How might design principles support workplace training for employees?

The design principles I have selected to guide design and innovation in my context are informed by our readings and assignments in the LRNT 524 course, and my own lived experience as a student in learning environments delivered online and in-person. I’ve benefitted from reading about concepts such as critical instructional design (Morris & Stommel, 2018), humanizing virtual learning (University of Waterloo, 2019), and reading about the design principles that guide the work of some of the most innovative, meaningful, and creative organizations toy companies to tech startups to health service providers.

The context which my project partner and I chose to examine was workplace training for managers in a decentralized organization where employee learners span different geographies, communities, leadership roles, subject expertise, and levels of experience with the employer. Through our Pecha Kucha assignment, we had the opportunity to work through this design challenge using the design thinking process, starting with empathy maps from the perspective of the trainers and the employee learner. We reflected on the tension that can exist between the intention of the employer and the needs of the employee learners. Using principles of Universal Design for Learning, we looked at how user needs and learner engagement could be strengthened through an intentional human-centered design process. The design principles I have for improving innovation and design thinking in this context are:

The principles:

  • Start with learners’ needs (and do the work to know what these are).
  • Design to support the desired end result or outcome.
  • Build inclusivity into everything you will ask a learner to do.
  • Be flexible and open to experiment, try new ideas, or approaches.
  • Make individual parts connect to the big picture. Keep it relevant.
  • Give people choice and agency on how to participate.

Our design challenge was partly informed by conversations with actual organizational development trainers who are in the process of redesigning manager training programs. They cited issues such as low participation, higher drop-out rates, inability of staff in smaller rural communities to access training, and the changing landscape that remote work has had on the practice of gathering people in-person for sustained, ongoing training programs. Making content relevant to learners’ disparate roles and areas of expertise is also a challenge.

These design principles that I’ve chosen would see learning designers do more upfront work to learn what present needs are though activities such as surveys and focus groups, and interviews. Strengthening inclusivity and choice could bolster participation and completion rates if learners had hybrid learning options, for example, where some lessons could be done remotely and asynchronously and not only in-person. Being flexible to suggestions for improvement, especially from the learners themselves might uncover a better way of doing things. For example, if learners wish to self-manage an online collaboration hub using a Slack channel to support one another, be open to how learners want to engage. Offering options on how learners can participate or giving a choice in learning activity can create a sense of agency and choice. Finally, especially for training programs that have a longer duration, making all the disparate learning components connect to the overall goal can help focus learners on the big picture. Taken together, these design principles could go a long way to support those delivering the training and the employees who are the intended audience.

design principles

References:

Morris, S. M., & Stommel, J. (2018). An urgency of teachers: The work of critical digital pedagogy. Hybrid Pedagogy. https://pressbooks.pub/criticaldigitalpedagogy/

University of Waterloo, Trent University, Conestoga College. (2019). Humanizing virtual learning: A guide to creating connection, engagement, and inclusivity. Published by same authors.

https://ecampusontario.pressbooks.pub/humanizinglearningonline/

The failed experiment of inBloom – a cautionary tale for EdTech

Learning innovation

inBloom was a non-profit organization which aimed to provide technology and data solutions to improve education in the United States. It was created in 2011 with the goal of creating a secure, standardized platform for student data to create more personalized and data-informed education. It was planned as a $100 million USD project for data sharing that was going to create an open source platform for learning apps and curricula. Much of the funding was backed by the Bill and Melinda Gates Foundation.

Value proposition

One of the intentions of inBloom was to create more tools for teachers, create shared standards for data collection, and increase access to instructional resources. In 2011, the Director of College-Ready Education Programs wrote a blog post describing InBloom as “a huge app store just for teachers – with the Netflix and Facebook capabilities we love the most” (Bulger, 2017). The proposed benefit of harnessing millions of data points from US students across school districts and states was to break down siloes and create more tailored experiences to bolster student achievement.

Reliance on technology

In order to develop, scale, and roll-out a project of this magnitude, inBloom required a consortium of software developers, technical experts, data experts, database developers, etc to create a massive data store to aggregate, clean, and present data so teachers could access it. It was an enormous endeavour to undertake financially, technologically, and logistically. It also suffered some differing views around risk tolerance given the information that it would collect would contain identifiable information about students such as names, addresses, ethnicity, test scores, special education status, and any disciplinary actions.

Risk

inBloom failed to prove to stakeholders including parents, teachers, and privacy advocates a compelling value proposition that outweighed risks inherent in the project. Critics posed concerns about the misuse and sharing of data, in particular to third-parties. It lost support from school districts and U.S. states who were uneasy about the growing opposition and lack of clarity about how the data would be safeguarded. The loss of this support also meant a drop in financial contributions, which made the project more difficult to realize. In 2014, InBloom shut down and deleted all the student data it had collected.

Reflection

In the years following its demise much has been said about the reasons why inBloom failed. It serves as a cautionary tale that any learning innovation must place pedagogy before technology. Though something is possible, is it relevant? Does it place students’ best interests first? Who is designing and conceiving the project? What biases are those parties coming to the table with? InBloom suffered from an inability to build transparency and trust and alienated key stakeholders along the way. It continues to serve as an example of how funding and enthusiasm for an innovation are only part of the required elements to achieve a meaningful, intentional, and ethical learning design.

References:

Bulger, M., McCormick, P., Pitcan, M., (2017). The Legacy of InBloom. Data & Society. https://datasociety.net/pubs/ecl/InBloom_feb_2017.pdf

Open AI. (2023). ChatGPT. (January 9 version) [Large language model]. https://chat.openai.com/

Schaffuauser, D. (2017, February 15). Autopsy for the failure that was inBloom. The Journal. https://thejournal.com/articles/2017/02/15/autopsy-for-the-failure-that-was-inbloom.aspx

Innovations in instructional design: a look at health care education

No shortage of programs in the field

Today, if you typed the words “artificial intelligence in health care program” into a search engine, you will get a plethora of search results describing programs from schools like Stanford, MIT,  University of Toronto, and many more. The role of artificial intelligence (AI) in health care education and practice even has its own place in the aptly named Healthcare Rounds, a series of webinars hosted by McMaster University’s School of Biomedical Engineering and Entrepreneurship. Interest in understanding and applying artificial intelligence in health care spaces seems to be having its moment in the sun.

Opportunities and pitfalls

Perhaps, but a literature search will also help us understand that technology mediated education in health care occupations is nothing new. Nursing education has used simulation for training since the 1990s. Since then, other technologies have become part of the way nursing education is delivered, including virtual reality, augmented reality, 360 video, and screen-based simulation (Aebersold, 2023). For those who are responsible for creating educational resources for nursing education, what opportunities and pitfalls does AI present?

One of the perceived benefits that we hear about when discussing machine learning is how to improve efficiency and productivity to free up human beings to perform tasks that machines cannot do as easily, or well. For example, an instructional designer can use AI to inform curriculum development and identify topics that should be integrated into course content. AI can also be used to help personalize or tailor assessments based on student performance. From a clinical perspective, AI can also support a nurse’s decision-making by analyzing data points to come up with the best course of action for a patient at a given time.

Learning outcomes and bias

As with most developments in learning and technology, there is need to review new practices with a critical lens. First, no matter how education is delivered, instructors need to be intentional about the desired learning outcomes intended. For example, if virtual reality isn’t the best tool to teach a certain skill, it shouldn’t be the only option for a student nurse to use to practice. With the use of AI, the presence of bias can profoundly alter the results of an analysis or recommendation. For example, in healthcare, if a certain ethnic population is overrepresented (or underrepresented) in the literature being analyzed, it is imperative to be aware of the biases being presented that could lead to poor health outcomes and experiences for marginalized groups (Aebersold, 2023).

Going forward, AI and other technology mediated instruction appear to be a growing part of training and development in health occupations. What isn’t as clear is how instructional designers will balance the benefits of using these tools, while still being learner-focused, and critically aware of the weaknesses in these tools.

References:

Aebersold, M., Gonzalez, L., (May 31, 2023) “Advances in Technology Mediated Nursing Education” OJIN: The Online Journal of Issues in Nursing Vol. 28, No. 2, Manuscript 6.