its roots in earlier approaches but has emerged as a distinct science in the early 21st century, and it is with this 21st century approach that the current study is concerned.

The current three-part series of articles examines network science as a continuation of Von Bertalanffy’s GST. In Part 1, network science is contextualized within the information revolution and the Connected Age (Watts, 2004), with specific reference to Von Bertalanffy’s insights in this regard. Parts 2 and 3 examine specific theoretical aspects of network science that are consistent with Von Bertalanffy’s insights. The aim is to show how network science realised Von Bertalanffy’s vision against the backdrop of the information revolution since the 1990s.

2. A note on the relationship between General Systems Theory and network science

Goldstein (2008:20) shows that complexity theory can be traced back to cybernetics, information theory, graph theory, GST, complex adaptive systems (CAS), game theory and far-from-equilibrium thermodynamics. Complexity theory is neither a unified theory nor a theory with singular roots – various authors have published in this domain, citing different influences. Von Bertalanffy’s GST has however become an important component of complexity theory.
As stated earlier, Von Bertalanffy (1972:416, 1968:21,90) included graph theory under the umbrella of GST. He (1972:416) writes,

System-theoretical approaches include general system theory (in the narrower sense), cybernetics, the theory of automata, control theory, information theory, set, graph and network theory, relational mathematics, game and decision theory, and computerization and simulation.

Network theory is sometimes considered to be the social branch of network science, whose history is e.g. documented in Freeman (2004). Graph theory is usually considered to be a branch of mathematics, and its history is described in Amaral and Ottino (2004). Note that Von Bertalanffy (1972:416) includes both network and graph theory as part of GST. As these branches developed, they were merged and combined with concepts from other branches, resulting in an entangled family tree. Watts and Strogatz (1998) is a case in point: they combine the theory of random graphs (mathematical graph theory) with social networks, in particular Milgram (1967). The result is a science of systems and networks that is difficult to demarcate and whose roots are difficult to separate.

Von Bertalanffy is seldom cited as a source in publications that use network theory, but this does not mean that his insights had no bearing on the discipline. Harary and Batell (1981) for instance use Von Bertalanffy as a source in one of the most influential journals in network theory, Social Networks. Note also that Freeman (2004:129) identifies Harary as one of the most influential authors in the social branch of network theory.

Like Von Bertalanffy, Harary and Batell (1981:30) see graph theory as an approach to studying systems,

… all conceptions of ‘system’ involve a set of units and their interrelationships. Since any binary relation has a natural representation as a graph or digraph, the points being the units (people, groups, or larger aggregations) and the lines the relations between them, a graph-theoretic approach immediately suggests itself.

Harari and Batell’s view of graph theory as a way of studying systems is found in numerous other publications as well. Glattfelder (2010:2) writes that any complex system “finds its natural formal representation in a graph,” and Kuhnert (2011:9) argues, “Complex networks can be represented formally by graphs and graph theory offers a mathematical framework for an exact treatment of such systems”1 (see also Mobus and Kalton, 2015:23, and Kuchaiev, Stevanović, Hayes and Pržulj, 2011:1).

In other words, while Von Bertalanffy conceived of network and graph theory as some of the theories incorporated under GST, GST itself has morphed and combined with other theories into what can broadly be termed systems science, of which network and graph theory remain part, although these theories have also evolved into what Barabási (2016) terms network science. A full discussion of the complex history of these disciplines will not be attempted here, but it should be noted that network science and GST form part of the complexity paradigm.

3. Three aspects of Von Bertalanffy’s vision

3.1 Interdependence and the Connected Age

The modern world is more interdependent than it has ever been, mainly due to the rapid advances in technology over the past few decades. The telegraph, telephone and radio all played a major role in making the world more interdependent, but technological advances since World War II, such as the introduction of television and satellites, had an even greater impact (Papp, Alberts and Tuyahov, 1997). Already before the advent of the World Wide Web, Senekal (1987:169) argued with reference to Afrikaans literature,

Even Afrikaans literary acts do not exist in isolation, but are closely intertwined with the international world and its thinking – to which it is indeed even electronically connected. This is much clearer today than in previous decades and even then, from the beginning of Afrikaans literature, there was a very strong import of other literatures into Afrikaans, from both Western and African traditions.2

Since the 1990s, however, technology has advanced even faster. The World Wide Web was founded in 1989, blogs in 1997, Google in 1998, Wikipedia in 2001, Myspace in 2003, Facebook in 2004, YouTube in 2005, Twitter in 2006 and Instagram in 2010 (Senekal and Brokensha, 2014). Along with these platforms, cell phones came into general use around the turn of the millennium and the iPhone was launched in 2007. Today, there are few households in the West that do not have access to the World Wide Web, it is estimated that there are more than 4 billion mobile phones worldwide, and Facebook has more than 2 billion users.

Due to information technology, information now spreads worldwide within seconds, bringing people into contact who would otherwise have been unconnected. Urry (2004:22) writes, “New technologies produce ‘global time’ by shortening or even ‘dematerializing’ distances between places and people.”3 South Africans became aware of the September 11 2001 terrorist attacks at the same time as Americans, while such information would have taken weeks to reach the Southern Hemisphere a hundred years ago. Through email, Skype, Facebook, Twitter, Instagram, WhatsApp and other online platforms, it is now possible to share in other people’s lives, no matter where they are.

Technology not only connects the world in terms of information: transport networks have evolved through the twentieth century that make different continents much more connected than before. Jan van Riebeeck’s journey to the Cape in 1652 took just over four months, while Magellan’s journey around the world (1519-1522) took three years, but such a journey can now be completed within hours. Luo, Yin, Di, Hardisty, MacEachren and Alan (2014) write that the world has recently become more interconnected, additionally with connections between different types of networks. For example, the World Wide Web (an information network) connects to transportation networks (technological networks) when a book is purchased online but delivered locally, or information networks maintain and expand social networks, or information networks allow terrorist networks to operate – groups such as the Islamic State of Iraq and Syria (ISIS) regularly use social media such as Twitter (Helmus and Bodine-Baron, 2017, Klausen, 2015), while Al-Qaeda’s affiliate, Al-Shabaab, also used this platform (Omand, Bartlett, and Miller, 2012:803). Heidtmann (2013:440) states, “our society [is] interconnected in many ways.”4

These links between entities and between networks have had a significant impact on culture, politics and the economy, including the use of information networks to resist governments (as seen during the recent Arab uprisings and the rise of the Islamic State in Iraq and Syria or ISIS), the global spread of epidemics such Covid-19, the increasing interdependence of economies, and on culture itself. Kaluza, Kӧlzsch, Gastner and Blasius (2010:1093) argue, “The ability to travel, trade commodities and share information around the world with unprecedented efficiency is a defining feature of the modern globalized economy.” In Watt’s (2004) view, we now live in an age that can be described as the Connected Age.

It is not only with regard to the present that interconnectedness is important: South Africa, for example, was established to function as an important node in the dominant transport network of the 17th century, namely the shipping network. Some historical networks have already been studied, of which Padgett and Ansell’s (1993) study of the Medici family in the 16th century and Pitts’s (1965) study of water transport networks in Russia during the Middle Ages are the most well-known. Interconnectedness has always been an important facet of the human environment and an emphasis on current technologies should not be seen as a denial that humanity was also interdependent in the past. What has changed, however, is that the level of interconnectedness has placed connectedness at the forefront to a greater extent than ever before.

This increasingly connected environment has meant that the term network has become a common term in the contemporary world (Barabási, 2016:22). Most people belong to some or other social networking platform, such as Facebook, Instagram or Twitter, connect to computers that are connected to the company or institution’s intranet or the global Internet, make cell phone calls through cell phone networks and so on. Schnelle (2018:49) writes that some people consider social network analysis (SNA) to mean analysing social media – a misconception I have found also. Social networking platforms are part of the network phenomenon, and so is the World Wide Web, but these are only part of this connectedness. As Easley and Kleinberg (2010:1) write, one can observe an increasing popular interest in interconnectedness during the first decade of the 21st century, and at the heart of this interconnectedness lies the concept of networks. By the turn of the millennium, Esterhuise (1999:19) remarked,

The consequence of this [the information revolution] is that the world has lost its density and hierarchical order. It’s turning into a web – with interdependent networks that are not only complex, but also fluid and turbulent.5

A number of popular science books were published on networks in the last two decades, including those of Barabási (2003; 2011), Buchanan (2003), Christakis and Fowler (2010) and Watts (2004; 2011). The growing popularity of network theory for scientific research is closely intertwined with this popular interest in networks; as Strogatz (2004:230) observes, “science itself reflects the network zeitgeist.” One could also argue that the increasing popularity of complexity theory – which is moreover related to network science – is also related to this zeitgeist.

This increasing interest in connectedness demands a re-evaluation of Von Bertalanffy’s contribution to science, as for instance done in Hammond (2019). As discussed in Part 2 of this article series, Von Bertalanffy emphasised that it is insufficient to study phenomena in isolation: an element’s connections are key to coming to a better understanding of the world. This emphasis on studying the ties between elements rather than the attributes of elements themselves lies at the heart of network science, as it does with GST. Von Bertalanffy, like network science, foregrounded connectedness.

3.2 Interdisciplinary science

Systems that are commonly seen as complex include governments, families, cultures, politics, traffic, the human body (from a physiological perspective), a person (from a psychosocial perspective), the brain, the immune system, ecosystems, the weather, swarms (birds or insects), economies, trade, language, socio-cultural systems and armed conflicts (Brownlee, 2007:2, Bar-Yam, 1997:2-4, Steyn, 1984:9). Complexity theory seeks to arrive at a better understanding of the functioning of these systems, and is “the ultimate of interdisciplinary fields” (Bar-Yam, 1997:1) or an “umbrella science” (Johnson, 2009:18) that “bridges” (Mobus and Kalton, 2015:6-7) various disciplines. Von Bertalanffy (1972:416) also suggested that GST is “by nature” interdisciplinary, as Senghaas (1968:51) explains,

The father of General Systems Theory assumed that if different sciences deal with systems, i.e. with relatively stable patterns of variables, it should be possible to develop a whole series of basic, common analytical concepts that can then be exchanged between any discipline. So the thesis of isomorphism was born anew, which is based on the assumption that there are indeed a whole series of structures and processes that could be worked out in all disciplines and applied to systems of all kinds.6

The same applies to network science, which has been applied in almost every discipline of science – from microbiology to archaeology, from epidemiology to education, from neurology to sociology (Barabási, 2016). Both network science and complexity theory bridge the gaps between disciplines and also transfer discoveries made in one field to another. As such, these theoretical perspectives not only study interactions, but also provide interactions between various disciplines; Barabási (2016:10) refers to “a cross-disciplinary fertilization of tools and ideas.” Stegbauer (2017:21) also argues,

Network research offers the opportunity for such an exchange; in this way, different specialist areas can learn from each other if the right contact surfaces can be constructed. In this case, you can transfer theories and methods between subjects and thereby advance your own discipline.7

Similarly, Heidtmann (2013:440) writes about the importance of network science in the mediation of interdisciplinary research,

As a result, the interdisciplinary research area of network science emerged with the aim of developing theoretical and practical ideas and methods to improve our understanding of networks of natural and human origin, amongst others through the use of ideas and results from mathematics, physics, computer science, operations research and from many other areas of natural, social and engineering sciences.8

This exchange of insights linked to different disciplines was Von Bertalanffy’s ideal for GST, and as such network science continues his vision in this regard. Von Bertalanffy (1950:142, 1968:80-81) writes,

… general system theory should be, methodologically, an important means of controlling and instigating the transfer of principles from one field to another, and it will no longer be necessary to duplicate or triplicate the discovery of the same principles in different fields isolated from each other. At the same time, by formulating exact criteria, general system theory will guard against superficial analogies which are useless in science and harmful in their practical consequences.

For this transfer of principles to occur between disciplines, GST had to identify and describe, “general system laws which apply to any system of a certain type, irrespective of the particular properties of the system or the elements involved” (Von Bertalanffy, 1950:138). This is precisely what authors such as Barabási (2011:15) found when applying network science,

By simultaneously looking at the World Wide Web and genetic networks, Internet and social systems, [network science] led to the discovery that despite the many differences in the nature of the nodes and the interactions between them, the networks behind most complex systems are governed by a series of fundamental laws that determine and limit their behaviour.

3.3 Network science and data science

Von Bertalanffy (1968:vii) suggests that the development of GST is closely linked to the development of digital computers by noting that it is “centred in computer technology, cybernetics, automation and systems engineering.” Elsewhere (1968:20) he argues that,

… computers have opened a new approach in systems research; not only by way of facilitation of calculations which otherwise would exceed available time and energy and by replacement of mathematical ingenuity by routine procedures, but also by opening up fields where no mathematical theory or ways of solution exist.

Barabási (2016:8) offers a similar perspective by arguing that the development of network science in the 21st century is due to two factors in particular: the development of large datasets as part of the information revolution, and the identification of universal characteristics shared by various networks. Improvements in information technology, in terms of software, hardware and data sets, mediated scientific investigations by the early 21st century that were not previously possible (Nistor, Pickl, and Zsifkovits, 2015:11, Heidtmann, 2013:441, Cohen and Havlin, 2010:16, Barabási, 2009:413, Kumpula, 2008:4).

Firstly, network science relies heavily on computer software, as for instance noted by Barabási (2016:11), Scott (2012:6), Freeman (2004:139), and Boissevain (1979:392). Although software played a role in network theory from at least as far back as the 1970s, software developments since the 1990s have led to significantly larger datasets being studied as a whole. Since the whole is always more than the sum of the parts – as discussed in the next article – the analysis of larger data sets as a whole could lead to new insights within network science. Software has also become more user-friendly and cheaper, which means more researchers have access to these technologies than was the case decades ago (Barabási, 2016:11).

Secondly, hardware has become exponentially faster over the past few decades. For example, the processing power of an Apple iPhone 6 surpasses the processing power of Cray-1, the world’s first supercomputer (Reed and Dongarra, 2015:59). Improving processor speed has played a crucial role in the development of network science (Van der Hofstad, 2014:1, Glattfelder, 2013:3), since astronomical data sets could now be studied with millions and even billions of data points. Together with the software that relies on this hardware and network science, researchers were able to observe similarities between different systems that were not previously possible.

Thirdly, the availability of large digital data sets has had a significant impact on the development of network science. The widespread use of computers since the 1990s has led to a data explosion, with data sets consisting of billions of data points but which can also be analysed in the finest detail. Authors such as Park and Leydesdorff (2013:757), Abreu and Acker (2013:549), Hitzler and Janowicz (2013:233) and Kitchin (2014:3) argue that we are currently on the brink of the fourth paradigm of science: the first was empirical science, the second the theoretical, the third – as Von Bertalanffy’s (1968:20) earlier quote indicates – computer-driven, and the fourth data science (Chen and Zhang, 2014:315). Data science enables us not only to study large, comprehensive datasets, but also to study those datasets as a whole. Barabási (2011:14-15) writes that the data explosion has created several opportunities for scientific investigations:

… something has changed in the past few years. The driving force behind this change can be condensed into a single word: data. Fuelled by cheap sensors and high-throughput technologies, the data explosion that we witness today, from social media to cell biology, is offering unparalleled opportunities to document the inner workings of many complex systems.

Similarly, Watts (2011:266) claims,

…just as the invention of the telescope revolutionized the study of the heavens, so too by rendering the unmeasurable measurable, the technological revolution in mobile, Web, and Internet communications has the potential to revolutionize our understanding of ourselves and how we interact. Merton was right: Social science has still not found its Kepler. But three hundred years after Alexander Pope argued that the proper study of mankind should lie not in the heavens but in ourselves, we have finally found our telescope. Let the revolution begin ….

Information technology is therefore linked to a general realization that the world has become more interdependent, but has also provided the data and tools (both hardware and software) to undertake large-scale investigations. Data is important for the development of a theory that seeks to gain a better understanding of complex systems, as Byrne and Callaghan (2014:40) for example criticize models of complexity that are not based on empirical research.

The importance of grounding theories and models in data cannot be overstated. As Von Bertalanffy (1968:23) already noted, there is often a gap between a scientific model and reality. In order to gain a better understanding of reality, models and theories must be calibrated with reality, as Watts and Strogatz (1998) and Barabási and Albert (1999) did with their small-world- and scale-free network models respectively.9 Freeman (2004:3) argues that social network analysis (SNA) is characterized by being based on “systematic, empirical data,” while Newman (2010:17) emphasizes that data is the starting point of almost any development within network science. Barabási (2011:15) argues,

These ideas have not been gleaned from toy models or mathematical anomalies. They are based on data and meticulous observations. The theory of evolving networks was motivated by extensive empirical evidence documenting the scale-free nature of the degree distribution, from the cell to the World Wide Web; the formalism behind degree correlations was preceded by data documenting correlations on the Internet and on cellular maps; the extensive theoretical work on spreading processes was preceded by decades of meticulous data collection on the spread of viruses and fads, gaining a proper theoretical footing in the network context. This data- inspired methodology is an important shift compared with earlier takes on complex systems. Indeed, in a survey of the ten most influential papers in complexity, it will be difficult to find one that builds directly on experimental data. In contrast, among the ten most cited papers in network theory, you will be hard pressed to find one that does not directly rely on empirical evidence.

In this respect, network science is also in line with data science and the shift in emphasis in the fourth paradigm from the deductive to the inductive method, as Csermely (2006:97) argues.

4. Conclusion

There is a lot of excitement in the scientific community about the promise of network science. Together with Amaral and Ottino (2004:147) and Maslov, Sneppen and Zaliznyak (2004:529), Barabási (2011:15) argues that network science has become an indispensable approach to the study of complex systems. This optimism often comes from physics, as evidenced by some statements made by some of the most influential network scientists:

  • “if we are ever to have a theory of complexity, it will sit on the shoulders of network theory” (Barabási, 2011:15).
  • “The network approach to the phenomenon of complexity has turned so beneficial that some researchers think of it to be the key to understand the principles of the complex systems structure and behaviour” (Kwapień and Drożdż, 2012:206).
  • “If the day should ever come that we understand how life emerges from a dance of lifeless chemicals, or how consciousness arises from billions of unconscious neurons, that understanding will surely rest on a deep theory of complex networks” (Strogatz, 2004:232).

While network science is still in its infancy, this article has shown that network science continues Von Bertalanffy’s vision for GST in three important ways:

  1. By building bridges between disparate fields,
  2. By foregrounding the connections between entities, and
  3. By using computers to study phenomena in new ways.

These basic tenets that network science shares with GST will be elaborated on in the second and third articles in this article series.


  1. Own translation from the original German, “Komplexe Netzwerke können formal durch Graphen dargestellt werden und die Graphentheorie bietet einen mathematischen Rahmen für eine exakte Behandlung solcher Systeme.”
  2. Own translation from the original Afrikaans, “Selfs Afrikaanse literêre handelinge bestaan nie in isolasie nie, maar is ten nouste verweef met die internasionale wêreld en sy denke – waarmee dit inderdaad selfs elektronies verbind is. Dit is vandag baie duideliker só as in vorige dekades en toe reeds, van die begin van die Afrikaanse literatuur af, was daar baie sterk import van ander literature na Afrikaans, uit sowel Westerse as uit Afrikatradisies.” It is also noteworthy that this statement was made at a time when South Africa was at its most isolated by sanctions, boycotts and the arms embargo. The opening of South Africa to international influences since De Klerk announced in 1990 that South Africa’s path to a majority government had begun is not the focus here, but the reader must keep in mind that international efforts to isolate South Africa were terminated at the same time that the Internet became widely used.
  3. Own translation from the original Dutch, “Nieuwe technologieën produceren ‘globale tijd’ doordat afstanden tussen plaatsen en mensen verkorten of zelfs ‘dematerialiseren’.”
  4. Own translation from the original German, “unsere Gesellschaft [is] in vieler Hinsicht vernetzt.”
  5. Own translation from the original Afrikaans, “Die gevolg hiervan [die inligtingrevolusie] is dat die wêreld sy digtheid en hiërargiese ordening verloor het. Dis in ʼn web verander – met interafhanklike netwerke wat nie alleen kompleks is nie, maar ook vloeibaar en onstuimig.”
  6. Own translation from the original German, “Die Väter der allgemeinen Systemtheorie gingen davon aus, daß, wenn verschiedene Wissenschaften sich mit Systemen, also mit relativ stabilen Mustern von Variablen, beschäftigen, es möglich sein müsse, eine ganze Reihe von grundlegenden gemeinsamen analytischen Konzeptionen zu erarbeiten, die dann zwischen beliebigen Disziplinen austauschbar wären. So wurde die These des Isomorphismus neu geboren, welcher eben die Annahme zugrunde liegt, daß es in der Tat eine ganze Reihe von Strukturen und Prozessen gibt, die in allen Disziplinen erarbeitet und auf Systeme aller Art angewandt werden könnten.”
  7. Own translation from the original German, “Die Netzwerkforschung bietet die Möglichkeit für einen solchen Austausch; so können unterschiedliche Fachgebiete voneinander lernen, wenn es gelingt, die richtigen Kontaktflächen zu konstruieren. In diesem Fall kann man Theorien und Methoden zwischen den Fächern übertragen und dadurch die jeweils eigene Disziplin voranbringen.” See also Heidtmann (2013:440), Kwapień and Drożdż (2012:205) for similar perspectives.
  8. Own translation from the original German, “Folglich entstand das interdisziplinäre Forschungsgebiet der Netzwissenschaft (Network Science) mit dem Ziel, theoretische und praktische Vorstellungen und Methoden zur Verbesserung unseres Verständnisses von Netzen natürlichen und menschlichen Ursprungs zu entwickeln, u. a. durch die Nutzung von Ideen und Ergebnissen aus der Mathematik, der Physik, der Informatik, dem Operations Research und aus vielen anderen Bereichen der Natur-, Sozial und Ingenieurwissenschaften.”
  9. For a discussion of these models, see Newman (2010:552-564, 500-502) and Cohen and Havlin (2010:31-49, 51-62).


Abreu, A. and Acker, A. 2013. Context and Collection: A Research Agenda for Small Data. iConference 2013 Proceedings:549-554.
Amaral, L. and Ottino, J. 2004. Complex networks. Augmenting the framework for the study of complex systems. European Physical Journal, 38:147–162.
Bar-Yam, Y. 1997. Dynamics of complex systems. Colorado: Westview Press.
Barabási, A.-L. and Albert, R. 1999. Emergence of scaling in random networks. Science, 286:509–511.
Barabási, A.-L. 2003. Linked. London: Plume.
Barabási, A.-L. 2009. Scale-free networks: a decade and beyond. Science, 325(5939):412-413.
Barabási, A.-L. 2011. Bursts. London: Plume.
Barabási, A.-L. 2011. The network takeover. Nature Physics, 8(1):14-16.
Barabási, A.-L. 2016. Network science. Cambridge: Cambridge University Pres.
Boissevain, J. 1979. Network Analysis: A Reappraisal. Current Anthropology, 20(2):392-394.
Brownlee, J. 2007. Complex Adaptive Systems. CIS Technical Report, Volume March:1-6.
Buchanan, M. 2003. Nexus: Small worlds and the groundbreaking science of networks. New York: W.W. Norton and Co.
Byrne, D. and Callaghan, G. 2014. Complexity Theory and the Social Sciences. The state of the art. Oxon: Routledge.
Chen, C. P. and Zhang, C.-Y. 2014. Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275:314–347.
Christakis, N. and Fowler, J. 2010. Connected. London: Harper.
Cohen, R. and Havlin, S. 2010. Complex networks: structure, robustness and function. Cambridge: Cambridge University Press.
Csermely, P. 2006. Weak links: Stabilizers of complex systems from proteins to social networks. Heidelberg: Springer.
Easley, D. and Kleinberg, J. 2010. Networks, crowds and markets. Reasoning about a highly connected world. New York: Cambridge University Press.
Esterhuyse, W. 1999. Wêreld word ‘n web. Beeld, 29 December:19.
Freeman, L. C. 2004. The development of Social Network Analysis. A study in the sociology of science. Vacouver: Empirical Press.
Geurts, J. L. A. 1974. Systeemleer, model en methode in de sociologie. Sociologische Gids, 21(4):209-224.
Glattfelder, J. B. 2013. Decoding Complexity: Uncovering Patterns in Economic Networks. Heidelberg: Springer.
Goldstein, J. 2008. Conceptual foundations of complexity science: Development and main constructs. In: M. Uhl-Bien and R. Marion, eds. Complexity leadership. Part 1: Conceptual foundations. Charlotte, North Carolina: Information Age Publishing:17-48.
Hammond, D. 2019. The legacy of Ludwig von Bertalanffy and its relevance for our time. Systems Research and Behavioural Science, 36:301– 307.
Harary, F. and Batell, M. F. 1981. What is a System? Social Networks, 3:29-40.
Heidtmann, K. 2013. Internet-Graphen. Informatik_Spektrum, 6:440-448.
Helmus, T. C. and Bodine-Baron, E. 2017. Empowering ISIS Opponents on Twitter. Perspective:1-15.
Hitzler, P. and Janowicz, K. 2013. Linked Data, Big Data, and the 4th Paradigm. Semantic Web, 4:233–235.
Johnson, N. 2009. Simply complexity. London: OneWorld.
Kaluza, P., Kӧlzsch, A., Gastner, M. T. and Blasius, B. 2010. The complex network of global cargo ship movements. Journal of the Royal Society Interface, 7(48):1093-1103.
Kitchin, R. 2014. Big Data, new epistemologies and paradigm shifts. Big Data and Society:1–12.
Klausen, J. 2015. Tweeting the Jihad: Social Media Networks of Western Foreign Fighters in Syria and Iraq. Studies in Conflict and Terrorism, 38(1):1-22.
Kumpula, J. 2008. Community structures in complex networks: detection and modeling. Unpublished PhD dissertation: Helsinki University of Technology.
Kwapień, J. and Drożdż, S. 2012. Physical approach to complex systems. Physics Reports, 515(3):115-226.
Luo, W. et al. 2014. A Geovisual Analytic Approach to Understanding Geo-Social Relationships in the International Trade Network. PloS one, 9(2):e88666.
Maslov, S., Sneppen, K. and Zaliznyak, A. 2004. Detection of topological patterns in complex networks: correlation profile of the internet. Physica, 333:529 – 540.
Milgram, S. 1967. The small world problem. Psychology Today, 2:60-67.
Mobus, G. E. and Kalton, M. C. 2015. Principles of systems science. Heidelberg: Springer.
Newman, M. E. J. 2010. Networks. Oxford: Oxford University Press.
Nistor, M. S., Pickl, S. W. and Zsifkovits, M. 2015. Visual Analytics of Complex Networks: A Review from the Computational Perspective. International Conference on Modeling, Simulation and Visualization Methods:10-15.
Omand, D., Bartlett, J. and Miller, C. 2012. Introducing Social Media Intelligence (SOCMINT). Intelligence and National Security, 27(6):801-823.
Padgett, J. F. and Ansell, C. K. 1993. Robust Action and the Rise of the Medici, 1400–1434. The American Journal of Sociology, 98(6):1259–1319.
Papp, D. S., Alberts, D. S. and Tuyahov, A. 1997. Historical Impacts of Information Technologies: An Overview. In: D. S. Alberts and D. S. Papp, eds. The information age: an anthology on its impact and consequences. s.l.:CCRP Publication Series:13-35.
Park, H. W. and Leydesdorff, L. 2013. Decomposing social and semantic networks in emerging “big data” research. Journal of Informetrics, 7:756–765.
Pitts, F. 1965. A graph theoretical approach to historical geography. The Professional Geographer, 17:15-20.
Reed, D. A. and Dongarra, J. 2015. Exascale computing and Big data. Communications of the ACM, 58(7):56-68.
Schnelle, S. 2018. Kartlegging av maritime hybride trusler. Kan bruk av stordata og sosial nettverksanalyse bidra til økt maritim situasjonsbevissthet?. Unpublished MA-dissertation: Forsvarets høgskole.
Scott, J. 2012. What is Social Network Analysis? London: Bloomsbury Academic.
Senekal, B. A. and Brokensha, S. 2014. Surfers van die tsunami. Navorsing en inligtingstegnologie binne die Geesteswetenskappe. Bloemfontein: SunPress.
Senekal, J. H. 1987. Literatuuropvattings: ‘wese’ en ‘waarhede’ van ‘n nuwe literêre teorie. Bloemfontein: Universiteit van die Oranje-Vrystaat.
Senghaas, D. 1968. Systembegriff und Systemanalyse: Analytische Schwerpunkte und Anwendungsbereiche in der Politikwissenschaft. Zeitschrift für Politik, 15(1):50-64.
Stegbauer, C. 2017. Interdisziplinäre Netzwerkforschung. Soziologie, 36(1):17-22.
Steyn, A. F. 1984. Buckley: die moderne oop-sisteembenadering. Suid-Afrikaanse Tydskrif vir Sosiologie, 15(1):5-16.
Strogatz, S. 2004. Sync. The emerging science of spontaneous order. London: Penguin.
Urry, J. 2004. Globale complexiteit en het autosysteem. Tijdschrift voor sociologie, 25(1):21-35.
Van der Hofstad, R. 2014. Random Graphs and Complex Networks. Eindhoven: Eindhoven University of Technology.
Von Bertalanffy, L. 1940. Der Organismus als physikalisches System betrachtet. Die Naturwissenschaften, 28:521-531.
Von Bertalanffy, L. 1950a. An Outline of General System Theory. The British Journal for the Philosophy of Science, 1(2):134-165.
Von Bertalanffy, L. 1950b. The Theory of Open Systems in Physics and Biology. Science, Volume 111:23-29.
Von Bertalanffy, L. 1952. Problems of Life. An Evaluation of Modern Biological Thought. London: Watts and Co.
Von Bertalanffy, L. 1956. A Biologist Looks at Human Nature. The Scientific Monthly, 82(1):33-41.
Von Bertalanffy, L. 1968. General systems theory: Foundations, development, applications. New York: George Braziller.
Von Bertalanffy, L. 1972. The History and Status of General Systems Theory. The Academy of Management Journal, 15(4):407-426.
Watts, D.J. and Strogatz, S. H. 1998. Collective dynamics of ‘small-world’ networks. Nature, 393(6684):409-410.
Watts, D.J. 2004. Six Degrees. The Science of a Connected Age. London: Vintage.
Watts, D.J. 2011. Everything is obvious. Once you know the answer. London: Atlantic.

Exploring circular systems-based education in South Africa: A collaborative approach to digital learning

Author: Dr Eben Haeser Swanepoel
Orcid Id: 0000-0003-3205-5244
Postdoctoral Researcher, North-West University, Economic and Management Sciences, GIFT.
PhD: Psychology of Education (UFS), M.Ed Psychology of Education (UFS), Hons Psychology (UNISA), B.Ed FET (UFS)

Ensovoort, volume 41 (2020), number 6: 1


South African education systems are at an exciting juncture. They are shaping the way into the Fourth Industrial Revolution and globalized access to learning. Online learning has gained traction internationally and been shown to be effective in curbing overcrowded classrooms at both schools and institutions of higher learning. Online solutions promote open-access learning at cost-effective rates also. South Africa is hindered, however, through unequal access to resources and inequality when it comes to the implementation of online learning as a uniform platform across locations. This article explores a circular model of sensitizing educators, by means of teacher induction, to the use of digital content through collaborative practice, whilst building a systemic autonomous platform of content which is contextual, reliable, and formally assessed before it is used in classrooms. Drawing on cybernetic theory, the article is framed by how systems open and close boundaries to information and how systemic structure is either maintained or lost during the process of adapting to incoming feedback from other systems. With the pressure to adapt to international ideals and globalization, it becomes increasingly important to explore how South African education systems steer information from teacher induction to the ultimate application at grassroots classroom level. Digitized learning, as proposed through this article, holds value when incorporated through collaborative practice where information boundaries from external systems are opened and closed for the purpose of maintaining the ideals of internationally bench-marked knowledge, while at the same time upholding a unified democratic front to the South African context.

Keywords: Cybernetics; Technology Education; Educational Systems; Teacher Induction; Circularity

1. Introduction   

The Fourth Industrial Revolution (4IR) has brought on disruptive technological advances which can potentially replace outdated and traditional ways of doing. Artificial Intelligence, Robotics and the Internet of Things (IoT), all characteristic of the Fourth Industrial Revolution’s innovations for transformative practice, introduce technological innovation which can potentially change the way we teach and prepare learners and students for the world of work (Xing & Marwala, 2017; Kayembe & Nel, 2019). The rapid trajectory of the Fourth Industrial Revolution’s globalization movement compels us to reflect on existing ways of doing and align the South African education system with international benchmarks and global trajectories in the area of vocational access. At the same time, there is a growing need to explore how, within the globalization movement, the South African educational system can retain a democratically unified system whilst simultaneously accounting for cultural diversity and the unequal access to information systems at sub-systemic levels due to past inequalities that hinder access and resource distribution (Meier & Hartell, 2009).

The relevance of 4IR to South Africa, as a Sub-Saharan country, has however been scrutinized. The country’s infrastructure deficits and unequal skill distribution are key challenges to aligning skillsets with the benchmarks of advanced economies (Ayentimi & Burgess, 2019), and public discourse centres on the need for better access and globally bench-marked educational reform. Historically, the Fees Must Fall movement is an example of societal feedback highlighting the dialogue of high-quality access to higher education platforms at lower costs (Pillay & Swanepoel, 2018). More recently, the global Covid-19 pandemic and the national lock-down have highlighted the importance of continuous instruction across all education platforms. Digitized learning platforms have become a go-to for instruction (Odendaal, 2020).

Especially post-apartheid, educational reform in South Africa has seen a paradigmatic emphasis on ways of doing, focussing especially on policy and curriculum reform to align with systemic injustices caused by past unequal access to education and resource distribution (Gumede & Biyase, 2016; Subreenduth, 2009). Social segregation and class privilege, however, still pose challenges when it comes to equal access to quality education. The desired change for socially just education reform has been slow to manifest at a practical level. An inter-generational cycle of unequal privilege and poverty now sees learners from sociohistorically hindered schools unable to accumulate the skills, especially in mathematics, to assimilate functional alignment with high-quality education access and lifelong learning opportunities (Spaull, 2015). There has been stringent progress in technologically enhanced learning methods in South African schools, and teacher training programs now need to be examined as a key component in aligning South Africa with the global trends that form part of the Fourth Industrial Revolution (Padayachee, 2017). Older teachers often evade technological disruption in the classroom, while younger teachers show greater confidence in the use of modern digitized learning methods (Msila, 2015). This reflects segregated learning experiences across spaces and places of learning, and articulates the cycle of unequal learning that permeates traditional classroom practices. It can be argued that how a learner in a specific school will be taught is then left to the luck of the draw and would depend on the particular level of classroom autonomy they are subjected to for a given learning experience.

The Fourth Industrial Revolution is characterised by innovative interdisciplinary teaching methods, with many South African teachers feeling uncomfortable teaching outside of their own discipline-knowledge systems. They may be slow when it comes to adopting enhanced methodological approaches towards cross-disciplinary collaboration (Chaka, 2019). It becomes increasingly difficult for teachers to use information in a funnelled manner, especially with the rapid digitization of information at a global scale and the need to facilitate both the subject knowledge as well as the technological skill-sets associated with finding and evaluating information in the classroom (Kultawanicha, Koraneekija & Na-Songkhlaa, 2015). The dissemination of information becomes an increasingly important skill-set. The wealth of information during the digitized age calls for greater scrutiny not only in terms of relevance, but also the applicability thereof, outside the classroom and within learners’ communities. A quality educational system should therefore be founded on skilled educators who implement and drive technological systems to optimize learning (Moodley, 2019) within relevant and applicable boundaries, for the purpose of mediating knowledge and information during the learning experience in a meaningful and contextual way.

Online learning, the Internet of Things, and the use of Artificial Intelligence for personalised learning are redefining the role of educational systems (Ally, 2019; Picciano, 2019; Pillay, Maharaj & van Eeden, 2018). Given how the use of smartphones, tablets and computers are becoming increasingly common among learners and students and the manner in which these technologies disrupt traditional pedagogical methods in classrooms on an ongoing basis (Laurillard & Kennedy, 2017; Zhamanov & Sakhiyeva, 2015), exploring a system of incorporating technology in a meaningful and collaborative way during classroom practice becomes increasingly viable. The shift towards digitized learning environments, in particular, but also the Fourth Industrial Revolution’s globalization trajectory would necessitate such an investigation. The systemic integrity of South African education, the inclusive nature of which extends across the space and place of all communities and stakeholders (be it national or international) should at the same time be retained. While there are a growing number of virtual and blended schools globally, further research is needed to determine the long-term viability thereof in terms of successful student retention and performance (Miron, Shank & Davidson, 2018). A lack of infrastructure, the skill-sets of teachers and overall expertise in optimally utilizing technology in the teaching-learning environment pose further limitations to modernizing digitized classroom practices in South Africa (Jantjies & Joy, 2016).

Specifically grounded on collaborative practice among various stakeholders toward global vocational access and quality education, this article aims to explore a circular model of teaching-learning which can potentially mediate geographical and spatial limitations to learning and sustain a continued and reliable method of delivery which brings transparent, reliable and valid information to learners and students. The circular method of delivery furthermore addresses the need to uphold a unified international standard of knowledge and information for the digitized age, while simultaneously retaining South African benchmarks and ideals as a means to retain systemic structure within the global front of information sharing and innovation as we move toward the Fourth Industrial Revolution.

2. First-order cybernetics: A systemic approach to information and education

Cybernetics derives from the Greek Kybernetes, which also translates to steersman (Heylighen & Joslyn, 2001). Aimed at investigating similarities amongst autonomous systems, cybernetics provides a lens for exploring how systems are structured and steered (at first-order level), and why they behave as they do (through a Second Order perspective). While the term cybernetics is often associated with the functioning of machine systems and robotics, it can also be used to observe and explore social systems and discourse (Umpleby, Medvedeva, Lepskiy, 2019). The theoretical lens through which this article is articulated is based on first-order cybernetics. Aligned with systems theories, cybernetics allows for the investigation and exploration of how systems are governed and maintained through patterns and rules that form boundaries to information. Especially useful in framing goal-related functioning of systems, the use of first-order cybernetics allows the researcher to investigate how a system functions through investigating the feedback processes of information which are allowed or rejected at specific intervals within the system to either initiate change or retain structure (Becvar & Becvar, 2012). As systems are governed by rules and patterns which form boundaries for attaining systemic structure, opening boundaries to allow information in, or in turn closing boundaries to reject information, helps provide a better understanding of how systems function in achieving a desired state or goal.

Accordingly, the use of first-order cybernetics provides a valuable framework for investigating the implementation of e-learning in classrooms across educational platforms. Simultaneously, it becomes possible to investigate how different systems within the wider South African system are shaped or restricted through open and closed boundaries, while at the same time investigating a model that allows for cohesive information flow to enter the South African system and school subsystems as self-sustaining entities without losing overall systemic integrity and entering a state of dysfunction (entropy).

3. Unifying the space and place of learning

The innovative use of digital learning aligns with the United Nation’s Sustainable Development Goal 4 and aims toward a 21st century paradigm of Education for All. This will deem the use of digitized learning as central to infrastructure limitations and extending learning to learners who reside in remote areas (Ally, 2019). Accordingly, the World Economic Forum’s Internet for All initiative looks at the deployment of training aimed at bridging the online access gap. South Africa is one of the countries that is aligned with the global Fourth Industrial Revolution initiative. By identifying challenging spaces of connectivity, especially in rural areas, the initiative is centred on “extending ICT infrastructure to underserved areas, lowering the costs of being online and cheaper gadgets, digitising local content and providing ICT and digital skills” (Matshediso, 2017). The unification of place and space, specifically to optimize learning, is twofold and encompasses the physical dimension as well as the unseen dimension (time and context). Encountering boundaries to learning in traditional classroom practice is commonplace, and online learning systems provide the opportunity to connect to other systems of learning and draw from external knowledge bases. Chilton (2019) expresses the value of connecting to prerecorded case study material or learners who are not able to access the main place of learning.

While the before-mentioned holds potential for bringing connectivity to areas otherwise hindered from achieving optimal access to online resources, there is a need to look at available tools for bridging the divide and achieving successful implementation. Jantjies and Joy (2016) draw on the lack of access to proper resources such as computers in schools, while maintaining that mobile phones have been effective in enhancing learning and teaching globally, especially in developing countries. The researchers go on to point out that multiple languages hinder the implementation of technological methods for blended learning in South Africa. While Western discourse is seen as a colonising tool deflecting from African knowledge systems (Pillay & Swanepoel, 2018), it becomes increasingly important to steer South African education in innovative ways and incorporating global innovations in a meaningful way whilst retaining the systemic integrity of the South African education system as whole. While also accounting for the resource constraints that underlie many spaces of learning, the multilingual citizenship characteristic of the South African context can, instead of closing the door to global adaption, become a strong motivator for conceptualizing an indigenously relevant system.

4. Establishing boundaries to open information: Toward ethical patterns of learning

The growing nature of the digitized learning and online access platforms for information is transcending closed boundaries to learning, and the digitized age is seeing a rapid increase in information availability (Ally, 2019). Gous (2019) postulates that the aim of education is to teach knowledge and information that is on par with the latest developments. Gous (2019) evaluates the applicability of information and knowledge through contextualized teaching. What is relevant and current will depend on the needs of the people that the information is intended to serve. Lambert and Gong (2010) emphasise that knowledge is power. Should information systems not be effectively employed, we risk a further divide and marginalization which would perpetuate past cycles. One example of this would be how literacy education has, through past policy and practice, enhanced white supremacy. Education did this through information control amongst groups which caused inequality in participation and vocational access, as well as further education and training (Reygan & Steyn, 2017).

South African education is based on rules and patterns of constitutional ideals enshrined in the South African Constitution (1996) that translate into idealized practice through the Curriculum and Assessment Policy Statements (Department of Basic Education, 2011). These rules encapsulate the boundaries of equitable information and provide for restorative action concerning historic inequalities, the promotion of messages and knowledge based on inclusivity, and further promoting transformation towards the overall system of South African citizenship. Closed micro exploration has, however, revealed that closed system instruction has retained dysfunction and teachings have not been aligned with the overall systemic boundaries of social justice and equal participation (Davids, 2104; Rooth, 2005). While closed systemic loops have widened the gap between theoretic ideals and practice, opening educational boundaries to information can potentially lead to entropy.

Platonova and Gous (2019) draw on open education as a meaningful tool for producing free-thinking and multiculturally articulate learners. However, the researchers refer to open systems such as online education as a tool for perpetuating ideology and advance knowledge and information which is construed by power. Drawing on Gee (2011), the researchers feel that learning is central to changing patterns of behaviour and the creation of socially situated identities. Through symbolic discourse and cultural control, uncritical mass learning can decontextualize learning and skew it to the ideas and values of systems which are not relevant or on par with the learner’s specific context, which is where behaviour manifests. This is especially important to note when it comes to the creation of democratically accessible information systems during the digitized age where boundaries to information have been opened to global citizenship. Anefuku (2017) notes that opening boundaries to information for globalized knowledge is futile without critically examining the systems which underlie the knowledge base itself.

South African education systems are perpetually criticized for being dysfunctional at the practical level of conveying core curriculum content. Educators lack subject knowledge and in some cases teach subjects that fall out of their scope of training (Moloi, 2019). According to Lee and Brett (2015), a firm understanding of subject knowledge and comprehension of aligned pedagogies is fundamental to successfully utilizing technology during classroom instruction. A deficit in subject expertise is in turn a closed boundary for adapting to modern digitized learning methods which is relevant and contextual to learner needs. Research has also revealed closed boundaries of information sharing during sensitive topics, especially topics that are related to Social-Justice education. During certain classroom teachings, teachers perpetuate incongruent messages to learners, or leave out core content completely due to personal beliefs (Reygan & Francis, 2015). In such instances, opening boundaries to digital material holds value, and the teacher’s role as facilitator then becomes key in facilitating activities amongst learners. The use of technology for such learning experiences is however closely monitored and cannot be implemented on a large scale (such as via MOOC) due to the cultural plurality of learners needing to be understood within boundaries that are specific to the South African context. The uncritical use of material in digital format on the part of the teacher can either sustain the lesson within closed boundaries of digital use or lead to further entropy when employed without careful consideration.

Clarà and Barberà (2013) warn against the problematic pedagogies which underlie many forms of MOOCs, deriving from behaviouristic foundations to learning that situate knowledge as observable and objectively attainable. Further uncritical pedagogies can potentially lead learning toward representations of the object of learning, which obscures knowledge not as constructed patterns through various perspectives, but a thing that is not dynamic and adaptable. The use of MOOCs, without critically reflecting on which knowledge boundaries are opened, leads to further entropy within the learning process. Wise, Cui, Jin and Vytasek (2017) comment on interactional patterns during online discussions used in digital learning communities. With the wide array of participants engaging in the online discussion, it becomes difficult and chaotic to successfully disseminate relevant information that is aligned with the purpose of the learning activity. Porter et al (2016) further draw on harassment and bullying as cornerstone malpractices that hinder the adoption of online learning platforms. The further spread and “trolling” of pornographic material is also prevalent on online platforms. Online learning, in turn, needs to be aligned with instructors who comprehend not only the context of their digital platforms and users, but also grasp ethical boundaries in creating a functional system that is shielded from learning hindrances through the use of intervention, guidance and active participation.

 5. Traditional hierarchical versus modern digitized educational instruction

Traditional education systems are characterized by pedagogies that emphasise knowledge as external, thus situating the role of a teacher as the knower who leads learners to a set reality of “knowing”. Subsequent paradigm shifts have seen the role of the teacher change to that of a facilitator whose primary role is to create a learning environment in which learners co-construct knowledge. Recently, the shift toward modern digitized pedagogies is seen through the implementation of web 2.0 and the technological move toward learning platforms where learners connect to online instructors and potentially a wide array of knowledge sources (Clarà and Barberà, 2013). It has been noted that while the uncritical pedagogical approaches utilized through MOOCs can lean toward traditional behaviourist methods of instruction, the potential opportunities created for collaborative modern pedagogies is of value when used within a multi-perspective and collaborative environment. Fourth Industrial Revolution learning environments are conducive to collaborative learning. The teacher brings innovative technological methods to the classroom to facilitate collaboration, innovation, and creativity (Adefila & Pillay, 2019; Maksimović & Dimić, 2016). The modern role of the educator is subsequently multi-dimensional and transcends subject knowledge, redefining the educator as a change agent skilfully navigating social tenancies. The role is characterised by reflective practise and ongoing learning and research to maintain relevance in the face of societal changes in dynamic and innovative ways (Maksimović & Dimić, 2016).

Online education in various forms, including asynchronous and synchronous learning methods, have become popular platforms for instruction delivery, with designed lessons incorporating blended learning methods and artificial intelligence software for delivering personalized learning experiences (Ally, 2019; Picciano, 2019). Ally (2019) and Damoensa (2003) refer to the importance of teaching for the future, and align the modern role of the educator with the need to dynamically prepare learners for vocational access to jobs that are not yet in existence. The need for face-to-face learning as traditional learning methods have been re-aligned with virtual teachings where learners take control of their own learning experiences and select learning content (Akpan, Etim & Ogechi, 2016). Flipped classrooms have also broken away from traditional linear models of instruction, leading the learner to actively co-construct knowledge (Hwang, Lai & Wang, 2015). Gross, Pietri, Anderson, Moyano-Camihort and Graham (2015) draw on blended learning as it is beneficial for pre-class preparation and providing more opportunities for in-class activities as well as active learning experiences. Specifically grounded through research based on STEM education (science, technology, engineering, and mathematics), the researchers point to beneficial outcomes when learning is balanced between on-line and in-class contact-based activities. Flipped and virtual classrooms have shown proven value through the recording of classes and lectures for later use. The proven effectiveness of this method has led to classes being pre-recorded to instil basic knowledge before formal instruction, which in turn has led to more time for in-class discussion and contextualization (Hwang, Lai & Wang, 2015).

The ‘leading’ of learners and students is important, as the flipped classroom can revert to autonomous systems of control should boundaries to learning be too rigid and narrow. Further entropy is also possible should boundaries be too open and allow for too much learner autonomy. The teacher is at the core of successful technological integration in the classroom. Successful integration of constructivist learning and the use of technology is driven by the beliefs of the educator who ultimately enters the classroom (Chen, 2008), and accordingly teacher induction and developmental programs should focus not only on skillset improvement, but also teachers’ beliefs about digital learning. Damoense (2003) noted that online learning enhances collaborative engagement. Traditional teacher instruction gives way to facilitating meaningful engagement with online activities. The wealth of online activities and resources make the act of leading learners and teacher beliefs cornerstone in ensuring that meaningful boundaries to information are opened. To attain systemic integrity within modern 21st century teaching-learning spaces, the use of e-learning, flipped and virtual classrooms need to be used effectively in the context within which learners reside (both at a micro and macro level). This is especially important in relation to the amount of learning that will stem from online methods and which learning will flow from face-to-face modes of delivery. As such, the awareness of systemic boundaries becomes a cornerstone during online pedagogies. Educators will need to open and close boundaries to online instruction in a manner that enhances the overall flow of the teaching-learning process.

 6. A circular approach to collaborative practice

An effective method of opening boundaries to enhance teacher adaptability toward digitized learning is found in the form of Communities of Practice (CoP). CoPs allow for collaborative practice, and when grounded through trusted patterns of interaction, enhance cross-platform learning that is stimulated through various stakeholders (Lee & Brett, 2015). According to Lee and Brett (2015), the use of online-learning design allows for teachers to cross-collaborate within a CoP, which enhances peer-teacher communication, stimulates self-reflection, and allows for stronger implementation of material within classroom practice. Lambert and Gong (2010) advocate for the use of technological systems during pre-service teacher instruction. They see it as vital for translating skill-sets during teaching when educators enter classroom practice. However, while many universities and institutions of learning provide computers for use during training, programs often miss the opportunities to effectively integrate digital systems in meaningful ways that advance post-training incorporation and use.

The remainder of the discussion will focus on a circular model of open-closed boundaries to digital content which emphasises collaborative practice and contextual awareness stemming from initial teacher induction towards the grassroots classroom use of media and material. Specifically grounded on a process of circularity pertaining to the knowledge-systems teachers draw on, the proposed model further envisages a South African knowledge base and context in a manner where information is mediated and steered toward the ideals of the South African system and unified and constructed through a wide array of perspectives, languages and contextual backgrounds. Intrinsic to the model is that the product of the information retains core knowledge integrity and is evaluated and steered through didactic approaches during teacher induction, as part of the process, instead of it being a process to engage with only once teachers actually start to teach. Skill-sets can, accordingly, be translated toward post-induction employment where teachers construct subsystems of evaluated and monitored content, thereby ensuring a uniform content base as teachers become comfortable with the use of digitizing learning and peer-support towards the goal of reliable and equitable information sharing.

Figure 1: Cyclical model of initiating cross-platform collaboration toward e-learning in South Africa (Author, 2020)

Figure 1 illustrates a circular approach to introducing a reliable and valid flow of e-learning content into classrooms while providing relief for teacher input during certain stages or cycles of curriculum coverage. The risk of an open system to online learning content has been discussed, as content drawn on from international sources or external open boundaries does not guarantee that contextual or relevant information is conveyed to learners. The aim is further to emphasise that digitized content should not dominate lessons but be used effectively and innovatively to enhance the learning that occurs during formal teaching hours. A circular method of introduction to core content, in collaboration with higher education instructors, and emanating through the teacher induction process, holds value both for ensuring valid and reliable use of digital content, as well as for allowing sub-system classroom level autonomy during the facilitation and contextualisation of information.

A process of circularity allows information to flow through the system in a controlled and goal-oriented manner, while providing for a basis of stored content which is accumulated and available to teachers post-induction for use and knowledge refinement. It is noteworthy that this article specifically draws on a model where information is continuously updatable. Opening too many boundaries to online learning is detrimental and will lead to entropy and systemic dysfunction. Thus, the proposed model is founded on shaping an autonomous community of content which is evaluated and on par with current trends in education, and stored in offline format for post-induction use. The material is shaped around core content. Through the process of teacher induction, it can utilize various forms of online instruction and assessment methods, which can be translated to the needs of the classroom system. With a wide array of material available across various subject didactics, it also leads to fewer challenges when looking to find content that is of value across different contexts of space and place.

Teachers entering their own space and place of teaching post-induction have an array of material and curriculum coverage available to them right at the onset. They can integrate the resources available during induction in a controlled environment alongside various stakeholders and peers from a plurality of backgrounds and skill-sets. The process stimulates access to resources and material which is contextually relevant and adaptable to a specific context. Boundaries to the material are opened and closed to learners either offline or online, and the overall integrity of the system’s use is adjusted to contextual constraints and resources, as material can be provided through various offline means. Importantly, a circular system, as proposed, provides teachers with opportunities to shape positive values and collaborative values about using technologically-based learning in classrooms without opening e-learning boundaries in a manner that is disadvantageous to other systems of learning situated in other contexts. Boundaries are therefore systematically removed in a way that does not cause a larger divide between schools systems and teachers who cannot employ sophisticated digital material in the classrooms.

A sensitization process is thus articulated where, through opening and closing systemic boundaries to material, the teacher can better navigate their own subject knowledge and continuous learning, while allowing for a stronger unified method of conveying base knowledge and giving way to the contextual application of skill-sets in the classroom. While there are various sources available for teachers to draw on content already, these sources are often expensive and not created with larger populations or context in mind. They are also not always reliable in terms of the encapsulated core ideals of national and international benchmarks. Ideally, to maintain systemic integrity, the overall aims of a model for implementing digital learning in classrooms should enhance teacher practice to align with reliability, and up-to-date ethical information. Ultimately, constructing a circular approach for reliable information and content sharing holds value for periods of instruction disruption while also allowing for teachers to shape values and autonomy to draw strategically on local and international content for enhancing practice, bypassing emotional bias during certain lessons, or reshaping and refining knowledge that is not on par to, or unaligned with, current trends and benchmarks.

7. Recommendations and research

There are various challenges to the implementation of a circular model for digitizing content for a unified information base. Firstly, cloud space is expensive, and the management of the proposed system can be costly and time-consuming. While the proposed system is autonomous and updates as teacher-induction takes place, further research is required to explore smaller data management systems that are both cost effective and reliable, especially in spaces where schools already employ online learning in effective and creative ways. Artificial intelligence and autonomous systems are feared as potentially replacing teachers. However, with careful consideration and implementation, the uses thereof can reduce administrative times and reduce the likelihood and severity of teacher burnout. The use thereof for system management and information sharing in strengthening classroom practice will be useful for stimulating public debate around the digitized paradigm shift in South Africa towards the ideals of globalization and technological advancement that characterise the Fourth Industrial Revolution.

8. Conclusion

The unequal distribution of resources and the post-apartheid ripples that hinder many schools’ systems from benefiting from real-time synchronous instruction that is relevant to the South African context, are impeding the adoption of technology in South African classrooms. While international systems have deep knowledge and information sharing pools to draw from, the boundaries of relevance and context are primary challenges in shaping a sustainable base from a South African perspective. This article subsequently explored a possible method of sensitizing teachers to online learning through a collaborative model based on circularity. The proposed model reflects the advantage of continuous content, which involves opening a system to allow for new content to flow in a directed manner, while closing boundaries to content that is not on par or reliably assessed before being widely used. The proposed model allows teachers to access an array of voices and methods pertaining to a specific lesson, so that they can reliably convey core content in a more effective and controlled manner. By providing more time for the teacher to facilitate context and application in the classroom, the value of a circular model lies therein that it further stimulates ongoing access to new methods and content coverage for self-reflection and peer engagement.

Limitations of resources and time constraints are curbed due to the initial construction of a community platform during induction. South Africa has the exciting opportunity to shape the adoption of digital learning in a manner that emphasises the active role of the teacher as both facilitator and lifelong learner, while also bridging contextual boundaries in new and innovative ways that promote the sustainability of the South African systemic context. At the forefront of the digitization process is the call for further research into refining an autonomous system which would reduce colonial powers’ monopoly on information and further investigating closed boundaries to unreliable material and content to the detriment of the learner and their subsequent context in terms of place and space. The move toward digitized learning can strengthen teacher practice and enhance collaboration. If not properly initiated and implemented, however, we may create a further divide amongst systems of learning, and not just among subsystems in South Africa. The overall integrity of the South African context of learning may be compromised, when compared with international benchmarks.

9. References

Adefila, A., & Pillay, P. (2019). Collaborative learning in the Fourth Industrial Revolution: implications for an African Renaissance. Journal of Gender, Information and Development in Africa (JGIDA), 8(Special Issue 2), 151-160.

Akpan, S. J., Etim, P. J., & Udom, S. O. (2016). Virtual Classroom Instruction and Academic Performance of Educational Technology Students in Distance Education, Enugu State. World Journal of Education, 6(6), 83-88.

Ally, M. (2019). Competency profile of the digital and online teacher in future education. International Review of Research in Open and Distributed Learning, 20(2).

Ayentimi, D. T., & Burgess, J. (2019). Is the fourth industrial revolution relevant to sub-Sahara Africa?. Technology Analysis & Strategic Management, 31(6), 641-652.

Chaka, J. N. (2017). A critical analysis of sexuality education in schools beyond the disciplinary boundaries of life orientation (Doctoral dissertation, University of the Free State).

Chen, C. H. (2008). Why do teachers not practice what they believe regarding technology integration?. The journal of educational research, 102(1), 65-75.

Chilton, M. A. (2019). Technology in the classroom: Using video links to enable long distance experiential learning. Journal of Information Systems Education, 23(1), 5.

Clarà, M., & Barberà, E. (2013). Learning online: massive open online courses (MOOCs), connectivism, and cultural psychology. Distance Education, 34(1), 129-136.

Damoense, M. Y. (2003). Online learning: Implications for effective learning for higher education in South Africa. Australasian Journal of Educational Technology, 19(1).

Davids, M. N. (2014). Using Foucauldian ‘discursive practices’ as conceptual framework for the study of teachers’ discourses of HIV and sexuality. Perspectives in Education, 32(3).

Department of Basic Education. (2011). Curriculum and Assessment Policy Statement Grades 7 9: Life Orientation. Pretoria, South Africa: Government Printers.

Gee, J.P., 2011, ‘Discourse analysis: What makes it critical?’, in R. Rogers (eds.), An introduction to critical discourse analysis in education, 2nd edn., pp. 23–46, Routledge, New York.

Gumede, V., & Biyase, M. (2016). Educational reforms and curriculum transformation in post-apartheid South Africa. Environmental Economics, 7(2), 69.

Heylighen, F., & Joslyn, C. (2001). Cybernetics and second-order cybernetics. Encyclopedia of physical science & technology, 4, 155-170.

Hwang, G. J., Lai, C. L., & Wang, S. Y. (2015). Seamless flipped learning: a mobile technology-enhanced flipped classroom with effective learning strategies. Journal of computers in education, 2(4), 449-473.

Jantjies, M., & Joy, M. (2016). Lessons learnt from teachers’ perspectives on mobile learning in South Africa with cultural and linguistic constraints. South African Journal of Education, 36(3).

Kayembe, C., & Nel, D. (2019). Challenges and opportunities for education in the Fourth Industrial Revolution. African Journal of Public Affairs, 11(3), 79-94.

Kultawanich, K., Koraneekij, P., & Na-Songkhla, J. (2015). A proposed model of connectivism learning using cloud-based virtual classroom to enhance information literacy and information literacy self-efficacy for undergraduate students. Social and Behavioral Sciences, 191, 87-92.

Lambert, J., & Gong, Y. (2010). 21st century paradigms for pre-service teacher technology preparation. Computers in the Schools, 27(1), 54-70.

Laurillard, D., & Kennedy, E. (2017). The potential of MOOCs for learning at scale in the Global South. Center for Global Higher Education.

Lee, K., & Brett, C. (2015). Dialogic understanding of teachers’ online transformative learning: A qualitative case study of teacher discussions in a graduate-level online course. Teaching and Teacher Education, 46, 72-83.

Maksimović, J., & Dimić, N. (2016). Digital technology and teachers’ competence for its application in the classroom. Istraživanja u pedagogiji, 6(2), 59-71.

Matshediso, M. (2017). ‘Internet for All’ by 2020. New report accessed online at https://www.sanews.gov.za/south-africa/internet-all-2020 on 20 April 2020.

Meier, C., & Hartell, C. (2009). Handling cultural diversity in education in South Africa. SA-eDUC Journal, 6(2), 180-192.

Miron, G., Shank, C., & Davidson, C. (2018). Full-time virtual and blended schools: Enrollment, student characteristics, and performance.

Moloi, K. (2019). Learners and educators as agents of social transformation in dysfunctional South African schools. South African Journal of Education, 39(4).

Moodley, M. (2019). WhatsApp: Creating a virtual teacher community for supporting and monitoring after a professional development programme. South African Journal of Education, 39(2).

Odendaal, N. (2020). Vodacom e-School registration reaches one-million users. News report accessed online at https://www.engineeringnews.co.za/article/vodacom-e-school-registration-reaches-one-million-users-2020-04-01 on 20 April 2020.

Picciano, A. G. (2019). Artificial Intelligence and the Academy’s Loss of Purpose. Online Learning, 23(3), 270-284.

Pillay, N., Maharaj, B. T., & Van Eeden, G. (2018). AI in engineering and computer science education in preparation for the 4th Industrial Revolution: A South African perspective. In 2018 World Engineering Education Forum-Global Engineering Deans Council (WEEF-GEDC) (pp. 1-5). IEEE.

Pillay, P., & Swanepoel, E. (2018). An exploration of higher education teachers’ experience of decolonising the Bachelor of Education honours curriculum at a South African university. Perspectives in Education, 36(2), 119-131.

Platonova, I., & Gous, I. G. (2019). The online educated or online indoctrinated human? Discourse analysis as a method to study ideologies disseminated by online courses. HTS Theological Studies, 75(1), 1-8.

Reygan, F., & Francis, D. (2015). Emotions and pedagogies of discomfort: Teachers’ responses to sexual and gender diversity in the Free State, South Africa. Education as Change, 19(1), 101-119.

Reygan, F., & Steyn, M. (2017). Diversity in basic education in South Africa: Intersectionality and critical diversity literacy. Africa Education Review, 14(2), 68-81.

Rooth, E. (2005). An investigation of the status and practice of Life Orientation in South African schools in two provinces (Unpublished doctoral dissertation). University of the Western Cape, Cape Town, South Africa.

Spaull, N. (2015). Schooling in South Africa: How low-quality education becomes a poverty trap. South African Child Gauge, 12, 34-41.

Subreenduth, S. (2009). CHAPTER 8: Post-apartheid Dilemmas: Black Teachers Theorizing Social Justice. Counterpoints, 369, 119-135.

The Bill of Rights of the Constitution of the Republic of South Africa. (1996). Government Gazette. (No. 17678).

Umpleby, S. A., Medvedeva, T. A., & Lepskiy, V. (2019). Recent Developments in Cybernetics, from Cognition to Social Systems. Cybernetics and Systems, 50(4), 367-382.

Wise, A. F., Cui, Y., Jin, W., & Vytasek, J. (2017). Mining for gold: Identifying content-related MOOC discussion threads across domains through linguistic modeling. The Internet and Higher Education, 32, 11-28.

Xing, B., & Marwala, T. (2017). Implications of the fourth industrial age for higher education. The_Thinker__Issue_73__Third_Quarter_2017.

Zhamanov, A., & Sakhiyeva, Z. (2015). Implementing flipped classroom and gamification teaching methods into computer networks subject, by using cisco networking academy. In 2015 Twelve International Conference on Electronics Computer and Computation (ICECCO) (pp. 1-4). IEEE.