Shunan Zhang 교수

Abstract Although previous studies have highlighted the problematic artificial intelligence (AI) usage behaviors in educational contexts, such as overreliance on AI, no study has explored the antecedents and potential consequences that contribute to this problem. Therefore, this study investigates the causes and consequences of AI dependency using ChatGPT as an example. Using the Interaction of the Person-Affect-Cognition-Execution (I-PACE) model, this study explores the internal associations be

How humans achieve long-term goals in an uncertain environment, via repeated trials and noisy observations, is an important problem in cognitive science. We investigate this behavior in the context of a multi-armed bandit task. We compare human behavior to a variety of models that vary in their representational and computational complexity. Our result shows that subjects' choices, on a trial-to-trial basis, are best captured by a forgetful" Bayesian iterative learning model in combination with a

Diffusion models are widely-used and successful accounts of the time course of two-choice decision making. Most diffusion models assume constant boundaries, which are the threshold levels of evidence that must be sampled from a stimulus to reach a decision. We summarize theoretical results from statistics that relate distributions of decisions and response times to diffusion models with time-varying boundaries. We then develop a computational method for finding time-varying boundaries from empir

Human subjects exhibit "sequential effects" in many psychological experiments, in which they respond more rapidly and accurately to a stimulus when it reinforces a local pattern in stimulus history, compared to when it violates such a pattern.This is often the case even if the local pattern arises by chance, such that stimulus history has no real predictive power, and therefore any behavioral adjustment based on these erroneous predictions essentially amounts to superstition.Earlier, we proposed

Learning from online videos using synchronized Danmu comments provides a co-learning experience. To explore the factors influencing learning with or without Danmu videos, an initial set of reasons and impediments was compiled based on a pilot study of 24 Chinese university students with learning experience using Danmu videos. Three hundred such students were surveyed to determine the factors that influence their motivations and hindrances with regard to using Danmu videos. The potential predicto

Abstract Prior research has explored the impact of diverse anthropomorphic interventions on the effectiveness of AI (artificial intelligence) instructors. However, the exploration of interpersonal communication skills (e.g., self-disclosure) as anthropomorphic conversational cues for AI instructors is rare. Considering the positive impact of the self-disclosure of human instructors and guided by the social penetration theory (Altman & Taylor, 1973) and computers are social actors (CASA) para

How people achieve long-term goals in an imperfectly known environment, via repeated tries and noisy outcomes, is an im-portant problem in cognitive science. There are two inter-related questions: how humans represent information, both what has been learned and what can still be learned, and how they choose actions, in particular how they negotiate the ten-sion between exploration and exploitation. In this work, we examine human behavioral data in a multi-armed bandit set-ting, in which the subj

MOOCs (massive open online courses) have attracted considerable attention from researchers. Fueled by constant change and developments in educational technology, the trends of MOOCs have varied greatly over the years. To detect and visualize the developments and changes in MOOC research, 4,652 articles published between 2009 and 2021 were retrieved from Web of Science and Scopus with the aid of CiteSpace. This study sought to explore the number of publications, co-citation network, cluster analy

The "wisdom of the crowds" refers to the idea that the aggregated performance of a group of people on a challenging task may be superior to the performance of any of the individuals.For some tasks, like estimating a single quantity, it is straightforward to aggregate individual behavior.For more complicated multidimensional or sequential tasks, however, it is not so straightforward.Cognitive models of behavior are needed, to infer what people know from how they behave, and allow aggregation to b

Owing to the limitations of computer-mediated communication (CMC), distributed CSCL (Computer-supported collaborative learning) has not always been as effective as desired. Despite recognizing the significance of group composition, the exploration of the function of teammate familiarity in distributed educational settings is restricted. This study explored the influence of teammate familiarity and social presence in a distributed CSCL setting by conducting an online survey of 288 Korean universi

Due to the tremendous growth in user population over online social networks (OSNs), interactive messaging traffic has experienced exponential increase, which consequently elevates the privacy leakage. Malicious users may utilize camouflage, phishing and other unconventional techniques to lure ordinary users to reveal their sensitive attributes in their interactive messages, when they least expect that the other sides are malicious. Therefore, it is very important to identify malicious users quic