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Collaborative Problem Solving (CPS) has received increasing attention for its role in promoting learners' cognitive and social development in STEM education. However, little is known about how learners interact dynamically within a group at different time granularities. This gap mainly resulted from overlooking the time dimension of interactions, leading to a lack of nuanced understanding of moment-to-moment interaction in CPS. In this study, we demonstrated the potential of "temporal group interaction density" in modeling online CPS interactions and investigated the impact of temporal interaction density on CPS processes and outcomes. Specifically, we proposed using cumulative weighted density to measure the holistic state of group interactions and explained the differences in group interactions with different collaborative performance and interaction densities by modeling the transition and evolution of interaction sequences through Apriori and cumulative relative centrality. Results indicated that group interaction density cannot directly predict their collaborative performance, but notable differences in interaction patterns existed in the high-performance groups with different interaction densities, while low-performance groups showed interactive commonalities towards the completion of CPS. The findings of this study guided the design of CPS interventions and supported the process mining of CPS interactions, with vital practical implications for CPS assessment and skills development.

This study aims to track college students' on-task rate during the teaching process and to analyze the influence of instructional strategies on on-task rate through the aspects of observable and internal engagement indicators. Thirty-six undergraduate students at a higher education institution in China participated in the study. Students' behaviors and their EEG signals were recorded from fifty-one learning activities. Analyses have been focused on identifying the determinants of student's engagement levels and revealing the impacts of behavioral sequences and cognitive sequences on student's engagement levels. The results show that: (1) instructional strategies, classroom behaviors, and cognitive states were significant predictors of students' on-task rate; (2) the continuity of classroom behaviors improved the on-task rate; and (3) the standard deviations of attention and cognitive load were positively correlated with the on-task rate. This study describes a case of integrating multimodal data analysis in classroom teaching and discusses practical implications for improving classroom teaching.

Blended learning, integrating online and in-person components, has been increasingly adopted in higher education to enhance students' learning experience and outcomes. While the advantages of blended learning are well-evidenced, research has primarily focused on the online pre-learning component, neglecting the significance of in-class activities. In-class activities play a crucial role in affording active learning opportunities (e.g., discussion, elaboration), necessitating a systemic understanding of their dynamics. The purpose of this study was thus to systemically investigate college students' learning behaviors during in-class activities in a blended course. In-class activities were video-recorded and labelled manually following a coding scheme. By establishing a linear regression model, the study identified listening to the instructor's lecture and taking notes as two predictors of students' learning gains. Additionally, sequential patterns of learning behaviors during in-class activities were examined. The reciprocal interactions between students' behavior of listening to the lecture and their note-taking actions were noted. The findings of this study contributed to a systemic view of blended learning by shedding light on students' learning behaviors and their implications for instructional practice.

Blended learning has been widely integrated in college-level computer science education. Despite evidence about benefits of blended learning, students' in-class activities remain underexplored. To afford effective blended learning experience, supporting students in both modalities is essential. This study thus took an initial step to fill the gap by investigating college students' in-class activities in a blended course from the perspective of attention. Using non-intrusive electroencephalography (EEG) instruments to collect attentional data, this study found students' attention in in-class activities positively correlated with their learning gains. Students' attention also varied across in-class activities, reaching a higher level in group discussions than in pre-tests and lectures. Linear regression analysis indicated students' length of time spent viewing online resources and their pre-test scores significantly predicted their in-class attention. The findings of the study provide insight into course design and facilitation for effective blended computer science courses.

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In order to realize the real-time processing and analysis of astronomical ultra-wide bandwidth signals, this study proposes a sub-band division algorithm based on RFSoC. The algorithm uses Kaiser window to design FIR prototype low-pass filter, adopts critical sampling polyphase filter bank to decompose ultra-wide bandwidth signal into several sub-bands, and encapsulates each sub-band into VDIF data frame and sends it to GPU server. The algorithm is implemented on RFSoC platform, and its effectiveness is verified by simulation and actual observation. The experimental results show that the algorithm can divide the astronomical ultra-wide bandwidth signal into multiple sub-bands in real time, packetize and transmit them to GPU. This research provides reproducible design and project for ultra-wide bandwidth signal sub-band division with low spectrum leakage and aliasing, high data accuracy, and fast processing speed.

With the surge in astronomical data volume, modern astronomical research faces significant challenges in data storage, processing, and access. The I/O bottleneck issue in astronomical data processing is particularly prominent, limiting the efficiency of data processing. To address this issue, this paper proposes a tiered storage algorithm based on the access characteristics of astronomical data. The C4.5 decision tree algorithm is employed as the foundation to implement an astronomical data access correlation algorithm. Additionally, a data copy migration strategy is designed based on tiered storage technology to achieve efficient data access. Preprocessing tests were conducted on 418GB NSRT (Nanshan Radio Telescope) formaldehyde spectral line data, showcasing that tiered storage can potentially reduce data processing time by up to 38.15%. Similarly, utilizing 802.2 GB data from FAST (Five-hundred-meter Aperture Spherical radio Telescope) observations for pulsar search data processing tests, the tiered storage approach demonstrated a maximum reduction of 29.00% in data processing time. In concurrent testing of data processing workflows, the proposed astronomical data heat correlation algorithm in this paper achieved an average reduction of 17.78% in data processing time compared to centralized storage. Furthermore, in comparison to traditional heat algorithms, it reduced data processing time by 5.15%. The effectiveness of the proposed algorithm is positively correlated with the associativity between the algorithm and the processed data. The tiered storage algorithm based on the characteristics of astronomical data proposed in this paper is poised to provide algorithmic references for large-scale data processing in the field of astronomy in the future.

This paper reports a method to measure the mechanical properties of a single cell using a microfluidic chip with integrated force sensing and a liquid exchange function. A single cell is manipulated and positioned between a pushing probe and a force sensor probe using optical tweezers. These two on-chip probes were designed to capture and deform the cells. The single cell is deformed by moving the pushing probe, which is driven by an external force. The liquid–liquid interface is formed between the probes by laminar flow to change the extracellular environment. The position of the interface is shifted by controlling the injection pressure. Two positive pressures and one negative pressure are adjusted to balance the diffusion and perturbation of the flow. The mechanical properties of a single Synechocystis sp. strain PCC 6803 were measured in different osmotic concentration environments in the microfluidic chip. The liquid exchange was achieved in approximately 0.3–0.7 s, and the dynamic deformation of a single cell was revealed simultaneously. Measurements of two Young’s modulus values under alterable osmotic concentrations and the dynamic response of a single cell in osmotic shock can be collected within 30 s. Dynamic deformations of wild-type (WT) and mutant Synechocystis cells were investigated to reveal the functional mechanism of mechanosensitive (MS) channels. This system provides a novel method for monitoring the real-time mechanical dynamics of a single intact cell in response to rapid external osmotic changes; thus, it opens up novel opportunities for characterizing the accurate physiological function of MS channels in cells.

Real-time emotion recognition in conversations (ERC), which relies on only the historical utterances to achieve ERC, has recently gained increasing attention due to its significance in providing real-time empathetic services. Although utilizing multimodal information can mitigate the issues of unimodal approaches, few real-time ERC studies consider the differences in representation ability of different modalities and explore comprehensive conversational context from different perspectives based on different structures. Furthermore, the heavy annotation cost makes it difficult to collect sufficient labeled data, which also limits the performance of current supervised ERC approaches. To address these issues, we propose a novel framework SMFNM for real-time ERC, which integrates semi-supervised learning with multimodal fusion under the guidance of main-modal. Specifically, SMFNM utilizes additional unlabeled data to extract high-quality intra-modal representations, and implements cross-modal interaction to capture complementary information to enhance the audio representations. Then SMFNM employs the directed acyclic graph and the Gated Recurrent Units for exploring more accurate conversational context from both the multimodal and main-modal perspectives, respectively. Finally, these two types of contextual features are fused for emotion identification. Extensive experiments on benchmark datasets (i.e., IEMOCAP (4-way), IEMOCAP (6-way) and MELD) demonstrate the effectiveness, superiority and rationality of our SMFNM.

Band structure modulation may drive topological phase transitions, but tuning topological phases within the same material is challenging. Here, quantum oscillations are used to map the Berry phase and Dirac bandgap closing and reopening in a strain-induced topological insulator phase transition in ZrTe5.

Abstract The purpose of this study aimed to analyze the process of online collaborative problem solving (CPS) via brain-to-brain synchrony (BS) at the problem-understanding and problem-solving stages. Aiming to obtain additional insights than traditional approaches (survey and observation), BS refers to the synchronization of brain activity between two or more people, as an indicator of interpersonal interaction or common attention. Thirty-six undergraduate students participated. Results indicate the problem-understanding stage showed a higher level of BS than the problem-solving stage. Moreover, the level of BS at the problem-solving stage was significantly correlated with task performance. Groups with all high CPS skill students had the highest level of BS, while some of the mixed groups could achieve the same level of BS. BS is an effective indicator of CPS to group performance and individual interaction. Implications for the online CPS design and possible supports for the process of online CPS activity are also discussed.

Quiz question annotation aims to assign the most relevant knowledge point to a question, which is a key technology to support intelligent education applications. However, the existing methods only extract the explicit semantic information that reveals the literal meaning of a question, and ignore the implicit knowledge information that highlights the knowledge intention. To this end, an innovative dual-channel model, the Semantic-Knowledge Mapping Network (S-KMN) is proposed to enrich the question representation from two perspectives, semantic and knowledge, simultaneously. It integrates semantic features learning and knowledge mapping network (KMN) to extract explicit semantic features and implicit knowledge features of questions,respectively. Designing KMN to extract implicit knowledge features is the focus of this study. First, the context-aware and sequence information of knowledge attribute words in the question text is integrated into the knowledge attribute graph to form the knowledge representation of each question. Second, learning a projection matrix, which maps the knowledge representation to the latent knowledge space based on the scene base vectors, and the weighted summations of these base vectors serve as knowledge features. To enrich the question representation, an attention mechanism is introduced to fuse explicit semantic features and implicit knowledge features, which realizes further cognitive processing on the basis of understanding semantics. The experimental results on 19,410 real-world physics quiz questions in 30 knowledge points demonstrate that the S-KMN outperforms the state-of-the-art text classification-based question annotation method. Comprehensive analysis and ablation studies validate the superiority of our model in selecting knowledge-specific features.