My teaching philosophy centers on three interconnected principles: prioritizing understanding, fostering a safe and supportive learning environment, and equipping students with the ability to generalize their knowledge and apply it in diverse contexts. These principles serve as the bedrock of my approach to teaching and inform my interactions with students.
As a course instructor specializing in educational technology, statistics, and data analytics, my primary objective is to cultivate an environment that facilitates students' comprehension of how to design and enhance learning experiences. This commitment to fostering understanding permeates every aspect of my teaching, from lectures and class activities to student reflections. Moreover, I am dedicated to maintaining a learning environment characterized by acceptance, respect, and encouragement, where students feel empowered to pursue personal and professional growth. My ultimate goal is to guide students toward not only mastering the course content but also developing the capacity to transfer the skills and strategies they have acquired to a wide range of real-world scenarios, thereby supporting their professional and personal aspirations.
For instance, when teaching big data analysis, I prioritize ensuring that students with limited or no prior experience can grasp the concept of big data and leverage it to assess and address their needs. I actively engage students by identifying their interests, skills, and pertinent questions that can be explored within the context of big data and its potential to create impactful learning environments. By connecting the purpose of big data to students' individual needs, I aim to make statistics and data analytics relevant, meaningful, and comprehensible.
To further support students' understanding, I incorporate cultural and theoretical frameworks as well as real-world case studies, enabling them to critically analyze the interplay between theory and practice. Additionally, I illustrate how data is generated and encourage students to examine and critique data derived from familiar contexts, fostering their ability to make informed, data-driven decisions. Through this approach, students not only gain a deeper understanding of the analytical value of different data forms but also develop an awareness of their limitations.
My role as an instructor extends to providing guidance and collaborative support as students navigate the processes of data discovery, analysis, and visualization. By emphasizing the procedures involved in data-informed decision-making and incorporating ongoing discussions about the why, when, and how of utilizing contextual data, I strive to equip students with practical, context-based data analysis experience. My overarching aim is to empower students to have firsthand experiences that contribute to their proficiency in data analytics while helping them circumvent the potential frustrations and challenges associated with programming, statistics, and the development of data-informed learning frameworks. Witnessing students utilize available data to address and interpret their own questions and concerns brings me immense satisfaction, as does supporting them in producing creative and critical work that integrates evidence-based adjustments to meet their needs.
While fostering meaningful understanding is crucial, the ability to generalize new knowledge and insights is equally vital. This is particularly evident in my course for students without technical backgrounds who are learning to apply data-analytic technology and statistics, where the capacity to generalize current understandings to new situations is essential due to the rapidly evolving nature of technology. To cultivate this ability, I have designed the course to include three summative assessments that provide opportunities for students to practice self-regulation strategies that support generalization.
Furthermore, I employ constructivist scaffolding and guided discovery teaching strategies to facilitate the learning process. Prior to the first assignment, I engage students in explicit discussions about task analysis, process monitoring, work assessment, performance reflection, and the utilization of gathered information to inform future iterations. Initial assignments are highly structured, featuring specific questions, tasks, and discussions. As the course progresses, the learning experience evolves into collaborative work, with students undertaking similar but increasingly complex tasks. Following this initial guidance, students are granted the autonomy to work independently within their chosen subject areas to create cases they deem valuable. This comprehensive process not only provides students with meaningful practice in effectively integrating technology into teaching and learning environments but also bolsters their self-efficacy in technology use and data analytics, thereby enhancing the self-regulation skills that enable them to transfer the knowledge and content acquired in the course to future applications. It is especially gratifying when students, long after completing the course, share with me how they have successfully incorporated the strategies they learned into their own practices.
In the AI era, my teaching philosophy is undergoing a dynamic and necessary transformation, adapting the core principles of learning to effectively integrate and leverage AI. I recognize that AI is not merely a tool but a fundamental shift in how knowledge is accessed, processed, and applied. My approach moves beyond traditional methods to cultivate a learning environment where students not only understand AI concepts but also critically engage with AI tools to enhance their own cognitive processes (especially metacognitive strategies and self-regulated learning), problem-solving abilities, and creative output. When teaching courses, instead of restricting AI use, I encourage students to complete ALL ASSINGMENTS with AI to exercise their judgement and align the assignment objectives to AI capabilities. I designed an AI evaluation system to grade students' AI-generated assignments and provide feedback to them. This approach works surprisingly well, resulting in a reduction of students' utilization of AI in assignments, which then remains at a consistently low level throughout the semester. Students indicate that completing these assignments serves as a prompt for them to make their own decisions rather than relying on AI to make the decisions for them, and they have learned when and how to AI as a cognitive partner.Â
My commitment to fostering a safe and supportive learning environment is exemplified by my open communication with students and my encouragement of experimentation. In my classroom, failure is not only accepted but viewed as a valuable learning opportunity, and risk-taking is actively encouraged. I invite students to offer constructive critiques of the data analytics cases used in previous lessons and to collaborate on identifying areas for improvement. This practice promotes self-regulated learning by empowering students to constructively acknowledge, analyze, and learn from their experiences.
Ultimately, my role as a course instructor is multifaceted. I strive to impart the content and skills necessary for students to thrive in their careers, guide them toward becoming independent learners capable of regulating their own learning and achieving lifelong goals, and equip them to navigate the complexities of an ever-changing world.
Updated on 2026-01-21