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What predicts adult readers’ understanding of STEM texts?
The current study examined the relations among key variables that underlie reading comprehension of expository science texts in a diverse population of adult native English readers. Using Mechanical Turk to sample a range of adult readers, the study also examined the effect of text presentation on readers’ comprehension and knowledge structure established after reading. In Study 1, ratings of situational interest, select reading background variables, and select measures of readers’ knowledge structure accounted for significant variance in comprehension. In Study 2, the knowledge structure metrics of primacy, recency, and node degree as well as several text ratings were found to be comparable across text presentation formats. Participants who read the text sentence-by-sentence obtained higher scores on measures of comprehension and provided higher ratings of situational interest than those who received the whole paragraph text at once. Knowledge structure measures for the sentence-by-sentence and paragraph formats were similar (68% overlap). We discuss implications for future research examining factors that underlie the successful comprehension of science texts.
Journal Article
SsciBERT: a pre-trained language model for social science texts
The academic literature of social sciences records human civilization and studies human social problems. With its large-scale growth, the ways to quickly find existing research on relevant issues have become an urgent demand for researchers. Previous studies, such as SciBERT, have shown that pre-training using domain-specific texts can improve the performance of natural language processing tasks. However, the pre-trained language model for social sciences is not available so far. In light of this, the present research proposes a pre-trained model based on the abstracts published in the Social Science Citation Index (SSCI) journals. The models, which are available on GitHub (
https://github.com/S-T-Full-Text-Knowledge-Mining/SSCI-BERT
), show excellent performance on discipline classification, abstract structure–function recognition, and named entity recognition tasks with the social sciences literature.
Journal Article
A Mathematics Course for Political and Social Research
Political science and sociology increasingly rely on mathematical modeling and sophisticated data analysis, and many graduate programs in these fields now require students to take a \"math camp\" or a semester-long or yearlong course to acquire the necessary skills. The problem is that most available textbooks are written for mathematics or economics majors, and fail to convey to students of political science and sociology the reasons for learning often-abstract mathematical concepts.A Mathematics Course for Political and Social Researchfills this gap, providing both a primer for math novices and a handy reference for seasoned researchers.
The book begins with the fundamental building blocks of mathematics and basic algebra, then goes on to cover essential subjects such as calculus in one and more than one variable, including optimization, constrained optimization, and implicit functions; linear algebra, including Markov chains and eigenvectors; and probability. It describes the intermediate steps most other textbooks leave out, features numerous exercises throughout, and grounds all concepts by illustrating their use and importance in political science and sociology.
Uniquely designed for students and researchers in political science and sociology Uses examples from political science and sociology Features \"Why Do I Care?\" sections that explain why concepts are useful to practicing political scientists and sociologists Includes numerous exercises Complete online solutions manual (available only to professors) Selected solutions available online to students
eBook
Development and Validation of a Reading in Science Holistic Assessment (RISHA): a Rasch Measurement Study
Researchers in science education lacks valid and reliable instruments to assess students’
disciplinary
and
epistemic
reading of scientific texts. The main purpose of this study was to develop and validate a Reading in Science Holistic Assessment (RISHA) to assess students’ holistic reading of scientific texts. RISHA measures students’ content, procedural, and epistemic domains of reading two texts, one history-of-science text and another socio-scientific text. The initial 24-item RISHA was administered to 161 Grade 9 students from 3 schools. The multidimensional Rasch partial credit model was used to analyze the reliability and validity of RISHA. All items demonstrated good fit and reliability. According to logit scores generated for each domain in Rasch analysis, students in our study performed better in content domain and less well in the epistemic domain. Students also performed significantly better in the epistemic domain of the socio-scientific text than in the history-of-science text. RISHA provides accurate measures in various domains of reading scientific texts and various contexts of scientific texts. We propose that RISHA could potentially be applied to studying the effect of reading-science intervention or predictors of students’ performance in each domain of reading scientific texts.
Journal Article
