UK PSF / SFHEA Portfolio

My teaching approach focuses on developing students’ ability to think algorithmically and understand computational systems structurally through the integration of algorithmic learning, data-centric reasoning, and exploration of authentic problems drawn from research and industry. Rather than focusing primarily on software tools or rapidly changing technologies, I aim to help students develop transferable ways of thinking that enable them to adapt to new computational challenges throughout their careers.

I am a lecturer in Applied Computer Science with expertise in artificial intelligence, machine learning, data science, and optimization algorithms. My teaching spans both foundational and advanced courses, including Design and Analysis of Data Structures and Algorithms, Computer Networks and Network Computing, Artificial Intelligence and Machine Learning, Computational Intelligence, Enterprise Resource Planning, Digital Marketing, and Big Data Analytics.

My teaching career began in 2009 as a guest lecturer at several universities before joining King Mongkut’s University of Technology Thonburi (KMUTT) as a full-time lecturer in 2019. Alongside university teaching, I have delivered professional training in data science and artificial intelligence, contributed to national AI talent development initiatives, and served as an instructor and coach in Computer Olympiad programmes. These experiences have enabled me to teach learners with diverse backgrounds, ranging from secondary school students to experienced professionals.

Through these experiences, I developed a system-based learning approach that encourages students to understand the structural foundations of computational systems before focusing on implementation. Learning begins with system architecture, data structures, and algorithmic reasoning, and then progresses towards applications in artificial intelligence, data analytics, enterprise systems, and other computational domains. This approach helps students connect fundamental concepts with real-world applications and transfer their knowledge across disciplines.

A second principle underlying my teaching is capability progression, where learning is intentionally designed to accumulate across courses. Assignments in foundational subjects act as building blocks for advanced learning. For example, students implement phylogenetic trees in the Data Structures and Algorithms course to establish conceptual foundations for hierarchical clustering studied later in Artificial Intelligence. This progression enables students to develop structural understanding rather than simply learning isolated techniques.

My teaching is closely connected to research and professional practice through a research–industry–course ecosystem, where research outcomes, industrial innovation, and authentic datasets are incorporated into learning activities and student projects. By working with real-world cases, students gain insight into the practical constraints, design decisions, and interdisciplinary nature of computational systems while recognising how academic knowledge can contribute to technological innovation.

Beyond classroom teaching, I supervise approximately 10–16 student projects each year and contribute to curriculum development at both undergraduate and postgraduate levels. These experiences have reinforced my belief that sustainable educational development depends on coherent programme-level learning design rather than isolated course improvements.

Ultimately, my goal is to create learning environments in which students actively explore, question, and experiment with computational ideas. By integrating teaching, research, and industry practice, I aim to develop graduates who possess strong algorithmic reasoning, system-level understanding, and the ability to apply computational methods to solve complex real-world problems.

My approach to learning design focuses on developing students’ structural understanding of computational systems. This approach emerged from a recurring observation in computer science education: many students are able to implement solutions using software tools or follow programming procedures, yet struggle to explain the underlying algorithmic principles or connect foundational concepts with more advanced applications.

To address this challenge, I design learning experiences using a capability progression approach, where student capabilities are intentionally developed across interconnected courses. Assignments in foundational subjects are designed not only to achieve immediate course outcomes but also to function as building blocks for subsequent learning. Rather than treating courses as isolated units, students progressively construct deeper conceptual understanding throughout the curriculum.

One example is the implementation of phylogenetic trees in the Design and Analysis of Data Structures and Algorithms course. This activity introduces hierarchical data structures that later support students’ understanding of hierarchical clustering and machine learning concepts in Artificial Intelligence. By designing learning tasks that extend across courses, students recognise how foundational computational ideas evolve into more advanced applications.

Another important feature of my course design is encouraging students to investigate how design choices influence computational performance. Instead of focusing solely on obtaining correct answers, students compare alternatives such as adjacency matrices and adjacency lists, or evaluate different data structures within shortest-path algorithms. These activities encourage students to reason about time–space trade-offs, algorithmic efficiency, and engineering decisions that underpin real computational systems.

I also emphasise interdisciplinary learning by connecting computational thinking with applications beyond traditional computer science. Network algorithms are used to explain search engine ranking in Digital Marketing, while data structures and algorithmic reasoning are linked to information management in Enterprise Resource Planning systems. These connections help students appreciate the transferability of computational thinking across diverse professional contexts.

Recognising that students learn in different ways, I incorporate principles of learner segmentation when designing learning activities. Students are encouraged to explore their own learning preferences and cognitive patterns, and anonymised class data may later become datasets for introductory machine learning activities. This approach promotes self-awareness while reinforcing the idea that learner diversity is itself a valuable learning resource.

My learning design is further strengthened through a research–industry–course ecosystem, where authentic research problems, industrial case studies, and real datasets are integrated into classroom activities. Students are therefore exposed not only to theoretical concepts but also to the practical constraints and decision-making processes encountered in professional computational practice.

Reflecting on my teaching, I have found that designing learning through capability progression, interdisciplinary integration, and authentic problem contexts enables students to build lasting conceptual understanding. More importantly, it equips them with the confidence to transfer computational thinking beyond individual courses and apply it to unfamiliar real-world challenges.

My teaching practice is centred on creating learning experiences in which students actively explore, experiment, and question computational ideas rather than relying solely on lecture-based instruction. I believe that meaningful learning in computer science and artificial intelligence comes from investigating alternative solutions and understanding design decisions, rather than simply memorising algorithms or learning how to use particular software tools.

One of my principal teaching strategies is algorithmic exploration, where students modify components of algorithms and observe how these changes influence system performance. For example, when studying shortest-path algorithms, students compare binary heaps with Fibonacci heaps and evaluate different graph representations, including adjacency matrices and adjacency lists. Through these activities, students examine trade-offs involving execution time, memory consumption, and computational complexity, recognising that engineering decisions often involve balancing competing constraints rather than identifying a single correct solution.

To make abstract concepts more accessible, I also integrate gamified learning into my teaching. For example, I use Scrabble as a context for teaching hashing, where students design anagram-based hashing structures capable of identifying valid words from a given set of letters. This activity provides a practical and engaging way for students to understand hash function design, collision handling, and efficient search strategies.

Another signature activity is TriChess, a three-player chess environment developed to explore multi-agent systems and non-zero-sum game dynamics. Students design autonomous agents, compete against one another, analyse outcomes, and iteratively improve their strategies. This process mirrors authentic algorithm development, encouraging experimentation, reflection, and evidence-based refinement rather than simply pursuing a predetermined solution.

In Machine Learning courses, I adopt a tool-independent learning philosophy. Students implement fundamental algorithms such as k-Nearest Neighbour and Naïve Bayes using multiple platforms, including Excel, Weka, Python, and SQL. By approaching the same concepts through different tools, students develop deeper understanding of algorithmic principles while recognising that data quality and model design are often more important than the software itself.

To encourage engagement from the beginning of the course, I also use classroom-generated data as learning material. Students first explore their own cognitive preferences and learning characteristics, after which anonymised class data becomes the dataset for introductory machine learning exercises. This approach promotes self-awareness, encourages discussion about learner diversity, and creates an inclusive learning environment in which students learn from both data and one another.

My support for student learning extends beyond the modules for which I am directly responsible. I regularly help students connect concepts across the curriculum by explaining relationships between subjects. Examples include discussing row-based and column-based storage in database systems, or explaining stack frames, dependency graphs, addressing modes, and memory hierarchy within computer architecture and operating systems. These discussions help students appreciate how lower-level system architecture influences higher-level algorithm and software design.

Reflecting on my teaching practice, I have found that exploration, experimentation, competition, and cross-course integration enable students to develop deeper system-level understanding than isolated instructional approaches alone. Rather than learning only how to implement algorithms, students become better equipped to explain design decisions, evaluate alternatives, and transfer computational thinking to unfamiliar real-world problems.

My approach to assessment is founded on the belief that problems in computer science and artificial intelligence rarely have a single correct solution. Students may develop different approaches to solving the same problem, each with its own strengths, limitations, and appropriate contexts. For this reason, I avoid assessment practices based solely on a single metric or rigid comparison between students. I often explain this philosophy using the metaphor that “we should not compare birds with fishes,” recognising that learners demonstrate strengths in different dimensions.

Accordingly, I adopt a multi-dimensional assessment approach that evaluates several aspects of student performance rather than focusing exclusively on correctness. Alongside accurate solutions, I consider algorithmic efficiency, computational resource usage, design rationale, and students’ ability to explain the trade-offs underlying their decisions. In algorithm-related projects, for example, students may be assessed according to correctness, execution speed, memory consumption, and the quality of their engineering justification.

This approach encourages students to develop diverse solutions. Some choose to maximise execution speed, others prioritise memory efficiency, while others seek balanced compromises between competing objectives. I regard this diversity as an essential component of learning because it reflects the realities of computational system design, where engineering decisions are typically shaped by multiple constraints rather than a single optimal criterion.

In Machine Learning courses, I extend this philosophy by designing assessments that better reflect professional practice. Instead of evaluating models solely by predictive accuracy, students are encouraged to consider training efficiency, model interpretability, feature selection, data quality, and the rationale behind model selection. This broader perspective helps students appreciate that successful AI systems require balancing multiple performance considerations.

Assessment also serves as a learning activity rather than merely a grading process. Following project presentations, students participate in structured discussions where they analyse different solutions by identifying strengths, limitations, and design trade-offs. These conversations encourage critical thinking, expose students to alternative perspectives, and reinforce the understanding that complex computational problems can often be solved in multiple valid ways.

My feedback practices therefore emphasise explanation, reflection, and future improvement rather than numerical scores alone. I seek to explain why particular design decisions are effective, where limitations exist, and how students might refine their approaches in subsequent work. In this way, feedback becomes an integral part of the learning cycle rather than simply the conclusion of assessment.

Reflecting on my teaching practice, I have found that this multi-dimensional approach promotes a more open learning environment, reduces excessive emphasis on grades alone, and supports students in developing deeper analytical thinking, engineering judgement, and system-level reasoning.

I aim to create learning environments that encourage exploration, experimentation, and the development of system-level thinking. In my view, learning in computer science and artificial intelligence should extend beyond classroom instruction to include research, authentic industry problems, and participation in academic communities. Such environments help students recognise the relevance of computational knowledge and appreciate its broader impact on society and technological innovation.

A central mechanism for achieving this is through capstone projects and student research projects, where students integrate knowledge from multiple courses to solve complex, real-world problems. I typically supervise around 10–16 projects each year, primarily in artificial intelligence, data analytics, and intelligent systems. These experiences enable students to develop algorithmic reasoning, data management skills, teamwork, and professional communication within authentic project contexts.

I also encourage students to disseminate their work through national and international academic conferences, including UAMC, IEEE ICSEC, iSAI-NLP, and INCIT. Presenting their work allows students to experience academic research, develop scholarly communication skills, and engage with wider research communities. Several student projects have received awards and recognition, reinforcing students’ confidence that their work can contribute meaningfully beyond the classroom.

My learning environment is further strengthened through a research–industry–course ecosystem, where authentic research, industrial case studies, and real datasets become part of classroom learning. Exposure to real systems helps students appreciate practical constraints, interdisciplinary collaboration, and the complexities involved in designing computational solutions for professional environments.

Alongside academic challenge, I believe that student well-being is an essential component of effective learning. Over the years, I have observed that some students experience considerable stress arising from academic demands, competition, and uncertainty about their future. I therefore strive to create an open learning environment in which students feel comfortable discussing ideas, asking questions, and seeking guidance when needed.

Beyond formal teaching, I have organised voluntary charity classes in mindfulness and meditation for students and interested participants. These activities encourage mindfulness, self-awareness, and stress management. Although they are not part of the formal curriculum, they have strengthened my skills in deep listening, empathetic communication, and informal mentoring, all of which have positively influenced my supervision and day-to-day interactions with students.

My experience as an instructor, coach, and trainer in the Computer Olympiad programme has further reinforced the importance of supporting learners in high-pressure environments. Preparing students for algorithmic programming competitions requires more than technical expertise; it also demands resilience, emotional balance, and sustained motivation. Through reflective dialogue, deep listening, and a supportive team culture, I aim to help students manage stress while continuing to develop their academic potential.

Where students require support beyond informal mentoring, I encourage them to access appropriate university services or qualified professionals so that they receive suitable and safe assistance.

Reflecting on my practice, I have come to believe that effective learning environments are created not only through intellectually challenging activities but also through psychological safety, mutual respect, and supportive academic communities. When students feel safe to question, experiment, and occasionally fail, they become more willing to take intellectual risks and develop into confident, independent learners.

My professional development is closely integrated with research, industry engagement, and continuous reflection on teaching practice. I believe that teaching in computer science and artificial intelligence should evolve alongside advances in technology, research, and the changing needs of society. Professional development therefore involves not only keeping pace with new knowledge but also transforming that knowledge into meaningful learning experiences for students.

My research spans several areas, including artificial intelligence, machine learning, optimisation algorithms, bioinformatics, natural language processing, and computational modelling. My work has been published in international conferences and journals, including IEEE, ECTI-CON, ICSEC, iSAI-NLP, and journals in bioinformatics and computational science. Working across multiple research domains has enabled me to identify connections between disciplines and to bring authentic research examples directly into the classroom.

Research has had a significant influence on my teaching practice. I regularly incorporate case studies from bioinformatics, natural language processing, and optimisation research to illustrate concepts such as feature engineering, search spaces, model design, and algorithmic reasoning in Artificial Intelligence, Machine Learning, and Data Structures courses. These examples help students recognise that fundamental computational principles are transferable across different application domains and remain highly relevant to contemporary research.

My professional development has also been enriched through interdisciplinary collaboration with researchers from several institutions, including BIOTEC (NSTDA), the Department of Industrial Engineering at Chulalongkorn University, the Department of Physics at King Mongkut’s University of Technology Thonburi, and researchers in marketing from Sukhothai Thammathirat Open University. Collaborating across disciplines has broadened my perspective on complex problems and strengthened my ability to integrate diverse forms of knowledge into teaching.

Industry engagement represents another important dimension of my professional development. I have designed and implemented AI-integrated Enterprise Resource Planning (ERP) systems that have been deployed in industrial settings. These experiences provide authentic case studies, real datasets, and practical design constraints that enrich classroom learning and help students understand how computational methods are applied in professional environments.

Reflecting on my practice, I have found that integrating research, interdisciplinary collaboration, and industrial innovation continually strengthens both my teaching and my professional growth. Looking forward, I intend to further develop my work in the Scholarship of Teaching and Learning (SoTL) by investigating how the capability progression model, the algorithmic learning framework, and the research–industry–course ecosystem can support student learning in artificial intelligence and data science. I hope these developments will contribute not only to my own teaching practice but also to the wider computer science education community.

In addition to my own teaching responsibilities, I contribute to programme-level educational development by working collaboratively with colleagues to strengthen connections across courses and support long-term student capability development. I believe that the quality of a degree programme depends not on individual modules in isolation but on how learning experiences are intentionally connected throughout the curriculum.

I have served on the Applied Computer Science Programme Committee since 2019, contributing to the curriculum revision completed in 2022 and the ongoing review for 2027. I have also participated in the development of the Applied Mathematics postgraduate programme during the 2022 and 2026 curriculum revision cycles. These roles have enabled me to contribute to discussions about curriculum coherence, programme structure, and the progressive development of graduate capabilities.

Within these collaborative discussions, I frequently introduce the concept of capability progression, encouraging colleagues to design assignments and learning activities in foundational courses that intentionally prepare students for more advanced study. Rather than viewing modules as isolated units, this approach supports a coherent curriculum in which knowledge and skills are progressively developed across the programme.

Beyond formal curriculum design, I also contribute to colleagues’ teaching by helping strengthen conceptual connections between related subjects. For example, I discuss row-based and column-based storage to connect database systems with data analytics, and explain concepts such as stack frames, memory hierarchy, and dependency graphs to illustrate how computer architecture influences algorithm and data structure design. These conversations help improve curriculum coherence and reinforce shared conceptual foundations across multiple courses.

I also contribute industry perspectives to programme development by bringing structured feedback from industrial partners into curriculum discussions. Using evidence gathered from industry engagement helps inform conversations about graduate competencies and future curriculum directions, encouraging evidence-informed programme development rather than decisions based solely on personal opinion.

Reflecting on these experiences, I have learned that sustainable educational development depends upon collaboration, constructive academic dialogue, and the willingness to share ideas across teaching teams. I therefore seek to contribute by sharing insights from my teaching practice, research, and industry engagement, supporting colleagues in developing coherent curricula and strengthening the educational experience of students across the programme.

Reflecting on my teaching practice over the past years has led me to recognise that learning in computer science and artificial intelligence is far more than acquiring technical knowledge or mastering software tools. Students need opportunities to develop structural understanding, connect ideas across disciplines, and transfer their knowledge to unfamiliar problems. These experiences have gradually shaped my belief that teaching should focus on the progressive development of student capability rather than treating each course as an isolated learning experience.

One of the central ideas emerging from this reflection is Capability Progression, in which assignments in foundational courses serve as building blocks for advanced learning. Another is the Algorithmic Learning Framework, encouraging experimentation, comparison, and reflection rather than dependence on software tools alone. Together, these approaches strengthen computational thinking, system-level reasoning, and knowledge transfer.

My experience across teaching, research, and industry has also led to the development of the Research–Industry–Course Ecosystem, which integrates research, industrial innovation, and classroom learning through student projects. These experiences have reinforced my belief that meaningful learning depends upon authentic problems, supportive learning environments, and opportunities for reflection.

Looking forward, I intend to strengthen evidence-informed teaching through learning analytics, student performance data, and industry feedback. I also plan to further develop the Capability Progression Model, the Algorithmic Learning Framework, and the Research–Industry–Course Ecosystem as contributions to the Scholarship of Teaching and Learning (SoTL).

Ultimately, I believe lasting educational impact comes from designing coherent learning systems, collaborating with colleagues, and fostering a culture of continuous learning. I hope to contribute more broadly to programme- and institutional-level educational development while continuing to share the ideas and practices developed throughout my teaching journey.