Grading programming assignments manually is a big burden for instructors who teach programming languages for university students due to complexity and subjectivity. The black test approach adopted by online judge systems can only outputs either an answer is correct or incorrect. This study proposes a Large Language Model (LLM) approach to automatically grade answers from students for programming assignments. A LLM mode formed by coder-decoder architecture is utilized to generate summarization from source code, then the summarization is compared to the textual assignment description by semantic similarity. Finally, the output is converted to five-score rating. CodeBERT and a Transformer model serve as coder and decoder respectively. The semantic similarity is computed by MiniLM-L6. The validation test shows that the accuracy of the suggested approach reaches 0.92.
The programmer’s attention have been utilized for the behavioral research in programming education. Existing approaches mainly focused on the programmer’s visual attention measured by gaze trackers. However, for the scenarios of programming and debugging, the measurement on just eye movements is not sufficient for understanding programming attention. The measurements on programmer’s other modals of behaviors, including mouse clicks and keyboard inputs, are also required. In this paper, we focus on the programming attention visualization problem in the debugging test scenario. A series of attention visualization approaches are proposed for the two main types of interactions, i.e. reading source code and editing/debugging by keyboard and mouse. We develop methods to visualize student’s attentions from either reading or debugging interactions, and then merge them into multimodal programming attention. The experiment results show that, the proposed multimodal attention graphs can show the diversity of students’ debugging strategies in terms of reading and editing focus in code line and time dimensions. It can help the teacher infer the students’ behavioral intentions and conduct individual diagnose in debugging tests.
Micro action recognition is an important research area in human motion analysis, which can be applied to the fields of healthcare, motion analysis and human-computer interaction. In this paper, we propose a 3D convolutional neural network-based micro-gesture recognition network to improve the performance, which combines skeletal and semantic embedding losses to enhance network discrimination. Specifically, first, various data enhancement techniques, such as level flipping and random Gaussian noise, are utilized to improve the robustness and generalization of the model. Horizontal flipping aims to help the model better recognise mirrored and stacked features in images by flipping them on the symmetry axis. Meanwhile, Gaussian noise, also known as white noise, is a set of random values with a normal distribution designed to force the model to learn features that are robust to small variations in the input, which can represent smudges or subtle absences in the image. We have evaluated our methods on a micro-posture recognition benchmark dataset, and these methods improve on previous methods. The model was tested on the iMiGUE dataset and achieved a Top1% accuracy of 59.01%-a notable improvement over other models.
Knowledge Tracing (KT) aims to trace students’ knowledge state and predict their future performance from their historical learning records. However, accurately capturing learning process is a challenging due to the complexity of the learning behaviors and learning patterns. In this paper, we propose a Convolution Attentive Knowledge Tracing with comprehensive behavioral features (CAKT). Specially, we propose an ensemble learning-based feature fusion approach that projects learning behavior information, including attempt counts, hint counts, first action, and response time to obtain complex learning behavior features. Furthermore, we propose a convolution attentive layer that mimics the learning patterns of the human brain during learning. This layer utilizes convolution to integrate exercise context information, allowing it to highlight the influence of recent relevant exercises and model forgetting behavior through a temporal decay function. We conduct experiments on several real-world educational datasets and the results demonstrate that CAKT outperforms state-of-the-art knowledge tracing models in predicting student performance. Our work offers an innovative approach to KT that better aligns with the authentic development and formation of learners’ knowledge.
Recent advancements in programming education have transformed it from a niche skill into a fundamental requirement across diverse industries, fostering innovation and problem-solving capabilities. An increasing number of non-computer science(CS) students are now engaging in programming learning. However, they often struggle in early programming courses. Numerous studies have explored the relationship between students' majors and their academic performance to understand the factors influencing their success. Previous studies primarily relied on outcome-based assessments, overlooking the significance of formative evaluations during the learning process. To address this gap, we conducted a study collecting process data from an introductory programming course for freshmen of mixed majors, analyzing learning trajectories using process-oriented methods. Our findings reveal significant differences in score trajectories between CS and non-CS groups, underscoring the need for targeted instructional improvements to enhance non-CS students' learning experiences.
