Writing an integrated essay based on multiple-documents requires students to both comprehend the documents and integrate the documents into a coherent essay. In the current study, we examined the effects of summarization as a potential reading strategy to enhance participants' multiple-document comprehension and integrated essay writing.…

This study examines the potential to use non-expert, crowd-sourced raters to score essays by comparing expert raters' and crowd-sourced raters' assessments of writing quality. Expert raters and crowd-sourced raters scored 400 essays using a standardised holistic rubric and comparative judgement (pairwise ratings) scoring techniques, respectively.…

Automated Essay Scoring (AES) is a well-studied problem in Natural Language Processing applied in education. Solutions vary from handcrafted linguistic features to large Transformer-based models, implying a significant effort in feature extraction and model implementation. We introduce a novel Automated Machine Learning (AutoML) pipeline…

This study examined the extent to which prior beliefs and reading instructions impacted elements of a reader's mental representation of multiple texts. College students' beliefs about childhood vaccinations were assessed before reading two anti-vaccine and two pro-vaccine texts. Participants in the experimental condition read for the purpose of…

This study examined a naturalistic, districtwide implementation of an automated writing evaluation (AWE) software program called "MI Write" in elementary schools. We specifically examined the degree to which aspects of MI Write were implemented, teacher and student attitudes towards MI Write, and whether MI Write usage along with other…

Human essay grading is a laborious task that can consume much time and effort. Automated Essay Scoring (AES) has thus been proposed as a fast and effective solution to the problem of grading student writing at scale. However, because AES typically uses supervised machine learning, a human-graded essay corpus is still required to train the AES…

Recent advances in automated writing evaluation have enabled educators to use automated writing quality scores to improve assessment feasibility. However, there has been limited investigation of bias for automated writing quality scores with students from diverse racial or ethnic backgrounds. The use of biased scores could contribute to…

The present study examined the extent to which adaptive feedback and just-in-time writing strategy instruction improved the quality of high school students' persuasive essays in the context of the Writing Pal (W-Pal). W-Pal is a technology-based writing tool that integrates automated writing evaluation into an intelligent tutoring system. Students…

Few studies have explored how general skills in both reading and writing influence performance on integrated, source-based writing. The goal of the present study was to consider the relative contributions of reading and writing ability on multiple-document integrative reading and writing tasks. Students in the U.S. (n=94) completed two tasks in…

Broadly defined, academic language (AL) is a set of lexical-grammatical norms and registers commonly used in educational and academic discourse. Mastery of academic language in writing is an important aspect of writing instruction and assessment. The purpose of this study was to use Natural Language Processing (NLP) tools to examine the extent to…

The current study examined the extent to which the cohesion detected in readers' constructed responses to multiple documents was predictive of persuasive, source-based essay quality. Participants (N=95) completed multiple-documents reading tasks wherein they were prompted to think-aloud, self-explain, or evaluate the sources while reading a set of…

This paper presents an investigation of using a co-attention based neural network for source-dependent essay scoring. We use a co-attention mechanism to help the model learn the importance of each part of the essay more accurately. Also, this paper shows that the co-attention based neural network model provides reliable score prediction of…

In this paper, we extracted content-based and structure-based features of text to predict human annotations for claims and nonclaims in argumentative essays. We compared Logistic Regression, Bernoulli Naive Bayes, Gaussian Naive Bayes, Linear Support Vector Classification, Random Forest, and Neural Networks to train classification models. Random…

We tested the long-term effects of a utility-value intervention administered in a gateway chemistry course, with the goal of promoting persistence and diversity in STEM. In a randomized controlled trial (N = 2,505), students wrote three essays about course content and its personal relevance or three control essays. The intervention significantly…

Despite increasing pressure for children to learn to write at younger ages, there are many unanswered questions about composition skills in early elementary school. The goal of this research was to examine the dimensionality of composition skills in kindergarten children, thereby adding to current knowledge about the measurement of young…