ethanporcaro/eric-fulltext-meta huggingface dataset:
Ice-Breaking as a Useful Teaching Policy for Both Genders
Author: Yeganehpour, Parisa
Date: 2017-00-00
Type: Journal Articles
Subjects: Teaching Methods,English (Second Language),Second Language Instruction,Teacher Attitudes,Adult Learning,Gender Differences,Language Teachers,Foreign Countries,Interaction,Classroom Environment,Interviews,Observation,Qualitative Research,Quasiexperimental Design,Vocabulary Development,Grammar,Pronunciation,Language Fluency,Pretests Posttests,Statistical Analysis

These days focus of interest in English learning changes to productive skills, in Turkey. This research study assesses the teachers' point of view about using ice-breakers for adult Turkish EFL learners in upper-intermediate level. It also anticipates finding valuable information with applying ice-breaker activities as a useful teaching policy that will contribute to the comparison of interest between male and female learners while trying to employ icebreaker activities and speak fluently in the target language. The present study concluded that teachers need to be informed about the usefulness of ice-breaker activities for adult learners. Also, according to the finding of this study, there is not any relationship between gender of Turkish EFL learners and the effect of using ice-breaker activities in improving their speaking ability.
Teaching iSTART to Understand Spanish
Author: Dascalu, Mihai
Date: 2017-00-00
Type: Speeches/Meeting Papers
Subjects: Spanish,Reading Comprehension,Natural Language Processing,Intelligent Tutoring Systems,Computer Assisted Instruction,Second Language Learning,Second Language Instruction,Scores,Teaching Methods

iSTART is a web-based reading comprehension tutor. A recent translation of iSTART from English to Spanish has made the system available to a new audience. In this paper, we outline several challenges that arose during the development process, specifically focusing on the algorithms that drive the feedback. Several iSTART activities encourage students to use comprehension strategies to generate self-explanations in response to challenging texts. Unsurprisingly, analyzing responses in a new language required many changes, such as implementing Spanish natural language processing tools and rebuilding lists of regular expressions used to flag responses. We also describe our use of an algorithm inspired from genetics to optimize the Fischer Discriminant Function Analysis coefficients used to determine self-explanation scores. [This paper was published in: R. Baker &amp; E. Andre (Eds.), &quot;Proceedings of the 18th International Conference on Artificial Intelligence in Education&quot; (pp. 485-489), Wuhan, China: Springer.]
Implementing Instructional Practices to Improve American Indian and Alaska Native Students&apos; Reading Outcomes: An Exploration of Patterns across Teacher, Classroom, and School Characteristics
Author: Vincent, Claudia G.
Date: 2017-00-00
Type: Reports - Research
Subjects: American Indian Education,American Indian Students,Alaska Natives,Culturally Relevant Education,Reading Instruction,Reading Improvement,Reading Achievement,Teacher Surveys,Educational Practices,Teacher Characteristics,Classroom Environment,Institutional Characteristics,Program Implementation,Native Language,Faculty Development,Elementary School Teachers,Secondary School Teachers,Grade 4,Grade 8,Factor Analysis

The Native Community strongly recommends integrating Native language and culture (NLC) into reading instruction to improve outcomes for American Indian/Alaska Native (AI/AN) students. However, little is known about the extent to which recommended practices are used and what might facilitate their implementation. The National Indian Education Study conducted by the U.S. Department of Education surveys teachers of AI/AN students on their instructional practices. This descriptive study builds on previous analysis of survey data which identified measurable dimensions of NLC in instruction. We now examine (a) the extent to which teachers implement these dimensions, and (b) what teacher, classroom, and school-wide characteristics facilitate implementation. Outcomes suggest that the recommended practices are rarely implemented, and that AI/AN teachers speaking Native language(s) and teaching in classrooms with high AI/AN enrollment located in schools employing AI/AN teachers and staff implement the recommended practices more often. We discuss implications for teacher education and support. [This paper was published in &quot;Journal of Teacher Education&quot; v68 n5 p435-450 Nov-Dec 2017 (EJ1158825).]
Early Numeracy Intervention Program for First-Grade Students with Mathematics Difficulties
Author: Bryant, Diane Pedrotty
Date: 2011-00-00
Type: Journal Articles
Subjects: Grade 1,Numeracy,Intervention,Mathematics Skills,Learning Problems,Program Effectiveness,Elementary School Students,Pretests Posttests,Randomized Controlled Trials,Hypothesis Testing,Problem Solving,Computation,Mathematics Instruction,Elementary School Mathematics,Comparative Analysis,Screening Tests,Progress Monitoring,Standardized Tests,Achievement Tests,Mathematics Tests,Fidelity,Observation,Rating Scales,Statistical Analysis

The purpose of this study was to determine the effects of an early numeracy preventative Tier 2 intervention on the mathematics performance of first-grade students with mathematics difficulties. Researchers used a pretest-posttest control group design with randomized assignment of 139 students to the Tier 2 treatment condition and 65 students to the comparison condition. Systematic instruction, visual representations of mathematical concepts, purposeful and meaningful practice opportunities, and frequent progress monitoring were used to develop understanding in early numeracy skills and concepts. Researchers med progress-monitoring measures and a standardized assessment measure to test the effects of the intervention. Findings showed that students in the treatment group outperformed students in the comparison group on the progress-monitoring measures of mathematics performance and the measures that focused on whole-number computation. There were no differences between groups on the problem-solving measures. [This article was published in &quot;Exceptional Children&quot; (EJ939950).]
iSTART-ALL: Confronting Adult Low Literacy with Intelligent Tutoring for Reading Comprehension
Author: Johnson, Amy M.
Date: 2017-00-00
Type: Speeches/Meeting Papers
Subjects: Reading Comprehension,Reading Instruction,Intelligent Tutoring Systems,Reading Strategies,Adult Literacy,Educational Technology,Adult Students,Student Attitudes,Instructional Effectiveness,Literacy Education,Reading Tests

There is little empirical research available on the substantial problem of adult low literacy rates, and limited educational technologies are available to address distinct instructional needs of this population. This paper reports on development and testing of a version of Interactive Strategy Training for Active Reading and Thinking (iSTART) for Adult Literacy Learners (iSTART-ALL) We describe modifications of iSTART to accommodate adult literacy learners, including new practice modules (i.e., summarization, question asking), a new library of texts, and an interactive narrative for adult literacy learners to engage in extended practice of reading comprehension strategies. We report results of a study examining reactions to iSTART-ALL and performance data while engaging with the interactive narrative. The attitudinal study, conducted with 38 adult literacy learners, demonstrated generally positive reactions to the narrative. Results also revealed that task performance was strongly related to individual difference scores on reading comprehension assessments, and more so with higher-level comprehension skills than basic word-level skills, providing concurrent validity for the interactive narrative tasks. [This paper was published in: E. Andre, R. Baker, X. Hu, M. M. T. Rodrigo, &amp; B. du Boulay (Eds.), &quot;Proceedings of the 18th International Conference on Artificial Intelligence in Education&quot; (pp. 125-136). Wuhan, China: Springer.]
Assessing Question Quality Using NLP
Author: Kopp, Kristopher J.
Date: 2017-00-00
Type: Speeches/Meeting Papers
Subjects: Reading Comprehension,Reading Instruction,Intelligent Tutoring Systems,Reading Strategies,Natural Language Processing,Questioning Techniques,Mathematics,Feedback (Response),Taxonomy,Artificial Intelligence,Prediction,Accuracy,Educational Technology,Multivariate Analysis

An NLP algorithm was developed to assess question quality to inform feedback on questions generated by students within iSTART (an intelligent tutoring system that teaches reading strategies). A corpus of 4575 questions was coded using a four-level taxonomy. NLP indices were calculated for each question and machine learning was used to predict question quality. NLP indices related to lexical sophistication modestly predicted question type. Accuracies improved when predicting two levels (shallow versus deep). [This paper was published in: E. Andre, R. Baker, X. Hu, M. M. T. Rodrigo, &amp; B. du Boulay (Eds.), &quot;Proceedings of the 18th International Conference on Artificial Intelligence in Education&quot; (pp. 523-527). Wuhan, China: Springer.]
iSTART Therefore I Understand: But Metacognitive Supports Did Not Enhance Comprehension Gains
Author: McCarthy, Kathryn S.
Date: 2017-00-00
Type: Speeches/Meeting Papers
Subjects: Reading Comprehension,Reading Instruction,Intelligent Tutoring Systems,Reading Strategies,Metacognition,Instructional Effectiveness,Educational Games,High School Students,Self Evaluation (Individuals),Experimental Groups,Control Groups,Comparative Analysis,Pretests Posttests,Statistical Analysis,Reading Tests

iSTART is an intelligent tutoring system designed to provide self-explanation instruction and practice to improve students' comprehension of complex, challenging text. This study examined the effects of extended game-based practice within the system as well as the effects of two metacognitive supports implemented within this practice. High school stu-dents (n = 234) were either assigned to an iSTART treatment condition or a control condition. Within the iSTART condition, students were assigned to a 2x2 design in which students provided &quot;self-assessments&quot; of their performance or were transferred to Coached Practice if their performance did not reach a certain &quot;performance threshold.&quot; Those receiving iSTART training produced higher self-explanation and inference-based comprehension scores. However, there were no direct effects of either metacognitive support on these learning outcomes. [This paper was published in: R. Baker &amp; E. Andre (Eds.), &quot;Proceedings of the 18th International Conference on Artificial Intelligence in Education&quot; (pp. 201-211), Wuhan, China: Springer.]
StairStepper: An Adaptive Remedial iSTART Module
Author: Perret, Cecile A.
Date: 2017-00-00
Type: Speeches/Meeting Papers
Subjects: Reading Comprehension,Reading Instruction,Intelligent Tutoring Systems,Reading Strategies,Scaffolding (Teaching Technique),Educational Games,Remedial Reading,Adult Literacy,Literacy Education,Multiple Choice Tests,Difficulty Level

This paper introduces StairStepper, a new addition to Interactive Strategy Training for Active Reading and Thinking (iSTART), an intelligent tutoring system (ITS) that provides adaptive self-explanation training and practice. Whereas iSTART focuses on improving comprehension at levels geared toward answering challenging questions associated with complex texts, StairStepper focuses on improving learners' performance when reading grade-level expository texts. StairStepper is designed as a scaffolded practice activity wherein text difficulty level and task are adapted according to learners' performance. This offers a unique module that provides reading comprehension tutoring through a combination of self-explanation practice and answering of multiple-choice questions representative of those found in standardized tests. [This paper was published in: E. Andre, R. Baker, X. Hu, M. M. T. Rodrigo, &amp; B. du Boulay (Eds.), &quot;Proceedings of the 18th International Conference on Artificial Intelligence in Education&quot; (pp. 557-560). Wuhan, China: Springer.]
Leading Education Innovations in Rural Schools: Reflections from i3 Grantees
Author: Fox, Tom
Date: 2017-09-00
Type: Reports - Research
Subjects: Rural Schools,Educational Innovation,Elementary Secondary Education,School Districts,Partnerships in Education,Educational Research,Sustainability,Capacity Building,Stakeholders,Program Implementation

The Investing in Innovation (i3) Improving Rural Achievement Community, a professional learning community of grantees under the US Department of Education's i3 program, created this document to share knowledge and experiences related to working in rural districts across the United States. Grounded in field-based experiences and lessons, it serves as a guide for researchers, non-profits, and others interested in partnering with rural districts to develop and implement innovations in K-12 schools. A total of 27 rural community participants working across 24 i3 projects contributed content as active participants in focus groups (via webinar), polls, emails, and/or in-person meetings during the guide development process. Following an introduction about why innovations in rural districts and schools matter, four key topics are addressed: (1) relationship-building with rural districts for innovation research and implementation, (2) establishing and maintaining partnerships with rural districts, (3) sustaining innovation in rural communities, and (4) scaling up innovation in rural communities. The document concludes with recommendations that offer encouragement and caution to innovators who seek to design and implement educational innovations in rural places. Five questions are offered to help guide planners toward a successful innovation journey in a rural context. An appendix lists the document's 27 contributors, the organization awarded the i3 grant, the project title, and the purpose of the project. An acknowledgments section identifies the leading contributors and co-writers of the document. [This report was produced by the i3 Improving Rural Achievement Community.]
Metacognitive Prompt Overdose: Positive and Negative Effects of Prompts in iSTART
Author: McCarthy, Kathryn S.
Date: 2017-00-00
Type: Speeches/Meeting Papers
Subjects: Metacognition,Prompting,Intelligent Tutoring Systems,Reading Instruction,Reading Strategies,Reading Comprehension,Educational Games,Scores,Feedback (Response),Self Evaluation (Individuals),Difficulty Level,High School Students,Statistical Analysis

Interactive Strategy Training for Active Reading and Thinking (iSTART) is an intelligent tutoring system that supports reading comprehension through self-explanation (SE) training. This study tested how two metacognitive features, presented in a 2 x 2 design, affected students' SE scores during training. The &quot;performance notification&quot; feature notified students when their average SE score dropped below an experimenter-set threshold. The &quot;self-rating&quot; feature asked participants to rate their own SE scores. Analyses of SE scores during training indicated that neither feature increased SE scores and, on the contrary, seemed to decrease SE performance after the first instance. These findings suggest that too many metacognitive prompts can be detrimental, particularly in a system that provides metacognitive strategy training. [This paper was published in: X. Hu, T. Barnes, A. Hershkovitz, &amp; L. Paquette (Eds.), &quot;Proceedings of the 10th International Conference on Educational Data Mining&quot; (pp. 404-405). Wuhan, China: International Educational Data Mining Society.]
Use of Native Language and Culture (NLC) in Elementary and Middle School Instruction as a Predictor of Mathematics Achievement
Author: Van Ryzin, Mark J.
Date: 2017-00-00
Type: Reports - Research
Subjects: Elementary School Students,Middle School Students,American Indian Students,Alaska Natives,Native Language,Culturally Relevant Education,American Indian Education,Mathematics Achievement,American Indian Languages,American Indian Culture,National Competency Tests,Grade 4,Grade 8,Cultural Context,Hierarchical Linear Modeling,Factor Analysis

Because students from American Indian/Alaska Native (AI/AN) backgrounds tend to lag behind their peers in academic achievement, researchers have recommended integrating Native Language and Culture (NLC) into instruction. However, existing evidence from large-scale studies finds a &quot;negative&quot; effect of the use of NLC on achievement, although this research does not take into account aspects of student background and the learning context. Using a nationally representative dataset, we found that use of NLC had a less negative and/or more positive effect on achievement among students whose families identified more strongly with their Native culture and who were in schools with larger percentages of AI/AN students. Our results support earlier contentions that the use of NLC can be effective in enhancing achievement for at least some AI/AN students; they also suggest that existing approaches to NLC should be expanded to accommodate AI/AN students in a wider range of contexts. [This paper was published in &quot;Journal of American Indian Education&quot; v56 n2 2017.]
Combining Machine Learning and Natural Language Processing to Assess Literary Text Comprehension
Author: Balyan, Renu
Date: 2017-00-00
Type: Speeches/Meeting Papers
Subjects: Artificial Intelligence,Natural Language Processing,Reading Comprehension,Literature,Comparative Analysis,Accuracy,Classification,Mathematics,Essays,College Students

This study examined how machine learning and natural language processing (NLP) techniques can be leveraged to assess the interpretive behavior that is required for successful literary text comprehension. We compared the accuracy of seven different machine learning classification algorithms in predicting human ratings of student essays about literary works. Three types of NLP feature sets: unigrams (single content words), elaborative (new) n-grams, and linguistic features were used to classify idea units (paraphrase, text-based inference, interpretive inference). The most accurate classifications emerged using all three NLP features sets in combination, with accuracy ranging from 0.61 to 0.94 (F = 0.18 to 0.81). Random Forests, which employs multiple decision trees and a bagging approach, was the most accurate classifier for these data. In contrast, the single classifier, Trees, which tends to &quot;overfit&quot; the data during training, was the least accurate. Ensemble classifiers were generally more accurate than single classifiers. However, Support Vector Machines accuracy was comparable to that of the ensemble classifiers. This is likely due to Support Vector Machines' unique ability to support high dimension feature spaces. The findings suggest that combining the power of NLP and machine learning is an effective means of automating literary text comprehension assessment. [This paper was published in: A. Hershkovitz &amp; L. Paquette (Eds.), &quot;Proceedings of the 10th International Conference on Educational Data Mining&quot; (pp. 244-249), Wuhan, China: International Educational Data Mining Society.]
Postsecondary Pathways and Persistence for STEM versus Non-STEM Majors; Among College Students with an Autism Spectrum Disorder
Author: Wei, Xin
Date: 2014-00-00
Type: Reports - Research
Subjects: Autism,Pervasive Developmental Disorders,College Students,Academic Persistence,Two Year Colleges,STEM Education,Majors (Students),College Transfer Students,Gender Differences,Racial Differences,Parent Background,Educational Attainment,Educational Policy,Community Colleges,Longitudinal Studies,Transitional Programs,Special Education,Statistical Analysis,Student Surveys,Parent Surveys,Regression (Statistics)

Little is known about postsecondary pathways and persistence among college students with an Autism Spectrum Disorder (ASD). This study analyzed data from the National Longitudinal Transition Study-2, 2001-2009, a nationally representative sample of students in special education with an ASD who progressed from high school to postsecondary education. Findings suggest that most college students with an ASD enrolled in a 2-year community college at some point in their postsecondary careers (81%). Those in science, technology, engineering and mathematics (STEM) fields were more likely to persist in a 2-year community college and were twice as likely to transfer from a 2-year community college to a 4-year university than their peers in the non-STEM fields. College persistence rates varied by gender, race, parent education level, and college pathway and major. Educational policy implications are discussed, including the understanding that students with an ASD may need a longer transition period between high school and an independent 4-year university. Community colleges may be a particularly ideal setting for students with an ASD as they enable students to receive postsecondary education while often still remaining at home, thus providing the continuity of a supportive and consistent environment. Additionally, these findings demonstrate the need for future research to further explore the barriers to college persistence and how these may vary depending on the field of study and student characteristics. [This report was published in &quot;Journal of Autism and Developmental Disorders&quot; (EJ1038463).]
Individualizing Teaching in Beginning Reading
Author: McDonald Connor, Carol
Date: 2014-00-00
Type: Reports - Descriptive
Subjects: Individualized Instruction,Beginning Reading,Teaching Methods,Elementary School Teachers,Kindergarten,Grade 1,Grade 2,Grade 3,Educational Assessment,Reading Instruction,Randomized Controlled Trials,Reading Achievement

The best way to teach children how to read has been debated for more than a century. Accumulating research now shows that there is no one best way to teach reading. The effect of various types of instruction depends on the constellation of skills learners bring to the classroom. In this article, we discuss the idea of child-characteristic-by-instruction interaction effects and how teachers can use this information to effectively individualize (or differentiate or personalize) their reading instruction more effectively from kindergarten to third grade (Year 1 to Year 4). [This paper was published in: &quot;Better: Evidence-Based Education&quot; by the Institute for Effective Education, University of York, Heslington, York and the Center for Research and Reform in Education at Johns Hopkins School of Education, Baltimore.]