Welcome to the Camtree Digital Library
The Camtree Digital Library publishes peer-reviewed research reports produced by educators from around the world. Library content is freely available to all readers.
Camtree supports educators to reflect on their practice and conduct research to improve learning in their own contexts and organisations, through its website at https://www.camtree.org. Camtree is based at Hughes Hall, University of Cambridge.
Recent Submissions
Item 人工智能助推“城市交通与土地利用”课程教学 AI-Enhanced Teaching of Urban Transportation and Land Use(2025)Context: This study was conducted within an advanced undergraduate course, Urban Transportation and Land Use, at Peking University. The course examines how transport systems and land use influence each other, and how this relationship affects urban development, sustainability, and equity. Given the technical nature of the course—requiring model building, data analysis, and programming—large language models (LLMs) such as ChatGPT were introduced to help students understand complex concepts and technical methods. Aims: The project aimed to examine how AI tools could enhance student learning in this domain, focusing on: (1) which parts of the learning process benefited from LLM support; (2) where the tools are limited; and (3) whether the use of AI creates confusion or distractions. Methods: In the Fall 2023 semester, LLMs were integrated into two major homework assignments on transport modelling and land use analysis. Students were asked to use tools like ChatGPT or Baidu Ernie Bot, and reflected on the process, including prompt design, comparisons between AI and manual outputs, and classroom discussions. Instructors collected and analysed these reflections to evaluate the role of AI tools in learning. Findings: Students generally found LLMs useful for understanding concepts, exploring modeling methods, and especially for coding and debugging. In the homeworks, AI tools improved efficiency in handling repetitive data tasks. However, they were less effective with “local” data or facts, and sometimes produced inaccurate or made-up results in tasks like image generation. Implications: The study shows that AI tools can reduce barriers in technically demanding courses and support both theoretical and practical learning. It also suggests that instructors should design assignments to distinguish between tasks suitable for AI support and those requiring independent thinking.Item Improving Students’ Analytical Writing Skills Through Effective Questioning Strategies in Chemistry Lessons(2025)Background and purpose: This study explores how effective questioning strategies can improve analytical writing skills among Year 11 and 12 students at Nazarbayev Intellectual School of Physics and Mathematics in Uralsk, Kazakhstan. Analytical writing is essential for success in the external summative assessment, which aligns with international CIE Cambridge A-level standards. These assessments require advanced skills in reasoning, evaluation, and writing well-structured responses, areas where students often face challenges. Given the importance of preparing students to succeed in these assessments, the study focuses on addressing skill gaps and fostering the competencies necessary for academic success. Aims: The study specifically focuses on how effective questioning techniques can enhance students’ abilities to analyse, plan, write well-reasoned conclusions, and evaluate information effectively. The primary objective of this study is to prepare students for the demands of external summative assessments. Study design or methodology: This study was conducted during 2023-2024 academic year with eight Year 11 students (aged 16-17), through collaboration between Chemistry and English language teachers. Lessons integrated CLIL (Content and Language Integrated Learning) strategies to build subject knowledge alongside analytical writing skills. Data collection included summative assessments, teacher observations, and student feedback to evaluate the effectiveness of questioning techniques in improving student readiness for the external summative assessment. Findings: The findings demonstrate that effective questioning strategies significantly enhanced students' skills in planning responses, constructing conclusions, and evaluating information critically. These strategies contributed to improved performance in both short-answer and extended-response tasks, which are integral components of the external summative assessment. The approach also demonstrated potential for broader application across all year level to support long-term academic development. Conclusions, originality, value and implications: This study highlights the importance of effective questioning strategies in preparing students for the external summative assessment. By improving essential skills such as planning, evaluation, and conclusion-writing, the study contributes to the school’s mission of fostering independent learners and critical thinkers. Future work will focus on refining these techniques and providing targeted support for struggling learners, ensuring consistent progress across all year level.Item Gamification in high school computer science: enhancing engagement and critical thinking through lesson study.(2025)Background and Purpose: Traditional teaching struggles to engage students. This study explores gamification as a strategy to boost motivation, participation, and critical thinking in high school computer science. Using Lesson Study, it integrates avatars, challenges, and teamwork to enhance learning. Aims: This study aims to enhance student engagement and motivation through gamification, strengthen critical thinking and problem-solving skills, optimize task completion rates via interactive learning strategies, and foster collaboration through structured, team-based activities. Methodology: This study followed a three-week Lesson Study cycle with 12 high school students (ages 15–16) at Nazarbayev Intellectual School. Gamification elements, including personalized avatars, level-based challenges, and structured argumentation tables, were integrated into computer science lessons. A mixed-methods approach combined task completion rates, time-on-task records, motivation surveys, classroom observations, and structured reflections to assess engagement and learning outcomes. Engagement was measured through participation tracking and task completion rates, while motivation was evaluated using self-reported Likert scale surveys (1-5). Observational data and teacher feedback provided qualitative insights into student collaboration, problem-solving, and analytical thinking. Data triangulation ensured the validity and reliability of the findings. Findings: Gamification significantly enhanced student engagement, motivation, and collaboration. Engagement levels increased by 15%, with task completion rates rising from 65% to 83%. Motivation scores improved from 3.3 to 4.2 on a five-point scale, while classroom participation grew by 15%, and student satisfaction reached 85%. Observations revealed that structured argumentation tables and team-based challenges improved problem-solving abilities, fostering analytical thinking and independent learning. Students demonstrated greater autonomy in structuring arguments and applying logical reasoning in coding tasks, highlighting the effectiveness of gamification in computer science education. Conclusions and Implications: Gamification has proven to be an effective strategy for enhancing student engagement, motivation, and critical thinking in high school computer science classes. The structured integration of game elements improved task completion rates, collaboration, and problem-solving skills. Findings suggest that gamification can serve as a scalable approach to improving learning outcomes, particularly in STEM education. Future research should explore its long-term impact across different subjects and student demographics.Item Using oracy to improve disadvantaged student attitudes, engagement, and reasoning(2025)Context: The study was conducted in an inner-city secondary school in Eastern England, serving a highly disadvantaged community. The research focused on a Year 10 history class (students aged 14–15), particularly examining students’ reasoning skills in discussions. The intervention followed a period of remote learning during the COVID-19 pandemic and aimed to address the language deficit observed in both disadvantaged and non-disadvantaged students. Aims: The research sought to determine whether an oracy-based learning intervention focused on exploratory talk could improve the reasoning capabilities of disadvantaged students. It also aimed to explore whether fostering oracy could positively impact students’ engagement and attitudes, though this aspect is to be reported separately. Methods: A pilot study identified students’ reliance on verbatim reading from textbooks and lack of reasoning in discussions. The intervention included ten research lessons structured around scaffolded exploratory talk, where students engaged in guided discussions with talk roles, modelling, and structured ground rules. Data were collected through classroom observations, audio recordings, surveys, and student interviews. Analysis involved interaction-level discourse coding to track improvements in student talk and reasoning. Findings: Compared to the pilot, students engaged in more meaningful discussions, demonstrating improved reasoning skills. Exploratory talk increased to 32% of total interactions, with students more frequently justifying opinions, building on others’ ideas, and engaging critically. Interviews revealed that students felt the intervention helped them articulate thoughts, debate ideas, and develop confidence. While disadvantaged students showed notable improvement, benefits were observed across all students. Implications: The research highlights the value of explicitly teaching reasoning through structured talk. Findings suggest that interventions targeting oracy can benefit students in disadvantaged contexts, though the official classification of disadvantage may not fully capture students’ linguistic needs. The study also underscores the role of collaborative teacher inquiry in refining pedagogical approaches.Item 利用大语言模型支持不同的学习任务——《信息资源建设》课的实证研究 Supporting different learning tasks with large language models – a field experiment in the course information resource development(2025)Context: The undergraduate course "Information Resource Development" at Peking University addresses the development and management of various types of information resources. It faces two persistent teaching challenges: the abstract nature of foundational concepts and the difficulty of simulating practical tasks within limited class time. With the rise of large language models (LLMs) such as ChatGPT, there is growing interest in exploring their role in improving learning engagement and personalisation. Aims: This study explored how LLMs could enhance learning in the course, focusing on two core issues: making abstract content more engaging and supporting students in developing practical skills. A further aim was to examine how the integration of AI tools might contribute to students' AI literacy within the context of professional education. Methods: A randomised controlled experiment was conducted with 37 students across four tasks reflecting different learning goals: factual knowledge, theoretical understanding, causal reasoning, and critical thinking. Students were divided into an AI-supported group using the LLM ‘Wenxin Yiyan (Ernie Bot)’ and a control group using traditional resources. Learning outcomes were assessed using t-tests and regression analysis. Post-experiment interviews explored students’ strategies and experiences. Findings: Students using the LLM performed significantly worse on tasks requiring factual accuracy and critical thinking. AI often generated inaccurate data and produced repetitive viewpoints. However, no significant differences were observed in tasks involving theory or causal reasoning, where LLMs offered quick overviews and illustrative examples. Interview data reflected overall cautious attitudes toward AI, with students noting both potential and limitations. Implications: The findings suggest that current LLMs may be helpful for introductory exploration of theoretical content but less effective for tasks requiring precision or original thought. Teachers might learn that thoughtful integration of AI tools depends on task type and critical guidance.
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