This study aimed to synthesize, develop, and evaluate the Micro Game-Based Learning Model on Intelligent Metaverse Learning Environment. The study integrated Microlearning, Game-Based Learning, Artificial Intelligence, and Metaverse technologies to create a learning environment that provides short learning units, interactive game-based activities, and simulated communication situations. The research objectives were to (1) synthesize the learning process, (2) develop the learning model, and (3) evaluate the learning model. The evaluation panel consisted of three experts from various academic institutions. The experts were selected based on their experience at least 3 years in education, instructional design and development, particularly in teaching English for communication. The research instruments consisted of the learning model, learning platform, and an expert evaluation questionnaire based on a 5-point Likert scale. Data were analyzed using descriptive and inferential statistics according to the research objectives. The findings indicated that the appropriateness of the developed learning model was evaluated at a highest level (Mean = 4.93, SD = 0.24). The findings suggest that integrating Microlearning, Game-Based Learning, Artificial Intelligence, and an Intelligent Metaverse Learning Environment provides a feasible approach for developing technology-supported English communication learning.
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
F. Xia, Amir Zamir, Zhi-Yang He et al.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9