Learning in vivo

Part of an active disaster management team as learning experience

Because competence in coping with natural and fabricated disasters is of increasing importance, disaster management became an integral part of the curriculum of, for example, nursing education. Often “first responders in the most acute phases of disaster to the long-term aftermath addressing loss, grief and posttraumatic stress disorders” include nurses, say Sickora et al. (2020: 30). Apart from “disaster preparedness theory and frameworks for intervention”, it is deemed essential to provide clinical “experiential learning opportunities”, which “often includes simulation experiences that range from mass casualty to active shooter”. Although less common, for students to become part of an active disaster management team, has been experienced as invaluable with remarks such as “I felt like a real nurse” and “This is the best experience I have had since I decided to become a nurse”. In this regard Strimel (2014) remark that natural disasters happen at the intersection between humans and nature, and having to face a current real-life issue, students are challenged with the issues at hand.

Disaster relief intervention, as experiential learning opportunity, is deemed to serve a dual purpose, namely (a) “meeting the needs of a community in real-time crisis”, and (b) providing “students with boots-on-the-ground disaster relief experiences”, say Sickora et al. (2020: 31). Similar to Buschlen, Warner and Goffnett (2015: 40), Sickora et al. (2020: 31) emphasise preparation of students before, and debriefing after their disaster intervention. Many students made requests to return, because of deeply affirming experiences, share Sickora et al. (2020).

Sickora et al. (2020: 32) caution that “organization is crucial throughout an effective disaster relief intervention and that initial logistics are challenging and time consuming”, as well as that all stakeholders need to be identified, and resources secured. “Coordination and collaboration with indigenous health care providers and other resources, such as charities and nongovernment organizations” are essential. Long-range preplanning is important, as is effective administration to “ensure proper tracking of disaster relief execution, patient medications, nurse and staff log-ins and log-outs, and patient contact information are also key factors in the effort”. “Keeping a cool head and rolling with the punches” while rendering disaster relief is vital. Practicalities, such as, translators make a significant difference. On-site, fellow relief “workers, such as those providing assistance in construction, debris removal, or road repair, are also in need of care for a variety of maladies, ranging from heat exhaustion to insect bites and wound care”, which suggest a need for multi-disciplinary collaboration.

Buschlen, E., Warner, C. & Goffnett, S. (2015). Leadership education and service: Exploring transformational learning following a tornado. Journal of Leadership Education, 14(1), 33-54. https://journalofleadershiped.org/wp-content/uploads/2019/02/14_1_buschlen.pdf

Sickora, C., Salt, R.J., Page, T.S., Lee, W., Cantu, A.G., Lee, M., Schwab, K.W., Martinez, M.L., Byrd, D. (2020). Evolution of experiential learning opportunities for nursing students after natural disaster. Journal of Nursing Education, 59(1), 30-33. Electronically accessible from https://doi.org/10.3928/01484834-20191223-07.

Strimel, G. (2014). Authentic education by providing a situation for student-selected problem-based learning. Technology and Engineering Teacher, 73 (7), 8-18. Abstract accessible from https://eric.ed.gov/?id=EJ1049199

Students deriving learning opportunities by responding to natural disasters

Universities often struggle to secure sufficient workplace learning opportunities. Disasters, however, may offer suitable learning opportunities. This post presents ‘prompt-based journaling’ as means of facilitation of learning.

“Each year, millions of people around the world are affected by natural disasters” say Buschlen, Warner and Goffnett (2015: 33). Often universities recognise both the service learning and development of responsible citizenship opportunities involved for students, through relief community engagements, to among others, implement theoretical concepts learned. Major events such as tornados, hurricanes, tsunamis, floods, earthquakes, fires, heat waves, droughts, epidemics, pandemics, diseases, and volcanic eruptions; all require substantial humanitarian logistics relief in order to alleviate suffering and reintroduce supply. “Climate change and environmental degradation, political instability, and chronic poverty and inequality are significant drivers of forced displacement” of people, say Wessells and Kostelny (2021: 225). There is an “increased global awareness of the need for mental health and psychosocial support amidst disasters” they state, and draw attention to Hippocrates ‘Do No Harm’ principle issues such as “poor coordination, the monetization of aid, excessive targeting of aid, the imposition of Western modes of psychiatric and medicalized support, too little attention to capacity building and sustainability, and the marginalization of collective self-help and indigenous, cultural and social supports”.

Often large numbers of people are subjected to traumatic events before, during, and after disasters, with result that trauma and post-traumatic stress disorder (PTSD) become prevalent. Forcibly displaced people furthermore often face myriad social, cultural, and everyday distresses as result of weakened or shattered social support and controls. Apart from lack of basic necessities such as food, health care, or shelter; there are family separation, ongoing insecurity and lack of protection, inability to engage in cultural or religious practices, sexual exploitation and abuse, trafficking, recruitment into armed forces or groups, etc.

Prompt-based journaling could be used as means of facilitating learning. Buschlen, Warner and Goffnett (2015: 40) recommend that journal prompts are written in advance and given in sealed envelopes to participanting students, taped inside the cover of a blank notebook. They made use of three prompts, each in a separate envelope, with specific instructions when to open, namely before travelling to the disaster site; during the relief work while on site; and 30 days after the experience; where after journals are completed and returned. Because of the emotional and reflective nature of the data, participating students may request the return of their journal after transcription.

Buschlen, Warner and Goffnett (2015: 36) point out that the ‘3 Rs’, reality, reflection, and reciprocity are essential design elements of service learning, where students learn “from a situation while meeting other process or stakeholder needs”. A natural disaster (reality) would provoke students to engage, “and their personal reflections can ultimately transform into permanent knowledge and skill”. Transformational learning is derived because “Reflections on the service experience expand knowledge to achieve deeper learning”.

Buschlen, E., Warner, C. & Goffnett, S. (2015). Leadership education and service: Exploring transformational learning following a tornado. Journal of Leadership Education, 14(1), 33-54. https://journalofleadershiped.org/wp-content/uploads/2019/02/14_1_buschlen.pdf

Wessells, M. & Kostelny, K. (2021). Do no harm issues in psychosocial support: Post-tsunami practitioner learning in Sri Lanka. Asian American Journal of Psychology, 12(3), 225–233. https://doi.org/10.1037/aap0000191

Natural disasters as authentic problem-based learning opportunities for students

Photos and videos of the April 2022 KwaZulu-Natal flooding damage prompted a literature search about ‘natural disasters as potential authentic learning experiences’ through university coordinated community engagement interventions. Occupational field practitioners, the community of practice, could possibly engage pro bono in order to provide expertise, supervision and guidance to students.

Strimel (2014) observe that seldom students are provided with authentic experiences that allow opportunity for solving real-life complex engineering design problems, which have meaning both to them and greater society. Design briefs are used in technology and engineering education to offer hands-on, problem-based learning opportunities to students, but may lack both in authenticity and true real-world problem-solving experiences. Natural disasters, however, happen at the intersection between humans and nature, say Strimel (2014), who adds that the engineering community holds the key to diminishing the threats (or impact) of natural hazards.

When faced with a current real-life issue, such as the flooding damage, students are provided with opportunities to analyse the issue at hand and define a problem based on the situation. They “can then focus on using the engineering design process to solve the problem while developing new knowledge that is essential in designing and creating a quality innovative solution”, say Strimel (2014: 9).

Authenticity is extremely important, because it eliminates students questioning why they are learning stuff. “In an authentic learning experience, students are doing work that is real and has meaning to them” say Strimel (2014: 9). It encapsulates the significant difference between ‘learning about engineering principles’ and ‘learning to be an engineer’. From a constructivist learning theory perspective real-world situations are important parts of a strong educational process, in that it allows students with opportunities to apply knowledge to real situations, and may provide a sense of accomplishment.

Strimel, G. (2014). Authentic education by providing a situation for student-selected problem-based learning. Technology and Engineering Teacher, 73 (7), 8-18. Abstract accessible from https://eric.ed.gov/?id=EJ1049199

The relevance of #DataMesh to work integrated learning

Good data management of various aspects of work integrated learning (WIL) is important and may include, for example:

• Contact details of stakeholders, decision-makers, influencers, key role players and members of the relevant community of practice in general
• Established providers of experiential learning opportunities and mentors at such
• Registered students, including their academic progression
• Actual placements of students, including the provider and mentor details, and the learning agreement of each student
• Capturing of the learning activities of students
• Records of mentoring or supervision, especially if a professional or statutory requirement
• Records of monitoring and progress tracking of students, and of communication that occur
• Records of facilitated reflection on learning (experiences) by students
• Formative assessments and summative evaluation of WIL

Generally, data such as mentioned above, are captured in a range of data bases, such as the institution’s official student administration system, and data bases of academic and administrative staff responsible for WIL modules in a variety of forms. What is deemed needed is integration of the various forms of data, which is where ‘data mesh’ is deemed relevant.

Zhamak Dehghani, while a principal technology consultant at Thoughtworks, coined the term ‘data mesh’ in 2019, say Christ, Visengeriyeva and Harrer (n.d.), who indicate that ‘data mesh’ is primarily an organizational approach, which cannot be bought from a vendor. Yuhanna (2022) remarks that “A data mesh offers the ability to optimise mixed workloads by matching processing engines and data flows with the right use cases. It interfaces to the event-driven architecture, enabling support for edge use cases.”

Dehghani (2019) found that many organisations are planning or building their third generation data and intelligence platform hoping to democratize data in order to provide business insights with the view of automated intelligent decisions. Proprietary enterprise data warehouse and business intelligence platforms, the first generation, mainly rendered tables and reports which only a small group of specialized people could understand with little positive impact on the business. Complex big data ecosystem with a data lake, the second generation, required hyper-specialized data engineers and similarly impacted little on the actual business. Unfortunately, data platforms that are streaming and analysing real-time data, the third generation, with cloud based storage and machine learning platforms, are addressing only some of the gaps of the previous generations. Dehghani (2019) recommends shifting from the centralized paradigm of a data lake (or predecessor ware house) and embracing a paradigm (data mesh) drawing from contemporary “distributed architecture: considering domains as the first class concern, applying platform thinking to create self-serve data infrastructure, and treating data as a product”.

Dehghani (2020) differentiates operational data from analytical data. Operational data has a transactional nature with regard to the applications running the business; whereas analytical data provide an aggregated view of the facts of the business over time. Analyses are often modelled to provide retrospective or future-perspective insight. Often existing technology, architecture and organization design reflect the divergence of these two data planes; integrated yet separate, two levels of existence. The divergence is causing fragility, as well as a need for continuous extract, transform, and load (ETL) activities; and ever growing complexity of labyrinth of data pipeline between the two planes, as illustrated below (Dehghani, 2020, Figure 1: The great divide of data).

The ‘data mesh’ paradigm “recognizes and respects the differences between these two planes: the nature and topology of the data, the differing use cases, individual personas of data consumers, and ultimately their diverse access patterns” say Dehghani (2020). However, she adds, the ‘data mesh’ paradigm entails an inverted model and topology based on domains. ‘Data mesh’ “offers a more holistic and efficient way to organise, process, and access data” says Cynozure (2022), and “is an approach to how teams and logistics are organised”. Dehghani (2020), Cynozure (2022) and Christ et al. (n.d.) specify four underpinning principles:

1. Domain-oriented decentralized data ownership and architecture — Splitting up the business goals into ideas and concepts within certain boundaries which smaller autonomous data teams can work on
2. Data as a product — The different data domains are productised. Yuhanna (2022) indicates that data as a service (DaaS) is also known as data as a product (DaaP), and that it “offers several business benefits, including supporting a common view of business and customer data using industry standard protocols”.
3. Creating self-serve data infrastructure — Dividing the different domains among smaller autonomous teams and making the self-serve data platforms
4. Federated computational governance — Creating a unified governance and helping to come up with ways for data to flow from one team to another

Gavish (2022) derived three key concepts pertaining a ‘data mesh’ to highlight how it differs from traditional data architectures.

• Domain-oriented data owners and pipelines — who are held accountable for providing their data as products, while also facilitating communication between distributed data across different locations. Domains are further tasked with managing ingestion, cleaning, and aggregation.
• Self-serve functionality — allowing users to abstract the technical complexity and focus on their individual data use cases
• Interoperability and standardization of communications — some data (both raw sources and cleaned, transformed, and served data sets) will be valuable to more than one domain. cross-domain collaboration is enabled by standardisation on formatting, governance, discoverability, and metadata fields, among other data features. Each data domain further define and agree on SLAs and quality measures that they will ‘guarantee’ to its consumers.

It appears a ‘mesh’ connecting the various data sources pertaining the WIL of qualifications is necessary.

Christ, J.; Visengeriyeva, L. & Harrer, S. (n.d.) Data mesh architecture from an engineering perspective. Electronically accessible from https://t.co/HCzrguXmrg

Cynozure. (2022). Data mesh as a framework for data-driven value at scale. Hub & Spoken, Episode 127, 7 April 2022. Electronically accessible from https://t.co/x8qnxRPyt1

Dehghani, Z. (2019). How to move beyond a monolithic data lake to a distributed data mesh. Martin Fowler articles. Electronically accessible from https://martinfowler.com/articles/data-monolith-to-mesh.html

Dehghani, Z.. Data mesh principles and logical architecture. Martin Fowler articles. Electronically accessible from https://martinfowler.com/articles/data-mesh-principles.html

Gavish, L. (2022) What is a data mesh — and is it right for me? HackerNoon, 15 April 2022. Electronically accessible from https://t.co/oMiqO99kBg

Yuhanna, N. (2022). Four emerging data integration trends to assess: To accelerate their performance in data integration, companies are evaluating and adopting a range of contributing technologies. Computer Weekly, 8-14 February 2022, 20-23.

Application mistakes that can easily be prevented

Application is a significantly important aspect of work integrated learning (WIL). Wendy Syfret, a Melbourne based writer, editor, and author tweeted (emphases in bold added) a thread on 18 April 2022, although not specific to WIL, but nevertheless deemed of value: “In my (digital media) career I've hired a lot of people and seen a lot of good/not good applications. People always make the same mistakes that knock them out of consideration. Often it's dumb/little things, but in a crowded field they matter. They include:

• Not reading the WHOLE job description. Sounds obvious but people embed small tasks at the bottom of copy to not only test your skills, but your ability to pay attention to a full brief.
• Not looking for a direct email to someone in the company. Don't apply via LinkedIn unless they directly say so. Again, often at the bottom of the JD [job description] there is a contact and only those applications get looked at. Sometimes this isn't possible, but at least try!
• Not updating their resume to lightly reference the job ad. Yes I know it takes time but seriously, you can change ONE line to show it's not a copy and paste job.
• Not having a website. Everyone has a LinkedIn and they are annoying to navigate. A simple, clean personal website with your bio, work examples, contact details makes you look like a functioning adult. You can cheaply make one with a web builder, don't overthink it.
• Lacking experience? I hear you. Try have a personal project to talk about. Not everyone has resources to put into unpaid efforts (fair) but think about non-work parts of your life where you might have gathered relevant skills. Ran the uni magazine? Work with an activist group?
• BUT, don't tell me about anything that happened in high (or primary) school! Unless you were Betty Cooper breaking national stories, it makes you look like you're living in the past and haven't been up to anything.
• Don't spend too much time explaining why you're passionate about what you do or love this work. If you're applying for a job I'm going to assume you have a passingly interested (or are willing to fake it). A couple of lines is enough, I don't need a "why I write" essay.
• If you made a point about being good at social media or design, make sure your personal social accounts look good. I hate the idea you need to have 100k followers to get a job (you don't) but don't you can demonstrate a level of style and skill through your own accounts.
• Don't show unpublished work. Even if it's amazing, my first question is: why didn't this run? Rather send me pitches/projects/ideas you think would be relevant to this role. Or, again, send cool personal work (a few great TikToks or Instagram explainers can go a long way)
• Everyone says they are good communicators, punctual and quick learners. Don't include super general statements. Be specific and demonstrate what you're uniquely good at. Maybe you're amazing at spotting fresh talent or trends? That's more valuable than knowing you can use Slack.”

https://twitter.com/WendyWends/status/1515847945169797122