Over the past nine weeks, my understanding of leadership has deepened significantly. When I first ranked the attributes of leadership, I emphasized qualities such as being forward-looking, competent, caring, and inspiring. These values reflected my commitment to guiding students through an ever-evolving digital world, especially in the field of computer science education. However, through studying change management theories and engaging in the collaborative development of a digital toolkit, I have begun to see leadership not only as a personal disposition but also as a strategic and relational practice.
One of the major shifts in my perspective relates to the role of cooperation and supportiveness. Initially ranked at mid-level in my list, I now recognize that these attributes are essential in fostering collective ownership of change. As Kotter (2012) emphasizes, successful change requires mobilizing a guiding coalition and sustaining a sense of urgency throughout the process. Our toolkit project demonstrated the importance of distributed leadership where influence is shared across the team and how this fosters innovation, resilience, and shared accountability (Spillane, 2006).
Moreover, the emphasis I placed on being forward-looking remains, but I now understand it must be grounded in listening and adapting. Change leadership is not only about having a vision but also aligning that vision with the needs and readiness of stakeholders (Fullan, 2011). My work on the digital toolkit underscored the need to continuously assess user experience, data-informed decision making, and inclusive design. These dimensions required me to adopt a more transformational leadership style, one that inspires, models, and empowers rather than directs (Bass & Riggio, 2006).
Furthermore, my view of competence has also evolved. Previously, I associated it primarily with subject expertise. However, through this course, I’ve come to see competence as including emotional intelligence, communication, and adaptability in navigating uncertainty (Goleman et al., 2013). This shift has influenced how I interact with students and colleagues, especially when introducing new technologies or instructional methods.
Finally, I’ve developed a deeper appreciation for self-awareness and reflection in leadership. Being able to critically assess my own assumptions, seek feedback, and adjust my approach has proven essential throughout the toolkit project. It’s clear to me now that leadership is not a fixed trait but a continuous learning process grounded in context and community (Heifetz et al., 2009).
In conclusion, this course has significantly broadened my understanding of leadership in digital learning environments. While I still value qualities like vision, inspiration, and expertise, I now place greater emphasis on collaboration, emotional intelligence, and strategic responsiveness. These are the capacities I aim to carry forward as I lead change within my educational context.
References
Bass, B. M., & Riggio, R. E. (2006). Transformational leadership (2nd ed.). Psychology Press.
Fullan, M. (2011). Change leader: Learning to do what matters most. Jossey-Bass.
Goleman, D., Boyatzis, R., & McKee, A. (2013). Primal leadership: Unleashing the power of emotional intelligence. Harvard Business Review Press.
Heifetz, R., Grashow, A., & Linsky, M. (2009). The practice of adaptive leadership: Tools and tactics for changing your organization and the world. Harvard Business Press.
Kotter, J. P. (2012). Leading change. Harvard Business Review Press.
Spillane, J. P. (2006). Distributed leadership. Jossey-Bass.
The implementation of Learning Management Systems (LMS) such as Moodle has become an integral part of educational institutions’ digital transformation strategies. However, despite its potential to enhance learning experiences, successful adoption depends on effective project planning and change management strategies. This blog post reflects on my experience as an end user of a Moodle implementation project that faced significant challenges. By analyzing the barriers and potential improvements, I explore how project management principles and alternative approaches, such as design thinking, could have influenced the outcome.
The Problem and Project Goals
The project aimed to introduce Moodle as the primary LMS during the COVID-19 pandemic to support remote learning. The primary goal was to ensure all educators could effectively use the platform to deliver and manage course content. The stakeholders included teachers, students, IT staff, and administrators. While some educators adapted quickly, many faced difficulties due to inadequate training and resistance to change.
As an end user, I observed that the project lacked a well-defined plan, leading to inconsistencies in adoption and technical issues. While there were some efforts to provide training materials and IT support, they were largely reactive rather than part of a structured rollout plan. A more structured project plan, incorporating phased implementation, stakeholder engagement, and continuous evaluation, could have helped mitigate these challenges. As Watt (2014) notes, stakeholder management and clear communication are crucial for project success. The absence of these elements contributed to a fragmented adoption process.
Who Benefited and Who Were the Stakeholders?
The primary beneficiaries of Moodle’s implementation were the students and teachers.
Students: While Moodle provided a centralized platform for learning, some students struggled with accessibility issues, particularly those with limited internet access or technological literacy.
Teachers: Many educators benefited from having a digital repository for resources and an asynchronous learning environment. However, resistance to change and insufficient training negatively impacted their adoption of the system.
Administrators: They gained a structured way to monitor course progress and engagement.
IT Staff: They played a crucial role in supporting the implementation but faced an overwhelming demand for troubleshooting and training support due to the rapid rollout.
A more comprehensive stakeholder analysis, as suggested by Watt (2014), could have identified key concerns early and provided targeted support.
Project Planning and Challenges Encountered
From my perspective as a user, the approach to introducing Moodle appeared largely reactive rather than pre-emptive. While there was an attempt to provide some guidance, there was no comprehensive project plan outlining phased implementation, training sessions, or contingency measures.
For instance, the initial introduction of Moodle was accompanied by a single email announcement with links to tutorials. However, many teachers, including myself, found these materials insufficient when encountering real-time technical issues or when attempting to structure online courses effectively. IT support was overwhelmed, and many teachers resorted to informal peer-led troubleshooting rather than relying on institutional training. This lack of foresight resulted in several barriers:
Lack of Staff Buy-in: Many educators were reluctant to transition from their familiar teaching methods to an online platform.
Insufficient Training: Teachers received minimal training before the system was implemented, leading to low confidence in using Moodle effectively.
Technical Issues: The platform required consistent IT support, but the available resources were inadequate.
Resistance to Change: As noted by Moskal, Dziuban, and Hartman (2013), blended learning initiatives challenge traditional educational models, often leading to skepticism among educators.
Overcoming Barriers: Lessons from Research
To improve implementation outcomes, integrating insights from both project management and change management literature would have been beneficial.
Stakeholder Engagement: A structured stakeholder analysis (Watt, 2014) could have helped anticipate concerns and tailor support accordingly.
Comprehensive Training: Research suggests that effective training programs enhance LMS adoption (Moskal et al., 2013). A structured training plan, including workshops and one-on-one support, would have increased confidence among educators.
Phased Implementation: Introducing Moodle in stages, rather than an abrupt transition, could have reduced resistance and allowed for iterative improvements (University of Calgary, 2014).
Design Thinking Approach: Unlike linear project management models, design thinking encourages iterative problem-solving (Ben Mahmoud-Jouini, Midler, & Silberzahn, 2016). This approach would have facilitated real-time feedback and adaptation.
Changes in Planning That Could Have Helped
If I were to suggest improvements based on my experience as a user, I would recommend the following strategies:
Pilot Testing: Before a full-scale rollout, a pilot phase with a small group of teachers could have identified early challenges and informed necessary adjustments.
Targeted Training Programs: A mix of self-paced modules and live workshops would have catered to different learning preferences, ensuring teachers felt more confident using Moodle.
Clear Communication Plan: Regular updates, check-ins, and dedicated Q&A sessions could have kept stakeholders informed and engaged, preventing confusion and resistance.
Ongoing Support Mechanism: A designated team for troubleshooting and follow-up training could have mitigated frustration and resistance, ensuring a smoother adoption process.
Application to My Future Practice
As a user rather than a project manager, I observed how the sudden shift to an LMS without adequate planning or training affected both educators and students. This experience has given me valuable insights into the challenges and opportunities of educational technology implementation.
In my future work, I plan to:
Advocate for early stakeholder involvement to address concerns proactively.
Prioritize phased implementation to allow smoother transitions and iterative improvements.
Develop robust training programs to ensure users are confident and competent in new technology adoption.
Utilize design thinking methodologies to create flexible, user-centered implementation plans.
Conclusion
The rapid and unplanned implementation of Moodle during the pandemic highlighted the importance of effective support systems for educators when adopting new technologies. A more structured rollout, phased implementation, and proactive training could have improved the experience for both teachers and students. While the circumstances were unprecedented, this experience has highlighted the need for institutions to prioritize usability, support, and adaptability in future digital learning initiatives.
References
Ben Mahmoud-Jouini, S., Midler, C., & Silberzahn, P. (2016). Managing innovative projects: The role of design thinking in project management. Journal of Business Research, 69(2), 471-480.
Moskal, P., Dziuban, C., & Hartman, J. (2013). Blended learning: A dangerous idea? Internet and Higher Education, 18, 15-23.
University of Calgary. (2014). Strategic framework for learning technologies: Report of the Learning Technologies Task Force.
Watt, A. (2014). Project management (2nd ed.). BCcampus.
Published by Joan Oladunjoye on the 4th of January 2025
Contextual Foundations for Inclusive and Sustainable Design
High school education today demands instructional approaches that are not only adaptable to diverse learner needs but also inclusive, empowering, and sustainable. This blog explores how the design thinking process shaped my principles for creating impactful instructional practices. Insights gained from the Pecha Kucha exercise in assignment one highlighted the importance of addressing barriers such as technological inequities and cultural inclusivity. These challenges informed my commitment to providing flexible, equitable, and accessible learning resources. Additionally, the iterative exploration of design models and educational theories during assignments two and three emphasized the need for responsiveness to evolving learner contexts. By integrating these frameworks into my practice, I aim to create learning environments that empower students, celebrate diversity, and promote lifelong learning. The principles below outline this vision in detail.
Designing for Impact: Inclusive and Sustainable Instruction for High School Education
Instructional design in high school education must address the diverse and changing needs of learners while fostering engagement and adaptability. My design principles, rooted in theoretical and practical considerations, provide a blueprint for equitable and engaging learning experiences.
Equity is foundational to inclusive education, ensuring all students have access to meaningful learning opportunities. Universal Design for Learning (UDL) provides a framework for offering multiple means of representation, engagement, and expression (CAST, 2018). For example, a coding lesson might include video tutorials, interactive exercises, and printed guides, accommodating varied learning preferences and resource access. This approach reflects a commitment to accessibility, ensuring no student is left behind.
Empowering students to take ownership of their learning fosters autonomy and deep engagement. Drawing on Vygotsky’s (1978) social constructivism, I design collaborative assignments that encourage teamwork and active participation. Examples include team-based projects where students create video tutorials or presentations on technological innovations. Regular feedback further personalizes learning, making it responsive to students’ evolving needs.
Culturally responsive practices ensure the curriculum reflects the diverse backgrounds of students, creating meaningful connections between their lived experiences and classroom learning. For instance, when teaching about artificial intelligence (AI), I guide students to explore Indigenous perspectives on technology, such as environmental stewardship and data sovereignty (Crichton & Childs, 2022). Guest speakers and case studies further enrich this exploration, offering authentic insights and practical examples.
Iterative approaches like the Successive Approximation Model (SAM) enable continuous refinement of instructional materials based on feedback and formative assessments (Ali, 2021). If students struggle with a concept, lessons can be adapted to include hands-on coding activities or real-world applications. Backward Design principles complement this process by aligning activities with measurable learning outcomes (Wiggins & McTighe, 2005).
Thoughtfully integrating technology enhances learning while promoting sustainability. Adaptive platforms and gamified environments personalize education, while offline resources support students with limited technology access (Selwyn, 2024). Collaborative projects addressing real-world challenges, such as climate change, develop critical thinking and prepare students for future success.
Constructive feedback is critical for deepening student understanding and building confidence. Tools like interactive coding exercises deliver immediate feedback, enabling students to identify and correct errors in real-time (CAST, 2018). This scaffolding not only supports mastery of key concepts but also nurtures student self-efficacy.
Reflections on the Design Thinking Process
The design principles presented here are deeply informed by the insights gained through the design thinking process in assignment 1. A notable moment during this unit involved identifying barriers such as limited technology access, which emphasised the importance of providing offline alternatives to digital resources. Another significant realization was the need for cultural inclusivity, which shaped my approach to integrating diverse perspectives into the curriculum, such as exploring Indigenous views on technology.
In assignments 2 and 3, iterative exploration of design models reinforced the necessity of refining instructional strategies to address evolving learner contexts. For instance, applying the Successive Approximation Model (SAM) in a hypothetical classroom scenario revealed the value of adapting materials dynamically based on student feedback. These experiences deepened my commitment to creating adaptable and inclusive educational practices.
Concluding Thoughts on Inclusive and Sustainable Design
By focusing on inclusivity, empowerment, and adaptability, these principles aim to create equitable and sustainable learning environments. While grounded in theory, such as Universal Design for Learning (CAST, 2018) and social constructivism (Vygotsky, 1978), they also prioritize practical applications like culturally responsive teaching and iterative design methods. Looking ahead, I envision classrooms where every learner thrives, supported by innovative yet sustainable practices.
Future goals include expanding the use of adaptive learning technologies to bridge resource gaps and developing collaborative projects that address global challenges, such as climate change and technological equity. When implemented thoughtfully, these strategies have the potential to transform education into a force for lifelong success and societal progress.
References
Ali, R. (2021). Designing for diversity: The importance of iterative design in education. Learning Journal, 15(3), 45–59.
CAST. (2018). Universal Design for Learning guidelines version 2.2. Retrieved from http://www.cast.org
Crichton, A., & Childs, M. (2022). Cultural responsiveness in education: The role of technology in promoting inclusivity. Educational Research Quarterly, 47(4), 78–90.
Selwyn, N. (2024). Technology and sustainability in the classroom: A balanced approach. Education and Technology Review, 33(1), 112–125.
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
Wiggins, G., & McTighe, J. (2005). Understanding by design. ASCD.
Published by Joan Oladunjoye on 30th November 2024
As a high school computer science teacher, my experiences as both an educator and a learner resonate deeply with the principles of instructional design (ID) models. This week’s readings and frameworks provide valuable insights into how structured approaches to design can transform teaching practices. Reflecting on these models underscores their influence on creating effective learning environments in my context as a high school computer science teacher, while also highlighting their relevance and potential integration into my future work.
The ADDIE model, consisting of analyze, design, develop, implement and evaluate serves as a foundational process for designing instruction. Its systematic approach aligns closely with my teaching strategies, particularly in identifying students’ performance gaps and developing targeted interventions (Dousay, n.d.). For example, when teaching Year 12 students HTML concepts, I analyze their baseline skills to design appropriate scaffolding activities, ensuring learners of varying competencies can engage effectively.
Similarly, Universal Design for Learning (UDL) emphasizes inclusivity by addressing diverse learner needs through multiple means of engagement, representation, and expression (Takacs et al., 2021). In practice, UDL has guided me in differentiating resources, such as using interactive coding simulations for kinaesthetic learners and detailed reading guides for those who prefer text-based material.
Among the various models, UDL aligns most closely with my practice due to its flexibility and learner-centered approach. The jaggedness principle, which acknowledges variability in learners’ abilities, underpins my efforts to create adaptable coding exercises tailored to individual strengths (Rose, 2016, as cited in Takacs et al., 2021). This approach fosters an environment where all students, regardless of their prior knowledge, feel capable of achieving learning outcomes. UDL’s focus on proactive planning resonates strongly with my teaching philosophy. By anticipating barriers and designing resources to address them, such as providing alternative assessment formats, I ensure that every student has equitable access to success. For instance, students struggling with syntax errors in programming benefit from debugging tools or pair programming opportunities that build their confidence and competence.
Building on these principles, I plan to integrate UDL strategies and the ADDIE process more fully into my instructional design practice. These strategies include:
Iterative feedback loops, where ADDIE’s evaluation phase helps refine lesson plans based on student feedback. For example, after each project-based coding activity, gathering input can improve clarity and engagement in future iterations.
Flexible assessment methods inspired by UDL, such as oral presentations on coding concepts or peer-reviewed projects, to cater to different learning styles.
Culturally responsive design principles (Heaster-Ekholm, 2020) to create inclusive resources acknowledging my students’ diverse backgrounds. For example, incorporating global coding challenges or exploring culturally significant technologies could further enrich their learning experience.
In conclusion, instructional design models provide a robust framework for creating effective and inclusive learning environments. While ADDIE offers a structured approach to addressing instructional challenges, UDL ensures that diverse learner needs are met through innovative and flexible strategies. By synthesizing these models, I aim to foster a classroom culture where all students can thrive, leveraging instructional design not just as a tool but as a transformative practice in high school computer science education.
References
Dousay, T. A. (n.d.). Instructional design models. Retrieved from uploaded document.
Heaster-Ekholm, K. L. (2020). Popular instructional design models: Their theoretical roots and cultural considerations. International Journal of Education and Development Using ICT, 16(3), 50–65.
Takacs, S., Zhang, J., Lee, H., Truong, L., & Smulders, D. (2021). Universal design for learning: A practical guide. Retrieved from uploaded document.
In this Pecha Kucha presentation, my partner and I, Jeremey explore the complexities and opportunities in designing inclusive, engaging, and effective educational strategies for high school students with cognitive impairments. Our design thinking challenge focuses on the Empathy and Define stages, emphasizing the critical need to understand learners’ unique perspectives and contextual barriers. This challenge is shaped by our diverse teaching experiences in specialized and alternative education settings, where we have seen both the successes of tailored instructional methods and the struggles arising from limited resources and training.
The goal of this project is to address the pressing need for personalized, interactive learning experiences that adapt to students’ abilities, reduce disengagement, and promote meaningful academic growth. By synthesizing insights from real-world teaching experiences, research, and collaboration, we aim to reframe the problem into actionable strategies. This introduction sets the stage for the journey ahead, a journey grounded in empathy and driven by the desire to design learning environments that empower every student to succeed.
Designing for Inclusion: A Pecha Kucha Journey Through Empathy and Understanding
Published by Joan Oladunjoye on the 26th October 2024
At the edge of a bustling city, amidst the noise of honking cars and the ever-present hum of digital activity, Layla Park finished her cup of tea and set it down next to her laptop. It wasn’t just another workday; it was the day her team would reveal the culmination of years of effort: an AI-driven learning platform designed specifically for K12 computer science education. Layla wasn’t just proud of the project; she was determined to ensure that it set a new standard for ethical technology integration in schools.
The year was 2030, and AI had already become a transformative force in education, personalizing lessons for individual students, predicting learning outcomes, and even suggesting tailored pathways based on a student’s learning style. Yet, Layla knew all too well the risks associated with this rapid evolution. As her fingers hovered over the keyboard, she reflected on the path that had brought her to this pivotal moment. Layla had always been fascinated by the power of AI to transform learning, but she also knew that with great power came great responsibility. In 2030, AI was ubiquitous in classrooms, but not all its effects were positive. Poorly designed AI systems had alienated students, compromised their privacy, and even contributed to environmental damage through the sheer amount of energy required to power the necessary data centers.
Her goal was different. She wanted to create an AI system that did more than just spit out personalized lesson plans. Layla envisioned a system that empowered students to think critically, question the systems around them, and build real-world problem-solving skills. As she fine-tuned the final details of the platform, Layla remembered the words of Díaz and Nussbaum (2024), who had inspired much of her team’s work. The Pedagogical Centered AI (PCAI) framework they developed emphasized that AI should always serve human teachers and learners, not the other way around. Layla’s platform would put control in the hands of educators, allowing them to guide AI-driven lessons without letting the technology overshadow their role in the classroom.
The challenge, however, was enormous. One of the biggest hurdles Layla faced was ensuring that AI didn’t just perpetuate existing educational inequalities. Bozkurt et al. (2023) had warned of this, showing how AI could further entrench the gap between well-funded schools and those in underprivileged areas. AI required not just devices but high-quality internet access, and both were luxuries many students lacked. Layla knew that her platform had to be accessible to all students, regardless of where they lived or what resources they had. To meet this challenge, her team worked tirelessly to design AI tools that could run smoothly on older devices and adapt to lower bandwidth settings. It was a logistical nightmare at times, but it was a critical step in ensuring that students in rural areas or underserved urban communities could have the same quality of learning experience as their peers in better-connected schools. Equality of access was a non-negotiable goal for Layla and her team.
The inequalities in AI’s reach were not the only concern on Layla’s mind. Environmental sustainability was another pressing issue. The proliferation of AI-driven educational platforms had led to a skyrocketing demand for data storage and processing, which consumed vast amounts of energy. As Selwyn (2021) had pointed out in a landmark study, the energy demands of AI technologies, especially those reliant on constant data collection and processing, were threatening to overwhelm global energy supplies and contribute to climate change. Layla was acutely aware of these risks. Early in the design process, she had made it clear to her team that they needed to focus on reducing the platform’s carbon footprint. Through rigorous testing and refinement, they managed to develop algorithms that were more energy-efficient than conventional models, minimizing the platform’s environmental impact. It wasn’t a perfect solution, but Layla believed it was a significant step in the right direction.
But there was another ethical dilemma Layla had wrestled with from the beginning: privacy. As schools increasingly relied on AI to monitor student behaviour, track performance, and predict future outcomes, Layla was deeply concerned about the potential for misuse. While these tools offered valuable insights to teachers, they also posed significant risks if used irresponsibly. Selwyn et al. (2020) had raised alarms about schools turning into surveillance spaces, where students felt as though they were constantly being watched. Layla was determined that her platform wouldn’t contribute to that dystopian vision. Instead, her team focused on creating an AI system that respected students’ privacy while still providing actionable insights to educators. Data collection would be minimized, and any information gathered would be anonymized wherever possible, ensuring that students felt safe and trusted in their learning environment.
Teachers, Layla knew, would play a pivotal role in ensuring that AI didn’t undermine student autonomy. The professional development of educators was essential to making sure that AI was used not just as a crutch, but as a tool that enriched the learning experience. Sun et al. (2022) emphasized the importance of equipping teachers with the necessary skills to navigate AI-driven tools, fostering a culture where educators could confidently guide their students through AI-enhanced lessons without feeling displaced by the technology. Layla’s platform integrated training modules that would allow teachers to become active facilitators of the technology, rather than passive users. These modules were designed not only to familiarize teachers with the platform but also to inspire them to use AI in ways that fostered critical thinking, creativity, and collaboration.
As the platform neared its launch, Layla couldn’t help but feel a mix of excitement and apprehension. The future of AI in education was exhilarating, but it also needed to be tempered with caution, thoughtfulness, and humanity. In her vision for 2030 and beyond, AI wouldn’t replace teachers or automate education. Instead, it would enhance learning experiences, foster creativity, and help students become the thinkers and innovators of tomorrow; all while being mindful of ethical, societal, and environmental impacts.
Looking even further into the future, Layla imagined a world where AI didn’t just personalize learning but actively promoted inclusivity and collaboration across cultural and economic divides. As Macgilchrist et al. (2020) proposed, AI had the potential to support collective problem-solving by integrating diverse knowledge systems, creating more inclusive and collaborative learning environments. The concept of a “decolonized AI” that Roberts (2023) had advocated for was especially close to Layla’s heart. She wanted her platform to be a tool not just for learning but for promoting social justice, ensuring that technology didn’t reinforce existing biases but instead helped to dismantle them.
With a deep breath, Layla hit the final key to confirm the platform’s upload. Her team’s work wasn’t about the next flashy tech innovation; it was about building a sustainable, equitable, and ethically sound future for education. And that, she thought as she closed her laptop, was the kind of progress worth fighting for.
References
Bozkurt, A., et al. (2023). Speculative futures on ChatGPT and generative AI. Asian Journal of Distance Education, 18(1).
Díaz, B., & Nussbaum, M. (2024). Artificial intelligence for teaching and learning in schools: The need for pedagogical intelligence. Computers & Education, 105071.
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(1), 76-89.
Roberts, J. S. (2023). Decolonizing AI ethics: Indigenous AI reflections. Accel.AI.
Selwyn, N. (2021). Ed-tech within limits: Anticipating educational technology in times of environmental crisis. E-Learning and Digital Media.
Selwyn, N., Pangrazio, L., Nemorin, S., & Perrotta, C. (2020). What might the school of 2030 be like? An exercise in social science fiction. Learning, Media and Technology, 45(1), 90–106.
Sun, T., Strobel, J., Kim, C., Gao, Y., & Luo, W. (2022). Enhancing K-12 teachers’ AI teaching competency: A TPACK-based professional development program. Journal of Educational Computing Research, 60(7), 1824-1845.
The integration of AI into the K12 computer science curriculum in Canada offers promising opportunities, yet presents significant challenges. AI can personalize learning, allowing students to engage with computer science, particularly coding, at their own pace. This democratizes education by providing equitable access, especially in underfunded schools, while enabling educators to focus on fostering higher-level problem-solving and creativity (Bozkurt et al., 2023). However, a cautious approach is needed to avoid potential pitfalls.
The readings stress the importance of ethical considerations in AI implementation. Selwyn (2024) highlights the risk of over-reliance on AI’s statistical models, which can oversimplify learning and reinforce biases, particularly affecting marginalized students. This raises concerns about perpetuating educational inequities through unchecked AI use.
In contrast, Roberts (2023) advocates for a decolonized AI ethics approach, promoting inclusivity and the integration of Indigenous knowledge systems. Such an approach would ensure that AI fosters equitable educational outcomes, rather than reinforcing existing colonial structures.
While the future of AI in K12 education appears promising, it must be guided by ethical principles that prioritize equity and inclusivity. This careful approach could lead to more balanced and just educational practice.
References:
Bozkurt, A., et al. (2023). Speculative Futures on ChatGPT and Generative AI. Asian Journal of Distance Education, 18(1).
Roberts, J. S. (2023). Decolonizing AI Ethics: Indigenous AI Reflections. Accel.AI.
Selwyn, N. (2024). On the Limits of AI in Education. Nordisk tidsskrift for pedagogikk og kritikk, 10, 3–14.
This Post was co authored with Lauren Chum. 21st of September 2024
In recent years, the rapid advancement of AI technologies has reignited debates about the role of media in education. From generative AI models to AI-powered assistants, educational technology is often considered game-changing. The real question is, are these tools genuinely revolutionary, or are they merely delivering instructional content more efficiently? To explore this, we revisit the debate of Richard E. Clark and Robert B. Kozma in 1994, applying their perspectives to two contemporary examples of techno-deterministic thinking. We aim to understand how Clark and Kozma might respond to these claims and what their views could mean for today’s educational landscape.
In the original debate between Clark and Kozma, Clark asserted that media are mere vehicles for delivering instruction, having no direct influence on learning outcomes. He argued that what matters is the instructional method, not the medium itself. For instance, switching from textbooks to video lectures or AI tools would not necessarily change the learning outcomes; the instruction and pedagogy design makes the difference—not the medium (Clark, 1994, p. 26). Conversely, Kozma (1994, p. 18) contended that different media offer unique affordances that can enhance learning experiences. For example, a video provides visual cues that text cannot, and AI tools can offer real-time feedback, potentially reshaping how students learn.
Considering these perspectives, we examined two current examples reflecting the ongoing hype around educational technology.
Chat-GPT-4
The first example is from the article “Using GPT-4 to Improve Learning in Brazil”. In this article, OpenAI claims that GPT-4, a generative AI model, revolutionizes learning in Brazil by providing personalized tutoring at scale (OpenAI, n.d.). The article highlights the AI’s ability to adapt to individual learners’ needs, offering explanations, feedback, and tailored learning materials. According to OpenAI, this innovation is poised to improve learning outcomes dramatically.
We can speculate on their responses after reviewing Clark and Kozma’s arguments. Clark would likely assert, “It’s the method, not the medium.” He would argue that despite the enthusiasm surrounding GPT-4, the underlying instructional design will ultimately determine whether students benefit. For Clark, GPT-4 is merely another tool parallel to a textbook or a video (Clark, 1994, pp. 21-22). If the pedagogical approach remains unchanged, swapping GPT-4 would not significantly impact outcomes. Clark would caution against assuming that AI leads to better learning outcomes, emphasizing instead the importance of evaluating teaching strategies (Clark, 1994, p. 29).
Kozma sees GPT-4 as a transformative tool that showcases how media can influence learning. He might argue that GPT—4’s real—time adaptability—offering personalized feedback and tailored content—introduces new affordances that traditional methods cannot match (Kozma, 1994, p. 11). AI has the potential to transform learning by making it more interactive, engaging, and responsive to students’ needs (Kozma, 1994, p. 12).
Microsoft Copilot
Our second example is from the article “Microsoft Copilot for Education.” Microsoft’s Copilot, an AI-powered assistant, is integrated into various software programs to assist learners by generating documents, summarizing information, and providing real-time guidance during tasks. The articleportrays Copilot as a “game-changer” for student productivity and creativity, asserting that this technology will significantly enhance learning outcomes (Microsoft, 2024).
Clark would likely respond by suggesting that “the hype is overblown.” He would express skepticism about Copilot being a “game-changer,” arguing that its mere presence does not necessarily lead to better learning. Like other technology, Copilot is simply a medium for delivering content (Clark, 1994, p. 26). If educators continue using traditional teaching methods such as lectures, worksheets, and tests, Copilot will be another way to support those methods. The tool is secondary to Clark’s instructional design and pedagogical approach.
Kozma’s argument supports the idea that different media, including AI tools, can enhance learning by providing unique affordances. He would likely argue that tools like Copilot promote creativity rather than limit it. According to Kozma, AI assistants offer new opportunities for learning by automating mechanical tasks (such as document formatting or information summarization), which allows students to focus on higher-order cognitive functions like creativity, analysis, and critical thinking. Kozma would see AI tools as enabling, not constraining, creativity by freeing students from routine tasks and providing interactive, real-time feedback that enhances the learning experience (Kozma, 1994, p. 13).
Both examples highlight a recurring theme in educational technology: techno-determinism, the belief that technology alone can drive educational transformation. Kozma might cautiously endorse this view, while Clark would vigorously critique it. As educators and technologists, it is crucial to remain mindful of overstating the impact of media on learning. Whether using AI tools, digital platforms, or traditional textbooks, these tools are only as effective as the instructional design behind them.
In conclusion, the Clark-Kozma debate remains highly relevant as educational technology continues to evolve with innovations like AI. Understanding the limitations and affordances of media helps educators make informed decisions about integrating technology into their teaching. While AI tools like GPT-4 and Microsoft Copilot offer exciting possibilities, they will not improve learning independently. Effective learning requires thoughtful instructional design, not just cutting-edge technology. By revisiting the Clark-Kozma debate, we hope to encourage critical thinking about the role of media in education and to prompt a more nuanced consideration of the claims made by educational technology advocates. As the debate continues, educators must balance embracing new technologies and staying grounded in sound pedagogical practices.
References
Clark, R. E. (1994). Media will never influence learning. Educational Technology Research and Development, 42(2), 21-29.
Kozma, R. B. (1994). Will media influence learning: Reframing the debate. Educational Technology Research and Development, 42(2), 7-19.
Published by Joan Oladunjoye on the 14th of September 2024
George Siemens is a prominent figure in educational technology, best known for developing the theory of connectivism, a fundamental framework for understanding learning in the digital age (Rudolph et al., 2020, p. 109). He is also a pioneer of Massive Open Online Courses (MOOCs) and has significantly impacted digital technology’s role in education (Rudolph et al., 2020, p. 115). Siemens believes that traditional educational models must be reevaluated to meet the evolving needs of today’s students (Siemens, 2005).
In addition to his theoretical contributions, Siemens has served as a college instructor and author and has been widely interviewed about his educational ideologies. His work continues to influence modern educational practices, particularly in online learning and digital pedagogy, where he advocates for using technology to foster more dynamic, learner-centered environments (Rudolph et al., 2020, p. 113).
I am particularly drawn to Siemens’ work due to his insights into how learning occurs in the digital age, especially through his theory of connectivism. This theory, which suggests that learning takes place across networks of people, tools, and shared information, is increasingly relevant with the rise of online education and social learning platforms. His focus on networked learning resonates with my interest in creating adaptive, flexible spaces where students engage meaningfully with content, peers, and digital resources, both in traditional classrooms and online.
Siemens’ work on MOOCs is also significant to my professional practice, as these platforms democratize education and make it accessible on a global scale. I aim to leverage these ideas to create collaborative, technology-enhanced environments that foster inclusive learning opportunities. In addition to his written work, Siemens has also shared his ideas in various multimedia formats. In a YouTube video titled Overview of Connectivism, he highlights how platforms like Twitter facilitate knowledge sharing, integrating technology and social systems into human knowledge, thus enhancing our overall capacity to understand (Siemens, 2014). This resonates with my belief in the power of digital tools and social platforms to foster collaboration, provide access to diverse perspectives, and promote continuous knowledge building.
Overall, Siemens’ contributions to educational technology, particularly through connectivism and MOOCs, make him a pivotal figure in shaping the future of learning in the digital age.
References
Rudolph, J., Siemens, G., & Tan, S. (2020). “As human beings, we cannot not learn”: An interview with Professor George Siemens on connectivism, MOOCs and learning analytics. Journal of Applied Learning & Teaching, 3(1). https://doi.org/10.37074/jalt.2020.3.1.15
Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1). https://www.itdl.org/Journal/Jan_05/article01.htm
Published by Joan Oladunjoye on the 6th of September 2024
I have experience teaching computer science in both public high schools and private schools, including special needs schools that cater to students on the autism spectrum. One key lesson I learned from reading “The Use of Blogs in Education (2003)” is the value of blogs as an educational tool.
Blogs provide students with a platform to express their thoughts, reflect on their learning, and take ownership of their academic journey. This is especially beneficial in special needs environments, where students have diverse learning styles and needs. Blogs enable differentiated instruction, allowing students to engage with content at their own pace and focus on topics that interest them.
In my experience teaching students with autism, I’ve seen how critical it is to accommodate different learning styles. Blogs offer students who may be less comfortable with traditional classroom discussions a space to explore and analyse subjects on their own terms. For students on the autism spectrum, blogs provide a structured yet flexible medium for communication and self-expression, helping build digital literacy and social skills in a supportive environment. Blogs effectively bridge formal and informal learning, enabling students to explore computer science concepts creatively while improving their writing and critical thinking skills.
On the other hand, a conflicting lesson arises from Weller’s 2008 work on “The Adoption of E-Portfolios“. While e-portfolios offer a comprehensive way to assess student skills, they can be challenging to implement and maintain. In a special needs school for example, the technical complexity and time required to manage e-portfolios may be overwhelming for both students and teachers, particularly when students already struggle with organisation and time management.
Moreover, the focus on digital portfolios may conflict with the need for more traditional, manageable forms of assessment that are easier to use in a busy classroom. This tension highlights the challenge of balancing innovative educational technologies with the practical realities of teaching, especially when working with diverse student populations who may not benefit equally from such tools.
References: Weller, M. (2020). 25 years of ed tech. Athabasca University Press.
Change in digital learning is inevitable, driven by emerging technologies, evolving pedagogical practices, and the demand for accessible education. The infographic provided outlines a structured approach to change management, covering the need for change, leadership roles, stakeholder engagement, implementation, challenges, and sustainability.
This text synthesizes established change management models and theoretical perspectives, drawing from academic literature and professional consultations. Insights from a colleague consultation using the CBI script highlight leadership challenges, while Christy and Sandra’s reflections on the Voices page provide real-world perspectives on resistance and engagement strategies. Together, these elements form a framework for navigating digital transformation in education.
The Need for Change
Identifying the need for change is the first step in successful change management. In digital learning, key drivers include student engagement issues, technological advancements, and shifts in institutional priorities (Khan, 2017). The CBI script highlights triggers such as new funding, staffing adjustments, and student performance data, emphasizing data-driven decision-making.
Kotter’s (1997) change model highlights the importance of establishing urgency, ensuring stakeholders understand why change is essential. Without clear communication, digital initiatives may face resistance, making it crucial for leaders to articulate the necessity and benefits of transformation.
Leadership and Stakeholder Roles
Effective leadership is essential in digital transformation. Kotter’s Change Model (1997) and ADKAR provide structured approaches, emphasizing broad stakeholder involvement. Christy’s experience transitioning healthcare practitioners from Zoom to Microsoft Teams illustrates the importance of strong communication, training, and psychological safety. Initial resistance stemmed from “change fatigue,” aligning with ADKAR’s “Awareness” and “Desire” stages, as well as Lewin’s (1947) Unfreeze-Change-Refreeze model.
Sandra’s perspective highlights teacher autonomy as both a facilitator and a barrier to change. While autonomy fosters innovation, it can hinder institutional initiatives if educators resist mandated tools. She emphasizes leadership support, ensuring changes align with teachers’ workloads and professional needs (Sandra Transcript, 2024).
Theories of distributed leadership (Huggins et al., 2017) suggest that empowering educators fosters a shared vision and smoother transitions. Kotter (1997) reinforces this by stressing broad engagement across an organization. By defining clear roles, leaders can reduce resistance and enhance stakeholder buy-in, ensuring sustainable change.
Developing the Vision
A compelling vision is crucial for digital transformation. Christy’s transcript emphasizes clear communication and stakeholder involvement in articulating a shared goal. Lewin’s (1947) Change Model highlights the “unfreezing” stage, where existing norms are challenged, and stakeholders are prepared for transition.
The CBI script notes that institutions often communicate their vision through Continuous Professional Development (CPD) sessions or phased rollouts, allowing gradual adoption rather than abrupt changes. This strategic approach ensures stakeholders are aligned with transformation goals.
Implementing Change
Successful implementation depends on addressing resistance and fostering long-term adoption. The CBI script highlights the importance of clear objectives, aligning with Kotter’s (1997) strategy of establishing urgency and generating short-term wins. However, Christy’s experience highlights the emotional aspects of change, where staff resistance stems from “change fatigue.”
ADKAR’s reinforcement stage emphasizes the importance of ongoing training and support, preventing regression. Workman and Cleveland-Innes (2012) argue that leadership without personal transformation is merely management. Leaders must ensure educators feel equipped to integrate digital tools into their pedagogy, reinforcing long-term adoption.
Challenges and Solutions
Resistance to change is a major challenge in digital transformation. Sandra’s experience highlights teacher autonomy’s dual nature while it fosters creativity, it can lead to resistance when educators perceive technology initiatives as unnecessary. The CBI script suggests that structured CPD and middle leadership involvement are sufficient for driving change.
Huggins et al. (2017) argue that leadership must extend beyond CPD by involving educators in decision-making processes. Distributed leadership fosters a participatory model that balances professional autonomy with institutional alignment. Instead of solely relying on CPD, organizations should implement structured peer mentorship programs where educators support each other in digital transitions.
The CBI script also emphasizes that while change is expected, poor communication can lead to friction. Collaborative decision-making, pilot programs, and stakeholder engagement create a sense of ownership, reducing resistance (Huggins et al., 2017).
Sustaining Change
Long-term sustainability depends on ongoing support structures, continuous professional development, and leadership commitment. The CBI script notes that institutions often measure success based on student performance, but sustainable transformation requires embedding new approaches into institutional culture (Kotter, 1997).
By reinforcing digital adoption through mentorship and professional learning communities, institutions can prevent reversion to outdated practices. Leaders must not only introduce change but also integrate it into daily workflows, ensuring digital transformation remains an enduring aspect of educational practice.
Conclusion
Managing change in digital learning environments requires more than structured models, it demands leadership that actively engages stakeholders, mitigates resistance, and fosters adaptability. Kotter’s 8-Step Model, Lewin’s Change Model, and ADKAR provide theoretical frameworks, but real-world insights from Christy, Sandra, and the CBI script illustrate that success also depends on professional support, autonomy, and participatory leadership.
Digital transformation is not just about implementing new technologies but about aligning change with institutional culture and human-centered leadership. A key takeaway is that effective change management requires both strategic planning and an empathetic understanding of the people involved.
References
Heifetz, R., Linsky, M., & Grashow, A. (2009). The practice of adaptive leadership: Tools and tactics for changing your organization and the world. Harvard Business Press.
Huggins, K. S., Klar, H. W., Hammonds, H. L., & Buskey, F. C. (2017). Developing leadership capacity in others: An examination of high school principals’ personal capacities for fostering leadership. International Journal of Education Policy & Leadership, 12(1). https://doi.org/10.22230/ijepl.2017v12n1a670
Khan, N. (2017). Adaptive or transactional leadership in current higher education: A brief comparison. International Review of Research in Open and Distributed Learning, 18(3), 179-183.
Kotter, J. P. (1997). Leading change: A conversation with John P. Kotter. Strategy & Leadership, 25(1), 18-23.
Lewin, K. (1947). Frontiers in group dynamics: Concept, method and reality in social science; social equilibria and social change. Human Relations, 1(1), 5-41.
