Self-organising university#

Summary of

Bain, A., & Zundans-Fraser, L. (2017). The Self-organizing University. Springer. https://doi.org/10.1007/978-981-10-4917-0

Annotations (4/4/2023, 3:30:10 PM)#

Learning and teaching represents the largest component of a university's operating budget yet we know little about how well this money is spent and the quality of what is produced (Norton and Cherastidtham 2015). Massy et al. (2012) describe the measurement of teaching quality as the elephant in the room in determining the productivity of learning and teaching in higher education noting that none of the (Bain and Zundans-Fraser, 2017, p. 1)

approaches currently employed are sufficiently robust to be included in a determination of quality. (Bain and Zundans-Fraser, 2017, p. 2)

There is also recognition within the sector that to improve the quality of learning and teaching, the discrete elements in the effort chain must be integrated and addressed holistically (Probert 2015). However, for reasons we will explain in this chapter and throughout the book, a scaled, integrated, and holistic approach has not occurred in higher education. We will show that each link in the chain falters because of a failure to fully understand the context in which learning and teaching occurs. (Bain and Zundans-Fraser, 2017, p. 2)

The cornerstone of a professional context in any field is comparable and visible professionally controlled practice (Bowker and Star 2000). Comparability means the use of evidence-based approaches across multiple settings and individuals. Visibility means work processes that are observable and make use of evidence-based approaches in ways that can differentiate among more or less effective practice (Bowker and Star 2000). Comparability and visibility produce workable distinctions in routine practice (Drengenberg and Bain 2016). In higher education, workable distinctions mean a visible and comparable difference in the quality of learning and teaching practice in normal work that can be used to make a valid and reliable determination of quality (Drengenberg and Bain 2016). Professional Control is a managed professional process that makes standardization and differentiation of practice possible while retaining a degree of professional flexibility and autonomy (Bowker and Star 2000) . The term control is used here to denote a process of dynamic management and evaluation of comparable and visible practice, as opposed to something that restricts or constrains. (Bain and Zundans-Fraser, 2017, p. 3)

Not only do universities lack the models of practice at scale required to impact learning and teaching quality, they lack the internal analytic and evaluation systems, methods and tools to measure what they are doing in valid and reliable ways (Bain and Drengenberg 2016). This is not because of a lack of research and practical guidance. Extensive longitudinal research exists related to efficacious practice in learning design (e.g., Biggs and Tang 2007; Koper 2006), assessment (e.g., Sadler 2005; Wiggins 1998), and pedagogy (e.g., Hattie 2008, 2015; Marzano 1998), and approaches for implementing protocols derived from these practices. The issue is whether universities have the capacity to design themselves in ways that employ an understanding of the learning and teaching context to produce professionally controlled practice for the benefit of all faculty and students. To alter these circumstances requires an understanding of what learning and teaching means with sufficient clarity to establish differences or distinctions in the quality of practice at scale. (Bain and Zundans-Fraser, 2017, p. 5)

Governance practices focus on mitigating organizational risk over curricular innovation or best practice by amplifying institutional requirements for efficiency and compliance that lack any known impact on student learning (Coate and Tooher 2010). For example, governance processes are frequently built around the kind of standards we just described that lack a foundation of visible and comparable evidence-based practice. (Bain and Zundans-Fraser, 2017, p. 7)

Satisfaction is no guarantee of quality under current circumstances. For example, consumers can be satisfied with a service/product that does not work as claimed or at all, is unnecessary, or performs poorly, if at a given point in time it represents state-of-the-art knowledge or is the subject of a successful marketing and sales effort. The history of all fields is replete with examples of this phenomenon (e.g., bloodletting in medicine, bottled water, snake oil in pharmacology). Student evaluations in universities are an expression of satisfaction with an experience that does not reflect evidence-based professional control at any scale beyond the practice of an individual teacher. (Bain and Zundans-Fraser, 2017, p. 9)

If an institution has not assigned value to what it means by pedagogy, feedback, appropriate resources, etc., (a model of learning and teaching), and then seeks to evaluate quality teaching, the best that can be expected from such an effort is a determination of student satisfaction with whatever a faculty member believes to be good teaching practice. (Bain and Zundans-Fraser, 2017, p. 9)

The fundamental problem: Their work does not occur within a context of professionally controlled practice that would make possible the scalable influence that is frequently at the core of their mandate. **Those who lead these centers are required to provide high-level advice and leadership although their roles rarely extend to creating the kind of context, the fundamental structural change required to realize the aspirations their organizations have for the centers they lead (Bain and Zundans-Fraser, 2017, p. 12) **

While there may be an intent to make universities better at learning and teaching as a whole, centers for learning and teaching are designed for something else: (Bain and Zundans-Fraser, 2017, p. 12)

To influence learning and teaching through elective, selective, and exemplary approaches that are incompatible with whole-of-organizational change. By elective, selective, and exemplary, we mean approaches that offer elective capacity building and support for promotion; recognize excellence selectively through awards and grants; seek to demonstrate impact by example as opposed to scalable systemic effect, and where all of these actions occur within a context free of professional control. The expectation seems to be that exemplary practice sponsored by such centers will somehow exert a kind of osmotic effect on the whole organization which never happens at the scale required to influence quality overall. (Bain and Zundans-Fraser, 2017, p. 12)

Without models of scalable practice, centers become supporters and guides on the side for good works and better practice, identifying and recognizing academics who pursue excellence, providing and facilitating grants for those who are trying to innovate, participating in the development of policies, frameworks, producing papers and disseminating resources about effective practice and assuming roles and responsibilities in the existing governance processes. (Bain and Zundans-Fraser, 2017, p. 12)

We explain how an understanding of context creates the conditions for a new approach entitled Edge technology (Bain and Weston 2011) that reflect the context and leverages emergent feedback. (Bain and Zundans-Fraser, 2017, p. 22)

So, the tools people use in normal work can help them design and deliver courseware in ways that make it easier to incorporate comparable and visible evidence-based practice. The design of the tools helps users to employ professionally controlled practice. In this way, the tools have a virtual form of agency independent of their users (Levy 2001). (Bain and Zundans-Fraser, 2017, p. 22)

Their design and functionality are a mirror of the university's learning and teaching model and context (Bain and Zundans-Fraser, 2017, p. 22)

An organization's design is an expression of its schema---its beliefs and process. Beliefs and process are expressed in methods and tools, roles and responsibilities, how individuals contribute, and the way the organization as a whole addresses problems and develops more or less successful courses of action. Both the schema and those that work with it possess agency that functions in a reinforcing feedback loop. The organization's schema drives roles and responsibilities that shape individual perspectives on needs, priorities, and performance indicators. How this happens will affect schema development (Auhl, in progress). (Bain and Zundans-Fraser, 2017, p. 28) get auhl

The Pre-contextual Grandbridge approach to learning and teaching is an example of the effort chain described in Chap. 1---a loose coupling (Weick 1976) of awards, grants, governance, capacity building, promotion frameworks, and surveys none of which are underpinned by professionally controlled practice at a whole-of-organization scale. In line with Conway's Law, Pre-contextual Grandbridge's strategic planning mirrors its effort chain schema and design and the agency of those who work within its social structure. Its strategic plan amplifies its existing approach with more capacity building, more awards, more gran (Bain and Zundans-Fraser, 2017, p. 30)

The greatest single challenge associated with the improvement of learning and teaching in higher education is recognizing that the current effort chain approach and the architectural structures that serve it are incompatible with the whole-of-organization improvement in learning and teaching represented in mission and value statements and strategic plans and so desired by universities. (Bain and Zundans-Fraser, 2017, p. 33)

Grandbridge SOU employs a process known as Commons-Based Peer Production (Benkler 2002) an approach used to develop open source software, industry standards and guidelines, and nanotechnologies (Maurer 2010b). Commons-Based Peer Production (CBPP) can be defined as any process whereby individuals can freely and openly contribute to a common pool (of knowledge, code, and design) (Bauwens 2014). CBPP is a decentralized alternative to hierarchical development processes for the peer production of what are frequently complex products (Benkler 2016). CBPP pools collective intelligence in a proactive production process that permits the inclusion of a broad base of perspectives and motivations (Vuculescu 2012). Wikipedia is the best known and most widely used example CBPP where anyone can author or edit a contribution within a simple rule and oversight structure. There are three phases in the CPBB for policy development in the SOU. First, the community builds definitions and examples to clarify the meaning of its commitments. The instantiated commitments are then used to develop policy statements. The statements are then evaluated, refined, and organized to become a policy document. Example 2.3 describes the process as employed by Grandbridge. (Bain and Zundans-Fraser, 2017, p. 40)

Must be supported by verifiable empirical research5; (Bain and Zundans-Fraser, 2017, p. 40)

5This rule was included recognizing the contestation that exists among educators about what constitutes evidence and empirical research support. The intent was to surface multiple perspectives and required respondents to make a case for their definitions. (Bain and Zundans-Fraser, 2017, p. 40)

As with the initial crowdsourcing, the request for participation is made to alumni, the university governance, and all academic and non-academic entities, student groups, and to every individual in the community. (Bain and Zundans-Fraser, 2017, p. 41) There is a touch of techno-solutionism here. The 90/9/1 rule means that this type of CBP may not be all roses

Crowdsourcing and CBPP outsource a traditionally exclusive process to those who have most at stake in the way an organization changes and the end product of that change; - An inclusive participatory approach to developing commitments sends a powerful message irrespective of the levels of participation in the process; (Bain and Zundans-Fraser, 2017, p. 45)

Bauwens, M. (2014). Commons based peer production: An introduction. Retrieved from https:// www.boell.de/en/2014/07/08/commons-based-peer-production-introduction. (Bain and Zundans-Fraser, 2017, p. 45)

Benkler, Y. (2016). Peer production and cooperation, forthcoming in J. M. Bauer & M. Latzer (Eds.), Handbook on the economics of the internet, (pp. 81--119). Cheltenham and Northampton, Edward Elgar. (Bain and Zundans-Fraser, 2017, p. 45)

According to Dede (2006, 2016), an innovation needs to possess the requisite scope and depth required to be scalable. Most change processes in education are dependent on altering the way educators engage in their normal work---how they think act and feel about what they do every day in their professional lives. While the specific target of an innovation may be a new approach to assessment, curriculum, teaching, or technology, the support required for successful implementation at scale is frequently of much broader scope and depth than that usually assumed by change agents. All too often, change efforts target the immediate conditions required to make the change happen (e.g., providing professional development to upskill faculty members in an assessment, curriculum or teaching approach, or ensuring a new technology is in place). This approach fails to recognize the way change impacts individual faculty members more broadly and the scope and depth of effort required to support a change at scale. (Bain and Zundans-Fraser, 2017, p. 49)

The point here is that major social and technological1 shifts require much more work than simply building out the core or primary innovation. They call for a network of broad and deep coevolving and reinforcing innovations and circumstances if they are to adapt dynamically to ongoing change. (Bain and Zundans-Fraser, 2017, p. 52)

We also know the efforts to border cross from old to new technologies (e.g., Weston and Brooks 2008) are made immensely challenging because these dynamic coevolving circumstances (the roads, gas stations, mechanics, etc.) rarely emerge as part of a smooth and timely transition. They all too frequently include many false starts, failures, gaps, and incomplete solutions before a new technology emerges at scale as a robust alternative to the prevailing approach. Further, in the early stages of an innovation the development of the many coevolving elements fall to the developers or change agents in the absence of the kind of dispersed control and modularization that occurs when an innovation is more mature (Horn 2014). (Bain and Zundans-Fraser, 2017, p. 52)

Agent-based design involves describing a system from the perspective of its constituent units in order to understand the interaction of key elements (Bonabeau 2002). The approach focuses on sampling and modeling the interaction of agents in a real working context to understand the topology of that work and the rules and relationships that apply, including the way those agents engage in networked collaboration with their peers (Macal and North 2010). This information can then be used to drive software design. Most models of agent-based design involve an analysis of the need or problem, the situation or context and then the development of tools or process required to solve the problem (Bain and Drengenberg 2016; Billari et al. 2006; Macal and North 2010; Doran 2006). (Bain and Zundans-Fraser, 2017, p. 55) Need to find out more about this and its relation to other approaches. Agile/distributed development works more in reality. Agent based approaches appear to assume you can model the system. Questionable?

The full scope of software development required to enable learning and teaching practice in the SOU approach is taken up in Chap. 7 as it relates to program design, program mapping, learning design assessment, and feedback. Example 3.1 shows in one area (designing cooperative learning experiences), the way evidence-based practice can be embedded in tools to produce workable distinctions in learning and teaching that can ultimately lead to the determination of the valid and reliable standards of practice. It is also important to note that we are not conflating the existence of the software with using it at scale to achieve better learning design. The existence of commitments, policy and tools represent a beginning in this regard although more needs to be done to create the coevolved and self-reinforcing circumstances necessary for professionally controlled practice at scale. (Bain and Zundans-Fraser, 2017, p. 60)

Emergent Peer feedback for an assessment rubric (Bain and Zundans-Fraser, 2017, p. 60) Can a uni afford this type of software development? Can it be implemented with COTS?

He is having a firsthand experience exerting an influence on the 25% of the variance in student achievement contributed by the quality of instruction (Bloom 1976). (Bain and Zundans-Fraser, 2017, p. 62) THere are suggestions that there are issues with this figure

We also noted in Chap. 1 that the uptake of professional development opportunities in learning and teaching is frequently low (Probert 2015) and faculty members are often skeptical about the value of those opportunities. This is unsurprising given the research on the efficacy of professional development (Bain and Zundans-Fraser, 2017, p. 67)

PD fails in the pre-contextual university because it is expected to be the silver bullet driver of change instead of filling a meaningful role within an embedded design. (Bain and Zundans-Fraser, 2017, p. 67)

By this we mean professional development needs to be a role player embedded within a broader interconnected and supportive learning and teaching context instantiated by commitments, policy, technology, role, governance and career trajectory that function interdependently and coevolve to create a new schema for learning and teaching practice. Most often PD is expected to carry forward a loosely articulated pre-contextual design approach (Bain and Zundans-Fraser, 2017, p. 67)

n interdependently and coevolve to create a new schema for learning and teaching practice. (Bain and Zundans-Fraser, 2017, p. 67)

The scope, depth, and coevolutionary nature of the embedded design process stands in contrast to the loose coupling (Weick 1976) that is the hallmark of the pre-contextual effort chain organizational structure of universities. Loose coupling is characterized by an absence of regulation, loose organizational arrangements, lack of consensus, and low levels of coordination (Weick 1976; Orton and Weick 1990). Since Weick's seminal work, many pre-contextual universities have worn loose coupling like a badge of honor invoking it as an effective organizational form consistent with effective adaptation to change, responsive decision-making, multiple decision-making pathways, a cultivator of institutional ingenuity, initiative, and flexibility (Orton and Weick 1990). These potentials may exist for organizations in fields that possess fundamental professional control. However, in an echo of Conway's Law, (Conway 1968) the extensive evidence about the inability of universities to determine what learning and teaching mean, the failure to establish what constitutes quality and productivity in relation to student outcomes, along with the inability to exert scalable influence on their core activity (learning and teaching practice) would suggest a symmetry between loose coupling in universities and its outcomes in the pre-contextual university. The touted benefits may reside more in the realm of myth than reality when applied to learning and teaching in higher educatio (Bain and Zundans-Fraser, 2017, p. 68)

The tighter coupling in the SOU creates the support and functional cohesion (TheBojan 2015) required for an organization to function professionally at scale. In essence, it is the order required to replace top down control (Bain 2007) where there exists a shared understanding and schema for collaborative professionally controlled practice. (Bain and Zundans-Fraser, 2017, p. 69)

Dede, C. (2016, February 9). Designing an educational innovation to achieve scale: Five critical concepts. The Evolution: A destiny solutions illumination. Retrieved from http://evolllution. com/managing-institution/operations_efficiency/designing-an-educational-innovation-toachieve-scale-five-critical-concepts/ Dede, C. (2006). Scaling up: Evolving innovations beyond ideal settings to challenging contexts of practice. In R. K. Sawyer (Ed.), Cambridge handbook of the learning sciences (pp. 551--566). Cambridge, England: Cambridge University Press. (Bain and Zundans-Fraser, 2017, p. 71)

The company's consultants know no more about professionally controlled learning and teaching practice than the Pre-contextual Grandbridge leadership. Taking up the inconsistency across faculties is a perfect target for them as that agenda lines up perfectly with the expectations of the Pre-contextual Grandbridge leadership, their (the consultants) capabilities, and avoids the difficult question of what should be happening in terms of learning and teaching at scale across the university. The inconsistencies across faculties are assumed to be problematic although no one really knows about the relationship between the support of learning and teaching and their quality and productivity at Pre-contextual Grandbridge or anywhere else (Drengenberg and Bain 2016). (Bain and Zundans-Fraser, 2017, p. 92)

The new experts and expanded entities will assume responsibility for learning and teaching to whom faculty members then become accountable. Because there is no shared schema and practice, roles intended to serve the faculty members function in the inverse. Faculty members simply get more work to do as they become subject to new requirements and accountabilities because the new entities and roles generate a need to be served. Some faculty members will attend professional development sessions conducted by the CELT. Others will not. This will stimulate high-level discussions about making professional development compulsory or finding ways to deliver content more easily online. Instead of a shared and collaborative enterprise, learning and teaching become a set of requirements, things that faculty members need to do in order to fulfill expectations and meet new quality standards. (Bain and Zundans-Fraser, 2017, p. 93)

The successful applicants will take up their positions and invent their own autonomously constructed personal model of Pre-contextual Grandbridge learning and teaching because the university does not have one. They will then report to other people higher up the organizational chart who have responsibility for them and the entities within which they work (Bain and Drengenberg 2016). Those people will use their individually and autonomously constructed personal schema for the Pre-contextual Grandbridge model to lead and judge those they supervise. The black-box reorganization of learning and teaching at Pre-contextual Grandbridge is unable to produce the workable distinctions in the quality and productivity of normal work necessary to lead and manage. Power is centralized with the connoisseurs, the mappers, the experts, and quality controllers whom along with those who lead them assume responsibility for what goes on. Coordination and control are exercised from top to bottom in ways that are directed at the community of learners and teachers not with them. (Bain and Zundans-Fraser, 2017, p. 94)

An SOU is comprised of a network of learning and teaching teams. All teams operate with the same simple rules or commitments. They are: 1. To implement the model of learning and professional practice; 2. To use emergent feedback to problem-solve learning and teaching needs; 3. To contribute to the ongoing evolution of the model and schema by sharing and acting upon feedback across the community. (Bain and Zundans-Fraser, 2017, p. 96)

The network is free of hierarchy. Information from discrete learning experiences within courses all the way to whole-of-university performance are available to all of the teams all of the time as they share feedback and problem-solve. (Bain and Zundans-Fraser, 2017, p. 97) Would this work in reality?

Collective intelligence, a term first coined by Douglas Engelbart referred to as collective IQ, meaning a measure of how well people can work together on important challenges. This means how quickly and intelligently they can anticipate or respond to a situation, leveraging their collective perception, memory, insight, vision, planning, reasoning, foresight, and experience into applicable knowledge (Doug Engelbart Institute 2016). Much of Engelbart's work related to augmenting human intellect with technologies to address wicked problems (Engelbart 1962) including the development of hypertext and the computer mouse. The big idea here is that teams are great places for people to share perspectives and insights and pool the collective capacities associated with their membership. (Bain and Zundans-Fraser, 2017, p. 103)

he cumbersome and disconnected committee structure and process in Pre-contextual Grandbridge is what happens when decision-making data is inadequate, and the edges connecting nodes in the network are either too long or do not exist at all. The idea of definitive high level decision-making is an illusion produced by a hierarchical organizational structure and insufficient feedback. (Bain and Zundans-Fraser, 2017, p. 105)

Further, attempting to change an organization in the manner described in Example 5.1 is unlikely to have any substantive effect because it does not address the fundamental distance problem between high-level planning and ground-level action and the centrality and complexity of the latter as the fundamental source of solution generation and adaptation in organizations. (Bain and Zundans-Fraser, 2017, p. 105)

This is more than an issue of elevation but a fundamental difference in what can be reasonably accomplished when information, power, and control exist (Bain and Zundans-Fraser, 2017, p. 105)

in an abstracted context and when that abstraction is privileged as the primary driver of organizational action. (Bain and Zundans-Fraser, 2017, p. 106)

This circumstance, which is highly similar from university to university exists in large measure because those leading learning and teaching only have an approximate or analogically reasoned2 understanding of how to build out a valid and reliable learning and teaching approach at scale in their organizations (Bain and Drengenberg 2016). (Bain and Zundans-Fraser, 2017, p. 108)

Further, given the widely recognized hierarchical and top-down organizational structure of most modern universities (Bleiklie and Kogan 2007), the impact of those leaders is profound. (Bain and Zundans-Fraser, 2017, p. 108)

Agency is a model of action made possible by a particular body of knowledge that is capable of altering outcomes (Hirschmann 2009). Agency reflects and is developed by the context in which it occurs. (Bain and Zundans-Fraser, 2017, p. 109)

According to Latour (1996) agency is not restricted to human activity. Latour coined the term actants to denote human and non-human actors in a network that shape what they do as a result of their interaction. (Bain and Zundans-Fraser, 2017, p. 109)

Both Conway and Chandler amplify the relationship between the non-human architecture of an organization and the behavior of human actors. (Bain and Zundans-Fraser, 2017, p. 109)

This connection (Bain and Zundans-Fraser, 2017, p. 109)

between agency and context is critical to our understanding of the problem addressed by the SOU approach as a strategic or design solution to the challenge of getting to scale with quality and distinctive learning and teaching in universities. (Bain and Zundans-Fraser, 2017, p. 110)

There can be nothing more wicked in problem solving than a circumstance whereby those individuals critically positioned to solve a problem do not believe they have one, or understand what the problem is. (Bain and Zundans-Fraser, 2017, p. 113)

Further, as we described in Chap. 3, the model of providing supplementary professional development in contexts that do not have preexisting professionally controlled practice has not worked in any sector of education (Cole 2012). (Bain and Zundans-Fraser, 2017, p. 115)

The Pre-contextual Grandbridge leaders have some sense of the interconnectedness that makes the assessment problem wicked although that understanding is constrained by the limits of their individually constructed effort chain schema. They can visualize the pieces in the jigsaw puzzle (e.g., technology, professional development, promotion framework) but there is no deep understanding of each piece and how they fit together because there is no deep understanding of the learning and teaching context and associated professional practice that would imbue the puzzle pieces with meaning. (Bain and Zundans-Fraser, 2017, p. 116)

The longer Pre-contextual Grandbridge persists with its effort chain approach the more likely it is that leaders will ultimately conceptualize the problem as a compliance issue whereby faculty members are not doing what they need or are meant to do. This is likely to happen because the way the university has separated its strategy from execution. Pressure will be brought to bear on middle managers for failing to effectively implement the leadership strategy at an operational level in ways that echo the separation between the roles of strategy and operations in Pre-contextual Grandbridge's organizational structure. (Bain and Zundans-Fraser, 2017, p. 118)

What do these quotes say about how we understand learning, teaching, and technology and why as the quotes imply do we see a need to affirm the teacher's role in ways that amplify the distinction between technology and teacher? At play in these quotes and the "technology as tool" mantra is an echo of Conway's Law (Conway 1968) first described in Chap. 2 whereby the field unable to define its practice at scale employs technology in a manner that reflects its current architecture and social construction. Technology is just a tool because it is only in the realm of automation (i.e., grades, scheduling, curriculum organization and support and learning management) that the field of education understands and possesses clear line of sight to the role and purpose of technology at scale in educational organizations. The use of technology mirrors the organization's schema for learning and teaching. (Bain and Zundans-Fraser, 2017, p. 130)

Pre-contextual Grandbridge is also pinning many of its hopes for better learning and teaching on the functionality of the new LMS (the collaboration tools, new learning analytics capability etc.,) and student learning. However, there is no body of research that establishes an attributable relationship between LMS use and student learning and achievement (Means et al. 2014). A recent large scale K-12 study by Kimmons (2015) found that learning management systems account for just 12% of the variance in achievement ratings, a finding that in direction is broadly consistent with those for other learning and teaching technologies including games and simulations, reading and math software, and tutoring systems that have not added significant value to student learning over the existing learning and teaching paradigm (Bain and Drengenberg 2016; Barbour and Reeves 2009; Dynarski et al. 2007; Santoro and Bishop 2010; Smith et al. 2005; Steenbergen-Hu and Cooper 2014). Further, it seems to make little difference whether these applications exist on the desktop, the local server, or in the cloud. Pre-contextual Grandbridge will instead rely on the developers' assertion that simply using the LMS features and functionality will produce successful learning outcomes. However, existing evidence would suggest this is unlikely given there is no known attributable efficacious relationship between the new Pre-contextual Grandbridge LMS and student learning outcomes at scale. (Bain and Zundans-Fraser, 2017, p. 132)

re-contextual Grandbridge could leverage the LMS for better learning outcomes. If the university possessed a model of professional practice, it could integrate the functionality of the LMS with professionally controlled practice and possibly develop the LMS technology into something new and distinctive. For example, the new chat room functionality of the LMS could be employed in combination with research-based cooperative learning (CL) to build group learning activities that exert a powerful influence on student learning. The interaction of the chat room functionality and CL would mean that well-known achievement-related features of CL including the way accountability, interdependence, and learning tasks (Slavin 1996) are organized and structured could be leveraged by and with the chat room tools for learning effect. Feedback about chat room use would focus on the key achievement-related features of CL. We know that there is a greater (Bain and Zundans-Fraser, 2017, p. 132)

likelihood of successful student outcomes when technologies are placed within a context of professionally controlled practices that have been shown to influence student learning (Bain and Drengenberg 2016; Hattie 2008; Weston and Bain 2014) (Bain and Zundans-Fraser, 2017, p. 133)

The university is hoping to assume a position of leadership and distinction in the sector although it is using the same LMS product as its peers in ways that are most likely to produce outcomes highly similar to those peer institutions. While promoted as a key tool for innovation, the reality is that the LMS will simply make pre-contextual Grandbridge more like the other pre-contextual universities with which it competes (Bain and Zundans-Fraser, 2017, p. 134)

The purpose of technology in the SOU approach is to represent and instantiate a university's learning and teaching model in day-to-day normal work, to improve student learning, generate emergent feedback, and build the organization's learning and teaching capacity. This means embedding the model of learning and teaching and professional practice in tools that generate emergent feedback and produce workable distinctions in routine practice at all levels of the university. Technologies possess the capacity to be agents of actual and virtual professional control (Levy 2001) when they function at the nexus of the learning context, known professional practice, the professional agency of the user, and the affordances of the technology. What is created at that nexus is the product of an interaction among context, user, and technology. (Bain and Zundans-Fraser, 2017, p. 134)

We describe ICT tools that serve to instantiate the professional context in interactive ways as edge technologies (Bain and Weston 2011). Edge technologies do five things, they: - enact evidence-based protocols for professionally controlled practice in terms of content and process; - connect design, enactment and engagement using the network capability of technology to join these nodes and shorten the edges between them. In network theory, an edge is the connection between two nodes on a network (Barabási 2002); - extend and distribute capacity through a collaborative human--machine relationship (Facer and Sandford 2009). They do so by translating protocols into conceptual and then relational schema for action; - enable emergent feedback by connecting learners in open collaborative platforms, shortening the cognitive distance in the learning and teaching network, making feedback an emergent expression of the routine work of design, enactment and engagement. They bring teachers and learners together for collaborative development and feedback; - build capacity. By engaging users routinely in a context that expresses a research-based approach to design, enactment and engagement, users build capacity with both the virtual and actual processes and practice that secure quality learning and teaching. They make research-based collaborative problem-solving possible. (Bain and Drengenberg 2016, p. 124) (Bain and Zundans-Fraser, 2017, p. 135)

Latour (1994) describes the way the designers of technologies employ scripts or protocols that mediate the interaction of technology and users. Like a movie or play, technologies possess a script that can prescribe the actions of those using them (Verbeek 2006). Scripts anticipate the way a technology will be used and the nature of user interaction. This concept has particular relevance to the role of technology in the SOU and specifically its agency in enabling a relationship between the professional context and the form or design of technological tools for enacting learning and teaching. (Bain and Zundans-Fraser, 2017, p. 135)

Jane need not have read the extensive literature or be an expert on CL to use the designer. As she interacts with the software she builds capacity with the teaching practice and the interaction between content and pedagogy (Shulman 1986) represented in her design work. Jane could build her learning experience using the pre-contextual LMS chat room to facilitate students working together in some generic sense. However, the chat room design does not possess a proximal connection to those features of CL that produce enhanced student learning outcomes and as a result will not build her capacity with the approach or realize the potential learning benefits to students. (Bain and Zundans-Fraser, 2017, p. 138)

The example described in this chapter comes from a suite of three technologies in development for higher education learning and teaching (Bain 2012) and described in (Bain and Drengenberg 2016). They include: Technologies for Program Design---The Programspace. The Programspace includes modules for program design including program conceptualization, standards mapping, program outcomes, assessment task, course, and module design. The modules for program design provide a starting point for the learning and teaching process that ultimately generates clear line of sight from design all the way to student performance. Analytic data reflect the connections. Technologies for Course Design and Enactment---The CourseSpace: The CourseSpace includes modules for designing and enacting course learning activities including the pedagogical approaches and the tools to assist users deliver those approaches. The CL designer examples and feedback came from the Coursespace. Student Technologies---The Learningspace: The Learningspace is where students engage with the learning experiences developed in Coursespaces using computer, tablet, or smartphone technology. (Bain and Zundans-Fraser, 2017, p. 139)

According to Verbeek (2006) technology has meaning within its context of use. Verbeek views the agency of technology as an interaction among context, technology, and user where the technology influences human behavior and capability interactively. In this integration, technologies become an extension of human capability and possess intentions that amplify certain functions and dampen others. They shape experience and contribute to moral decision-making (Verbeek 2006). (Bain and Zundans-Fraser, 2017, p. 139)

Jane's design for her evaluation course is an expression of her agency and role as an academic at Grandbridge SOU; the context of professional practice that exists at the university, and a technology designed specifically to articulate or express features of that context; in this instance the practice of cooperative learning. By way of comparison, the pre-contextual approach at Grandbridge described in the LMS Example 7.1 engenders a separation of agent, context, and technology because there is no-overarching professional understanding of learning and teaching to bring context, agency, and technology together interactively in professionally controlled ways. Pre-contextual Grandbridge cannot employ knowledge of cooperative learning or other research-based practices to the (Bain and Zundans-Fraser, 2017, p. 140)

We contend there is a strong similarity in using the features and functionalities of an LMS without knowledge of professionally controlled visible and comparable professional practice and the hypothetical use of the ultrasound without knowledge of prenatal development or the CL designer without knowledge of cooperative learning. Both scenarios would result in a highly attenuated role for the technology that is more likely to engender a perspective that separates context, agency and technology resulting in the focus on technology as a tool or thing. (Bain and Zundans-Fraser, 2017, p. 141)

As we have shown throughout the book, these assumptions can only be secured when they reflect the challenging day-to-day work of a professionally controlled organization. Because the professional context for learning and teaching at Pre-contextual Grandbridge is essentially a black-box, learning and teaching quality cannot be assumed or assured. At Pre-contextual Grandbridge, the focus is fixed on the "type" of box and its "location" as surrogates for the more difficult conversation about what the box contains. Bricks and mortar or virtual become proxies for a weak understanding of the true professional learning context. (Bain and Zundans-Fraser, 2017, p. 141)

Fourth, when an expression of the learning context, technologies mediate action in ways that make it difficult and largely unproductive to sort out or separate the role of the technology from the actor and the professional context (Latour, 1994). In this contextual interaction, attention is removed from the univariate and discrete contribution of context, actor and technology to a more powerful focus on the interaction among all three. The physician does not focus on the characteristics of the ultrasound machine as a thing (Verbeek 2006) or a tool but on the image and what it shows and means. Jane does not focus on the CL designer. She is focused on building a quality learning experience. The more the technology reflects and expresses an understanding of the professional context, the less likely it is to be of singular focus or attention. (Bain and Zundans-Fraser, 2017, p. 142)

In doing so, it exerts a virtual influence beyond that which occurs when using the system. "As these systems become more incorporated into everyday academic practices, they will work to shape and even define teachers' imaginations, expectations and behaviours. (Coates et al. 2005, p. 27)" (Bain and Zundans-Fraser, 2017, p. 143)

P. P. (2006). 'Materializing Morality---Design ethics and technological mediation. Retrieved from https://www.utwente.nl/bms/wijsb/organization/verbeek/materializingmorality.pdf. (Bain and Zundans-Fraser, 2017, p. 146)

This issue is not within the CFOs sphere of influence or responsibility. He simply assumes the data produced by the software reflects "good learning and teaching" as expressed in the Pre-contextual Grandbridge strategic plan and mission statement. If this is not the case, then the Vice-President (Academic), the Human Resource Division or someone else needs to address the issue. The CFO's assumptions reflect the way responsibility is partitioned in an organization that does not understand what learning and teaching means in a professionally controlled sense. (Bain and Zundans-Fraser, 2017, p. 152) Handle weak understanding by allocating areas of responsiblity. CASA works to learn empircally how to stretch the iron triangle.

Horn's, description of more or less mature technologies and unbundling offers a profound insight to the transposition problem in the pre-contextual university. Unbundling presumes higher education learning and teaching to be a mature technology ready to move beyond a whole system architecture. The reality is that pre-contextual universities are yet to develop sufficient understanding of what learning and teaching means to build the foundational architecture for the kind of technology (Bain and Zundans-Fraser, 2017, p. 154)

As we have seen throughout the book, it is not whether there is a lecture that is important but how a learning experience is designed and delivered to maximize student outcomes. The true value of preparation time is not about whether it happens but what transpires as a result. Imagine determining the productivity of a medical or legal practice by establishing whether patients or clients are seen, hours/appointments billed, and resources consumed, without knowing whether the use of time and consumption of resources results in the successful resolution of cases or whether patients regained their health. If a university wants to save money associated with the cost of lectures or any other learning and teaching element, it has no standards, no term of reference to make those decisions beyond looking at others doing the same thing. This absence of understanding about efficacy and quality, while acknowledged is simply accepted as the normative state of affairs in pre-contextual productivity modelling in higher education (Sullivan et al. 2012). (Bain and Zundans-Fraser, 2017, p. 155)

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understanding of context creates the conditions for a new approach entitled Edge technology (Bain and Weston 2011) that reflect the context and leverages emergent feedback. Edge technologies extend the agency of users, enable collaboration and improve the network capability of the organization (Bain and Zundans-Fraser, 2017, p. 1)

creates the conditions for a new approach (Bain and Zundans-Fraser, 2017, p. 1)

if an organization adopts a particular visible and comparable approach to design its curriculum or specific pedagogical strategies (e.g., cooperative learning or criterion-based assessment), the key features of those approaches and especially those that influence achievement can be designed into the technologies used across the community. (Bain and Zundans-Fraser, 2017, p. 1)

Their design and functionality are a mirror of the university's learning and teaching model and context (Bain and Zundans-Fraser, 2017, p. 1)