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About

This work arose from the depths of an institutional LMS migration (Blackboard Learn to Canvas). In particular, the observation that the default migration processes required an awful lot of low level manual labour. Methods that appeared to reduce the quality of the migration process and increase the cost. Hence we started developing different methods. As the migration project unfolded we kept developing and refining. Building on what we’d done before and further decreasing the cost of migration, increasing the quality of the end result, and increasing the scale and diversity of what we could migrate.

We were stretching the iron triangle (Ryan et al, 2021). Since stretching the iron triangle a key strategic issue for higher education (Ryan et al, 2021), questions arose, including:

1. What was different between the two sets of orchestrations? Why are our orchestrations better than the default at stretching the iron triangle?
2. Might those differences help stretch the iron triangle post-migration (i.e. business as usual – BAU)?
3. Can we refine and improve those differences?

The work here is an initial exploration into answering the first question.

Table of Contents

Below you will find

• An abstract for the ASCILITE’2022 poster.
• An embedded copy of the poster.
• A sequence of videos providing detailed comparisons of the two sets of orchestrations for three of the migration tasks:
  1. Migrating echo360 embedded videos;
  2. Performing quality assurance checks on migrated course sites; and,
  3. Designing Canvas course sites with high levels of usability.
• More information on the software underpinning these and other orchestrations, including: the Course Analysis Report (CAR) process; Word2Canvas; and, Canvas Collections.
• And finally a couple of reference lists, including those for the poster and the abstraction.

Abstract

A key strategic issue for higher education is how to maximise the accessibility, quality, and cost efficiency of learning and teaching (Ryan et al., 2021). Higher education’s iron triangle literature (Daniel et al, 2009; Mulder, 2013; Ryan et al, 2021) argues that effectively addressing this challenge is difficult, if not impossible, due to the “iron” connections between the three qualities. These iron connections mean maximising one quality will inevitably result in reductions in the other qualities. For example, the rapid maximisation of accessibility required by the COVID-19 pandemic resulted in a reduction in cost efficiency (increased staff costs) and a reduction in the perceived quality of learning experiences (Martin, 2020). These experiences illustrate higher education’s on-going difficulties in creating orchestrations that stretch the iron triangle by sustainably and at scale fulfilling diverse requirements for quality learning, (Bennett et al., 2018; Ellis & Goodyear, 2019). This exploratory case study aims to help reduce this difficulty by answering the question: What characteristics of orchestrations help to stretch the iron triangle?

An LMS migration is an effective exploratory case for this research question since it is one of the most labour-intensive and complex projects undertaken by universities (Cottam, 2021). It is a project commonly undertaken with the aim of stretching the iron triangle. Using a socio-material perspective (Ellis & Goodyear, 2019; Fawns, 2022) and drawing on Dron’s (2022) definition of educational technology the poster examines three specific migration tasks: migrating lecture recordings; designing quality course sites; and, performing quality assurance checks. For each task, two different orchestrations – organized arrangements of actions, tools, methods, and processes (Dron, 2022) – are described and analysed. The institutional orchestrations developed by the central project organising the migration of an institution’s 4500+ courses, and the group orchestrations developed, due to perceived limitations of the institutional orchestrations, by a sub-group directly migrating 1700+ courses.

Descriptions of the orchestrations are used to identify their effectiveness in sustainably and at scale satisfying diverse quality requirements – stretching the iron triangle. Analysis of these orchestrations identified three characteristics that are more likely to stretch the iron triangle: contextual digital augmentation; meso-level automation; and, generativity and adaptive reuse. Each of these characteristics, their presence in each orchestration, the relationships between these characteristics; linkages with existing literature and practice; and their observed impact on the iron triangle qualities is described. These descriptions are used to illustrate the very different assumptions underpinning the two sets of orchestrations. Differences in relationships evident in the orchestrations and which mirror the distinctions between ‘smooth users’ and ‘collective agency’ (Macgilchrist et al., 2020); and, industrial and convivial tools (Illich, 1973). The characteristics identified by this exploratory case study suggest that an approach that is less atomistic and industrial, and more collective and convivial may help reconnect people with educational technology more meaningfully and sustainably. Consequently this shift may also help increase higher education’s ability to maximise the accessibility, quality, and, cost efficiency of learning and teaching.

Poster

The postere is embedded below and also available directly from Google slides. The Enter full screen option available from the “three dots” button at the bottom of the poster embed is useful for viewing the poster.

Comparing orchestrations

The core of this exploratory case study is the comparison of two sets of orchestrations and how they seek to fulfill the same three tasks.

echo360 migration

Course site QA

Course site usability

About the orchestrations

The orchestrations discussed typically rely on software that we’ve developed by building on the shoulders of other giants of open source software. Software that we’re happy to share with others.

Course Analysis Report (CAR) process

The CAR process started as an attempt to make it easier for migration staff to understand what was in a Blackboard course site. It started with a gather that extract the contents of each Blackboard course site into an offline data structure. A data structure that provided a foundation for much, much more.

The echo360 migration video offers some more detail. The following image is from that video. It shows the CAR folder for a sample Blackboard course. Generated by the CAR script this folder contains

• A folder (contentCollection) containing copies of all the files uploaded to the Blackboard course site.
  The files are organised in two ways to help the migration:
  1. Don’t migrate files that are no longer used in the course site; and,
     Files are placed into an attached or unattached folder depending on whether they are still used by the Blackboard course site.
  2. Don’t migrate all the files in one single unorganised folder.
• A folder (coursePages) containing individual Word documents containing the content of course site pages.
• A CAR report.
  A Word document that summarises the content, structure and features used in a course site.
• A pickle file.
  Contains a copy of all the course site details and content in a machine readable format.

While the CAR code is not currently publicly available we are happy to share.

Word2Canvas

Word2Canvas is Javascript which modifies the modules page on a Canvas course site. It provides a button that allows you to convert a specially formatted Word document into Canvas module.

The coursePages folder produced by the CAR process generates these specially formatted Word documents. Enabling migration to consist largely of minor edits of a Word document and using word2canvas to create a Canvas module.

The echo360 migration video offers some more detail, including an example of using the CAR. The Word2Canvas to site provides more detail again, including how to install and use word2canvas.

Canvas Collections

Canvas Collections is also Javascript which modifies the Canvas modules page. However, Canvas Collections’ modifications seek to improve the usability and visual design of the modules page. In doing so it addresses long known limitations of the Modules page, as the following table summarises.

Limitation of Canvas modules	Collections Functionality
Lots of modules leads to a long list to search	Group modules into collections that are viewed separately
An overly linear and underwhelming visual design	Ability to select from, change between, and create new representations of collections and their modules.
No way to add narrative or additional contextual information about modules to the modules page.	Ability to transform vanilla Canvas modules into contextual objects by adding additional properties (information) for modules that are used in representations and other functionality.

The course site usability video provides more detail on Canvas Collections, as does the Canvas Collections site. Canvas Collections is available for use now, but is continually being developed.

References – Poster

Arthur, W. B. (2009). The Nature of Technology: What it is and how it evolves. Free Press.

Bygstad, B. (2017). Generative Innovation: A Comparison of Lightweight and Heavyweight IT: Journal of Information Technology, 32(3), 180-193. https://doi.org/10.1057/jit.2016.15

Cottam, M. E. (2021). An Agile Approach to LMS Migration. Journal of Online Learning Research and Practice, 8(1). https://doi.org/10.18278/jolrap.8.1.5

Dron, J. (2022). Educational technology: What it is and how it works. AI & SOCIETY, 37, 155–166. https://doi.org/10.1007/s00146-021-01195-z

Ellis, R. A., & Goodyear, P. (2019). The Education Ecology of Universities: Integrating Learning, Strategy and the Academy. Routledge.

Fawns, T. (2022). An Entangled Pedagogy: Looking Beyond the Pedagogy—Technology Dichotomy. Postdigital Science and Education, 4(3), 711–728. https://doi.org/10.1007/s42438-022-00302-7

Goodhue, D., & Thompson, R. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236.

Illich, I. (1973). Tools for Conviviality. Harper and Row.

Jones, D., & Clark, D. (2014). Breaking BAD to bridge the reality/rhetoric chasm. In B. Hegarty, J. McDonald, & S. Loke (Eds.), Rhetoric and Reality: Critical perspectives on educational technology. Proceedings ascilite Dunedin 2014 (pp. 262–272). http://ascilite2014.otago.ac.nz/files/fullpapers/221-Jones.pdf

Macgilchrist, F., Allert, H., & Bruch, A. (2020). Students and society in the 2020s. Three future ‘histories’ of education and technology. Learning, Media and Technology, 45(0), 76–89. https://doi.org/10.1080/17439884.2019.1656235

Mulder, F. (2013). The LOGIC of National Policies and Strategies for Open Educational Resources. International Review of Research in Open and Distributed Learning, 14(2), 96–105. https://doi.org/10.19173/irrodl.v14i2.1536

Ryan, T., French, S., & Kennedy, G. (2021). Beyond the Iron Triangle: Improving the quality of teaching and learning at scale. Studies in Higher Education, 46(7), 1383–1394. https://doi.org/10.1080/03075079.2019.1679763

References – Abstract

Bennett, S., Lockyer, L., & Agostinho, S. (2018). Towards sustainable technology-enhanced innovation in higher education: Advancing learning design by understanding and supporting teacher design practice. British Journal of Educational Technology, 49(6), 1014–1026. https://doi.org/10.1111/bjet.12683

Cottam, M. E. (2021). An Agile Approach to LMS Migration. Journal of Online Learning Research and Practice, 8(1). https://doi.org/10.18278/jolrap.8.1.5

Dron, J. (2022). Educational technology: What it is and how it works. AI & SOCIETY, 37, 155–166. https://doi.org/10.1007/s00146-021-01195-z

Ellis, R. A., & Goodyear, P. (2019). The Education Ecology of Universities: Integrating Learning, Strategy and the Academy. Routledge.

Fawns, T. (2022). An Entangled Pedagogy: Looking Beyond the Pedagogy—Technology Dichotomy. Postdigital Science and Education. https://doi.org/10.1007/s42438-022-00302-7

Illich, I. (1973). Tools for Conviviality. Harper and Row.

Macgilchrist, F., Allert, H., & Bruch, A. (2020). Students and society in the 2020s. Three future ‘histories’ of education and technology. Learning, Media and Technology, 45(0), 76–89. https://doi.org/10.1080/17439884.2019.1656235

Martin, L. (2020). Foundations for good practice: The student experience of online learning in Australian higher education during the COVID-19 pandemic). Tertiary Educational Quality and Standards Agency. https://www.teqsa.gov.au/latest-news/publications/foundations-good-practice-student-experience-online-learning-australian

Ryan, T., French, S., & Kennedy, G. (2021). Beyond the Iron Triangle: Improving the quality of teaching and learning at scale. Studies in Higher Education, 46(7), 1383–1394. https://doi.org/10.1080/03075079.2019.1679763

Introduction

All university strategies for learning and teaching seek to maximise: accessibility (as many people as possible can participate – feel the scale – in as many ways as possible); quality (it’s good); and, cost effectiveness (it’s cheap to produce and offer). Ryan et al (2021) argue that this is a “key issue for contemporary higher education” (p. 1383) due to inevitable cost constraints, the benefits of increased access to higher education, and requirements to maintain quality standards. However, the literature on the “iron triangle” in higher education (e.g. Daniel et al, 2009; Mulder, 2013; Ryan et al, 2021) suggests that maximising all three is difficult, if not impossible. As illustrated in Figure 1 (adapted from Mulder, 2013, p. 100) the iron triangle suggests that changes in one (e.g. changing accessibility from on-campus to online due to COVID) will have negatively impact at least one, but probably both, of the other qualities (e.g. the COVID response involving increase in workload for staff and resulting in less than happy participants).

Much of the iron triangle literature identifies different strategies that promise to break the iron triangle. Mulder (2013) suggests open educational resources (OER). Daniel et al (2009) suggest open and distance eLearning. Ryan et al (2021) suggest high-quality large group teaching and learning; alternative curriculum structures; and automation of assessment and feedback.

I’m not convinced that any of these will break the iron triangle. Not due to the inherent validity of the specific solutions (though there are questions). Instead my doubts arise from how such suggestions would be implemented in contemporary higher education. Each would be implemented via variations on common methods. My suspicion is that these methods are likely to limit any attempts to break the iron triangle because they are incapable of effectively and efficiently orchestrating the entangled relations that are inherent to learning and teaching.

Largely because existing methods are based on atomistic, and deterministic understandings of education, technology, and organisations. The standard methods – based on practices like stepwise refinement and loose coupling – may be necessary but aren’t sufficient for breaking the iron triangle. These methods decompose problems into smaller black boxes (e.g. pedagogy before technology; learning and teaching, requirements and implementation; enrolment, finance, and HR; learning objects etc.) making it easier to solve the smaller problem within the confines of its blackbox. The assumption is that solving larger problems (e.g. designing a quality learning experience or migrating to a new LMS) is simply a matter of combining different black boxes like lego blocks to provide a solution. The following examples illustrate how this isn’t reality.

Entangled views of pedagogy (Fawns, 2022), educational technology (Dron, 2022), and associated “distributed” views (Jones and Clark, 2014) argue that atomistic views are naive and simply don’t match the reality of learning and teaching. As Parrish (2004) argued almost two decades ago in the context of learning objects, decontextualised black boxes place an increased burden on others to add the appropriate context back in. To orchestrate the entangled relations between and betwixt the black boxes and the context in which they are used. As illustrated in the examples below, current practice relies on this orchestration being manual and time consuming. I don’t see how this foundation enables the iron triangle to be broken.

Three examples from an LMS migration

We’re in the process of migrating from Blackboard Learn to Canvas. I work with one part of an institution and we’re responsible for migrating some 1400 courses (some with multiple course sites) over 18 months. An LMS migration “is one of the most complex and labor-intensive initiatives that a university might undertake” (Cottam, 2021, p. 66). Hence much of the organisation is expending effort to make sure it succeeds. This includes enterprise information technology players such as the new LMS vendor, our organisational IT division, and various other enterprise systems and practices. i.e. there are lots of enterprise black boxes available. The following seeks to illustrate the mismatch between these “enterprise” practices and what we have to actually do as part of an LMS migration.

In particular, three standard LMS migration tasks are used as examples, these are:

1. Connect the LMS with an ecosystem of tools using the Learning Tools Interoperability (LTI) standard.

2. Moving content from one LMS to another using the common cartridge standard.

3. “to make teaching and learning easier” using a vanilla LMS.

The sections below describe the challenges we faced as each of these standardised black boxes fell short. Each were so disconnected from our context and purpose to require significant manual re-entanglement to even approach being fit-for-purpose. Rather than persevere with an inefficient, manual approach to re-entanglement we did what many, many project teams have done before. We leveraged digital technologies to help automate the re-entanglement of these context-free and purposeless black boxes into fit-for-purpose assemblages that were more efficient, effective, and provided a foundation for on-going improvement and practice. Importantly, a key part of this re-entanglement was injecting some knowledge of learning design. Our improved assemblages are described below.

1. Connect the LMS with an ecosystem of tools using the LTI standard

Right now we’re working on migrating ~500 Blackboard course sites. Echo360 is used in these course sites for lecture capture and for recording and embedding other videos. Echo360 is an external tool, it’s not part of the LMS (Blackboard or Canvas). Instead, the Learning Tools Interoperability (LTI) standard is used to embed and link echo360 videos into the LMS. LTI is a way to provide loose coupling between the separate black boxes of the LMS and other tools. It makes it easy for the individual vendors – both LMS and external tools – to develop their own software. They focus on writing software to meet the LTI standard without a need to understand (much of) the internal detail of each other’s software. Once done, their software can interconnect (via a very narrow connection). For institutional information technology folk the presence of LTI support in a tool promises to make it easy to connect one piece of software to another. i.e. it makes it easy to connect the Blackboard LMS and Echo360; or, to connect the Canvas LMS and Echo360.

From the teacher perspective, one practice LTI enables is a way for an Echo360 button to appear in the LMS content editor. Press that button and you access your Echo360 library of videos from which you select the one you wish to embed. From the student perspective, the echo360 video is embedded in your course content within the LMS. All fairly seamless.

Wrong purpose, no relationship, manual assemblage

Of the ~500 course sites we’re currently working on there are 2162 echo360 embeds. Those are spread across 98 of the course sites. Those 98 course sites have on average 22 echo360 videos. 62 of the course sites have 10 or more echo360 embeds. One course has 142 echo360 embeds. The ability to provide those statistics is not common. We can do that because of the orchestration we’ve done in the next example.

The problem we face in migrating these videos to Canvas is that our purpose falls outside the purpose of LTI. Our purpose is not focused on connecting an individual LMS to echo360. We’re moving from one LMS to another LMS. LTI is not designed to help with that purpose. LTI’s purpose (one LMS to echo360) and how it’s been implemented in Blackboard creates a problem for us. The code to embed an echo360 video in Blackboard (via LTI) is different to the code to embed the same video in Canvas (via LTI). If I use Blackboard’s Echo360 LTI plugin to embed an echo360 video into Blackboard the id will be f34e8a01-4f72-46e1-XXXX-105XXXXXf75f. If I use the Canvas Echo360 LIT plugin to embed the very same video into Canvas it will use a very different id (49dbc576-XXXX-4eb0-b0d6-6bXXXXX0707). This means that to migrate from Blackboard to Canvas each of the 2162 echo360 videos in our 500+ courses we will need to regenerate/identify a new id.

The initial solution to this problem was:

1. A migration person manually searches a course site and generates a list of names for all the echo360 videos.

2. A central helpdesk uses that list to manually use the echo360 search mechanism to find and generate a new id for each video and update the list

   Necessary because in echo360 only the owner of the video or the echo360 “root” user can access/see the video. So either the video owner (typically an academic) or the “root” user generate the new ids. From a risk perspective, only a very small number of people should have root access, it can’t be given to all the migration people.

3. The migration person receives the list of new video ids and manually updates the new Canvas course site.

…and repeat that for thousands of echo360 videos.

It’s evident that this process involves a great deal of manual work and a bottleneck in terms of “root” user access to echo360.

Orchestrating the relationships into a semi-automated assemblage

A simple improvement to this approach would be to automate step #2 using something like Robotic Process Automation. With RPA the software (i.e. the “robot”) could step through a list of video names, login to the echo360 web interface, search for the video, find it, generate a new echo360 id for Canvas, and write that id back to the original list. Ready for handing back to the migration person.

A better solution would be to automate the whole process. i.e. have software that will

1. Search through an entire Blackboard course site and identify all the echo360 embeds.

2. Use the echo360 search mechanism to find and generate a new id for each video.

3. Update the Canvas course site with the new video ids.

That’s basically what we did with some Python code. The Python code helps orchestrate the relationship between Blackboard, Canvas, and Echo360. It helps improve the cost effectiveness of the process though doesn’t shift the dial much on access or quality.

But there’s more to this better solution than echo360. Our Python code needs to know what’s in the Blackboard course site and how to design content for Canvas. The software has to be more broadly connected. As explained in the next example.

Moving content from one LMS to another using the common cartridge standard

Common Cartridge provides “a standard way to represent digital course materials”. Within the context of an LMS migration, common cartridge (and some similar approaches) provide the main way to migrate content from one LMS to another. It provides the black box encapsulation of LMS content. Go to Blackboard and use it to produce a common cartridge export. Head over to the Canvas and use its import feature to bring the content in. Hey presto migration complete.

If only it were that simple.

2. Migrating content without knowing anything about it or how it should end up

Of course it’s not as simple as that, there are known problems, including:

1. Not all systems are the same so not all content can be “standardised”.

   Vendors of different LMS seek to differentiate themselves from their competitors. Hence they tend to offer different functionality, or implement/label the same functionality differently. Either way there’s a limit to how standardised digital content can be and not all LMS support the same functionality (e.g. quizzes). Hence a lot of manual work arounds to identify and remedy issues (orchestrating entangled relations).

2. Imports are ignorant of learning design in both source and destination LMS.

   Depending on the specific learning design in a course, the structure and nature of the course site can be very different. Standardised export formats – like common cartridge – use standardised formats. They are ignorant of the specifics of course learning design as embodied in the old LMS. They are also ignorant of how best to adapt the course learning design to the requirements of the new LMS.

3. Migrating information specific to the old LMS.

   Since common cartridge just packages up what is in the old LMS, detail specific to the old LMS gets ported to the new and has to be manually changed. e.g. echo360 embeds as outlined above, but also language specific to the old lms (e.g. Blackboard) but inappropriate to the new.

4. Migrating bad practice.

   e.g. it’s quite common for the “content collection” area of Blackboard courses to collect a large number of files. Many of these files are no longer used. Some are mistaken left overs, some are just no longer used. Most of the time the content collection is one long list of files with names like lecture 1.pptx, lecture 1-2019.pptx, lectures 1a.pptx. The common cartridge approach to migration packages up all that bad practice and ports it to the new LMS.

All these problems contribute to the initial migration outcome not being all that good. For example, the following images. Figure 2 is the original Blackboard course site. A common cartridge of that Blackboard course site was created and imported into Canvas. Figure 3 is the result.

It’s a mess and that’s just the visible structure. What were separate bits of content are now all combined together, because common cartridge is ignorant of that design. Some elements that were not needed in Canvas have been imported. Some information (Staff Information) was lost. And did you notice the default “scroll of death” in Canvas (Figure 3)?

The Canvas Files area is even worse off. Figure 4 shows the files area of this same course after common cartridge import. Only the first four or five files were in the Blackboard course. All the web_content0000X folders are added by the common cartridge import.

You can’t leave that course in that stage. The next step is to manually modify and reorganise the Canvas site into a design that works in Canvas. This modification relies on the Canvas web interface. Not the most effective or efficient interface for that purpose (e.g. the Canvas interface still does not provide a way to delete all the pages in a course). Importantly, remember that this manual tidy up process has to be performed for each of the 1400+ course sites we’re migrating.

The issue here is the common cartridge is a generic standard. Its purpose (in part) is to take content from any LMS (other other tool) and enable it to be imported into another LMS/tool. It has no contextual knowledge. We have to manually orchestrate that back in.

Driving the CAR: Migration scaffolded by re-entangling knowledge of source and destination structure

On the other hand, our purpose is different and specific. We know we are migrating from a specific version of Blackboard to a specific version of Canvas. We know the common approaches used in Blackboard by our courses. We eventually developed the knowledge of how what was common in Blackboard must be modified to work in Canvas. Rather than engage in the manual, de-contextualised process above, a better approach would leverage our additional knowledge and use it to increase the efficiency and the effectiveness of the migration.

To do this we developed the Course Analysis Report (CAR) approach. Broadly this approach automates the majority of the following steps:

1. Pickle the Blackboard course site.

   Details of the structure, make up, and the HTML content of the Blackboard course site is extracted out of Blackboard and stored into a file. A single data structure (residing in a shared network folder) that contains a snapshot of the Blackboard course site.

2. Analyse the pickle and generate a CAR.

   Perform various analysis and modifications to the pickle file (e.g. look for Blackboard specific language, modify echo360 embeds, identify which content collections files are actually attached to course content etc.) stick that analysis into a database, and generate a Word document providing a summary of the course site.

3. Download the course files and generate specially formatted Word documents representing course site content.

   Using our knowledge of how our Blackboard courses are structured and the modifications necessary for an effective Canvas course embodying a similar design intent create a couple of folders in the shared course folder containing all of the files and Word documents containing the web content of the Blackboard course. Format these files, folders, and documents to scaffold modification (using traditional desktop tools). For example, separate out the files from the course into those that were actually used in the current course site and those that aren’t. Making it easy to decide not to migrate unnecessary content.

4. Upload the modified files and Word documents directly into Canvas as mostly completed course content.

   Step #3 is where almost all the design knowledge necessary gets applied to the migrate the course. All that’s left is to upload it into Canvas. Uploading the files is easy and supported by Canvas. Uploading the Word documents into Canvas as modules is done via word2Canvas a semi-automated tool.

Steps #1 and #2 are entirely automatic as is the download of course content and generation of the Word documents in step #3. These are stored in shared folders available to the entire migration team (the following table provides some stats on those folders). From there the migration is semi-automated. People leveraging their knowledge to make decisions and changes using common desktop tools.

Development Window	# course sites	# of files	Disk Usage
1	219	15,213	1633Gb
2	555	2531	336Gb

Figures 5 and 6 show the end result of this improved migration process using the same course as the Figures 3 and 4. Figure 5 illustrates how the structure of “modules” in the Blackboard site has been recreated using the matching Canvas functionality. What the figures don’t show is that Step 3 of the CAR process has removed or modified Blackboard practices to fit the capabilities of Canvas.

Figure 6 illustrates a much neater Files area compared to Figure 4. All of the unnecessary common cartridge crud is not there. Figure 5 also illustrates Step 3’s addition of structure to the Files area. The three files shown are all within a Learning Module folder. This folder was not present in the Blackboard course site’s content collection. It’s been added by the CAR to indicate where in the course site structure the files were used. These images were all used within the Learning Modules content area in the Blackboard course site (Figure 2). In a more complex course site this additional structure makes it easier to find the relevant files.

Figure 5 still has a pretty significant whiff of the ‘scroll of death’. In part because the highly visual card interface used in the Blackboard course site is not available in Blackboard. This is a “feature” of Canvas and how it organises learning content in a long, visually boring scroll of death. More on that next.

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3. Making teaching and learning easier/better using a vanilla LMS

There’s quite a bit of literature and other work arguing about the value to learning and the learning experience of the aesthetics, findability, and usability of the LMS and LMS courses. Almost as much as there is literature and work expounding on the value of consistency as a method for addressing those concerns (misguided IMHO). Migrating to a new LMS typically includes some promise of making the experience of teaching and learning easier, better, and more engaging. For example, one of the apparent advantages of Canvas is it reportedly looks prettier than the competitors. People using Canvas generally report the user interface as feeling cleaner. Apparently it “provides students with an accessible and user-friendly interface through which they can access course learning materials”.

Using a overly linear, visually unappealing, context-free, generic tool constrained by the vendor

Of course beauty is in the eye of the beholder and familiarity can breed contempt. Some think Canvas “plain and ugly”. As illustrated above by Figures 2 and 4 the Canvas Modules view – the core of how students interact with study material – is known widely (e.g. University of Oxford) to be overly linear, involve lots of vertical scrolling, and not be very visually appealing. Years of experience has also shown that the course navigation experience is less than stellar for a variety of reasons.

There are common manual workarounds that are widely recommended to teaching staff. There is also a community of third party design tools intended to improve the Canvas interface and navigation experience. As well as requests to Canvas to respond to these observations and improve the system. Some examples include: a 2015 request; a suggestion from 2016 to allow modules within modules; and another grouping modules request in 2019. The last of which includes a comment touching on the shortcomings of most of the existing workarounds. The second of which includes comment from the vendor explaining there are no plans to provide this functionality.

As Figure 2 demonstrates, we’ve been able to do aspects of this since 2019 in Blackboard Learn, but we can’t in the wonderful new system we’re migrating to. We’ll be losing functionality (used in hundreds of courses.

Canvas Collections: Injecting context, visual design, and alternatives into the Canvas’ modules page

Canvas Collections is a work-in-progress designed to address the shortcomings of the current Canvas modules page. We’re working through the prevailing heavyweight umwelt in attempt to move it into production. For now, it’s working as a userscript. Illustrating the flexibility of light-weight approach, it’s currently updated to semi-automate the typical Canvas workaround for creating visual home pages. Canvas Collections is inspired by related approaches within the Canvas Community, including: CSS-based approaches to creating interactive cards; and, Javascript methods for inserting cards into Canvas which appears to have gone into production at the University of Oxford. But also draw upon the experiences of developing and supporting the use of the Card Interface in Blackboard.

Canvas Collections is Javascript to modify the Canvas modules view by adding support for three new abstractions. Each of the abstractions represent different ways to orchestrate entangled relations. The three abstractions are:

1. Collections;

   Rather than a single, long list of modules. Modules can be grouped into collections that align with the design intent of the course. Figures 7 and 8 illustrate a common use of two collections: course content and assessment. A navigation bar is provided to switch between the two collections. When viewing a collection you only see the modules that belong to that collection.

2. Representations; and,

   Each collection can be represented in different ways. No longer limited to a text-based list of modules and their contents. Figures 7 and 8 demonstrate use of a representation that borrows heavily from the Card Interface. Such representations – implemented in code – can perform additional tasks to further embed context and design intent.

3. Additional module “metadata”.

   Canvas stores a large collection of generic information about Modules. However, as you engage in learning design you assign additional meaning and purpose to modules, which can’t be stored in Canvas. Canvas Collections supports additional design-oriented metadata about modules. Figures 7 and 8 demonstrate the addition to each module of: a description or driving question to a module to help learners understand the module’s intent; a date or date period when learners should pay attention to a module; a different label to a module to further refine its purpose; and, a picture to visually representation ([dual-coding](https://en.wikipedia.org/wiki/Dual-coding_theory) anyone?).

Figures 7 and 8 illustrate each of these abstractions. The modules for this sample course have been divided into two collections: Course Content (Figure 7) and Assessment (Figure 8). Perhaps not very creative, but mirroring common organisational practice. Each Canvas module is represented by a card, which includes the title (Canvas), a specific image, a description, relevant dates, and a link to the module.

The dates are a further example of injecting context into a generic tool to save time and manual effort. The provision of specific dates (e.g. July 18, Friday, September 2) would require manual updating every time a course site was rolled over to a new offering (at a new time). Alternatively, Canvas Collections Griffith Cards representation knows both the Griffith University calendar and how Griffith’s approach to Canvas course ids specify the study period for a course. This means dates can be specific in a generic study period format (e.g. Week 1, or Friday Week 11) and the representation can figure out the actual date.

Not only does Canvas Collections improve the aesthetics of a Canvas course site it improves the findability of information within the course site by making it possible to explicitly represent the information architecture. Research (Simmunich et al, 2015) suggests that course sites with higher findability lead to increases in student reported self-efficacy and motivation, and a better overall experience. Experience with the Card Interface and early experience with Canvas Collections suggest that it is just not the students which benefit. Being able to improve a course site using Canvas Collections appears to encourage teaching staff to think more explicitly about the design of their course sites. Being asked to consider questions like: What are the core objects/activities in your course? How should they be explained? Visually represented?

Conclusions

The argument here is that more effective orchestration of entangled relations will be a necessary (though not sufficient) enabler for breaking the iron triangle in learning and teaching. On-going reliance on manual orchestration of entangled relations necessary to leverage the black-boxes of heavyweight IT will be a barrier to breaking the iron triangle. In terms of efficiency, effectiveness, and novelty. Efficiency because manual orchestration requires time-consuming human intervention. Effectiveness, at least because the time requirement will either prevent it from being done or, if one, increase significantly the chance of human error. Novelty because – as defined by Arthur (2019) – technological evolution comes from combining technologies where technology is “the orchestration of phenomena for some purpose” (Dron, 2021, p. 155). It’s orchestration all the way down. The ability to creatively orchestrate the entangled relations inherent to learning and teaching will be a key enabler to developing new learning and teaching practices.

What we’re doing is not new. In the information systems literature it has been labelled light-weight Information Technology (IT) development defined as “a socio-technical knowledge regime driven by competent users’ need for solutions, enabled by the consumerisation of digital technology, and realized through innovation processes” (Bygstad, 2017, p. 181). Light-weight IT development is increasingly how people responsible for solving problems with the black boxes of heavyweight IT (a different socio-technical knowledge regime) leverage technology to orchestrate the necessary entangled relations into contextually appropriate assemblages to solve their own needs. It is how they do this in ways that save time and enable new and more effective practice. The three examples above illustrate how we’ve done these in the context of an LMS migration and the benefits that have arisen.

These “light-weight IT” practices aren’t new in universities or learning and teaching. Pre-designed templates for the LMS (Perämäki, 2021) are an increasingly widespread and simple example. The common practice within the Canvas community of developing and sharing userscripts or sharing Python code are examples. More surprising examples is the sheer number of Universities which have significant enterprise projects in the form of Robotic Process Automation (RPA) (e.g. the University of Melbourne, the Australian National University, Griffith University, and the University of Auckland). RPA is a poster child example of lightweight IT development. These significant enterprise RPA projects are designed to develop the capability to more efficiently and effectively re-entangle the black boxes of heavyweight IT. But to date universities appear to be focusing RPA efforts on administrative processes such as HR, Finance, and student enrolment. I’m not aware of any evidence of institutional projects explicitly focused on applying these methods to learning and teaching. In fact, enterprise approaches to the use of digital technology appear more interested in increasing the use of outsourced, vanilla enterprise services. Leaving it to us tinkerers.

A big part of the struggle is that lightweight and heavyweight IT are different socio-technical knowledge regimes (Bygstad, 2017). They have different umwelten and in L&T practice the heavyweight umwelten reigns supreme. Hence, I’m not sure if I’m more worried about the absence of lightweight approaches to L&T at universities, or the nature of the “lightweight” approach that universities might develop given their current knowledge regimes. On the plus side, some really smart folk are starting to explore the alternatives.

References

Arthur, W. B. (2009). The Nature of Technology: What it is and how it evolves. Free Press.

Bygstad, B. (2017). Generative Innovation: A Comparison of Lightweight and Heavyweight IT: Journal of Information Technology. https://doi.org/10.1057/jit.2016.15

Cottam, M. E. (2021). An Agile Approach to LMS Migration. Journal of Online Learning Research and Practice, 8(1). https://doi.org/10.18278/jolrap.8.1.5

Daniel, J., Kanwar, A., & Uvalić-Trumbić, S. (2009). Breaking Higher Education’s Iron Triangle: Access, Cost, and Quality. Change: The Magazine of Higher Learning, 41(2), 30–35. https://doi.org/10.3200/CHNG.41.2.30-35

Dron, J. (2022). Educational technology: What it is and how it works. AI & SOCIETY, 37, 155–166. https://doi.org/10.1007/s00146-021-01195-z

Fawns, T. (2022). An Entangled Pedagogy: Looking Beyond the Pedagogy—Technology Dichotomy. Postdigital Science and Education. https://doi.org/10.1007/s42438-022-00302-7

Jones, D., & Clark, D. (2014). Breaking BAD to bridge the reality/rhetoric chasm. In B. Hegarty, J. McDonald, & S. Loke (Eds.), Rhetoric and Reality: Critical perspectives on educational technology. Proceedings ascilite Dunedin 2014 (pp. 262–272). http://ascilite2014.otago.ac.nz/files/fullpapers/221-Jones.pdf

Mulder, F. (2013). The LOGIC of National Policies and Strategies for Open Educational Resources. International Review of Research in Open and Distributed Learning, 14(2), 96–105. https://doi.org/10.19173/irrodl.v14i2.1536

Perämäki, M. (2021). Predesigned course templates: Helping organizations teach online [Masters, Tampere University of Applied Sciences]. http://www.theseus.fi/handle/10024/496169

Ryan, T., French, S., & Kennedy, G. (2021). Beyond the Iron Triangle: Improving the quality of teaching and learning at scale. Studies in Higher Education, 46(7), 1383–1394. https://doi.org/10.1080/03075079.2019.1679763