Summary of Flight Path 

As an educator with international experience teaching several subjects across multiple curricula, my teaching practice is rooted in a continual search for effective pedagogies that can be integrated into both traditional and technology-enhanced environments. My professional growth has been shaped by each new context I have entered. Each country, school system, and subject area has offered new insights into student engagement, assessment, and learning theory.

My journey into MET began during COVID-19, a period that accelerated the growth of online and hybrid teaching models. I saw the affordances of educational technology, particularly its capacity to differentiate instruction, provide flexible pacing, and facilitate deeper engagement through multimedia and interactive content (Hodges et al., 2020).

At the onset of MET, my vision was to contribute to a learning system where independent and motivated students could pursue personalized learning paths aided by technology while leaving more of my time to support other students who needed targeted interventions. I remain committed to this goal: to leverage digital tools to enable differentiated instruction, formative assessment, and learner autonomy. These principles are foundational to 21st-century education (Fullan & Langworthy, 2014).

One of my earliest realizations, dating back to my teacher practicum, was the gap between the idealism of digital integration and the practical engagement of students. I created a class website only to be told by my supervisor that only highly motivated students would use it. That comment stayed with me because it reflected a harsh truth in that the presence of technology does not ensure its use or effectiveness.

Later, when teaching hybrid classes to student-athletes, I discovered a reversed situation where despite their constant smartphone use, many lacked basic digital literacy skills required to navigate learning management systems or complete assignments online. This disconnect between personal and academic digital fluency continues to shape my interest in designing more intuitive, learner-centred platforms.

In entering ETEC 524, my specific learning goals were twofold: to understand how to evaluate learning technologies systematically and to improve the quality and design of my assessments using educational technology. In the high-stakes educational environment of Hong Kong, where exam performance often overshadows deeper learning, my challenge is to design assessments that both prepare students for standardized tests and promote conceptual understanding and critical thinking. I want to learn how to create formative and summative assessments that go beyond rote memorization, using technology to encourage metacognition, application, and synthesis (Anderson & Krathwohl, 2001).

Overall Course Experience

ETEC 524 has significantly deepened my ability to evaluate educational technologies in a structured and purposeful way. Assignment 1, in particular, pushed me to think critically about the criteria used to assess the suitability of digital tools within specific educational contexts. Working collaboratively with peers to design an evaluation rubric helped surface a range of perspectives, ultimately resulting in a set of criteria that was both concise and relevant. This process clarified not only how to assess technology but also how to approach its design through the lens of real educational challenges and goals.

One of the most valuable aspects of the course was learning to view educational technology through the eyes of different stakeholders, including learners, educators, administrators, and technology developers, each with unique needs and constraints. I appreciated how the course emphasized the intersection between pedagogy, usability, and institutional context. These three dimensions are critical when determining whether a particular technology genuinely supports teaching and learning (Bates, 2019). The weekly discussions were especially valuable for bridging theory and practice. They offered the chance to analyze authentic educational scenarios through various theoretical lenses and to engage in meaningful dialogue with classmates. These exchanges helped refine my understanding of how technology can be evaluated and implemented effectively. They also modeled the kinds of conversations that should happen in real-world decision-making processes.

The course also served as an exemplar of what it aimed to teach. Its design, featuring asynchronous forums, structured modular content, and opportunities for both independent and collaborative work, reflected many of the best practices we studied. The careful balance between cognitive engagement, peer interaction, and instructor guidance reflected the Community of Inquiry model (Garrison, Anderson, & Archer, 2000), reinforcing how teaching presence, social presence, and cognitive presence can work together to create a meaningful learning environment.

This meta-learning experience led me to reflect on my own course design practices. I found myself thinking more deliberately about how digital platforms could be structured to enhance student autonomy, collaboration, and engagement. The emphasis on learner interaction and feedback mechanisms has influenced how I now approach planning for both in-person and hybrid environments. It reminded me that digital learning is not merely a matter of content delivery, but of designing thoughtful, interactive experiences that empower students to construct meaning.

However, I did notice a significant overlap between this course and ETEC 510, particularly in the theoretical frameworks emphasized. While revisiting key theories such as TPACK, SAMR, and Bates’ SECTIONS model did reinforce foundational knowledge, I would have welcomed more opportunities for hands-on exploration of emerging technologies. My personal interests lie in assessment, learner analytics, and adaptive learning, so I was hoping to engage more directly with platforms and tools that support these domains. There seemed to be an overemphasis on applying theoretical models to a single artifact instead of providing a broader toolkit of technologies to experiment with and evaluate.

Despite this, one of the key takeaways from the course was the importance of aligning technology selection with instructional goals and learner needs. This principle now guides my thinking far more than before. Rather than starting with a tool and designing activities around it, I now begin with clear pedagogical objectives and evaluate which digital resources best serve those goals. The course also encouraged me to analyze why some technologies fail, whether due to lack of training, poor alignment with learning outcomes, or cultural misfit, which is essential when planning for scalable and sustainable implementations.

Ultimately, ETEC 524 has equipped me with a stronger conceptual and practical toolkit for evaluating and integrating technology. I now approach digital learning design with more intentionality, and I am better prepared to lead conversations about technology integration within my professional context. More importantly, the course reaffirmed a central belief I hold: that successful educational technology implementation depends less on the tools themselves and more on thoughtful instructional design, contextual relevance, and empathy for learners.

Next Steps in Educational Technology Practice

Looking ahead, I intend to engage more deeply with artificial intelligence and its applications in education. While concerns about AI’s ethical implications remain important, I believe its potential for enhancing both teaching and learning is immense. AI can help personalize instruction, automate administrative tasks, and generate real-time feedback, thus freeing educators to focus on higher-value work such as mentoring and relationship-building (Luckin, 2018).

One area I am eager to explore is using generative AI to support planning and lesson ideation. I envision using AI tools to generate creative learning activities that are rooted in sound pedagogy. For example, AI can assist in creating inquiry-based tasks, case scenarios, or simulations that encourage students to think critically and apply their knowledge. Aligning these activities with constructivist frameworks could make abstract concepts more concrete and personalized, especially in subjects like science and mathematics.

In addition to lesson design, I see great value in automating repetitive and time-consuming tasks through custom AI bots. Tasks such as generating quiz questions, categorizing student responses, or compiling assessment data could be handled more efficiently. This would allow teachers to spend more time analyzing learning progress and offering detailed feedback. These time savings could directly benefit learners, particularly those who require more individualized support.

I am also optimistic about AI’s potential to improve the logistical systems that often limit teaching and learning. For instance, AI could be used to optimize school timetables, better match students with teachers, and dynamically manage class groupings. These kinds of applications, although less discussed than AI-generated essays or chatbots, could have a major impact on teacher morale and instructional effectiveness by reducing administrative bottlenecks.

Perhaps most crucially, I hope educational technology will transform assessment practices. Traditional paper-based tests often fall short in evaluating complex cognitive skills, creativity, or applied knowledge. I envision a future where adaptive testing platforms use multimedia-rich questions, simulations, and open-ended tasks to assess students across the full spectrum of Bloom’s taxonomy. Such systems could provide real-time insights into student understanding and even tailor subsequent instruction accordingly. This would make it easier to teach for mastery rather than merely preparing students for exams.

Additionally, AI-powered rubrics and feedback generators could assist in providing more consistent and timely evaluations. These tools could help reduce teacher workload while maintaining or even improving the quality and fairness of assessment. They could also assist novice teachers in calibrating their marking to school or departmental standards, enhancing transparency and accountability.

As I move forward, I plan to stay engaged with emerging research and developments in educational technology. Attending conferences, participating in online professional learning communities, and contributing to school-based innovation initiatives are ways I hope to sustain momentum and stay connected to the broader conversation. Lifelong learning, particularly in such a fast-evolving field, is not optional. It is essential.

Importantly, I do not intend to adopt every new trend or platform uncritically. Instead, I will continue to evaluate tools based on their pedagogical value, their alignment with learner needs, and their practical feasibility in my context. My approach will be guided by cautious optimism. I will remain open to innovation while staying anchored in evidence and purpose.

The next stage in my professional practice will focus on applying what I have learned in this course and the MET program more broadly. Whether through designing more effective digital assessments, mentoring colleagues in technology integration, or advocating for ethical AI use in education, I aim to be a thoughtful and informed leader in my school’s ongoing digital transformation.

References

Anderson, L. W., & Krathwohl, D. R. (Eds.). (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Longman.

Bates, T. (2019). Teaching in a digital age: Guidelines for designing teaching and learning. BCcampus.

Fullan, M., & Langworthy, M. (2014). A rich seam: How new pedagogies find deep learning. Pearson.

Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2(2-3), 87–105.

Hodges, C., Moore, S., Lockee, B., Trust, T., & Bond, A. (2020). The difference between emergency remote teaching and online learning. Educause Review.

Luckin, R. (2018). Machine learning and human intelligence: The future of education for the 21st century. UCL IOE Press.