6 research outputs found
Related Data for: Peer feedback feature analysis with large language models: An exploratory study
No full textPeer feedback is a pedagogical strategy for peer learning. Despite recent indications of Large Language Models (LLMs) ' potential for content analysis, there is limited empirical exploration of their application in supporting the peer feedback process. This study enhances the analytical approach to peer feedback activities by employing state-of-the-art LLMs for automated peer feedback feature detection. This research critically compares three models—GPT-3.5 Turbo, Gemini 1.0 Pro, and Claude 3 Sonnet—to evaluate their effectiveness in automated peer feedback feature detection. The study involved 69 engineering students from a Singapore university participating in peer feedback activities on the online platform Miro. A total of 535 peer feedback instances were collected and human-coded for eleven features, resulting in a dataset of 5,885 labeled samples. These features included various cognitive and affective dimensions, elaboration, and specificity. The results indicate that GPT-3.5 Turbo is the most effective model, offering the best combination of performance and cost-effectiveness. Gemini 1.0 Pro also presents a viable option with its higher throughput and larger context window, making it particularly suitable for educational contexts with smaller sample sizes. Conversely, Claude 3 Sonnet, despite its larger context window, is less competitive due to higher costs and lower performance, and its lack of support for training and fine-tuning with researchers' data weakens its learning capabilities. This research contributes to the fields of AI in education and peer feedback by exploring the use of LLMs for automated analysis. It highlights the feasibility of employing and fine-tuning existing LLMs to support pedagogical design and evaluations from a process-oriented perspective
Spatial Distribution Pattern, Evolution and Influencing Mechanism of Ecological Farms in China
No full textNowadays, the challenges of energy depletion, environmental pollution and food security caused by extensive agriculture development are attracting global attention. In China, the construction of ecological farms is a key initiative to effectuate the goal of peaking carbon dioxide emissions and achieving carbon neutrality, contributing to high-quality agricultural development. Based on this, this study selects the national-level ecological farms directories issued by the Ministry of Agriculture and Rural Affairs (MARA) of China in 2021 and 2022, and collects the corresponding economic, social and physical geographic data for GIS spatial analysis and Geodetector. The results are as follows: (1) The distribution of ecological farms in various provinces of China is uneven and spatially clustered. It generally presents a ‘high in the east and low in the west with concentrated cores’ pattern. The construction scope significantly expanded over time, and the high-value areas of nuclear density are concentrated in East China, with the development core transitioned from East China to Central China. (2) Environmental conditions, industrial foundation, economic and social development level, science and technology level and financial support all significantly affect the spatial distribution of ecological farms in China, among which the science and technology level has the most significant enhancement effect on other factors. (3) Environmental conditions provide the construction basis for ecological farms, while economic and social development level and financial support determine the number of ecological farms. The industrial foundation affects the scale of ecological farms in China, while the level of science and technology eliminates the restrictions of other factors to a certain extent. This study provides a reference for optimizing the spatial distribution pattern of ecological farms in China and promoting ecological agriculture. In addition, it presents a viable approach to safeguarding food security
Machine Learning Models to Predict Individual Cognitive Load in Collaborative Learning: Combining fNIRS and Eye-Tracking Data
No full textEffectively leveraging cognitive load predictions helps optimize collaborative learning design and implementation. This study explored the feasibility of predicting individual learners’ cognitive load during collaborative learning using a combination of functional near-infrared spectroscopy (fNIRS) and eye-tracking data. A total of 188 valid collaborative events collected from 78 graduate students who engaged in three collaborative ideation tasks were analyzed using various machine learning algorithms applied to classify cognitive load levels. Nine features, derived from both fNIRS and eye-tracking data, were used as input for the models. Results demonstrated that machine learning models could accurately predict individual cognitive load, with the Random Forest model achieving the highest performance (F1 score = 0.84). Furthermore, the integration of fNIRS and eye-tracking data significantly enhanced predictive performance, with the multimodal model achieving an F1 score 0.87—outperforming the eye-tracking-only model (F1 = 0.79) by 8% and the fNIRS-only model (F1 = 0.68) by 19%. Analysis of feature importance revealed that “Total Fixation Duration”, “Average Inter-Fixation Degree”, and prefrontal cortex activity were among the strongest predictors of learners’ cognitive load. These findings have implications for understanding cognitive load dynamics and designing effective collaborative learning environments and human–computer interfaces
AI fusion of multisource data identifies key features of vitiligo
No full textAbstract Vitiligo is a skin disorder that is associated with a decreased risk of skin cancer, but it can lead to increased susceptibility to sunburn, psychological distress, and disruptions in daily life, consists of two primary subtypes: segmental and nonsegmental vitiligo, each with distinct underlying mechanisms. However, the reliable identification of diagnostic markers and the ability to differentiate between these subtypes have remained elusive challenges. This study aims to pioneer predictive algorithms for vitiligo diagnosis, harnessing the capabilities of AI (Artificial Intelligence) to amalgamate multisource data and uncover essential features for distinguishing vitiligo subtypes.An ensemble algorithm was thoughtfully developed for vitiligo diagnosis, utilizing a spectrum of machine learning techniques to evaluate the likelihood of vitiligo, whether segmental or nonsegmental. Diverse machine learning methodologies were applied to distinguish between healthy individuals and vitiligo patients, as well as to differentiate segmental from nonsegmental vitiligo. The ensemble algorithm achieved a remarkable AUC (Area Under the Curve) of 0.99 and an accuracy of 0.98 for diagnosing vitiligo. Furthermore, in predicting the development of segmental or nonsegmental vitiligo, the model exhibited an AUC of 0.79 and an accuracy of 0.73. Key parameters for vitiligo identification encompassed factors such as age, FBC (full blood count)-neutrophils, FBC-lymphocytes, LKF(liver and kidney function)-direct bilirubin, LKF-total bilirubin, and LKF-total protein levels. In contrast, vital indicators for monitoring the progression of segmental and nonsegmental vitiligo included FBC-B lymphocyte count, FBC-NK (Natural Killer) cell count, and LKF-alkaline phosphatase levels. This retrospective study underscores the potential of AI-driven analysis in identifying significant risk factors for vitiligo and predicting its subtypes at an early stage. These findings offer great promise for the development of effective diagnostic tools and the implementation of personalized treatment approaches in managing this challenging skin disorder
Students' verbal interaction patterns in computer-supported collaborative learning: The role of individual preparation
No full textThis study explores students' verbal interaction dynamics in two computer-supported collaborative learning (CSCL) environments: immediate collaboration and individual preparation (IP) followed by group collaboration. Although verbal interactions are not always central to all CSCL designs, they are critical in contexts that emphasize face-to-face or synchronous communication, where they facilitate negotiation, idea sharing, and collaborative knowledge construction. By applying content analysis and lag sequential analysis (LSA), this study examined the verbal interaction behavioral sequences of students in both conditions to understand how IP influences collaborative dynamics. The findings highlight the crucial role of IP in enhancing collaborative dynamics, suggesting that well-structured preparatory activities can significantly improve group interaction efficiency. This research contributes valuable insights for refining CSCL instructional strategies, emphasizing the need to balance structured preparation with opportunities for spontaneous interaction to optimize collaborative learning outcomes. By managing distractions and maintaining task focus, educators can create more effective collaborative learning environments.Published versionRS 1/22 CW
