Category: LRNT 524

In our latest assignment for LRNT 524, we were asked to use a design thinking process to emphasize, ideate, and determine a possible solution for a design problem that we encounter in the real world. My partner, Jessica Gemella, and I looked at how we could create learning environments that minimize stress and anxiety due to rigid structures and policies and exacerbated by collective trauma resulting from the Covid-19 pandemic. As a follow-up to that process and assignment, I have been considering how I might use some of these learnings, such as design for well-being, to create design principles that can be applied to my own context where I facilitate professional learning and collaboration concerning comprehensive school health for educators in the K-12 system. 

Considering the context around comprehensive school health, a collaborative learning environment in which well-being is valued, supported, and modelled is a natural extension of the learning content. As such, the following design principles can help guide the creation of synchronous learning environments that foster and reflect well-being while minimizing stress and anxiety, specifically in a virtual setting. 

Don’t Assume Digital Literacy 

Not understanding how to use technology can result in an inability to cope and can negatively impact the learner’s mental health and learning outcomes (Biggins & Holley, 2022; Bondanini et al., 2020). Support digital literacy by providing an orientation to the platform and tools that will be used in the learning environment and provide technical support as needed. 

Foster a Sense of Belonging  

Fostering a sense of belonging can be supported by including and valuing diverse perspectives and worldviews, ways of knowing and thinking, and culturally sensitive and responsive design practices (Adams et al., 2021; DeLorme, 2018; Gunawardena, 2020). A sense of belonging can help learners feel more comfortable and open to sharing knowledge while creating space for diverse meaning-making. 

Be Flexible  

Designing for flexibility in a virtual synchronous learning environment means creating opportunities for participation, allowing for spontaneous discussions, the use of chat and mic features for communication, representing content in a variety of ways, and offering options for self-directed learning pathways (Racheva, 2018; Yamagata-Lynch, 2014). Flexibility can increase motivation and engagement of learners and can help improve their overall learning experience and outcomes. 

Build Social Presence 

Social connection is an essential dimension of a learning environment that supports well-being. Using a social-constructivist framework to inform learning design can help develop social presence, enhance well-being, and positively affect learning outcomes (Mäkelä, 2018). Moreover, taking time for informal sharing helps to build trust among learners and can lead to more open knowledge sharing and collaboration (Holton, 2021). 

Be Clear 

Adult learners are goal orientated and like to know why what they are learning is important to them (Merriam & Bierema, 2014). Being explicit about the value of the learning material to the learner’s context, developing and sharing meaningful learning outcomes, using simple language, and providing clear instruction can help provide clarity and direction for the learner while limiting confusion and stress. 

Promote Self-Care & Mindfulness 

Opportunities to practice self-care and mindfulness in the learning environment can enhance well-being while modelling best practice for learning design that reflects and supports well-being. Incorporating mindfulness activities can create an inviting learning environment that is open and flexible, helps learners feel connected and cared for, develops strong self-regulation skills, and benefits learners experiencing psychological distress (Palalas, 2020; Roddey et al., 2017).

References

Adams, S., Bali, M., Eder, Z., Fladd, L., Garrett, K., Garth-McCullough, R., Gibson, A. M., Gunder, A., Iuzzini, J., Knott, J. L., Rafferty, J. & Weber, N. L. (2021). Caring for students playbook: Getting started with key terms and challenges. Every Learner Everywhere. https://www.everylearnereverywhere.org/resources/ 

Biggins, D., & Holley, D. (2022). Student wellbeing and technostress: critical learning design factors. Journal of Learning Development in Higher Education (25). https://doi.org/10.47408/jldhe.vi25.985 

Bondanini, G., Giorgi, G., Ariza-Montes, A., Vega-Muñoz, A., & Andreucci-Annunziata, P. (2020). Technostress dark side of technology in the workplace: A scientometric analysis. International Journal of Environmental Research and Public Health, 17(21), 8013. https://doi.org/10.3390/ijerph17218013 

DeLorme, C. M. (2018). Quilting a journey: Decolonizing instructional design. AlterNative: An International Journal of Indigenous Peoples, 14(2), 164–172. https://doi.org/10.1177/1177180118769068 

Gemella, J., Yardley L. (2023, January 8). Design challenges in a post-pandemic world [Video]. Canva. https://www.canva.com/design/DAFWuaVFBoc/EEUDFFvkYimI53ZhbDuEJQ/watch?utm_content=DAFWuaVFBoc&utm_campaign=designshare&utm_medium=link&utm_source=publishsharelink

Holton, J.A. (2001). Building trust and collaboration in a virtual team. Team Performance Management, 7(3), 36-47. https://doi.org/10.1108/13527590110395621 

Joint Consortium for School Health. (2023). What is comprehensive school health? http://www.jcsh-cces.ca/en/concepts/comprehensive-school-health/

Mäkelä, T.E. (2018). A design framework and principles for co-designing learning environments fostering learning and wellbeing. Jyväskylä Studies in Education, Psychology and Social Research. https://www.jyu.fi/edupsy/fi/tohtorikoulu/kasvatustieteiden-tohtoriohjelma/valmistuneet-vaitoskirjat/makela_tiina_vaitoskirja.pdf 

Merriam, S.B., & Bierema, L.L. (2014). Adult learning: Linking theory and practice. Jossey-Bass. 

Palalas, A., Mavraki, A., Drampala, K., Krassa, A., & Karakanta, C. (2020). Mindfulness practices in online learning: Supporting learner self-regulation. The Journal of Contemplative Inquiry, 7(1). https://journal.contemplativeinquiry.org/index.php/joci/article/view/222 

Racheva, V. (2018). Social aspects of synchronous virtual learning environments. AIP Conference Proceedings, 2048(1), 020032. https://doi.org/10.1063/1.5082050  

Roddy, C., Amiet, D.L., Chung, J., Holt, C.J., Shaw, L.K., Mckenzie, S., Garivaldis, F.J., Lodge, J.M., & Mundy, M. (2017). Applying best practice online learning, teaching, and support to intensive online environments: An integrative review. Frontiers in Education, 2(59). https://doi.org/10.3389/feduc.2017.00059 

Yamagata-Lynch, L.C. (2014). Blending online asynchronous and synchronous learning. The International Review of Research in Open and Distributed Learning, 15(2), 189-212. https://doi.org/10.19173/irrodl.v15i2.1778 

In a recent assignment for LRNT 524, I was asked to research and evaluate an instructional or learning design innovation. I chose to focus on a learning design innovation known as adapted learning in which technology is used to provide customized learning experiences for learners based on the needs of the individual by creating unique pathways and progression of learning material and activities (Leaders, 2022). As part of this discussion, I shared how artificial intelligence (AI) has progressed adapted learning, introducing tools such as ones that can detect the learners cognitive state based on conversational elements which it uses to guide the learner through productive conversations to enhance learning (Capuano & Caballé, 2020). 

As a follow up blog post, I have been asked to explore a learning innovation and discuss it’s impact. Upon further research in adapted learning, I discovered deep learning which is a AI-based technology that attempts to mimic the human brain through image or object detection using a multi-layererd approach to make intelligent decisions (Han & Xu, 2020; IBM, n.d.). Deep learning is a tool that can enhance adapted learning through a complex analysis of objects that goes beyond a linear process of adaptation, similar to neural connections and processing in the human brain. After an initial exploration of the literature, I found that there were many interpretations of what deep learning is and that the term is often used interchangeably with machine learning and deep neural networks. Kavlakoglu (2020) at IBM contends that deep learning is a subset of machine learning with deep neural networks making up its algorithms. The lack of common language and understanding in the literature of what deep learning is made my research on this topic more difficult. 

Furthermore, there does not seem to be a lot of literature about the use of deep learning technology in the context of learning in formal education. Deep learning is most known for its use in other sectors and innovations such as driverless cars where a car must learn how to detect a stop sign (Venkateswaran et al., 2021), a true focus on machine learning rather than human learning. However, I do see that deep learning can have a large impact on human learning, but perhaps in more of an ‘unlearning’ way. As deep learning technology advances in an effort to simulate processes of the human brain, less effort is required from humans to learn and complete tasks. This exclusive reliance on technology to make decisions and problem solve could be problematic for two reasons. Firstly, AI technology, including deep learning is still far from matching human intelligence, making outputs not always accurate (McClelland & Botvinick, 2020). And secondly, (and this is my own pondering) I am left wondering if the long-term use of AI, including deep learning technology, will change the architecture and performance of the human brain over time. With the use of AI driven deep neural networks, are we losing the opportunity to be develop our own neural networks in our own brains through problem solving? 

I see some potential for deep learning technology in creating more meaningful learning experiences for culturally diverse learners. For example, this technology could adapt learning content and images to represent the culture and worldview of the learner while still meeting the learning outcomes. In that case, this could lead to greater inclusion and sense-making according to diverse worldviews. However, there could still be bias present as there is a wide range of diversity within a culture, which will have to be considered in the design and data collection for deep learning technology. 

References

Baker, C. (2022, June 3). What is adaptive learning and can It work for business? Leaders. https://leaders.com/articles/innovation/adaptive-learning/ 

Capuano, N., & Caballé, S. (2020). Adaptive learning technologies. AI Magazine, 41(2), 96-98. https://doi.org/10.1609/aimag.v41i2.5317  

Han, Z., & Xu, A. (2020). Ecological evolution path of smart education platform based on deep learning and image detection. Microprocessors and Microsystems, 80, 103343. https://doi.org/10.1016/j.micpro.2020.103343 

IBM (n.d.). What is deep learning? https://www.ibm.com/topics/deep-learning 

Kavlakoglu, E. (2022, May 27). AI vs. machine learning vs. deep learning vs. neural networks: What’s the difference? IBM. https://www.ibm.com/cloud/blog/ai-vs-machine-learning-vs-deep-learning-vs-neural-networks  

McClelland, J. L., & Botvinick, M. M. (2020). Deep learning: Implications for human learning and memory. PsyArXiv. https://doi.org/10.31234/osf.io/3m5sb 

Venkateswaran, C., Amudha, M., Ramachandran, M., Saravanan, V., Vennila, T. (2021). A study on artificial intelligence with machine learning and deep learning techniques. Data Analytics and Artificial Intelligence 1(1). https://secureservercdn.net/50.62.90.29/d8a.8cf.myftpupload.com/wp-content/uploads/2022/01/A-Study-on-Artificial-intelligence-with-Machine-learning-and-Deep-Learning-Techniques.pdf 

The beginning of LRNT 524 had us exploring popular design models in an effort to understand the landscape of instructional design (ID). What became a prevalent theme throughout the readings is that there is no ‘one size fits all’ approach to ID and that many factors may lead to the use of one design model over another – or perhaps a blend of several. Assuming one ID model to inform all course design is a disservice to the learner and the model or process used should be appropriate and well-suited to the learning context. 

When considering instructional or learning design, an important distinction to make is between an ID model and an ID process. Dousay (2018) describes the ID process as steps taken to achieve the end result whereas the ID model takes a more specific representation of a process. The ADDIE process (assessment, design, develop, implement, evaluate) can be viewed as an overarching framework for informing ID, regardless of the model used. 

Another distinction to make is between the design for instruction and the design for learning. This has been a distinction I have been reflecting on a lot since the beginning of the MALAT program and as I learn more, the more I feel compelled to prioritize a learning design approach. Universal Design for Learning (UDL) takes a learner or user-centred approach focusing on learner engagement and developing ‘expert learners’ in which learners are “purposeful, motivated, resourceful, knowledgeable, strategic and goal-orientated” (Takacs et al., 2021, p. 31). In my opinion, the notion of developing learners in this way is a more holistic approach to learning and also demonstrates the need for careful, thoughtful and purposeful design. 

An area that I would like to learn more about is cultural inclusion in learning and instructional design. Although many popular ID models and processes reflect the consideration of diverse learners, there remains a gap in guidance for creating learning environments that embrace and reflect cultural diversity and inclusion (Heaster-Ekholm, 2020). In a time where we are making efforts to foster decolonization many of our systems and processes, learning that supports and reflects cultural inclusion is of utmost importance. 

Parchoma et al. (2020) introduce the idea of designing for learning in the yellow house. The yellow house, referencing Van Gogh’s Yellow House painting, is a place where there exists a metaphorical third place or room where instructional design and learning design can come together in an effort tto support growth, creativity and change (2020). This yellow house analogy has created space in my mind and professional practice to consider the possibilities of learning design while granting me permission to get creative. I look forward to learning more as we progress through this course.

References

Dousay, T. A. (2018). Instructional Design Models. In West, R (Ed.), Foundations of learning and instructional design technology: The past, present, and future of learning and instructional design technology. EdTech Books. https://edtechbooks.org/lidtfoundations/instructional_design_models 

Heaster-Ekholm, K. L. (2020). Popular instructional design models: Their theoretical roots and cultural considerations. International Journal of Education and Development Using Information and Communication Technology, 16(3), 50–65. https://files.eric.ed.gov/fulltext/EJ1275582.pdf 

Parchoma, G., Koole, M., Morrison, D., Nelson, D., & Dreaver-Charles, K. (2020). Designing for learning in the Yellow House: A comparison of instructional and learning design origins and practices. Higher Education Research & Development, 39(5), 997–1012. https://doi.org/10.1080/07294360.2019.1704693 

Takacs, S, Zhang, J., Lee, H., Truong, L., & Smulders, D. (2021). A comprehensive guide to applying Universal Design for Learning. Justice Institute of British Columbia. https://pressbooks.bccampus.ca/jibcudl/