The Next Frontier in Communication and the ECLIPPSE Study: Bridging the Linguistic Divide in Secure Messaging

abstract: Health systems are heavily promoting patient portals. However, limited health literacy (HL) can restrict online communication via secure messaging (SM) because patients’ literacy skills must be sufficient to convey and comprehend content while clinicians must encourage and elicit communication from patients and match patients’ literacy level. This paper describes the Employing Computational Linguistics to Improve Patient-Provider Secure Email (ECLIPPSE) study, an interdisciplinary effort bringing together scientists in communication, computational linguistics, and health services to employ computational linguistic methods to (1) create a novel Linguistic Complexity Profile (LCP) to characterize communications of patients and clinicians and demonstrate its validity and (2) examine whether providers accommodate communication needs of patients with limited HL by tailoring their SM responses. We will study >5 million SMs generated by >150,000 ethnically diverse type 2 diabetes patients and >9000 clinicians from two settings: an integrated delivery system and a public (safety net) system. Finally, we will then create an LCP-based automated aid that delivers real-time feedback to clinicians to reduce the linguistic complexity of their SMs. This research will support health systems’ journeys to become health literate healthcare organizations and reduce HL-related disparities in diabetes care.The article is published at https://www.hindawi.com/journals/jdr/2017/1348242

Automated essay scoring in applied games:Reducing the teacher bandwidth problem in online training

This paper presents a methodology for applying automated essay scoring in educational settings. The methodology was tested and validated on a dataset of 173 reports (in Dutch language) that students have created in an applied game on environmental policy. Natural Language Processing technologies from the ReaderBench framework were used to generate an extensive set of textual complexity indices for each of the reports. Afterwards, different machine learning algorithms were used to predict the scores. By combining binary classification (pass or fail) and a probabilistic model for precision, a trade-off can be made between validity of automated score prediction (precision) and the reduction of teacher workload required for manual assessment. It was found from the sample that substantial workload reduction can be achieved, while preserving high precision: allowing for a precision of 95% or higher would already reduce the teacher’s workload to 74%; lowering precision to 80% produces a workload reduction of 50%

Automated Essay Evaluation Using Natural Language Processing and Machine Learning

The goal of automated essay evaluation is to assign grades to essays and provide feedback using computers. Automated evaluation is increasingly being used in classrooms and online exams. The aim of this project is to develop machine learning models for performing automated essay scoring and evaluate their performance. In this research, a publicly available essay data set was used to train and test the efficacy of the adopted techniques. Natural language processing techniques were used to extract features from essays in the dataset. Three different existing machine learning algorithms were used on the chosen dataset. The data was divided into two parts: training data and testing data. The inter-rater reliability and performance of these models were compared with each other and with human graders. Among the three machine learning models, the random forest performed the best in terms of agreement with human scorers as it achieved the lowest mean absolute error for the test dataset

Cohesive Features of Deep Text Comprehension Processes

This study investigates how cohesion manifests in readers’thought processes while reading texts when they areinstructed to engage in self-explanation, a strategy associatedwith deeper, more successful comprehension. In Study 1,college students (n = 21) were instructed to either paraphraseor self-explain science texts. Paraphrasing was characterizedby greater cohesion in terms of lexical overlap whereas self-explanation included greater lexical diversity and moreconnectives to specify relations between ideas. In Study 2,adolescent students (n = 84) were provided with instructionand practice in self-explanation and reading strategies across8 sessions. Self-explanations increased in lexical diversity butbecame more causally and semantically cohesive over time.Together, these results suggest that cohesive featuresexpressed in think alouds are indicative of the depth ofstudents’ comprehension processes

ReaderBench goes Online: A Comprehension-Centered Framework for Educational Purposes

International audienceIn this paper we introduce the online version of our ReaderBench framework, which includes multi-lingual comprehension-centered web services designed to address a wide range of individual and collaborative learning scenarios, as follows. First, students can be engaged in reading a course material, then eliciting their understanding of it; the reading strategies component provides an in-depth perspective of comprehension processes. Second, students can write an essay or a summary; the automated essay grading component provides them access to more than 200 textual complexity indices covering lexical, syntax, semantics and discourse structure measurements. Third, students can start discussing in a chat or a forum; the Computer Supported Collaborative Learning (CSCL) component provides in- depth conversation analysis in terms of evaluating each member’s involvement in the CSCL environments. Eventually, the sentiment analysis, as well as the semantic models and topic mining components enable a clearer perspective in terms of learner’s points of view and of underlying interests

ReaderBench goes Online: A Comprehension-Centered Framework for Educational Purposes

International audienceIn this paper we introduce the online version of our ReaderBench framework, which includes multi-lingual comprehension-centered web services designed to address a wide range of individual and collaborative learning scenarios, as follows. First, students can be engaged in reading a course material, then eliciting their understanding of it; the reading strategies component provides an in-depth perspective of comprehension processes. Second, students can write an essay or a summary; the automated essay grading component provides them access to more than 200 textual complexity indices covering lexical, syntax, semantics and discourse structure measurements. Third, students can start discussing in a chat or a forum; the Computer Supported Collaborative Learning (CSCL) component provides in- depth conversation analysis in terms of evaluating each member’s involvement in the CSCL environments. Eventually, the sentiment analysis, as well as the semantic models and topic mining components enable a clearer perspective in terms of learner’s points of view and of underlying interests

The Poetry of Prompts: The Collaborative Role of Generative Artificial Intelligence in the Creation of Poetry and the Anxiety of Machine Influence

2022 has been heralded as the year of generative artificial intelligence (AI). Generative AI like ChatGPT and Stable Diffusion, along with a host of others, launched late in the year and immediately disrupted the status quo of the literary and art worlds, leading to outcries to ban “AI Art” and spawning an entirely new market of NFTs. Fears over the “death of the artist” and the “death of college composition,” however, are unfounded when considering the historical adoption of emerging technologies by creatives and the reconsideration of authorship that began with post structuralism and the Foucauldian Death of the Author in 1967. Contemporary scholarship has faced challenges in reconciling the function of the human author in conjunction with artificial intelligence (AI) due to the progressive sophistication and selfsufficiency of generative code. Nonetheless, it is erroneous to establish the threshold for authorship based on the development or advancement of AI or robotics, as it falls within the realm of ontology. Instead, assertions of AI authorship stem from a romanticized perception of both authorship and AI during a period in which neither holds significance. A new discussion on the role of the human agent in the writing process, particularly in the creative process like poetry, should prioritize the practical aspects of what an author does. This study examines how AI is increasingly becoming involved in collaborative efforts to create poetry and aims to explore the potential of this trend. Furthermore, the study seeks to provide empirical evidence on the boundaries of AI\u27s ability to replicate human thought and experience. Through generating content in the creative written arts using ChatGPT-3, poetry analysis revealed that, in fact, such new generative models can imitate the vocabulary, language choices, style, and even rhythm of famous poets such as Keats, it is unable to generate emotions that it has not experienced. The questions that will continue to be raised on the nature of humanity, existence, and creative capabilities should be reframed with the concept of fear fore grounded to assist in understanding the uniquely human anxiety and drive to create in an attempt to communicate across the gulf what it “feels” like to be human as a phenomenology of experience

The Poetry of Prompts: The Collaborative Role of Generative Artificial Intelligence in the Creation of Poetry and the Anxiety of Machine Influence

2022 has been heralded as the year of generative artificial intelligence AI Generative AI like ChatGPT and Stable Diffusion along with a host of others launched late in the year and immediately disrupted the status quo of the literary and artworlds leading to outcries to ban AI Art and spawning an entirely new market of NFTs Fears over the death of the artist and the death of college composition however are unfounded when considering the historical adoption of emerging technologies by creatives and the reconsideration of authorship that began with poststructuralism and the Foucauldian Death of the Author in 196

Automated assessment of non-native learner essays: Investigating the role of linguistic features

Automatic essay scoring (AES) refers to the process of scoring free text responses to given prompts, considering human grader scores as the gold standard. Writing such essays is an essential component of many language and aptitude exams. Hence, AES became an active and established area of research, and there are many proprietary systems used in real life applications today. However, not much is known about which specific linguistic features are useful for prediction and how much of this is consistent across datasets. This article addresses that by exploring the role of various linguistic features in automatic essay scoring using two publicly available datasets of non-native English essays written in test taking scenarios. The linguistic properties are modeled by encoding lexical, syntactic, discourse and error types of learner language in the feature set. Predictive models are then developed using these features on both datasets and the most predictive features are compared. While the results show that the feature set used results in good predictive models with both datasets, the question "what are the most predictive features?" has a different answer for each dataset.Comment: Article accepted for publication at: International Journal of Artificial Intelligence in Education (IJAIED). To appear in early 2017 (journal url: http://www.springer.com/computer/ai/journal/40593

Finding the right words : language technologies to support formulation

This chapter explores the ability of digital technologies to provide language support for writers. With such ability, technologies directly intervene into the productive act of language creation, which we refer to by the traditional term formulation. Formulation here is defined as the kind of thinking that happens when a writer tries to linearize thought by using language. In written communication, formulation happens during interaction with an inscription tool and is strongly influenced by the kind of technology used. In this chapter, we look into some of the changes in formulation and language crafting that followed the introduction of digital technologies. We attempt to estimate where the developments are heading by addressing four issues: (1) support for the preparation of formulation, (2) real-time support during inscription, (3) support for the choice of words and collocations, and (4) support for language use at the revision stage by automated feedback and intelligent tutoring. The contribution concludes with some thoughts about future directions