This paper examines the effectiveness of the Differential autoregressive integrated moving average (ARIMA) model in comparison to the Long Short Term Memory (LSTM) neural network model for predicting Wordle user-repor...This paper examines the effectiveness of the Differential autoregressive integrated moving average (ARIMA) model in comparison to the Long Short Term Memory (LSTM) neural network model for predicting Wordle user-reported scores. The ARIMA and LSTM models were trained using Wordle data from Twitter between 7th January 2022 and 31st December 2022. User-reported scores were predicted using evaluation metrics such as MSE, RMSE, R2, and MAE. Various regression models, including XG-Boost and Random Forest, were used to conduct comparison experiments. The MSE, RMSE, R2, and MAE values for the ARIMA(0,1,1) and LSTM models are 0.000, 0.010, 0.998, and 0.006, and 0.000, 0.024, 0.987, and 0.013, respectively. The results indicate that the ARIMA model is more suitable for predicting Wordle user scores than the LSTM model.展开更多
Research and games both require the participant to make a series of choices.Active learning is a process borrowed from machine learning for algorithmically making choices that has become increasingly used to accelerat...Research and games both require the participant to make a series of choices.Active learning is a process borrowed from machine learning for algorithmically making choices that has become increasingly used to accelerate materials research.While this process may seem opaque to researchers outside the field of machine learning,examining active learning in games provides an accessible way to showcase the process and its virtues.Here,we examine active learning through the lens of the game Wordle to both explain the active learning process and describe the types of research questions that arise when using active learning for materials research.展开更多
文字云图是通过文字云图工具制作而成的反映文字频率的可视图的一种形象比喻,可以作为一种有效的文本分析工具应用在教学中。文章梳理了国内外文字云图应用研究现状,分析了文字云图在英语阅读教学中应用的可视化表征、生成线索词、语义...文字云图是通过文字云图工具制作而成的反映文字频率的可视图的一种形象比喻,可以作为一种有效的文本分析工具应用在教学中。文章梳理了国内外文字云图应用研究现状,分析了文字云图在英语阅读教学中应用的可视化表征、生成线索词、语义和表象的双重表征等优势,并以Wordle为例选择高中英语教材中的一节课"Gettingto Know Steven Spielberg"进行了研究设计,研究结果表明使用文字云图能够调动学生的兴趣,给学生的英语阅读带来积极的促进作用。展开更多
文摘This paper examines the effectiveness of the Differential autoregressive integrated moving average (ARIMA) model in comparison to the Long Short Term Memory (LSTM) neural network model for predicting Wordle user-reported scores. The ARIMA and LSTM models were trained using Wordle data from Twitter between 7th January 2022 and 31st December 2022. User-reported scores were predicted using evaluation metrics such as MSE, RMSE, R2, and MAE. Various regression models, including XG-Boost and Random Forest, were used to conduct comparison experiments. The MSE, RMSE, R2, and MAE values for the ARIMA(0,1,1) and LSTM models are 0.000, 0.010, 0.998, and 0.006, and 0.000, 0.024, 0.987, and 0.013, respectively. The results indicate that the ARIMA model is more suitable for predicting Wordle user scores than the LSTM model.
文摘Research and games both require the participant to make a series of choices.Active learning is a process borrowed from machine learning for algorithmically making choices that has become increasingly used to accelerate materials research.While this process may seem opaque to researchers outside the field of machine learning,examining active learning in games provides an accessible way to showcase the process and its virtues.Here,we examine active learning through the lens of the game Wordle to both explain the active learning process and describe the types of research questions that arise when using active learning for materials research.
文摘文字云图是通过文字云图工具制作而成的反映文字频率的可视图的一种形象比喻,可以作为一种有效的文本分析工具应用在教学中。文章梳理了国内外文字云图应用研究现状,分析了文字云图在英语阅读教学中应用的可视化表征、生成线索词、语义和表象的双重表征等优势,并以Wordle为例选择高中英语教材中的一节课"Gettingto Know Steven Spielberg"进行了研究设计,研究结果表明使用文字云图能够调动学生的兴趣,给学生的英语阅读带来积极的促进作用。
