Engaging students with effective learning materials continues to be a significant challenge in programming education. Analogies are commonly used to simplify complex topics, enabling learners to relate unfamiliar concepts to familiar ones. Additionally, visual representations of these analogies can enhance engagement and improve the overall learning experience. This work presents a prototype of a novel AI tool that generates analogy-based explanations and corresponding video animations for programming education. The tool leverages Large Language Models (LLMs) for analogy generation and a structured animation workflow for visualization. This poster invites discussion on the effectiveness of AI-generated educational content and its implications for programming education.

Learner behaviours often provide critical clues about learners' cognitive processes. However, the capacity of human intelligence to comprehend and intervene in learners' cognitive processes is often constrained by the subjective nature of human evaluation and the challenges of maintaining consistency and scalability. The recent widespread AI technology has been applied to learning analytics (LA), aiming at a more accurate, consistent and scalable understanding of learning to compensate for challenges that human intelligence faces. However, machine intelligence has been criticized for lacking contextual understanding and difficulties dealing with complex human emotions and social cues. In this work, we aim to understand learners' internal cognitive processes based on the external behavioural cues of learners in a digital reading context, using a hybrid intelligence (HI) approach, bridging human and machine intelligence. Based on the behavioural frameworks and the insights from human experts, we scope specific behavioural cues that are known to be relevant to learners' attention regulation, which is highly relevant for learners' cognitive processes. We utilize the public WEDAR dataset with 30 subjects' video data, behaviour annotation and pre–post tests on multiple choice and summarization tasks. We apply the explainable AI (XAI) approach to train the machine learning model so that human evaluators can also understand which behavioural features were essential for predicting the usage of the cognitive processes (ie, higher-order thinking skills [HOTS] and lower-order thinking skills [LOTS]) of learners, providing insights for the next-round feature engineering and intervention design. The result indicates that the dominant use of attention regulation behaviours is a reliable indicator of low use of LOTS with 79.33% prediction accuracy, while reading speed is a valuable indicator for predicting the overall usage of HOTS and LOTS, ranging from 60.66% to 78.66% accuracy, highly surpassing random guess of 33.33%. Our study demonstrates how various combinations of behavioural features supported by HI can inform learners' cognitive processes accurately and interpretably, integrating human and machine intelligence. Practitioner notes What is already known about this topic Human attention is a cognitive process that allows us to choose and concentrate on relevant information, which leads to successful learning. In affective computing, certain behavioural cues (eg, attention regulation behaviours) are used to indicate learners' attentional states during learning. What this paper adds Attention regulation behaviours during digital reading can work as predictors of different levels of cognitive processes (ie, the utilization of higher-order thinking skills [HOTS] and lower-order thinking skills [LOTS]), leveraged by computer vision and machine learning. By developing an explainable AI model, we can predict learners' cognitive processes, which often cannot be achieved by human observations, while understanding behavioural components that lead to such machine decisions is critical. It can provide valuable machine-driven insights into the relationship between humans' external and internal states in learning. Based on the frameworks spanning cognitive AI, psychology and education, expert knowledge can contribute to initial feature selection and engineering for the hybrid intelligence (HI) model development and next-round intervention design. Implications for practice and/or policy Human and machine intelligence form an iterative cycle to build a HI to understand and intervene in learners' cognitive processes in digital reading, balancing each other's strengths and weaknesses in decision-making. It can eventually inform automated feedback loops in widespread e-learning, a new education norm since the COVID-19 pandemic. Our framework also has the potential to be extended to other scenarios with digital reading, providing concrete examples of where human intelligence and machine intelligence can contribute to building a HI. It represents more systematic supports that apply to real-life practices.

We present ReproducedPapers.org : an open online repository for teaching and structuring machine learning reproducibility. We evaluate doing a reproduction project among students and the added value of an online reproduction repository among AI researchers. We use anonymous self-assessment surveys and obtained 144 responses. Results suggest that students who do a reproduction project place more value on scientific reproductions and become more critical thinkers. Students and AI researchers agree that our online reproduction repository is valuable.

Many universities digitize exams or the process of grading the exams. This potentially allows for faster grading, is less labor intensive and less error-prone. But are the grades produced by online grading consistent with how we grade on paper? In this paper we present preliminary results of the comparison between scores given by grading online and grading on paper.