ethanporcaro/eric-fulltext-meta huggingface dataset:
Local School Finance Study, 2013
Author: Swift, Emma
Date: 2013-12-00
Type: Numerical/Quantitative Data
Subjects: Educational Legislation,Federal Aid,Federal Legislation,Federal Programs,Educational Finance,State Aid,Local Government,Taxes,Counties,Expenditure per Student,Elementary Secondary Education,Socioeconomic Influences,Charter Schools,Real Estate,Costs,Salaries,Operating Expenses,Income,Public Schools

For more than 25 years the Public School Forum of North Carolina has isolated state and federal spending to examine the capacity and effort counties make to support their schools. The intent of the annual Local School Finance Study is not to blame counties for their effort to support schools, but to examine the capacity and actual effort the state's 100 counties make to support 115 school districts. The 2013 Local School Finance Study examines data from the 2011-12 school year. The primary source of financial information is the Financial and Business Services Division at the North Carolina Department of Public Instruction, which provided the non-property tax revenue, low wealth and small county supplemental funding information, final average daily membership numbers, effective county tax rate, adjusted revenue tax base, and current expense. Other sources include the North Carolina Department of Revenue's Tax Research and Ad Valorem Tax Divisions, which provided the 2011-12 property tax valuations and tax rates. The state's public schools spent $11.2 billion in the 2011-12 school year using a combination of state, federal, and local resources. State funding accounts for 62 percent of expenditures, federal funding accounts for 14 percent, and local funding accounts for 24 percent of spending. According to a recent National Education Association survey, North Carolina ranks 11th in the Nation and 2nd in the Southeast for the highest percentage of funds from state revenue. The National Average is 45.5 percent. [For the 2012 report, see ED585782.]
The Effect of Task-Based and Topic-Based Speaking Activities on Speaking Ability of Iranian EFL Learners
Author: Yegani, Hossein
Date: 2017-00-00
Type: Journal Articles
Subjects: Foreign Countries,English (Second Language),Second Language Learning,Comparative Analysis,Interviews,Teaching Methods,Pretests Posttests,Conventional Instruction,Oral Language,Language Skills,Language Proficiency,Adult Learning,Language Tests,Scores,Student Attitudes,Reading Comprehension,Reading Tests,Vocabulary

The current study was conducted to investigate the effect of task-based and topic-based speaking activities on improving the speaking ability among Iranian EFL learners. The study used quasi-experimental design. The subjects consisted of 60 male and female students who were selected from among 90 EFL learners by applying the nelson proficiency test. 30 subjects formed a control group which received conventional method. The other with 15 subjects formed experimental group A which received the task-based speaking activities. Another 15 formed the experimental group B which received the topic-based speaking activities. Oral interviews were used both as the pretest and posttest in order to evaluate the speaking proficiency of subjects before and offer the treatment, the results indicated that task-based speaking activities enhanced speaking ability of Iranian EFL learners, but not significantly enough to reject the stated null hypothesis.
Examination of Activities in the Primary School Life Sciences Textbooks in Turkey
Author: Ütkür, Nur
Date: 2018-00-00
Type: Journal Articles
Subjects: Foreign Countries,Textbooks,Elementary School Science,Textbook Content,Biological Sciences,Learning Activities,Content Analysis,Grade 2,Grade 3,Grade 1

Students can find elements from life in one of the elementary school courses, life sciences. For this reason, it is important that the quality of the activities in this course. The aim of this study is classification of activities in textbooks of primary school life sciences taught in 2016-2017 academic year in our country. Document analysis technique is used in this study which has qualitative research feature. As a result of the analyzes, the total number of activities in the first grade life sciences textbook is 295, number 388 in the second grade textbook and 363 in the third grade textbook. The largest number of events in all of the textbooks is &quot;expressing thought, value judgment, assumption.&quot; This is followed by the &quot;interpretation of photographs and pictures&quot; events in all books. In the least number of activity types in the textbooks, it is determined that &quot;singing&quot; is the first grade textbook. The types of activities that have only one in the second grade textbook are &quot;discussing,&quot; &quot;making calculation,&quot; &quot;singing,&quot; &quot;doing acrostic work&quot; and &quot;doing experiment.&quot; In the 3rd grade textbook, &quot;slogan writing,&quot; &quot;classification, listing, matching&quot; and &quot;making table, making graphic, concept mapping&quot; activities are at least in number.
Motivation in Second Language Acquisition: A State of the Art Article
Author: Zareian, Gholamreza
Date: 2015-00-00
Type: Journal Articles
Subjects: Second Language Learning,Learning Motivation,State of the Art Reviews,Individual Differences,Context Effect,Models,Learning Theories,Self Determination,Goal Orientation,Attribution Theory,Neurology,Attitude Change

Success in second language learning is often related to the concept of 'motivation'. Motivation is the most used concept for explaining the failure or success of a language learner. This paper collected and reviewed the literature of motivation in second language acquisition (SLA) form prominent journals in the field. The role of motivation as one of the most important constructs of individual differences (IDs) in foreign language learning and their relationships with second/foreign language learning are investigated. The wider concluding argument of this paper is that motivation and its constructs are context dependent and, therefore, any language learning situation has its own unique motivational model.
From the State of Motivated to Demotivated: Iranian Military EFL Learners&apos; Motivation Change
Author: Jodaei, Hojjat
Date: 2018-00-00
Type: Journal Articles
Subjects: Foreign Countries,Military Personnel,English (Second Language),Second Language Learning,Learning Motivation,Young Adults,Questionnaires,Interviews,Student Motivation,Anxiety,Barriers,Student Attitudes,Attitude Change

Second language learning motivation has always been considered to be one of the most influential factors in language learning due to its undeniably substantial impacts. In comparison with research conducted in other areas of L2 motivation, there has been a paucity of research on L2 motivation change, especially in the Iranian language learning context. This study aimed at investigating motivation change among Iranian military EFL learners in an intensive English course. The participants included 61 Iranian military staff aged 23-35, who took part in an intensive English course at the military university's foreign language center. The data collection was conducted using a background information questionnaire, a contextualized and translated version of the mini-AMTB (Attitude Motivation Test Battery) and an interview with the selected participants. Quantitative descriptive analysis showed that the participants were highly motivated, with relatively low anxiety and high integrative motivation. Furthermore, a qualitative content analysis showed that situation-specific factors including management problems both at macro and micro levels, class timing, lesson planning, organizational interventions, teacher-related factors, and facilities were all motivational factors that played a significant role in motivating or demotivating learners. The results also showed that the learners moved from a state of high motivation to low motivation due to context-specific demotivational factors.
Developing Pedagogically-Guided Algorithms for Intelligent Writing Feedback
Author: Roscoe, Rod D.
Date: 2013-00-00
Type: Journal Articles
Subjects: Writing Instruction,Intelligent Tutoring Systems,Computer Assisted Testing,Writing Evaluation,Formative Evaluation,Feedback (Response),Natural Language Processing,Discourse Analysis,Connected Discourse,Semantics,Grammar,Essays,High School Students,College Freshmen,Scoring Formulas

Various computer tools have been developed to support educators' assessment of student writing, including automated essay scoring and automated writing evaluation systems. Research demonstrates that these systems exhibit relatively high scoring accuracy but uncertain instructional efficacy. Students' writing proficiency does not necessarily improve as a result of interacting with the software. One question is whether these systems offer appropriate or sufficient formative feedback to students about their writing. To motivate further research in this area, we present a straightforward methodology for constructing automated feedback algorithms that are grounded in writing pedagogy and assessment. The resulting threshold algorithms are demonstrated to be meaningfully related to essay quality and informative regarding individualised, formative feedback for writers. Potential applications and extensions of this methodology are discussed.
A Hierarchical Classification Approach to Automated Essay Scoring
Author: McNamara, Danielle S.
Date: 2015-00-00
Type: Journal Articles
Subjects: Automation,Scoring,Essays,Persuasive Discourse,Grade 9,Grade 11,College Freshmen,Time,Accuracy,Feedback (Response),Word Frequency,Classification,Rating Scales,Multivariate Analysis,Vocabulary

This study evaluates the use of a hierarchical classification approach to automated assessment of essays. Automated essay scoring (AES) generally relies onmachine learning techniques that compute essay scores using a set of text variables. Unlike previous studies that rely on regression models, this study computes essay scores using a hierarchical approach, analogous to an incremental algorithm for hierarchical classification. The corpus in this study consists of 1243 argumentative (persuasive) essays written on 14 different prompts, across 3 different grade levels (9th grade, 11th grade, college freshman), and four different time limits for writing or temporal conditions (untimed essays and essays written in 10, 15, and 25 minute increments). The features included in the analysis are computed using the automated tools, Coh-Metrix, the Writing Assessment Tool (WAT), and Linguistic Inquiry and Word Count (LIWC). Overall, the models developed to score all the essays in the data set report 55% exact accuracy and 92% adjacent accuracy between the predicted essay scores and the human scores. The results indicate that this is a promising approach to AES that could provide more specific feedback to writers and may be relevant to other natural language computations, such as the scoring of short answers in comprehension or knowledge assessments.
Predicting Math Performance Using Natural Language Processing Tools
Author: Crossley, Scott
Date: 2017-00-00
Type: Speeches/Meeting Papers
Subjects: Predictor Variables,Mathematics Achievement,English (Second Language),Natural Language Processing,Problem Solving,Online Courses,Educational Technology,Technology Uses in Education,Pretests Posttests,Scores,Student Characteristics,Fractions,Mathematics Instruction,Intelligent Tutoring Systems,Grade 4,Grade 5,Elementary School Students

A number of studies have demonstrated links between linguistic knowledge and performance in math. Studies examining these links in first language speakers of English have traditionally relied on correlational analyses between linguistic knowledge tests and standardized math tests. For second language (L2) speakers, the majority of studies have compared math performance between proficient and non-proficient speakers of English. In this study, we take a novel approach and examine the linguistic features of student language while they are engaged in collaborative problem solving within an on-line math tutoring system. We transcribe the students' speech and use natural language processing tools to extract linguistic information related to text cohesion, lexical sophistication, and sentiment. Our criterion variables are individuals' pretest and posttest math performance scores. In addition to examining relations between linguistic features of student language production and math scores, we also control for a number of non-linguistic factors including gender, age, grade, school, and content focus (procedural versus conceptual). Linear mixed effect modeling indicates that non-linguistic factors are not predictive of math scores. However, linguistic features related to cohesion affect and lexical proficiency explained approximately 30% of the variance (R[superscript 2] = 0.303) in the math scores. [This paper was published in: &quot;LAK '17&quot; (Vancouver, British Columbia, Canada, March 13-17, 2017). ACM. (ISBN 978-1-4503-4870-6)]
Natural Language Processing in an Intelligent Writing Strategy Tutoring System
Author: McNamara, Danielle S.
Date: 2013-00-00
Type: Journal Articles
Subjects: Intelligent Tutoring Systems,Natural Language Processing,Writing Instruction,Feedback (Response),Models,Syntax,Word Frequency,Vocabulary,Semantics,Essays,College Freshmen,Regression (Statistics),Scoring,Automation,Educational Technology,Technology Uses in Education

The Writing Pal is an intelligent tutoring system that provides writing strategy training. A large part of its artificial intelligence resides in the natural language processing algorithms to assess essay quality and guide feedback to students. Because writing is often highly nuanced and subjective, the development of these algorithms must consider a broad array of linguistic, rhetorical, and contextual features. This study assesses the potential for computational indices to predict human ratings of essay quality. Past studies have demonstrated that linguistic indices related to lexical diversity, word frequency, and syntactic complexity are significant predictors of human judgments of essay quality but that indices of cohesion are not. The present study extends prior work by including a larger data sample and an expanded set of indices to assess new lexical, syntactic, cohesion, rhetorical, and reading ease indices. Three models were assessed. The model reported by McNamara, Crossley, and McCarthy (&quot;Written Communication&quot; 27:57-86, 2010) including three indices of lexical diversity, word frequency, and syntactic complexity accounted for only 6% of the variance in the larger data set. A regression model including the full set of indices examined in prior studies of writing predicted 38% of the variance in human scores of essay quality with 91% adjacent accuracy (i.e., within 1 point). A regression model that also included new indices related to rhetoric and cohesion predicted 44% of the variance with 94% adjacent accuracy. The new indices increased accuracy but, more importantly, afford the means to provide more meaningful feedback in the context of a writing tutoring system.
Investigating Boredom and Engagement during Writing Using Multiple Sources of Information: The Essay, the Writer, and Keystrokes
Author: Allen, Laura K.
Date: 2016-04-00
Type: Speeches/Meeting Papers
Subjects: Learner Engagement,Psychological Patterns,Writing Instruction,Essays,Intelligent Tutoring Systems,Natural Language Processing,Writing Evaluation,Individual Differences,Measures (Individuals),Feedback (Response),Scoring,Undergraduate Students,Writing Processes,Keyboarding (Data Entry),Writing Apprehension,Tests

Writing training systems have been developed to provide students with instruction and deliberate practice on their writing. Although generally successful in providing accurate scores, a common criticism of these systems is their lack of personalization and adaptive instruction. In particular, these systems tend to place the strongest emphasis on delivering accurate scores, and therefore, tend to overlook additional indices that may contribute to students' success, such as their affective states during writing practice. This study takes an initial step toward addressing this gap by building a predictive model of students' affect using information that can potentially be collected by computer systems. We used individual difference measures, text indices, and keystroke analyses to predict engagement and boredom in 132 writing sessions. The results suggest that these three categories of indices were successful in modeling students' affective states during writing. Taken together, indices related to students' academic abilities, text properties, and keystroke logs were able classify high and low engagement and boredom in writing sessions with accuracies between 76.5% and 77.3%. These results suggest that information readily available in writing training systems can inform affect detectors and ultimately improve student models within intelligent tutoring systems. [This paper was published in: &quot;LAK '16&quot; (Edinburgh, United Kingdom, April 25-29, 2016) (p.114-123). ACM. (ISBN 978-1-4503-4190-5)]
Pssst… Textual Features… There Is More to Automatic Essay Scoring than Just You!
Author: Crossley, Scott
Date: 2015-03-00
Type: Speeches/Meeting Papers
Subjects: Automation,Scoring,Essays,Evaluation Methods,Student Characteristics,Intelligent Tutoring Systems,Natural Language Processing,High School Students,Reading Tests,Tests,Writing Apprehension,Educational Technology,Technology Uses in Education

This study investigates a new approach to automatically assessing essay quality that combines traditional approaches based on assessing textual features with new approaches that measure student attributes such as demographic information, standardized test scores, and survey results. The results demonstrate that combining both text features and student attributes leads to essay scoring models that are on par with state-of-the-art scoring models. Such findings expand our knowledge of textual and nontextual features that are predictive of writing success. [This paper was published in: &quot;LAK '15&quot; (Poughkeepsie, New York, March 16-20, 2015). ACM. (ISBN 978-1-4503-3417-4)]
The Motivation and Mastery Cycle Framework: Predicting Long-Term Benefits of Educational Games
Author: Jackson, G. Tanner
Date: 2017-00-00
Type: Reports - Research
Subjects: Educational Games,Intelligent Tutoring Systems,Educational Benefits,Learning Motivation,Mastery Learning,Learner Engagement,Academic Persistence,Retention (Psychology),Educational Research

By combining the benefits from Intelligent Tutoring Systems (ITSs) and video games, educational games offer the unique potential to capture learners' interest, compel them to persist with targeted tasks, and can result in improved mastery of educational content and skills. These benefits are described within the context of educational game research and incorporated into two new potential frameworks. The potential frameworks are designed to help structure findings from various literature bases in a manner that helps to inform future system designs and illustrates possible contributions to and the importance of considering timescales for evaluating effects of educational games. Tentative empirical support for these frameworks is provided through empirical results with iSTART-ME, along with general conclusions from the research presented. [This chapter was published in: Y. Baek (Ed.), &quot;Game-Based Learning: Theory, Strategies and Performance Outcomes,&quot; (pp.97-121). Hauppauge, NY:? Nova Science Publishers, Inc., 2017. (ISBN-978-1-5361-1900-8)]
Developing Component Scores from Natural Language Processing Tools to Assess Human Ratings of Essay Quality
Author: Crossley, Scott A.
Date: 2014-00-00
Type: Speeches/Meeting Papers
Subjects: Essays,Computer Assisted Testing,Writing Evaluation,Scores,Natural Language Processing,Scoring,Intelligent Tutoring Systems,College Freshmen,Prompting,Standardized Tests,Grade 9,Grade 10,Grade 12,High School Students

This study explores correlations between human ratings of essay quality and component scores based on similar natural language processing indices and weighted through a principal component analysis. The results demonstrate that such component scores show small to large effects with human ratings and thus may be suitable to providing both summative and formative feedback in an automatic writing evaluation systems such as those found in Writing-Pal. [This paper was published in: &quot;Proceedings of the Twenty-Seventh International Florida Artificial Intelligence Research Society Conference&quot; (p.381-386). Association for the Advancement of Artificial Intelligence, 2014.]
From Generating in the Lab to Tutoring Systems in Classrooms
Author: McNamara, Danielle S.
Date: 2015-00-00
Type: Journal Articles
Subjects: Learning Processes,Instructional Design,Intelligent Tutoring Systems,Writing Instruction,Writing Strategies,Reading Comprehension,Persuasive Discourse,Essays,Feedback (Response),Writing Skills,Skill Development,Writing Evaluation,Natural Language Processing,Writing (Composition)

Work in cognitive and educational psychology examines a variety of phenomena related to the learning and retrieval of information. Indeed, Alice Healy, our honoree, and her colleagues have conducted a large body of groundbreaking research on this topic. In this article we discuss how 3 learning principles (the generation effect, deliberate practice and feedback, and antidotes to disengagement) discussed in Healy, Schneider, and Bourne (2012) have influenced the design of 2 intelligent tutoring systems that attempt to incorporate principles of skill and knowledge acquisition. Specifically, this article describes iSTART-2 and the Writing Pal, which provide students with instruction and practice using comprehension and writing strategies. iSTART-2 provides students with training to use effective comprehension strategies while self-explaining complex text. The Writing Pal provides students with instruction and practice to use basic writing strategies when writing persuasive essays. Underlying these systems are the assumptions that students should be provided with initial instruction that breaks down the tasks into component skills and that deliberate practice should include active generation with meaningful feedback, all while remaining engaging. The implementation of these assumptions is complicated by the ill-defined natures of comprehension and writing and supported by the use of various natural language processing techniques. We argue that there is value in attempting to integrate empirically supported learning principles into educational activities, even when there is imperfect alignment between them. Examples from the design of iSTART-2 and Writing Pal guide this argument.