Coh-Metrix analyzes texts on multiple measures of language and discourse that are aligned with multilevel theoretical frameworks of comprehension. Dozens of measures funnel into five major factors that systematically vary as a function of types of texts (e.g., narrative vs. informational) and grade level: narrativity, syntactic simplicity, word concreteness, referential cohesion, and deep (causal) cohesion. Texts are automatically scaled on these five factors with Coh-Metrix-TEA (Text Easability Assessor). This article reviews how these five factors account for text variations and reports analyses that augment Coh-Metrix in two ways. First, there is a composite measure called formality, which increases with low narrativity, syntactic complexity, word abstractness, and high cohesion. Second, the words are analyzed with Linguistic Inquiry and Word Count, an automated system that measures words in texts on dozens of psychological attributes. One next step in automated text analyses is a topics analys...

/pdf/coh-metrix-measures-text-characteristics-at-multiple-levels-1309yf2boc.pdf

Coh-Metrix Measures Text Characteristics at Multiple Levels of Language and Discourse.

Responsive teaching promotes students' mathematical reasoning and positive attitudes toward mathematics. Due to the complexity of the work of teaching, preservice teachers (PSTs) have been provided with approximated opportunities to practice responsive teaching skills in teacher education programs. Although increasing demand for adaptive learning reinforces the need for research on artificial intelligence (AI) in education, there have been few approaches that engaged learners in meaningful interactions. Our goal was to develop an AI-based chatbot that engaged PSTs in an authentic, meaningful, and open-ended teaching situation to enhance PSTs' responsive teaching skills, specifically questioning skills through approximations of practice. The chatbot was designed to act as a virtual student who displayed misconceptions on the topic of fractions. By employing design-based research, we examined 1) design features and structure of the chatbot, 2) coverage of users' input, 3) PSTs' questioning patterns, and 4) users' experiences. Two iterations of design, implementation and evaluation took place in an elementary mathematics education methods course. To build the chatbot we qualitatively analyzed the training data, categorized them into the smallest meaningful intents of users, and prepared corresponding responses to each intent. At the final iteration, the refined chatbot adequately covered PSTs’ questions and provided realistic responses. We found a pattern of PSTs asking similar questions repeatedly in the conversation data. Through multiple iterations, certain design features could lead to improved questioning patterns and user perceptions, including sequential responses, informing responses, and personification. Implications, design features, and limitations are discussed. 

Developing an AI-based chatbot for practicing responsive teaching in mathematics

Reading literacy has been considered one of the essential vital competencies in modern society and has thus gained increasing attention in research. With both qualitative and quantitative research methods, this study aimed to investigate the overall picture in this research field and investigate the role of reading motivation and online reading activities and how online reading literacy was assessed. The top ten organizations, countries with the highest publications, author keywords, all keywords, cited references, cited sources, and cited authors were visualized via VOSviewer clustering and counting techniques. Reading motivation, online reading activities, and digital reading literacy assessment tests were also explored through the visualization citation network in CitNetExplorer. In conjunction with the citation network, 13 peer-reviewed articles were selected for further analysis based on the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocol (PRISMA-P). The results show that reading motivation played an essential role in reading literacy achievement in formal schooling, while online reading activities could both positively and negatively affect digital reading literacy due to their multifaceted nature. The digital reading literacy assessment tests vary across the world. Implications for widely investigating adults and L2 reading literacy and relevant support or interventional measures to boost reading literacy were also discussed.

/pdf/a-bibliometric-review-study-on-reading-literacy-over-2oni9m7g.pdf

A Bibliometric Review Study on Reading Literacy over Fourteen Years

Research on Artificial Intelligence (AI) in language education becomes more popular. This revolutionary technology is regarded as a promising tool to assist language learning environments by empowering automatic feedback, intelligent tutoring, and personalization. While designing Intelligent Computer Assisted Language Learning (ICALL) environments, educators have followed the First Principles of Instruction (FPI), including problem-centered, activation, demonstration, application, and integration. These instructional design principles were adoptable since they provided a problem-solving focus to the learning activities, satisfying learners’ need for relatedness to the process. Although different instructional design principles were used in ICALL environments and their corresponding effects on student learning outcomes have been reported, few researchers have examined the relevant studies in a systematic review approach, and even fewer scholars have explored the challenges of the instructional design principles adopted. To fill the gap, we conducted a systematic review with 83 related papers screened from the Web of Science, ERIC, Scopus, and ProQuest Social Science Database. We aimed at investigating the application of FPI in ICALL with two research questions: (1) How do ICALL environments foster learning from the perspective of FPI? (2) What are the challenges of ICALL environments? Content analysis and thematic analysis were used to analyze the papers collected. Our research findings disclosed the application of demonstration and application principles for automatic feedback, the activation principle for intelligent tutoring, and the problem-centered and integration principles for personalization. Meanwhile, we discussed the challenges of these studies and proposed instructional design insights and empirical suggestions for ICALL educational practice. 

Instructional design and learning outcomes of intelligent computer assisted language learning: Systematic review in the field

AbstractAutoTutor-ARC (adult reading comprehension) is an intelligent tutoring system that uses conversational agents to help adult learners improve their comprehension skills. However, in such a system, not all lessons and items optimally serve the same purposes. In this paper, we describe a method for classifying items that are instructive, evaluative, motivational, versus potentially flawed based on analyses of items’ psychometric properties. Further, there is no a priori way of determining which lessons are optimal given the learner’s reading profile needs. To address this, we evaluate how assessing learner component reading skills can inform various aspects of learner needs on AutoTutor lessons. More specifically, we compare learners who were classified as proficient, underengaged, conscientious, versus struggling readers based on their experiences with AutoTutor. Together, these analyses suggest the utility of integrating assessments with instruction: efficient, adaptive learning at the lesson level, more efficient and valid post-testing, and consequently, recommendations for more targeted, adaptive pathways through the instructional program/system.KeywordsIntelligent tutoring systemsReading skillsPsychometrics 

How Item and Learner Characteristics Matter in Intelligent Tutoring Systems Data

A common goal of Intelligent Tutoring Systems (ITS) is to provide learning environments that adapt to the varying abilities and characteristics of users. This type of adaptivity is possible only if the ITS has information that characterizes the learning behaviors of its users and can adjust its pedagogy accordingly. This study investigated an intelligent tutoring system with computer agents (AutoTutor) designed to improve comprehension skills in adults with low reading literacy. One goal of this study was to classify adults into different clusters based on their behavioral patterns (accuracy and response time to answer questions) while they interacted with AutoTutor to help them improve their reading comprehension skills. A second goal was to investigate whether adults’ behaviors were associated with different reading components. A third goal was to assess improvements in reading comprehension skills, based on psychometric tests, of different clusters of readers. Performance on AutoTutor was collected in a targeted 100-hour hybrid intervention for adult readers (n = 252) that included both human teachers and the AutoTutor system. The adults’ average accuracy and response time in AutoTutor were used to cluster the adults into four categories: higher performers (comparatively fast and accurate), conscientious readers (slow but accurate), under-engaged readers (fast at the expense of somewhat lower accuracy) and struggling readers (slow and inaccurate). Two psychometric tests of comprehension were used to assess comprehension. Gains in comprehension scores were highest for conscientious readers, lowest for struggling readers, with higher performing readers and under-engaged readers in between. The results provide guidance to enhance the adaptivity of AutoTutor.

/pdf/patterns-of-adults-with-low-literacy-skills-interacting-with-1k6qln1yre.pdf

Ying Fang

Papers

Patterns of Adults with Low Literacy Skills Interacting with an Intelligent Tutoring System