Comprehension monitoring of written discourse across early-to-middle adolescence

Comprehension monitoring is conceptualized as a metacognitive process involving monitoring and control of ongoing discourse processing. The error-detection paradigm was used in an experiment in which 315 7th-, 9th-, and 11th-grade students of low-to-high reading ability monitored and controlled their reading as they searched three times through a text. Search One showed that although all readers failed to monitor many problems, monitoring at lexical, syntactic, and particularly semantic levels increased with age and reading ability. Monitoring for low-ability readers remained low at all three grades. Search Two showed an asymmetry in the effects of instruction to search for errors, and some students exhibited constraints on monitoring and control. Finally, Search Three showed that some students had knowledge necessary to monitor more errors but failed to apply that knowledge to the text.     

《中国古籍善本书目》补正

《中国古籍善本书目》一书共收录山西师范大学图书馆藏善本古籍210部。笔者将馆藏善本古籍与《中国古籍善本书目》一一核对,发现该书在著录馆藏善本上有一些失误。本文试将这些失误作以纠正,以便读者查阅。     

浅议课堂教学中教师的“错误”观

在课堂教学中,学生经常会出现各种各样的错误。面对学生的错误,教师所持的态度直接影响着教学的效果和学生错误潜在价值的充分发挥。教师的“错误”观是教师教育观、课程观、学生观的集中反映,也是其教学能力、评价能力的重要体现。教师在课堂教学中面对学生错误时,应该具有科学的“错误”观,如课程资源观、启发契机观、审视教学观、评价信息观。教师应该在这些“错误”观的指导下,采取正确的策略去帮助学生纠正错误。     

四库提要订正两则

本文对《四库全书总目》中两则提要进行考察,订正其中存在的错讹之处。     

Automated monitoring of online news accuracy with change classification models

In the past decade, news consumption has shifted from printed news media to online alternatives. Although these come with advantages, online news poses challenges as well. Notable here is the increased competition between online newspapers and other online news providers to attract readers. Hereby, speed is often favored over quality. As a consequence, the need for new tools to monitor online news accuracy has grown. In this work, a fundamentally new and automated procedure for the monitoring of online news accuracy is proposed. The approach relies on the fact that online news articles are often updated after initial publication, thereby also correcting errors. Automated observation of the changes being made to online articles and detection of the errors that are corrected may offer useful insights concerning news accuracy. The potential of the presented automated error correction detection model is illustrated by building supervised classification models for the detection of objective, subjective and linguistic errors in online news updates respectively. The models are built using a large news update data set being collected during two consecutive years for six different Flemish online newspapers. A subset of 21,129 changes is then annotated using a combination of automated and human annotation via an online annotation platform. Finally, manually crafted features and text embeddings obtained by four different language models (TF-IDF, word2vec, BERTje and SBERT) are fed to three supervised machine learning algorithms (logistic regression, support vector machines and decision trees) and performance of the obtained models is subsequently evaluated. Results indicate that small differences in performance exist between the different learning algorithms and language models. Using the best-performing models, $F_2$-scores of 0.45, 0.25 and 0.80 are obtained for the classification of objective, subjective and linguistic errors respectively.