期刊：
BRITISH JOURNAL OF PSYCHOLOGY,2024年 ISSN：0007-1269
作者机构：
[Liu, Zhenzhen] School of Psychology, Central China Normal University, Wuhan, Hubei, China;[Ma, Rongzi; Sun, Xiaomin] Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, China;[Bao, Ruiji] Faculty of Business and Economics, The University of Hong Kong, Hong Kong, SAR, China
摘要：
Greedy phenomena have dramatically increased in societies. However, despite the universality of greedy behaviour, empirical research on the causes of greed is scarce. In this context, we propose that perceived economic inequality may be an important factor influencing greed. Study 1 provided primary evidence of a positive relationship between perceived economic inequality and greed, based on data from a large-scale social survey (CFPS 2018, N = 14,317). Employing well-established questionnaires, Study 2A (N = 200) and Study 2B (N = 399) revealed that perceived economic inequality positively predicts greed, with relative deprivation playing a mediating role. Study 3A (N = 200) and Study 3B (N = 200) manipulated perceived economic inequality to provide causal evidence of its effects on greed and to replicate the mediating effect of relative deprivation. Finally, Study 4 (N = 372), using a blockage manipulation design, showed that the effect of perceived economic inequality on greed significantly decreases when relative deprivation is suppressed. In summary, the results of these six studies consistently suggest that perceived economic inequality positively affects greed and that this effect is mediated by relative deprivation.
期刊：
British Journal of Educational Technology,2024年55(3) ISSN：0007-1013
通讯作者：
Wang, Zhifeng;Luo, H
作者机构：
[Liao, Xiaofang; Luo, H; Wang, Zhifeng; Luo, Heng] Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.;[Zhang, Xuedi] South China Normal Univ, Zengcheng Sch, High Sch, Guangzhou, Peoples R China.
通讯机构：
[Luo, H ; Wang, ZF] C;Cent China Normal Univ, Fac Artificial Intelligence Educ, Wuhan 430079, Peoples R China.
摘要：
Abstract Formative assessment is essential for improving teaching and learning, and AI and visualization techniques provide great potential for its design and delivery. Using NLP, cognitive diagnostic and visualization techniques designed to analyse and present students' monthly exam data, we developed an AI‐enabled visual report tool comprising six modules and conducted an empirical study of its effectiveness in a high school biology classroom. A total of 125 students in a ninth‐grade biology course were assigned to a treatment group (n = 63) receiving AI‐enabled visual reports as the intervention and a control group (n = 62) receiving overall oral feedback from the teacher. We present the main statistical results of the within‐subjects design and the between‐subjects design respectively, to better capture the main findings. Repeated measures ANOVA revealed a significant interaction effect of intervention and time on learning achievement, and the paired‐sample Wilcoxon test indicated that the treatment group had experienced increasing learning anxiety (Cohen's d = 0.203, p = 0.046) and self‐efficacy (Cohen's d = 1.793, p = 0.000) over time. Moreover, we conducted a series of non‐parametric tests to compare the effects of AI‐enabled visual reports and teacher feedback, but found no significant differences except for an increased self‐efficacy (Cohen's d = 0.312, p = 0.046). Additionally, we had the students in the treatment group rate their favourable modules in the AI‐enabled visual report and provide evaluative feedback. The study results provide important insights into the design and implementation of effective formative assessment supported by artificial AI and visualization techniques. Practitioner notes What is already known about this topic Formative assessment is essential for improving teaching and learning. Traditional formative assessment tools lack accurate data‐oriented assessment and usability. AI and visualization techniques have great potential for formative assessment. What this paper adds This study designs and implements an AI‐enabled visual report tool that generates data‐driven, user‐friendly reports. The AI‐enabled visual report can not only enhance students' learning achievement and self‐regulated learning over time but also increase their test anxiety. The AI‐enabled visual report has a comparable effect with teacher feedback but leads to increased self‐efficacy. Implications for practice and/or policy We recommend using the AI‐enabled visual report in large‐size classes for its overall positive effects on both learning achievement and self‐regulated learning. We recommend using the AI‐enabled visual report over teacher feedback for its capacity to enhance students' self‐efficacy. We recommend prioritizing the modules of Performance Ranking, Personal Mastery and Knowledge Alert when designing the AI‐enabled visual report.
摘要：
The neural ordinary differential equation (NODE) has attracted much attention for its applicability in dynamic system modeling and continuous time series analysis. When the sample size is limited, models often exhibit weak generalizability and robustness and are susceptible to overfitting. To address this, a novel multivariate grey neural differential equation model is proposed based on the grey model and NODE. The new model leverages the small -sample modeling capabilities of grey systems to enhance the overall generalizability. When the neural network structure changes, the proposed model can degenerate into other grey models, enhancing inclusiveness and adaptability. Two energy forecasting cases show that the new model achieves average MAPE values of 0.82% and 1.13% on the test sets. These values are significantly better than those of the other 10 benchmark models. Furthermore, the proposed model exhibits superior performance in terms of the MAE, RMSE, STD, and APE metrics compared to those of all contrastive models. This study demonstrates that the new model effectively enhances its predictive capabilities on limited nonlinear data, showcasing higher prediction accuracy, stronger adaptability, and better stability.
摘要：
Educators dynamically adjust their teaching strategies by tracing the development of students' knowledge states. Knowledge Tracing (KT) plays a role similar to that of educators in online teaching. By analyzing past performances, KT identifies learners' knowledge states and predicts the outcomes of future exercises. However, the existing KT models assume that the learner's performance is a binary variable (i.e., correct or incorrect) without refining learner performance or differentiating knowledge states. Multiple-choice tests employ distractors that engage learners in different knowledge states, with each distraction implying a specific error. In multiple-choice exercises, we propose an option -weighting -enhanced mixture -of -expert knowledge tracing (WEKT) method that assigns weights to different options based on improved option weighting scoring. The option weights affirm partial knowledge and refine the knowledge state. Building on the multi -task learning strategy, we design a mixture -of -experts framework that simultaneously predicts correctness and options, traces students' specific errors, and refines students' performances. The expert structure combines cognitive theory with deep learning technology, taking into consideration the differences between experts and students. Extensive experiments on large-scale datasets indicate that WEKT can refine knowledge states and attain more precise predictions of student performance.
作者：
Elizabeth Koh;Lishan Zhang;Alwyn Vwen Yen Lee;Hongye Wang
期刊：
IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES,2024年17:1416-1427 ISSN：1939-1382
作者机构：
[Elizabeth Koh; Alwyn Vwen Yen Lee] National Institute of Education, Nanyang Technological University, Singapore;[Lishan Zhang; Hongye Wang] Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, China
摘要：
Generative artificial intelligence (AI) has the potential to revolutionize teaching and learning applications. This article examines the word cloud, a toolkit often used to scaffold teaching and learning for reflection, critical thinking, and content learning. Addressing the issues in traditional word clouds, semantic word clouds have been developed but they are technically challenging to develop and still problematic. However, generative AI has the potential to develop efficient, accurate, creative, and accessible word clouds. Three different methods representing three major approaches of word cloud generation were developed, implemented, and user evaluated—traditional (baseline), semantic (natural language processing enhanced), and generative AI (generative pretrained transformer based)—in two different language contexts—Chinese (China case) and English (Singapore case). The findings of the study show the technical robustness of the methods, as well as provide key pedagogical insights from the user perspective of instructors of higher education courses in China and Singapore. Implications to the design of word clouds and their application in teaching and learning are discussed.
摘要：
A graph G has a
$${\mathcal {P}}_{\geqslant k}$$
-factor if G has a spanning subgraph H such that every component of H is a path of order at least k. A graph G is
$${\mathcal {P}}_{\geqslant k}$$
-factor deleted if
$$G-e$$
has a
$${\mathcal {P}}_{\geqslant k}$$
-factor for each edge e of G. In this paper, we give two necessary and sufficient conditions defining a
$${\mathcal {P}}_{\geqslant 2}$$
-factor deleted graph and a
$${\mathcal {P}}_{\geqslant 3}$$
-factor deleted graph, respectively. Based on the result of
$${\mathcal {P}}_{\geqslant 2}$$
-factor deleted graphs, we establish respectively a lower bound on the size and a lower bound on the spectral radius to ensure whether or not a graph is
$${\mathcal {P}}_{\geqslant 2}$$
-factor deleted. Furthermore, by constructing extremal graphs, we show that all the above bounds are best possible.
期刊：
IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES,2024年17:1353-1366 ISSN：1939-1382
作者机构：
[Xu Chen; Di Wu] Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, China
摘要：
Generative artificial intelligence (AI) is widely recognized as one of the most influential technologies for the future, having sparked a paradigm shift in scientific research. The field of education has also been greatly impacted by this transformative technology, with researchers exploring the applications of generative AI, particularly ChatGPT, in education. However, existing research primarily focuses on generating text from text, and there remains a relative scarcity of studies on leveraging multimodal generation capabilities to address key challenges in multimodal data supported instruction. In this article, we present a technical framework for generating Tang poetry situational videos, emphasizing the utilization of generative AI to address the need for multimedia teaching resources. Our framework comprises three main modules: textual situational comprehension, image creation, and video generation. Moreover, we have developed a situational video generation system that incorporates various technologies, including text-to-text generation models, text-to-image generation models, image interpolation, text-to-speech synthesis, and video synthesis. To ascertain the efficacy of the modules within the Tang poetry situational video generation system, we undertook a comparative analysis utilizing the prevalent text-to-image and text-to-video generation models. The empirical findings indicate that our approach is capable of generating images that exhibit greater semantic similarity with the poems, thereby enabling a better comprehension of the poem's connotations and its key components. Concurrently, the Tang poetry videos generated can significantly contribute to the reduction of cognitive load and the enhancement of understanding during the learning process. Our research showcases the potential of generative AI in the education field, specifically in the domain of multimodal teaching resources.
