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Automated Evaluation of Text and Discourse with Coh-Metrix: Introduction

Text Coherence and Judgments of Essay Quality: Models of Quality and Coherence Scott A. Crossley (scrossley@gsu.edu) Department of Applied Linguistics/ESL, Georgia State University Atlanta, GA, 30303 USA Danielle S. McNamara (dsmcnamara1@gmail.com) Department of Psychology, Institute for Intelligent Systems, The University of Memphis Memphis TN 38152 USA Abstract This study investigates the importance of human evaluations of coherence in predicting human judgments of holistic essay quality. Of secondary interest is the potential for computational indices of cohesion and coherence to model human judgments of coherence. The results indicate that human judgments of coherence are the most predictive features of holistic essay scores and that computational indices related to text structure, semantic coherence, lexical sophistication, and grammatical complexity best explain human judgments of text coherence. These findings have important implications for understanding the role of coherence in writing quality. Keywords: Coherence; Writing Quality; Cohesion, Computational Linguistics, Computational Models. Introduction Writing is an important aspect of communication because it provides the opportunity to articulate ideas and synthesize perspectives in a persuasive manner that is independent of time and space constraints (Crowhurst, 1990). Learning how to convey meaning competently in written texts is a crucial skill for academic and professional success. Indeed, the writing skills of college freshmen are among the best predictors of academic success (Geiser & Studley, 2001). The value of writing in academic and professional settings renders the understanding of writing and, particularly, the difference between good and poor writing, an important objective, both for theoretical and applied reasons. The primary goal of this study is to identify the features of essays that are most predictive of overall writing quality with a specific emphasis on the role text coherence plays in essay quality. Our secondary interest is in modeling human judgments of coherences using new computational indices related to text cohesion and text coherence. Cohesion refers to the presence or absence of explicit cues in the text that allow the reader to make connections between the ideas in the text, whereas coherence refers to the understanding that the reader derives from the text, which may be more or less coherent depending on a number of factors, such as prior knowledge, textual features, and reading skill (McNamara, Kintsch, Songer, & Kintsch, 1996). There is a general sense that essay quality is highly related to the cohesion of a text, and, by proxy, text coherence. This is reflected in the literature about writing as well as textbooks that teach students how to write. Until recently, there were few studies that had empirically investigated the role of cohesion cues in essays. However, studies by McNamara, Crossley, and McCarthy (2010) and Crossley and McNamara (in press) have found no evidence that cohesion cues and essay quality are related. McNamara et al. (2010) found no differences between high and low proficiency essays according to indices of cohesion. In contrast, indices related to language sophistication did show significant differences between the groups. Crossley and McNamara (2010) also found that linguistic sophistication characterized essays rated as higher quality. In addition they found that an index related to text cohesion (aspect repetition) correlated negatively with human scores of essay quality indicating that more cohesive essays were rated as lower quality. In a continuation of these studies, Crossley and McNamara (2010) investigated the degree to which analytical rubric scores of essay quality (e.g., essay cohesion, essay coherence, essay structure, strength of thesis, conclusion type) predicted holistic essays scores. This analysis permitted an examination of relations between holistic essay scores and analytic factors to determine the importance of these features in predicting essay quality. They found that human judgments of text coherence were the most informative predictor of human judgments of essay quality, explaining 65% of the variance. Crossley and McNamara (2010) also examined links between the cohesive devices reported by Coh-Metrix (e.g., semantic coreference, causal cohesion, spatial cohesion, temporal cohesion, connectives and logical operators, anaphoric resolution, word overlap) and human judgments of coherence. Among these variables, only one index (subordinating conjunctions) demonstrated positive, significant correlations with the human ratings of coherence; however, this index also had strong links to syntactic complexity. The majority of the cohesion indices correlated negatively to the human ratings, indicating an inverse relation between the selected cohesion variables and the human judgments of coherence. Thus, while the Crossley and McNamara (2010) study indicated that human ratings of coherence were important indicators of holistic evaluations of essay proficiency, how human raters construct a coherent mental representation did not correlate with the cohesive devices provided by Coh-Metrix.

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Text Coherence and Judgments of Essay Quality: Models of Quality and Coherence - eScholarship

Learning mathematics has been considered as a great challenge for many students. The advancement of computer technologies, in particular, artificial intelligence (AI), provides an opportunity to cope with this problem by diagnosing individual students’ learning problems and providing personalized supports to maximize their learning performances in mathematics courses. However, there is a lack of reviews from diverse perspectives to help researchers, especially novices, gain a whole picture of the research of AI in mathematics education. To this end, this research aims to conduct a bibliometric mapping analysis and systematic review to explore the role and research trends of AI in mathematics education by searching for the relevant articles published in the quality journals indexed by the Social Sciences Citation Index (SSCI) from the Web of Science (WOS) database. Moreover, by referring to the technology-based learning model, several dimensions of AI in mathematics education research, such as the application domains, participants, research methods, adopted technologies, research issues and the roles of AI as well as the citation and co-citation relationships, are taken into account. Accordingly, the advancements of AI in mathematics education research are reported, and potential research topics for future research are recommended.

https://www.mdpi.com/2227-7390/9/6/584/pdf

Roles and Research Trends of Artificial Intelligence in Mathematics Education: A Bibliometric Mapping Analysis and Systematic Review

Automated Evaluation of Text and Discourse with Coh-Metrix: The Introduction

Scholars have indicated the importance of providing guidance and support for individual learners. In the past decades, most studies have developed adaptive learning systems to address this issue mainly based on students’ cognitive status, such as their learning achievements. However, several educators have pointed out the need to take learners’ affective status into account. Therefore, this study proposed an expert system approach by taking into account both the affective and cognitive status of individual learners. An adaptive learning system was implemented based on the proposed approach. In addition, an experiment was conducted in a fifth-grade Mathematics course to compare the learning performances and perceptions of the students who learned with the adaptive learning system with affective and cognitive status analysis, a cognitive-status-based adaptive learning system, and a conventional learning system. The ANCOVA results revealed that the adaptive learning model with the affective and cognitive performance analysis mechanism outperformed the other two approaches in terms of improving the students’ learning achievement (F ​= ​3.12, p ​

A fuzzy expert system-based adaptive learning approach to improving students’ learning performances by considering affective and cognitive factors

The present study aims to examine the pedagogical effectiveness of a Chinese mathematical dialogue-based intelligent tutoring system used for teaching mathematics. The mathematical unit ‘multiplica...

An application of Chinese dialogue-based intelligent tutoring system in remedial instruction for mathematics learning

The current study explores students’ collaboration and problem solving (CPS) abilities using a human-to-agent (H-A) computer-based collaborative problem solving assessment. Five CPS assessment unit...

Computer-Based Collaborative Problem-Solving Assessment in Taiwan.

This paper proposed a PM 2.5 forecasting model using Long Short-Term Model (LSTM) sequence to sequence combined with the statistical method. Correlation Analysis, XGBoost, and Chemical processed are used as the methods to select the essential features. The air pollution data is extracted from Taiwan Environmental Protection Agency (EPA) for the Taichung City dataset in 2014–2018. The study points out that chemical processed model of particulate matter 10 micrometers or less in diameter (PM10), Sulfur Dioxide (SO2), and Nitrogen Dioxide (NO2) have the highest accuracy or lowest Root Mean Square Error (RMSE) and more short training and testing time among the other models. The chemical processed model of PM10, SO2, and NO2 (model B) has the highest accuracy (lowest RMSE), approximately 1 point lower RMSE values, and the shortest training and testing period among the other models. Furthermore, RMSE calculations based on the stations reveal that training with the entire station dataset has a 3 point higher RMSE value than training with each station dataset.

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PM2.5 Forecasting Model Using a Combination of Deep Learning and Statistical Feature Selection

Machine learning (ML) model is increasingly used to predict short-term outcome in critically ill patients, but the study for long-term outcome is sparse. We used explainable ML approach to establish 30-day, 90-day and 1-year mortality prediction model in critically ill ventilated patients.We retrospectively included patients who were admitted to intensive care units during 2015-2018 at a tertiary hospital in central Taiwan and linked with the Taiwanese nationwide death registration data. Three ML models, including extreme gradient boosting (XGBoost), random forest (RF) and logistic regression (LR), were used to establish mortality prediction model. Furthermore, we used feature importance, Shapley Additive exPlanations (SHAP) plot, partial dependence plot (PDP), and local interpretable model-agnostic explanations (LIME) to explain the established model.We enrolled 6994 patients and found the accuracy was similar among the three ML models, and the area under the curve value of using XGBoost to predict 30-day, 90-day and 1-year mortality were 0.858, 0.839 and 0.816, respectively. The calibration curve and decision curve analysis further demonstrated accuracy and applicability of models. SHAP summary plot and PDP plot illustrated the discriminative point of APACHE (acute physiology and chronic health exam) II score, haemoglobin and albumin to predict 1-year mortality. The application of LIME and SHAP force plots quantified the probability of 1-year mortality and algorithm of key features at individual patient level.We used an explainable ML approach, mainly XGBoost, SHAP and LIME plots to establish an explainable 1-year mortality prediction ML model in critically ill ventilated patients. 

/pdf/explainable-machine-learning-to-predict-long-term-mortality-1hh2xpqz.pdf

Explainable machine learning to predict long-term mortality in critically ill ventilated patients: a retrospective study in central Taiwan

The present study aimed at proposing a Chinese automated essay scoring model to assess college students writing quality. Thirtyone related Chinese linguistic indicators were developed based on Coh-Metrix indices and characteristics of Chinese texts. Essay collected from 277 college students were analyzed using automated Chinese text analyze tool. A stepwise regression was used to explain the variance in human scores. The number of words, number of low strokes, content words frequency, minimal edit distance (all words) and minimum frequency for content words predicted 55.8% variance in human scores. On the other hand, seven indicators: number of words, content words frequency, concreteness, Measure of Textual Lexical Diversity, minimal edit distance (part of speech), minimal edit distance (all words) and words per sentence were predictive of human essay ratings by using discriminant analysis. The present study further explored the effectiveness of the Chinese automated essay scoring model by using three different methods: stepwise linear regression, discriminant analysis, and Nonparametric Weighted Feature Extraction classification (NWFE). The preliminary results showed that NWFE classification method produced higher exact matches (51.3%) between the predicted essay scores and the human scores than stepwise regression (47.3%) and discriminant analysis (47.3%).

https://educationaldatamining.org/EDM2017/proc_files/papers/paper_151.pdf

Kai-Chih Pai

Papers

An application of Chinese dialogue-based intelligent tutoring system in remedial instruction for mathematics learning

Computer-Based Collaborative Problem-Solving Assessment in Taiwan.

PM2.5 Forecasting Model Using a Combination of Deep Learning and Statistical Feature Selection

Explainable machine learning to predict long-term mortality in critically ill ventilated patients: a retrospective study in central Taiwan

Developing Chinese Automated Essay Scoring Model to Assess College Students' Essay Quality.