In automated essay scoring (AES), essays are automatically graded without human raters. Many AES models based on various manually designed features or various architectures of deep neural networks (DNNs) have been proposed over the past few decades. Each AES model has unique advantages and characteristics. Therefore, rather than using a single-AES…

Automated Essay Scoring (AES) technologies provide innovative solutions to score the written essays with a much shorter time span and at a fraction of the current cost. Traditionally, AES emphasized the importance of capturing the "coherence" of writing because abundant evidence indicated the connection between coherence and the overall…

The 21st century learning demanded students to master the 4C competencies: critical thinking and problem-solving, communication, collaboration, and creativity and innovation, with problem-solving skills significantly influencing students' cognitive learning outcomes. This study utilized the Flipped Classroom instructional model, assisted by the…

This study compared the effects of worked example and scripting on students' argumentative peer feedback, essay and learning qualities. Participants were 80 BSc students who were randomly divided over 40 dyads and assigned to two experimental conditions (worked example and scripting). An online peer feedback environment named EduTech was designed…

In automated essay scoring (AES), scores are automatically assigned to essays as an alternative to grading by humans. Traditional AES typically relies on handcrafted features, whereas recent studies have proposed AES models based on deep neural networks to obviate the need for feature engineering. Those AES models generally require training on a…

Automated essay scoring (AES) has emerged as a secondary or as a sole marker for many high-stakes educational assessments, in native and non-native testing, owing to remarkable advances in feature engineering using natural language processing, machine learning, and deep-neural algorithms. The purpose of this study is to compare the effectiveness…

Automated Essay Scoring (AES) can reliably grade essays at scale and reduce human effort in both classroom and commercial settings. There are currently three dominant supervised learning paradigms for building AES models: feature-based, neural, and hybrid. While feature-based models are more explainable, neural network models often outperform…

The goal of this study is to model pauses extracted from writing keystroke logs as a way of characterizing the processes students use in essay composition. Low-level timing data were modeled, the interkey interval and its subtype, the intraword duration, thought to reflect processes associated with keyboarding skills and composition fluency.…

The hierarchical rater model (HRM) recognizes the hierarchical structure of data that arises when raters score constructed response items. In this approach, raters' scores are not viewed as being direct indicators of examinee proficiency but rather as indicators of essay quality; the (latent categorical) quality of an examinee's essay in turn…

Most automated essay scoring programs use a linear regression model to predict an essay score from several essay features. This article applied a cumulative logit model instead of the linear regression model to automated essay scoring. Comparison of the performances of the linear regression model and the cumulative logit model was performed on a…

Students with emotional and behavioral disorders often experience difficulties in expressive writing and social outcomes in school and beyond. Therefore, writing instruction and self-determination skills are critical for this population. This research study, in which special education teachers were trained to be implementers, successfully…

In this research, we investigated the suitability of implementing "e-rater"® automated essay scoring in a high-stakes large-scale English language testing program. We examined the effectiveness of generic scoring and 2 variants of prompt-based scoring approaches. Effectiveness was evaluated on a number of dimensions, including agreement…

This study analyzes the ability of advanced Russian learners of English as a foreign language (FL) to compose authentic essays in the target language (TL) reflecting morphosyntactic, discursive, and cross-cultural expectations. Thirty-four students at Russian universities were asked to utilize their TL, English, to write compositions focusing on…

The 13th iteration of the International Conference on Educational Data Mining (EDM 2020) was originally arranged to take place in Ifrane, Morocco. Due to the SARS-CoV-2 (coronavirus) epidemic, EDM 2020, as well as most other academic conferences in 2020, had to be changed to a purely online format. To facilitate efficient transmission of…

Presents a model-based approach to rater reliability for essays read by multiple raters. The approach is motivated by generalizability theory, and variation of rater severity and rater inconsistency is considered in the presence of between-examinee variations. Illustrates methods with data from standardized educational tests. (Author/SLD)