The CC2020 Report highlights the importance of transitioning from knowledge-based to competency-based CS education. Given that proficient programming is considered a foundational skill for CS majors, some researchers have developed top-down qualitative frameworks for assessing programming competency. However, the lack of quantitative competency models makes it challenging to conduct competency-oriented assessments in CS courses, especially for introductory programming courses such as CS1. To address this challenge, our study tracks the learning activities of 209 students in a CS1 course, including 10 formative tests and 44590 code submissions. The five-channel learning sequences (score, engagement, code metrics, programming skills, coding style) are established to capture the knowledge, skill, and dispositions of the CS1 competency model for each student, with profiles in each channel characterized by five indicators: mean values, entropy, turbulence, proficiency, and resilience. This approach enables multi-dimensional competency assessment with visualization throughout the learning process, providing timely guidance for both teaching and learning. This work is a preliminary exploration in CS1 towards quantitative programming competency models in CS education via integrating multidimensional data, employing appropriate data granularity, and visualizing observable patterns.

TPS (Tumor Proportion Score) serves as a crucial indicator to determine the eligibility of cancer patients for PD-L1 immunohistochemistry (IHC) treatment. In clinical practice, pathologists assess TPS primarily using IHC histopathological slides, which is time-consuming and incurs high treatment costs. In this paper, we introduce a novel approach for predicting PD-L1 positive cells using cost-effective and widely accessible Hematoxylin and Eosin (H&E) stained slides. Here, we first extract features of positive cells from PD-L1 slides. Then mapping the position and expression status of these cells onto H&E slide to generate cell-level pseudo-labels. Finally, we develop a deep correlation model that links PD-L1’s cellular expression patterns to the morphological features identifiable in H&E stains. This model enables the direct extraction of PD-L1 markers from H&E-stained slides, not only saving pathologists valuable diagnostic time but also avoiding the high costs associated with PD-L1 staining processes. Extensive results highlight the potential of our model as a promising alternative for assessing PD-L1 in cancer.Clinical Relevance—The proposed cell level detection model can achieve high accuracy. This indicates that predicting PD-L1 expression status from H&E is feasible and helps simplify the detection of immunohistochemistry in clinical practice.

Automatic segmentation of coronary artery is a crucial step in computer-aided diagnosis and treatment planning of coronary artery disease (CAD). A precise coronary artery mask aids clinicians in identifying potential stenosis and determining appropriate interventional treatment, signifying crucial medical importance in the efficient management of CAD. However, existing coronary segmentation methods encounter challenges, manifesting in complications like discontinuity of vessel mask and the mis-segmentation of small branches attributed to the intricate tree-like tubular structure of coronary artery. In this paper, we propose a novel coronary artery segmentation framework (called SACH-Net), which enhances segmentation effect by introducing shape-adaptive convolution (SA-Conv) and hierarchical topology constraints (HTC). Specifically, SA-Conv adjusts the convolution kernel adaptively based on the vessel shape to effectively learn the tree-like tubular feature representation, overcoming challenges posed by the intricate vascular structure. In addition, HTC module is introduced to supervise the feature expression of the network in three dimensions of continuity, overlap, and topological correctness, to alleviate the situation of segmentation fracture and discontinuity. The experimental results on the public dataset ARCADE show that SACH-Net significantly outperforms the state-of-the-art methods in coronary artery segmentation. The code is available at https://github.com/shbc2001/SACH-Net.Clinical Relevance—This research improves the accuracy of coronary segmentation and provides a more comprehensive evaluation, holding promising clinical implications for medical image analysis and healthcare applications.

Interactive fusion methods have been successfully applied to multimodal sentiment analysis, due to their ability to achieve data complementarity via interaction of different modalities. However, previous methods treat the information of each modality as a whole and usually treat them equally, failing to distinguish the contribution of different semantic regions in non-textual features towards textual features. It caused that the public regions fail to be captured and private regions are hard to be predicted only with textual. Meanwhile, these methods use sentiment-independent encoder to encode textual features, which may mistakenly identify syntactically irrelevant contextual words as clues for predicting sentiment. In this paper, we propose a coordinated-joint translation fusion framework with sentiment-interactive graph to solve these problems. Specifically, we generate a novel sentiment-interactive graph to incorporate sentiment associations between different words into the syntactic adjacency matrix. The relationships between nodes are no longer limited to the sole existence of syntactic associations but fully consider the interaction of sentiment between different words. Then, we design a coordinated-joint translation fusion module. This module utilizes a cross-modal masked attention mechanism to determine whether there is a correlation between the text and non-text inputs, thereby identifying the most relevant public semantic features in the visual and acoustic modalities corresponding to the text modality. Subsequently, a cross-modal translation-aware mechanism is used to calculate the differences between the visual and acoustic modalities features transformed into the text modality and the text modality itself, which allows us to reconstruct the visual and acoustic modalities towards text modality to obtain private semantic features. In addition, we construct a multimodal fusion layer to fuse textual features and non-textual public and private features to improve multimodal interaction effects. Experimental results on publicly available datasets CMU-MOSI and CMU-MOSEI illustrate that our proposed model achieve a best accuracy of 86.5% and 86.1%, and best F1 of 86.4% and 86.1%. A series of further analyses also indicate the proposed framework effectively improve the sentiment identification capability.

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.

Sentiment analysis (SA) aims to understand the attitudes and views of opinion holders with computers. Previous studies have achieved significant breakthroughs and extensive applications in the past decade, such as public opinion analysis and intelligent voice service. With the rapid development of deep learning, SA based on various modalities has become a research hotspot. However, only individual modality has been analyzed separately, lacking a systematic carding of comprehensive SA methods. Meanwhile, few surveys covering the topic of multimodal SA (MSA) have been explored yet. In this article, we first take the modality as the thread to design a novel framework of SA tasks to provide researchers with a comprehensive understanding of relevant advances in SA. Then, we introduce the general workflows and recent advances of single-modal in detail, discuss the similarities and differences of single-modal SA in data processing and modeling to guide MSA, and summarize the commonly used datasets to provide guidance on data and methods for researchers according to different task types. Next, a new taxonomy is proposed to fill the research gaps in MSA, which is divided into multimodal representation learning and multimodal data fusion. The similarities and differences between these two methods and the latest advances are described in detail, such as dynamic interaction between multimodalities, and the multimodal fusion technologies are further expanded. Moreover, we explore the advanced studies on multimodal alignment, chatbots, and Chat Generative Pre-trained Transformer (ChatGPT) in SA. Finally, we discuss the open research challenges of MSA and provide four potential aspects to improve future works, such as cross-modal contrastive learning and multimodal pretraining models.

Students who have been taught new ideas need to develop their skills by carrying out further work in their own time. This often consists of a series of exercises which must be completed. While students can choose exercises themselves from online sources, they will learn more quickly and easily if the ex-ercises are specifically tailored to their needs. A good teacher will always aim to do this, but with the large groups of students who typically take advantage of open online courses, it may not be possible. Exercise prediction, working with large-scale matrix data, is a better way to address this challenge, and a key stage within such prediction is to calculate the probability that a student will answer a given question correctly. Therefore, this paper presents a novel approach called Weighting-based Student Ex-ercise Matrix Factorization (Wse-MF) which combines student learning ability and exercise difficulty as prior weights. In order to learn how to complete the matrix, we apply an iterative optimization method that makes the approach practical for large-scale educational deployment. Compared with eight models in cognitive diagnosis and matrix factorization, our research results suggest that Wse-MF significantly outperforms the state-of-the-art on a range of real-world datasets in both prediction quality and time complexity. Moreover, we find that there is an optimal value of the latent factor K (the inner dimension of the factorization) for each dataset, which is related to the relationship between skills and exercises in that dataset. Similarly, the optimal value of hyperparameter c 0 is linked to the ratio between exercises and students. Taken as a whole, we demonstrate improvements to matrix factorization within the context of educational data. (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

Both traditional teaching and online teaching advocate individualized education. One of the difficulties on exploring possible improvements of instructional design is the challenging process of data collection. Existing research mainly focuses on the exam score of students but pays little attention to students' daily practice. As an effective method to handle time-series dataset, the generalized estimating equations (GEE) have not been used in this research field. Considering above issues, we first propose an experimental paradigm of programming performance analysis based on the performance record of students’ daily practice-exam and finish collecting a complete time-series dataset in one semester, including students’ individual attributes, learning behavior, and learning performance. Then, we propose an approach that analyzes practice-exam time-series dataset based on GEE to study the influence of individual attributes and learning behavior on learning performance. It is the first time to apply the GEE method for ordinal multinomial responses in this research field, by which we conclude several results that gender or major does have a certain difference on the programming learning. The longer the answer time and the less the cost time, the better the students' performance. Regardless of gender, students tend to cram for the exam and perform a little worse in the daily exercise. Finally, targeting at two important individual attributes, we give corresponding teaching mode decisions that university should teach students programming by major and teacher should give different teaching methods to students of different genders at different time points.