International Conference on Computers in Education
The International Conference on Computers in Education (ICCE) is organized by the Asia-Pacific Society for Computers in Education (APSCE). Accepted papers in the main conference, workshops, Early Career Workshop, Doctoral Student Consortium and Work-in-Progress Posters will be published in proceedings, which will be submitted to Elsevier for inclusion in Scopus. Proceedings of the main conference will also be submitted to Clarivate for inclusion in the Conference Proceedings Citation Index.
Asia-Pacific Society for Computers in Education (APSCE)
Front Matter: The 33rd International Conference on Computers in Education
This PDF file contains the front matter associated with ICCE Proceedings including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.
Copyright (c) 2025 International Conference on Computers in Education
Viat-map: A Constructivist, Toulmin-Based Framework for Enhancing EFL Reading Comprehension
This paper introduces Viat-map, a technology-enhanced learning tool designed to improve EFL learners’ reading comprehension by guiding them in reconstructing argumentative structures using Toulmin’s model. Viat-map’s closed-ended, visual interface scaffolds learners through claim, ground, and warrant identification, promoting active engagement and reflection. Experimental results demonstrate that learners using Viat-map significantly outperform those using traditional summarization methods in both immediate and delayed comprehension assessments. Log data analysis further reveals that iterative feedback cycles enhance learners’ accuracy, while behavioral profiles show varied engagement patterns linked to learning outcomes. The latest AI-enhanced Viat-map incorporates adaptive learning pathways and gamification, enabling personalized pacing and richer insights into learner processes. Together, these findings highlight Viat-map’s potential as a data-informed, defensible platform for fostering critical thinking and argumentation skills in multilingual educational contexts.
Copyright (c) 2025 Journal of Public Knowledge
Designing AI-powered Scaffolds to Foster Metacognition and Agency in EFL Writing
Learners in English as a Foreign Language (EFL) writing often struggle with limited metacognitive strategy use, weak feedback literacy, and insufficient self-regulation. EFL writing requires the coordination of language knowledge, cognitive strategies, and metacognitive skills, which makes these challenges especially significant. While AI writing assistants offer personalized support, most emphasize performance optimization and error correction, which risks reducing learner agency and hindering metacognitive development. This research aims to create an AI-enhanced writing platform designed to foster both writing performance and learner agency. It integrates LLM-based metacognitive prompts, adjustable agency control, and analytics dashboard within a digital writing environment, guided by theories of self-regulated learning, structure and agency, and recursive writing process. Using a Design-Based Research approach, this research involves iterative design, classroom-based pilot studies, and mixed-method evaluation. It has potential contributions include empirical evidence for balanced AI support, theoretical expansion of agency framework into AI-enhanced writing, and practical design principles to enhance learner agency. Preliminary findings and future directions will be discussed in the workshop.
Privacy-Focused AI Avatar for Educational Robotics in the Metaverse
The convergence of immersive environments and educational robotics is transforming STEM learning by enabling students to design and deploy robots across both virtual and physical contexts. This paper presents a privacy-first conversational AI avatar that enables seamless voice interaction with robots in simulation and reality. All processing runs locally, safeguarding student data while ensuring real-time responsiveness. The system integrates Whisper for speech recognition, a lightweight Llama 3.2 model for intent understanding, and Glow-TTS for natural feedback. Robot commands are strictly validated against a JSON schema and executed via ROS 2, allowing seamless transfer between NVIDIA Isaac Sim and a Jetson Orin Nano-powered robot. A prototype workshop confirmed smooth interaction, safe command execution, and high engagement, highlighting potential for immersive, privacy-preserving robotics education.
LLM Agent Collaboration for Educational Strategy Design
Large language models (LLMs) can be organized into multi-agent systems (MAS) that simulate expert panels, but coordination remains a challenge. In education, effective support often requires integrating insights from data analytics, curriculum design, and pedagogy. We present a moderated, embedding-driven MAS framework for educational decision-making. Role-specialized LLM agents generate structured recommendations, which are embedded into a shared semantic space. A central moderator measures alignment and agreement, computes a consensus embedding, and translates it back into natural language. This consensus is returned to the agents in subsequent rounds, guiding convergence while preserving distinct perspectives. A simulated case study illustrates the process in the context of secondary education. The results highlight the potential of embedding-based moderation to produce coherent, interpretable recommendations.
Exploring the Holistic Impact of Adaptive Formative Assessment for Novice Programmers
The Introductory Programming module is the first step in the software development related courses. The learning outcomes of this module are fundamental concepts of programming. These independent components increase the difficulties for novices including student struggle, and low self-confidence. To motivate novice students to learn programming languages, lecturers employ a variety of exercises. Inability to address errors while programming can lead novice students to lose interest, making the deliberate introduction of common code errors—alongside strategies to increase motivation and confidence — a key element in supporting their comprehension in programming courses. Formative assessment is one of the approaches for effective programming learning that aims to increase student understanding, instructor instruction, and learning by providing feedback on students’ progress. Most research focuses on either formative assessment or adaptive learning, but not their intersection in programming. Inspired by this approach, this study proposes the use of adaptive formative assessment as a pedagogical intervention to enhance student confidence and support learning Introductory Programming. The experiment is based on lessons learned from the literature and pedagogical theories that support learning through assessment and scaffolding. This study investigated how effectively these assessments helped students understand, learn and develop a sense of their ability to develop computer programs by introducing common programming errors. The self-confidence of students to learn programming is measured through a survey questionnaire and the assessments impact. Findings showed that employing adaptive formative assessment was more likely to motivate students and increase their self-confidence.
Advancing Research in CS Ethics Education, EdTech Implementation Frameworks, and Neurodivergent Learning
My research focuses on three interconnected areas of educational technology investigation: ethics education in computer science curricula, frameworks for EdTech implementation, and inclusive technology design for neurodivergent children. The first strand examines the integration of ethical considerations in CS education in India, addressing a critical gap in preparing future technologists for responsible innovation. The second develops comprehensive frameworks and metrics for EdTech implementation emphasizing evidence-based approaches to technology adoption in a genAI-driven educational landscape. The third strand extends these principles to create inclusive EdTech specifically designed for neurodivergent learners. Together, these research areas contribute to more responsible, impactful, and equitable technology development and application in educational contexts.
Learning Analytics for the Metaverse: A Review of Descriptive Analytics and Visualization Tools for Metaverse-Based Learning Environments
The adoption of metaverse technology in education offers new possibilities for experiential, collaborative, and immersive learning, while simultaneously generating complex, multimodal learner data. Interpreting this dense information, spanning spatial navigation, object manipulation, avatar embodiment, and communication patterns poses a significant challenge for both educators and learners. This review synthesizes 19 peer-reviewed studies identified through systematic database searches and snowballing, focusing on the current state of data visualization and descriptive learning analytics in metaverse-based Learning Environments. The analysis reveals that while platforms like Learningverse and tools such as PLUME enable non-interactive or post-hoc data exploration, there is a lack of real-time, pedagogically integrated visualization systems. Current approaches are largely descriptive, fragmented, and disconnected from immediate instructional support or learner reflection. This review highlights critical research gaps, including the absence of runtime interactive tools, underutilization of immersive analytics, and limited alignment with educational theory. Addressing these gaps is essential to transforming rich multimodal data into actionable insights that can enhance teaching and learning within the metaverse.
Reducing Teachers’ Techno-anxiety and Technostress through AI-TPACK
The use of information technology in education, including AI-based tools, can lead to increased anxiety and become a significant source of technostress. One possible mitigating factor for these effects is teachers' knowledge of technology, pedagogy and content related to artificial intelligence (AI-TPACK). This hypothesis was examined in a cross-sectional correlational study involving pre-service teachers. The results showed low levels of technical anxiety and technostress and moderate levels of AI-TPACK. Techno-anxiety and technostress were strongly and positively correlated, while both were negatively associated with AI-TPACK. Moreover, AI-TPACK significantly moderated the relationship between techno-anxiety and technostress in selected configurations. In line with the assumed buffering function of AI-TPACK, the relationship between anxiety and stress was strongest among teachers with the lowest knowledge.
An Online Mathematics Learning Platform for Thai Student Based on a 5-levels Math Problem Difficulty Model
Thai students often struggle on international assessments, even though mathematics problem-solving is a critical component of the curriculum and is studied extensively. This paper proposes an online mathematics learning platform based on a 5-level problem difficulty model to provide adaptive learning for Grade 8 and 9 students. An experiment involving 50 students demonstrated that those using the platform achieved statistically significant improvement based on paired-sample T-test (p < 0.001) compared to a control group. These findings suggest that adaptive learning tools can enhance mathematics proficiency and engagement.
Designing a Computer-Supported Collaborative Argumentation Learning Environment to Counteract Misinformation in Socio-Scientific Issues
This paper details the design-based research of a Computer-Supported Collaborative Argumentation (CSCA) environment to help secondary school students counter misinformation in socio-scientific issues. A pilot study revealed challenges in students' warrant articulation, source evaluation, and collaborative dynamics. These findings informed an iterative refinement of the design, which now incorporates structured protocols for systematic fact-checking and scripted, rotating roles to balance discourse. This work offers evidence-based design principles for integrating argumentation with misinformation literacy and proposes a refined pedagogical model for further testing.
Preliminary Insights on Learners’ Preferences on Pursuing Academic Micro Credentials at Universiti Putra Malaysia
With the rising demand for personalised learning, micro-credentials offer learners a pathway to tailor their education to their individual goals. This pilot study explored the preferences of four undergraduate learners enrolled in micro-credential courses through a focus group discussion. Thematic analysis identified three key dimensions influencing the focus of this study: value of micro-credentials, quality of learning experience, and shaping learning path. These dimensions reflect factors shaping learners' interest in pursuing academic courses offered through the micro-credentials mode of delivery. These preliminary findings contribute to the growing literature on academic micro-credential implementation.
A Study on the Performance of a RAG-Augmented Interview Chatbot
Along with the increasing importance of communication skills for the jobseekers, many students face challenges in preparing for interviews, both in terms of knowledge, experience, and self-confidence. This research explores the effectiveness of a RAG-augmented chatbot for job interview preparation, combining information retrieval and large language models (LLMs) to generate contextually relevant responses. Two LLM models, Google/gemma-2-2b-it and deepseek-ai/DeepSeek-V3, were compared across three scenarios: True-True (TT), True-False (TF), and False-False (FF). Results show that DeepSeek-V3 outperformed Google/gemma-2-2b-it in generating more accurate and relevant responses, though both models struggled in False-False scenarios. The System Usability Scale (SUS) testing indicated that the chatbot was perceived as easy to use and effective, with scores above the average threshold of 68. However, feedback highlighted concerns regarding response time and the complexity of some features. Overall, the findings suggest that the RAG-based chatbot is a promising tool for interview preparation, though improvements in model performance, system responsiveness, and interface simplicity are recommended for future development.
Formative Assessment with Technology Integration to Enhance Preservice Geography Teachers’ Learning Outcomes: A Quasi-Experimental Study
This study examines the effectiveness of formative assessment integrated with educational technologies in enhancing the learning outcomes of preservice geography teachers at Can Tho University, Vietnam. A quasi-experimental design was employed over two academic years, involving 144 participants. Students were randomly assigned to an experimental group (n = 70) and a control group (n = 74). The experimental group underwent frequent formative assessments embedded in the learning process, utilizing tools such as Google Classroom, Google Forms, and Canva, with structured criteria and timely feedback. The control group primarily relied on conventional classroom practices and email-based feedback, with limited use of formative assessments. Data were collected through pre- and post-intervention surveys assessing students' attitudes and perceptions, as well as achievement tests that combined multiple-choice and essay items. Results from paired-samples t-tests showed significant improvements in both groups, but independent-samples t-tests and ANCOVA confirmed that the experimental group demonstrated significantly greater progress (Cohen’s d = 1.30; partial η² = .365). The findings highlight that formative assessment with technology integration not only improves students' test performance but also fosters positive changes in learning attitudes and engagement. The study contributes evidence supporting the integration of formative assessment and digital tools in teacher education to promote effective and sustainable learning.
Is the Polish school system keeping pace with the digital revolution? Artificial intelligence in the context of the PISA and PIAAC results
The increasingly widespread use of digital tools in teaching and learning is becoming one of the key factors shaping modern education systems. It is therefore particularly interesting to observe how different regions of the world are addressing this issue. The article examines the integration of digital technologies and artificial intelligence in Polish education system. Using a mixed-methods approach, the study combines quantitative analysis of PISA and PIAAC reports with a qualitative review of policy documents, academic literature, and national strategies to identify the current status of Technology-Enhanced Learning in Poland, highlights challenges, factors which help to explain Poland’s relatively weak performance in PISA and PIAAC studies, proposes areas for improvement and potential fields for international collaboration.
IEC Driven Guitar Etude Optimization System Considering Learner Preferences and Skills
While interest in playing musical instruments continues to grow, self-directed learning remains challenging, leading many learners to abandon their practice. Previous research on musical instrument learning support has primarily focused on performance evaluation and feedback, with insufficient attention given to practice materials themselves. This study proposes an Interactive Evolutionary Computation (IEC) based system that optimizes guitar practice materials according to individual learner preferences and skill levels to enhance learning motivation. Through experimental evaluation with 15 participants, we found that approximately 87% reported increased motivation when practicing with personalized materials. These results suggest that tailoring practice materials to individual preferences and abilities can significantly improve learning motivation for musical instrument learners.
Evolving Teacher Professional Development in the Age of AI: Revisiting PCK, TPACK, and the DECODE Model
Current teacher professional development (TPD) models often face challenges of sustainability, contextual adaptability, and limited integration of emerging technologies. This study introduces the DECODE model, a cyclical, AI-supported professional development (PD) framework designed to enhance teachers' Technological Pedagogical Content Knowledge (TPACK), academic resilience, and instructional competence in post-pandemic contexts. Grounded in TPACK, Pedagogical Content Knowledge (PCK), and self-regulated learning, the DECODE model includes four phases: DEMO, CO-design/teach, feedback, and DEbriefing. AI is integrated to provide adaptive feedback, automate lesson analysis, and scale individualized coaching. Synthesizing five empirical studies conducted across Taiwan, Indonesia, India, and Vietnam, the findings confirm DECODE's positive impact on TPACK development, reflective thinking, and adaptive instruction. Participants reported increased technological confidence, resilience, and engagement. The study highlights DECODE's scalability and potential for AI-enhanced teacher education in diverse cultural contexts, with future directions focusing on embedding DECODE into formal TPD systems and institutional practices.
From Use to Insight: Employee Perceptions and Barriers to E-Training Apps in Kuwait’s Civil Service
This research examines employee perceptions and barriers to e-training applications at Kuwait's Civil Service Commission. Qualitative methods were employed, and the results show that it is flexible, engaging, and cost-effective, but they also show that there are problems with infrastructure, language, and organisational support. Using Diffusion of Innovation, Unified Theory on Acceptance and Use of Technology, and Interest-Driven Creator frameworks, findings of this study highlight both the benefits; such as flexibility, productivity, and engagement; and the barriers, including infrastructural, cultural, and organisational challenges, that influence adoption and sustained use. This study offers practical recommendations for localisation, user-centred design, and policy alignment to advance workforce development under Vision 2035.
Toward “AI-Centered Student”: Making Sense of Learning Environment, Epistemic Beliefs, and Self-Regulated Learning Using Epistemic Network Analysis
This preliminary study is designed to explore the multifaceted impact of Generative Artificial Intelligence (GenAI) integrated into digital learning platforms, namely AISI, on students' science learning, self-regulation, and epistemic beliefs from 129 college students. Leveraging empirical study data, Epistemic Network Analysis (ENA) was utilized to quantitatively explore relationships between students' learning preferences (high AI-centered preference group and low AI-centered preference group), their epistemic beliefs concerning AI (certainty, justification, complexity), and their self-regulated learning strategies (adaptation, planning). The findings show that students in the high AI-centered preference group tend to believe that knowledge provided by GenAI is uncertain and complex. This study aims to contribute to GenAI-enhanced learning environments and pedagogical practices that foster critical AI literacy and adaptive self-regulated learning.
Multi-Dialogue Agents for Collaborative Learning Support Through Adaptive Opinion Integration
This research presents a collaborative learning support system that enhances learner motivation by creating psychologically safe discussion environments using multiple LLM-powered agents. These agents are designed to validate and integrate user opinions into learning dialogues dynamically. While agent-based collaborative learning offers flexibility in time and location, achieving truly adaptive learning environments remains challenging. Our system addresses this by leveraging agents that affirm learner contributions through strategically aligned responses during discussions, fostering positive psychological effects of acceptance and belonging. Experimental evaluation with 11 participants demonstrated that 91% experienced increased learning motivation when interacting with opinion-affirming agents. This research contributes to the emerging field of LLM-based educational technology by demonstrating the effectiveness of adaptive multi-agent systems in supporting collaborative learning.
Objective Evaluation Metrics from Behavioral Logs in Metaverse-Based International Virtual Exchange: Total Movement Distance and Learners’ Self-Reported Intercultural Understanding
We propose an evaluation approach that uses behavioral logs as objective indicators in metaverse-based international virtual exchange (VE). Using VRChat logs, we computed total movement distance during presentations and examined its association with self-reported active participation and the deepening of intercultural understanding. In course presentation activities (N = 9 with valid survey responses), total movement distance showed moderate positive associations with both outcomes, suggesting that behavioral logs may be related to learning effects in metaverse classes.
Enhancing Reading Motivation and Engagement through Virtual Pet-Based Gamification
This study presents the design of a gamified reading support platform that combines virtual pet nurturing with AI-based analytics to address the challenge of sustaining students' reading motivation and long-term engagement. In the system, teachers provide guidance and feedback, students complete reading logs and discussion tasks to earn resources for caring for virtual pets, and parents contribute by supporting family reading activities. Reading activities are converted into in-game resources, with pets evolving in appearance as students progress. The platform integrates AI modules for learning diagnosis, emotion generation, growth visualization, and class-level analytics, together with gamification modules including quests, rewards, and progression loops. By fostering emotional connections and providing instant feedback, the system supports the development of stable reading habits, while offering educators and parents real-time data for tailored interventions. This work contributes a design case that bridges the theoretical foundations of Pet-like Learning Companion Systems (P-LCSs) with educational gamification, highlighting the role of affective interaction and cross-context continuity in promoting autonomous and lifelong learning.
Computer-Mediated Learner-Centered Error-Correction Activities: Opportunities and Challenges
This work aims to leverage technological affordances—specifically, adaptability, interactivity, choice, feedback, high processing speed, and immense storage space—to better support error-correction activities following drill-and-practice activities. An evaluative study adopting a within-subjects quasi-experimental design with seventh-grade students (n=57) was conducted. The major findings are as follows: First, all purposefully designed features and functionalities received strong affirmation for their respective learning supportiveness. Second, with the provision of (a) instant feedback on student performance, which is critical for initiating error-correction activities, and (b) a customizable correction steps along with an additional layer of hints for challenging steps—ranked the highest and second highest in terms of perceived supportiveness—the results further underscore the advantages of computer-mediated arrangements for error-correction activities over traditional paper-based formats. Students’ explanatory comments illuminate the opportunities offered by a computer-mediated, learner-centered error-correction approach, while also noting challenges associated with different error-correction modes.
Method for Extracting Unique Meanings of Synonyms Using Word2Vec for Vocabulary Learning
In this study, we propose a method for extracting word unique meanings of synonyms using Word2Vec for the purpose of supporting vocabulary learning. In conventional vocabulary learning, it was difficult for the system to teach the meaning differences between synonyms. This study attempts to propose a method for extracting words that represent unique meaning of each synonym pair using Word2Vec. Our method derives a vector of a unique meaning of the target word by subtracting the common vector of the two words from the vector of the target word. In the verification experiment, the proposed method was able to extract unique meaning of Japanese synonym pairs that contain one same Chinese character at a maximum rate of 64%.
Support System for Understanding Class Design with Reusability and Maintainability
In object-oriented programming (OOP), creating class designs with high reusability and maintainability is important. Structure with high reusability and maintainability can be created by using hierarchical structures based on inheritance. Hierarchical structure can be created by defining abstract classes that abstract similar classes. However, OOP beginners may struggle to identify which similarities to abstract to achieve reusability and maintainability, potentially preventing them from creating such structure. This research aims to teach OOP beginners to recognize similarities between classes, enabling them to create structure that satisfy reusability and maintainability requirements.
Proposal of Visualization for Test Execution Results of programming with Automated Testing
In recent years, automated testing has been used in many software developments. Automated testing has advantages such as the early detection of errors through continuous test execution. However, research has reported that software testing is difficult for novices who have not yet acquired basic programming skills. Previous research has observed that even novice learners can perform coding activities and identify bugs in a function in an environment where automated testing is already prepared, but they could not identify the line that has defects in a function and could not eliminate the defect. This study proposes a learning support tool to help novice learners learn how to identify defects in a function using automated testing in programming exercises. Result of evaluation indicated that the proposed tools can help learn a method for identifying the location of defects.
Balancing Wellbeing, Equity and Workforce preparation through ‘Art with Shapes’
This paper presents Art with Shapes, a cross-disciplinary middle school lesson integrating mathematics, art, and computational thinking (CT) in Indian public schools. Grounded in India's National Education Policy (NEP 2020) and aligned with the OECD Learning Compass and UNESCO’s Happy Schools, the lesson exemplifies effective ICT education goals and the challenges of translating them into sustainable classroom practice. Blending geometry, art, and coding, Art with Shapes offers culturally responsive learning and serves as a touchstone to illustrate the practical interplay between the educational aims of wellbeing, equity and workforce preparation. Narrative, process-focused and choice-based tasks foster equity, address the digital divide, and promote wellbeing through personal creative expression, while developing computational thinking skills for workforce readiness. The paper analyzes the lesson, identifies tensions between curriculum goals and classroom realities, and calls for teacher participation in learning design, targeted professional development, and policy-aligned strategies to advance equitable and sustainable computational thinking education. Art with Shapes is now embedded in Telangana’s SCERT Digital Literacy curriculum and 8,783 teachers were trained via state-level workshops. Through Codemitra app, 4,000+ students across 30+ states participated, with over 70% of learners in Maharashtra, Chhattisgarh, and Jammu & Kashmir completing more than half the activities, demonstrating widespread reach and sustained engagement.
Viat-Map vs Open-Ended Toulmin Arguments: Effects in Low- vs High-Ability EFL Learners
Comparing closed-ended and open-ended learning in understanding argumentation using the Toulmin method for second-year students at the State Polytechnic of Malang, this study evaluated how free-form learning and constructivism influenced student engagement, and the extent to which low and high pre-test proficiency moderated the impact of both platforms on learning improvement. High-ability students were grouped based on high pre-test scores, while low-ability students were grouped differently. Pre-test and post-test data from each learning group were classified and analyzed using ANOVA. The results showed that the parametric ANOVA interaction was marginal (p = 0.06), but Aligned-Rank ANOVA confirmed that the interaction between device and proficiency yielded significant results (p = 0.045). These findings suggest that constructivism improved more than free-form learning alone among low-ability students. Meanwhile, high-ability students showed minimal improvement in both conditions, with no significant differences between the two learning conditions. This study provides theoretical and practical information, which guides the design of more effective and adaptive educational technology.
Enhancing Systemic Understanding of Complex Biological Phenomena through SBF Analogical Stories
Understanding complex biological systems requires learners to reason about how structures interact dynamically to achieve functional outcomes. Yet students often describe biological processes in superficially overlooking the causal mechanism and interdependencies that define systemic behavior. The Structure–Behavior–Function (SBF) framework offers a powerful lens for supporting such reasoning by making visible the roles of system components, their interactions, and purposes. This dissertation investigates how SBF based analogical stories accessible across mobile and classroom environments can foster systemic reasoning in biology education. This research will be done in three different phases, phase 1 focuses on design and development of SBF based systemic analogical stories as an intervention and study how these supports students SBF reasoning compared to piecemeal analogies. Phase 2 focuses on training teachers for co-designing with AI guidelines to develop SBF based systemic analogies and Phase 3 implements these teacher-developed digital analogies in authentic classroom settings, examining technology integration and ubiquitous learning opportunities.
Towards Pedagogical Design Thinking: Exploring the Role of Scaffolding in Pre-Service Teachers Development as Learning Designer
In today's educational context, teachers are expected to design meaningful learning experiences that respond to diverse learning needs. Yet, pre-service teacher education programs often emphasize prescribed knowledge and lesson planning, preparing teachers more as implementers than as reflective designers of learning. Design Thinking (DT) offers potential to foster creativity, empathy, and problem-solving. However, existing DT models are either overly procedural or too open-ended, creating challenges for novice teachers without structured support. This research proposes a scaffolding framework that conceptualizes and integrates two novel supports for PSTs: Noticing-with-Video-based-Student-Personas to strengthen empathy and problem framing skills, and Expert Modelling to guide ideation and prototyping. Using a Design-Based Research approach, the research aims at conceptualizing and designing scaffolds and understanding how scaffolds influence DT processes and pedagogical reasoning of PSTs. The expected outcome is a validated scaffolding framework for Pedagogical Design Thinking that advances teachers as designers of learning, which can also inform design guidelines for teacher education programs.