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Interactive teaching is very popular in the field of education, especially in college preschool education teaching. In this study, the experimental performance of the evaluation system based on scientific emotion construction is analyzed by the machine learning algorithm. The experimental results show that the classification model realized by the machine learning algorithm is feasible and effective for the construction of an interactive design teaching evaluation system of preschool education in colleges and universities. The main research content of this experiment is for the teaching evaluation text, this paper intends to use NB, KNN, LR, and SVM algorithms as the meta-learning algorithm in the framework, the classification effect of the meta-classifier is ranked as SVM > LR > KNN > NB. From the above data comparison, the SVM meta-classifier in the algorithm in this paper has achieved the best classification effect. The SVM meta-classifier based on the algorithm in this paper performs well, and the classification accuracy rate reaches 92.1%. This shows that this model is very suitable for studying the. And the classification accuracy rate reaches 92.1%. This shows that this model is very suitable for studying the construction of interactive teaching evaluation systems under emotional education. • The experimental results show that the classification model realized by machine learning algorithm. • This shows that this model is very suitable for studying the construction of interactive teaching. • The main research content of this experiment is for the teaching evaluation text. • This paper intends to use NB, KNN, LR and SVM algorithms as the meta-learning algorithm. [ABSTRACT FROM AUTHOR]

Most prior work on online reviews assumes that the value of reviews for consumers lies only in providing information about the product or the consumer's experience with the product. Yet, consumers also post and interact with pseudo-reviews, defined as reviews that describe exaggerated product experiences while trying to be entertaining. To address the nature and consequences of pseudo-reviews, this study develops a framework for understanding their value and how they affect consumer decision-making. First, we define how pseudo-reviews differ from other types of reviews. Then, we empirically examine how pseudo-reviews moderate the relationship between online review valence (how positive or negative the review is) and purchase intention. Finally, using a dual-processing theoretical lens, we examine how pseudo-reviews affect consumer decision-making through systematic and affective processing. We test our model through two experiments. The first study finds that pseudo-reviews reduce the impact of review valence on purchase intention when compared to genuine reviews. The second study shows that pseudo-reviews affect uncertainty and amusement, and these, in turn, affect purchase intention. We discuss the implications of pseudo-reviews for theory and practice. • Previous studies on online reviews often ignore the amusement value of online reviews. • The paper focuses on pseudo-reviews and develops a framework to understand their influence on consumer decisions. • For positive reviews, pseudo-reviews decrease the positive effect of review valence on purchase intention. • Pseudo-reviews affect consumer decisions by increasing both uncertainty and amusement. [ABSTRACT FROM AUTHOR]

Augmented Reality (AR) is an emerging concept that impacts many disciplines, such as business, marketing, tourism, gaming, human–computer interaction, and manufacturing. Surprisingly, many scholarly and practical discussions overlook the fundamental primary factors that distinguish AR from other concepts, namely, that it involves a computing device that integrates virtual content into a consumer's perception of the real world in a specific context. The current article addresses this gap in the literature by proposing the 4C framework (based on the 4Cs: consumer, content, context, and computing device; pronounced: foresee) that highlights the importance of, and interplay among these four factors. Building on configurational theory, the framework calls for the systematic identification of additional AR-relevant factors across the 4Cs. Scholars can use this framework to systematically identify research gaps and variables of interest. Practitioners across various disciplines can employ the framework to systematically assess, communicate, and develop AR use cases. • The 4C framework highlights four unique characteristics of AR: Consumer, Content, Context, and Computing Device. • Engagement with AR derives from an alignment of the 4 Cs. • The paper advances theory by using the lens of complexity theory to explain how to engage consumers in AR experiences. • The framework offers significant implications for managers and scholars. • It allows systematic approaches to assess, communicate, and develop AR applications across various disciplines. [ABSTRACT FROM AUTHOR]

Modeling reading comprehension processes is a critical task for Learning Analytics, as accurate models of the reading process can be used to match students to texts, identify appropriate interventions, and predict learning outcomes. This paper introduces an improved version of the Automated Model of Comprehension, namely version 4.0. AMoC has its roots in two theoretical models of the comprehension process (i.e., the Construction-Integration model and the Landscape model), and the new version leverages state-of-the-art Large Language models, more specifically ChatGPT, to have a better contextualization of the text and a simplified construction of the underlying graph model. Besides showcasing the usage of the model, the study introduces three in-depth psychological validations that argue for the model's adequacy in modeling reading comprehension. In these studies, we demonstrated that AMoC is in line with the theoretical background proposed by the Construction-Integration and Landscape models, and it is better at replicating results from previous human psychological experiments than its predecessor. Thus, AMoC v4.0 can be further used as an educational tool to, for example, help teachers design better learning materials personalized for student profiles. Additionally, we release the code from AMoC v4.0 as open source in a Google Collab Notebook and a GitHub repository. • Introduce the Automated Model of Comprehension version 4.0 that integrates state-of-the-art Large Language models - ChatGPT. • Perform 3 in-depth psychological validation studies to support the model's adequacy in modeling reading comprehension. • AMoC v4.0 aligns with theoretical frameworks, namely Construction-Integration and Landscape models. • The model outperforms its predecessor and replicates results from human psychological experiments. • AMoC v4.0 can serve as an educational tool for designing personalized learning materials. [ABSTRACT FROM AUTHOR]

With the exponential growth of new technology, it is timely that social psychologists continue to develop new and effective solutions to old but existing problems, including prejudice. One such solution is virtual reality (VR). This critical review examines VR as a promising new media tool to reduce prejudice and improve intergroup relations. Unlike traditional prejudice reduction strategies, VR provides researchers with a high degree of experimental control and flexibility. However, VR prejudice reduction research is in its infancy, and a myriad of challenges have emerged, including the: i) difficulty of cross-study comparisons and navigation of existing literature due to lack of standardized VR methodologies; ii) limitations of adapting traditional prejudice reduction strategies into VR; and iii) the lack of measurement of behaviors. This paper critically evaluates each of these limitations and, in doing so, proposes a new working framework incorporating two dimensions, the: i) VR input device, and ii) level of interactivity and mobility in the VR environment. This framework attempts to address the lack of standardization of immersion and presence in current VR research by helping guide future VR researchers to better identify the type of research they are conducting, evaluate existing research findings, and thus make cross-comparisons in VR prejudice reduction findings more meaningful. • Highlights three main limitations of current VR prejudice reduction strategies. • Conceptual framework is developed to standardize the immersion and presence in VR. • Solutions to address the limitations of VR prejudice research is proposed. • Call attention to the importance of VR as a research tool to measure prejudice. • Future directions of VR prejudice reduction research are outlined. [ABSTRACT FROM AUTHOR]