Across two studies, children ages 6-9 (N = 160, 82 boys, 78 girls; 75% White, 91% non-Hispanic) rated an inaccurate expert's knowledge and provided explanations for the expert's inaccurate statements. In Study 1, children's knowledge ratings decreased as he provided more inaccurate information. Ratings were predicted by age (i.e., older children…

This article introduces a novel method that integrates collaborative filtering into the naive Bayes model to enhance predicting student academic performance. The combined approach leverages collaborative user behavior analysis and probabilistic modeling, showing promising results in improved prediction precision. Collaborative Filtering explores…

Datasets are critical for emotion analysis in the machine learning field. This study aims to explore emotion analysis datasets and related benchmarks in online learning, since, currently, there are very few studies that explore the same. We have scientifically labeled the topic and nine-category emotion of 4715 comment texts in online learning…

Recently, Bayesian diagnostic classification modeling has been becoming popular in health psychology, education, and sociology. Typically information criteria are used for model selection when researchers want to choose the best model among alternative models. In Bayesian estimation, posterior predictive checking is a flexible Bayesian model…

This article presents an exploration of college student perspectives of their zeitgeist over five semesters during and after the COVID-19 pandemic. At the start of each semester, from Spring 2021 through Spring 2023, between 100 and 150 individuals offered words and/or phrases which they believed characterized the spirit of the time. While this…

Student academic performance prediction is a significant area of study in the realm of education that has drawn the interest and investigation of numerous scholars. The current approaches for student academic performance prediction mainly rely on the educational information provided by educational system, ignoring the information on students'…

Recently, researchers have focused on various factors influencing work engagement, particularly in the EFL context. In this vein, this study was carried out to investigate the relationship among proactive personality, flow, and work engagement in China. In so doing, three instruments including Proactive Personality Scale, Work-Related Flow…

Higher education institutions have consistently strived to provide students with top-notch education. To achieve better outcomes, machine learning (ML) algorithms greatly simplify the prediction process. ML can be utilized by academicians to obtain insight into student data and mine data for forecasting the performance. In this paper, the authors…

Futures of education are increasingly defined through predictive technologies and methods. We conceptualize 'algorithmic futuring' as the use of data-driven digital methods and predictive infrastructures to anticipate educational futures and animate actions in the present towards their materialization. Specifically, we focus on algorithmic…

One challenging issue in improving the teaching and learning methods in MOOCs is to construct potential knowledge graphs from massive learning resources. Therefore, this study proposes knowledge graphs driving online learning behaviour prediction and multi-learning task recommendation in MOOCs. Based on the knowledge graphs supported by…

Creativity, and more recently dark creativity, have yet to be studied in relation to moral foundations, especially against the background of dark traits. This study identified moral foundations that predicted creativity, particularly malevolent creativity, after accounting for Dark Triad/Tetrad traits. Data (N = 529, M[subscript age] = 20.10…

Drawing on an economic, value-based framework of creative idea appraisals, we predict that the attributes of idea usefulness and novelty jointly inform people's decisions to allocate time toward collaborative implementation efforts. In a correlational design, we found support in our first study (n = 82) for an interaction between perceived idea…

The meta-analytic-predictive (MAP) approach is a Bayesian meta-analytic method to synthesize and incorporate information from historical controls in the analysis of a new trial. Classically, only a single parameter, typically the intercept or rate, is assumed to vary across studies, which may not be realistic in more complex models. Analysis of…

Conspiracy beliefs have negative effects on decision making in several life areas including health, ethical, political and environmental domains. But their influence on financial decisions is not known. The current study examines the mediational role of social trust in the relationship between non-financial conspiracy beliefs and stock market…

Meta-reasoning requires monitoring and controlling one's reasoning processes, and it often begins with an assessment of problem solvability. We explored whether "Judgments of Solvability (JOS)" for solvable and unsolvable anagrams discriminate and predict later problem-solving outcomes once anagrams solved during the JOS task are…