ethanporcaro/eric-fulltext-meta huggingface dataset:
Predicting Reading Comprehension Scores of Elementary School Students
Author: Nie, Bruce
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Reading Comprehension,Word Order,Sentence Structure,Grade 3,Foreign Countries,Difficulty Level,Oral Language,Vocabulary Skills,Natural Language Processing,Student Needs,Syntax

A child's ability to understand text (reading comprehension) can greatly impact both their ability to learn in the classroom and their future contributions to society. Reading comprehension draws on oral language; behavioural measures of knowledge at the word and sentence levels have been shown to be related to children's reading comprehension. In this study, we examined the impact of word and sentence level text-features on children's reading comprehension. We built a predictive model that uses natural language processing techniques to predict the question-level performance of students on reading comprehension tests. We showed that, compared to a model that used measures of student knowledge and subskills alone, a model that used features of sentence complexity, lexical surprisal, rare word use, and general context improved prediction accuracy by more than four percentage points. Our subsequent analyses revealed that these features compensate for the shortcomings of each other and work together to produce maximal performance. This provides insight into how different characteristics of the text and questions can be used to predict student performance, leading to new ideas about how text and reading comprehension interact. Our work also suggests that using a combination of text features could support the adaptation of reading materials to meet student needs. [For the full proceedings, see ED623995.]
Insta-Reviewer: A Data-Driven Approach for Generating Instant Feedback on Students&apos; Project Reports
Author: Jia, Qinjin
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Student Projects,Feedback (Response),Natural Language Processing,Guidelines,Intelligent Tutoring Systems,Computer Software,Learning Analytics,Student Evaluation,Ethics,Instructional Effectiveness,Evaluators,Comparative Analysis,Artificial Intelligence,Privacy

Providing timely feedback is crucial in promoting academic achievement and student success. However, for multifarious reasons (e.g., limited teaching resources), feedback often arrives too late for learners to act on the feedback and improve learning. Thus, automated feedback systems have emerged to tackle educational tasks in various domains, including novice programming, short-essay writing, and open-ended questions. However, to the best of our knowledge, no previous study has investigated automated feedback generation on students' project reports. In this paper, we present a novel data-driven system, named &quot;Insta-Reviewer,&quot; for automatically generating instant feedback on students' project reports, using state-of-the-art natural language processing (NLP) models. We also propose a framework for manually evaluating system-generated feedback. Experimental results show that feedback generated by Insta-Reviewer on real students' project reports can achieve near-human performance. Our work demonstrates the feasibility of automatic feedback generation for students' project reports while highlighting several prominent challenges for future research. [For the full proceedings, see ED623995.]
Investigating Temporal Dynamics Underlying Successful Collaborative Problem Solving Behaviors with Multilevel Vector Autoregression
Author: Zhou, Guojing
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Cooperative Learning,Problem Solving,College Students,Physics,Videoconferencing,Educational Games,Science Instruction,Comparative Analysis,Peer Relationship,Guidelines,Models,Coding,Interaction Process Analysis

In collaborative problem solving (CPS), people's actions are interactive, interdependent, and temporal. However, it is unclear how actions temporally relate to each other and what are the temporal similarities and differences between successful vs. unsuccessful CPS processes. As such, we apply a temporal analysis approach, Multilevel Vector Autoregression (mlVAR) to investigate CPS processes. Our data were collected from college students who collaborated in triads via a video-conferencing tool (Zoom) to collaborately engage a physics learning game. Video recordings of their verbal interactions were transcribed, coded using a validated CPS framework, and organized into sequences of 10-second windows. Then, mlVAR was applied to the successful vs. unsuccessful CPS sequences to build temporal models for each. A comparison of the models together with a qualitative analysis of the transcripts revealed six temporal relationships common to both, six unique to successful level attempts, and another eight unique to unsuccessful level attempts only. Generally, for successful outcomes, people were likely to answer clarification questions with reasons and to ask for suggestions according to the current game situation, while for unsuccessful CPS level attempts, people were more likely to struggle with unclear instructions and to respond to inappropriate ideas. Overall, our results suggest that mlVAR is an effective approach for temporal analyses of CPS processes by identifying relationships that go beyond a coding and counting approach. [For the full proceedings, see ED623995.]
Investigating the Effect of Automated Feedback on Learning Behavior in MOOCs for Programming
Author: Gabbay, Hagit
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Automation,Feedback (Response),Student Behavior,MOOCs,Programming,Computer Science Education,Electronic Learning,Learner Engagement,Academic Persistence,Academic Achievement

The challenge of learning programming in a MOOC is twofold: acquiring programming skills and learning online, independently. Automated testing and feedback systems, often offered in programming courses, may scaffold MOOC learners by providing immediate feedback and unlimited re-submissions of code assignments. However, research still lacks empirical evidence of their effect on learning behavior of MOOC learners, with diverse backgrounds and goals. Addressing this gap, we investigated the connections between the use of automated feedback system and learning behavior measures, relevant for MOOCs: engagement, persistence and performance. Further, two subjective measures of success are examined: sense of learning and intention fulfilment. In an experimental design, we analyzed data of active learners in a Python programming MOOC (N=4652), comparing an experimental group provided with automated feedback with a control group that did not. In examining the effect of automated feedback, prior knowledge of programming and Python was considered. Empirical evidence was found for the relation between automated feedback usage and a higher engagement and better performance, as well as higher attendance in &quot;active watchers&quot; and &quot;high-performed completers&quot; clusters, obtained by cluster analysis. Learners reports on their experience with the automated feedback system supported these findings. Regarding the subjective measures of success, however, no difference was found between groups. Our study and the offered future research may contribute to the considerations regarding the integration of automated feedback in MOOCs for programming. [For the full proceedings, see ED623995.]
Combining Domain Modelling and Student Modelling Techniques in a Single Automated Pipeline
Author: Picones, Gio
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Prediction,Academic Achievement,Learning Analytics,Concept Mapping,Mastery Learning,Scores,Programming Languages,Computer Science Education,Accuracy,Least Squares Statistics,Evaluation Methods,High School Students,Models,Computer Software

In this paper, we propose a novel approach to combine domain modelling and student modelling techniques in a single, automated pipeline which does not require expert knowledge and can be used to predict future student performance. Domain modelling techniques map questions to concepts and student modelling techniques generate a mastery score for a concept. We conducted an evaluation using six large datasets from a Python programming course, evaluating the performance of different domain and student modelling techniques. The results showed that it is possible to develop a successful and fully automated pipeline which learns from raw data. The best results were achieved using alternating least squares on hill-climbing Q-matrices as domain modelling and exponential moving average as student modelling. This method outperformed all baselines in terms of accuracy and showed excellent run time. [For the full proceedings, see ED623995.]
Is There Method in Your Mistakes? Capturing Error Contexts by Graph Mining for Targeted Feedback
Author: Jahnke, Maximilian
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Programming,Coding,Computer Science Education,Error Patterns,Feedback (Response),Graphs,Data Analysis,Students

The value of an instructor is that she exactly recognizes what the learner is struggling with and provides constructive feedback straight to the point. This work aims at a step towards this type of feedback in the context of an introductory programming course, where students perform program execution tracing to align their understanding of Java instructions with reality. The students' submissions are analyzed for repeating mistakes across different exercises by representing the context surrounding the error by a graph and applying graph mining techniques to discover their common grounds. The patterns need to be annotated only once and help to address misconceptions of individual students. They may also be used to select follow-up exercises automatically, that contain the same intricacy. [For the full proceedings, see ED623995.]
Materialist Epistemology Lends Design Wings: Educational Design as an Embodied Process
Author: Nathan, Mitchell J.
Date: 2021-00-00
Type: Journal Articles
Subjects: Epistemology,Instructional Design,Video Games,Technology Uses in Education,Secondary School Science,Geometry,High Schools,Mathematical Logic,Validity,Educational Technology

Materialist design is presented as an embodied perspective on educational design that can be applied to redesign of classroom-based learning environments. Materialist design is informed by a framework of materialist epistemology, which positions material innovation on equal placement with symbol-based formal theory. Historical examples of Einstein's conceptual reliance on trains for his Theory of Relativity and the Wright brothers' use of wind tunnels in aeronautics illustrate how materialist design drives progress on complex design problems. A key aspect is the application of scale-down methodology, where complex systems are reconceptualized as interactions among nearly decomposable subsystems that can be redesigned and integrated back into the entire system. The application of materialist design is illustrated with the redesign of an embodied video game that uses real-time motion capture technology to promote high school geometry reasoning and proof, following its use in an ethnically and linguistically diverse classroom. Our embodied perspective offers particular insights for understanding and implementing designs of complex learning environments, and assessing their influences on educational practices and student outcomes. [This article was published in &quot;Educational Technology Research and Development&quot; (EJ1310136).]
&quot;Wearing a Mask Won&apos;t Protect Us from Our History&quot;: The Impact of COVID-19 on Black Children and Families. Social Policy Report. Volume 35, Number 2
Author: Bogan, Erin
Date: 2022-00-00
Type: Reports - Evaluative
Subjects: COVID-19,Pandemics,African Americans,Racial Differences,Racism,Public Policy,Educational Policy,Equal Education,Housing,Ownership,Equal Opportunities (Jobs),Children,Adults,United States History,At Risk Persons,Distance Education,Educational Technology,School Closing,Access to Computers,Discipline,Educational Finance,Family Environment

The data on COVID-19 show an irrefutable and disturbing pattern: Black Americans are contracting and dying from COVID-19 at rates that far exceed other racial and ethnic groups. Due to historical and current iterations of racism, Black Americans have been forced into conditions that elevate their risk for COVID-19 and consequently place Black children at the epicenter of loss across multiple domains of life. The current paper highlights the impact of the pandemic on Black children at the individual, family, and school levels. Based on an understanding of the influence of structural racism on COVID-19 disparities, policy recommendations are provided that focus on equitable access to quality education, home ownership, and employment to fully address the needs of Black children and families during and after the pandemic. Research, practice, and policy recommendations are made to journal editors, funding agencies, grant review panels, and researchers regarding how research on COVID-19 should be framed to inform intervention efforts aimed at improving the situation of Black children and families.
State of Working Pennsylvania 2019
Author: Herzenberg, Stephen
Date: 2019-08-00
Type: Numerical/Quantitative Data
Subjects: Economic Development,Wages,Unemployment,Employment Patterns,Labor Market,Minimum Wage,Working Hours,Teacher Salaries,African Americans,Salary Wage Differentials

Tom Wilson, the chair of the executive committee of the U.S. Chamber of Commerce published an op-ed titled &quot;Save Capitalism by Paying People More.&quot; Wilson acknowledges in blunt terms that ordinary working Americans are not flourishing economically. This year's annual &quot;The State of Working Pennsylvania&quot; documents the accuracy of Tom Wilson's observation in Pennsylvania. In 2018, for the first time since 2001, Pennsylvania workers enjoyed wage increases across the board--3.1% on average across the entire wage distribution (i.e. from the 10th percentile to the 90th percentile). These gains reflect what is now the longest economic expansion in U.S. history and an unemployment rate in Pennsylvania below 4% for the first time since before 1976. This report recommends following Wilson's advice that business leaders create specific goals and execute a plan to grow more higher-paying jobs. The authors propose using the already existing Middle Class Task Force, which includes labor as well as business representation, to develop the plan and execution strategy. After 40 or more years of business leadership focused single-mindedly on profits, it will take a sustained team effort to create a kinder, gentler capitalism that we can all embrace.
Competency-Based Education Pilot Program Evaluation and Recommendation: Pursuant to 110 ILCS 148/
Author: Illinois State Board of Education (ISBE)
Date: 2022-08-00
Type: Reports - Descriptive
Subjects: Competency Based Education,Pilot Projects,Program Evaluation,Elementary Secondary Education

In accordance with the Postsecondary and Workforce Readiness (PWR) Act, the Illinois State Board of Education conducted an evaluation of the Competency-Based Education Pilot Program during the 2021-22 school year using external evaluators who analyzed data collected from school districts participating in the program. This report includes a summary of the evaluation and the Superintendent's recommendation to continue the pilot program for a minimum of three years.
English Learners in Illinois, SY 2020-21. Statistical Report
Author: Illinois State Board of Education (ISBE)
Date: 2022-06-00
Type: Reports - Descriptive
Subjects: English Language Learners,Enrollment,Preschool Education,Elementary Secondary Education,Bilingual Education,Student Placement,Teacher Qualifications,English (Second Language),Geographic Regions,Counties,Instructional Program Divisions,Racial Differences,Ethnicity,Students with Disabilities,Individualized Education Programs,Teaching Methods,Spanish,Language of Instruction,Self Contained Classrooms,Language Proficiency,Academic Achievement,Standardized Tests

The Illinois State Board of Education (ISBE) is committed to the cognitive, linguistic, and socio-emotional development of all children in Illinois. ISBE's Multilingual Department is focused on ensuring that students with diverse linguistic repertoires and those who may benefit from additional support have equitable access to academic instruction in English. Data for this report were extracted by the Data Strategies and Analytics Department at the Illinois State Board of Education from the SY 2020-21 Student Information System Enrollment, EL Record Data, and I-Star Student Approval. The data values were calculated based on the 2021 Report Card Snapshot. This report provides statistics regarding: (1) English Learners (EL) Enrollment per Type of Program; (2) Endorsements of Teachers Serving EL; (3) EL Student Enrollment by County; (4) EL Student Count by Language; (5) EL Student Count by Grade and Race/Ethnicity; (6) EL Students with Individualized Education Programs (IEPs); (7) EL Student Count by Instructional Design; (8) EL Student Performance on ACCESS; and (9) EL Students Performance on State ELA and Math Assessments. [This report was produced by the Illinois State Board of Education's Multilingual Department and the Data Strategies and Analytics Department. For the 2019-20 report, see ED618683.]
ISBE Strategic Plan: 2021-2022 Annual Status Report
Author: Illinois State Board of Education (ISBE)
Date: 2022-06-00
Type: Reports - Descriptive
Subjects: Strategic Planning,COVID-19,Pandemics,State Boards of Education,Teacher Effectiveness,Equal Education,School Districts,Elementary Secondary Education,Well Being,Educational Environment,Educational Strategies,Capacity Building,Advisory Committees,Academic Achievement,Achievement Gains,Barriers,Curriculum Implementation,State Standards,Faculty Development,Educational Improvement,Safety,Culturally Relevant Education,Minority Groups,History,Inclusion,Resource Allocation,Enrollment Trends,Minority Group Students,White Students,Special Education,Preschool Education,Bilingual Education Programs,Partnerships in Education,Teacher Persistence

After almost two years of uncertainty and unparalleled challenges for students, parents, teachers, staff, and administrators across the state, the Illinois State Board of Education continues to look to their Strategic Plan for clarity and confidence in helping them navigate toward their agency's vision as they look past the COVID-19 pandemic. This annual status report describes their progress toward implementing the Strategic Plan in the 2021-2022 academic year. For each of the following goals, a table describing &quot;where we started,&quot; activities and accomplishments, and what's next is included: (1) Equity goal; (2) Student learning; (3) Learning conditions; and (4) Elevating Educators. [For &quot;ISBE Strategic Plan: 2020-2021 Annual Status Report,&quot; see ED624115. For &quot;Illinois State Board of Education 2020-2023 Strategic Plan,&quot; see ED624118.]
State of Working Pennsylvania 2021
Author: Herzenberg, Stephen
Date: 2021-09-00
Type: Numerical/Quantitative Data
Subjects: Economic Development,Wages,Unemployment,Labor Market,Employment Patterns,Minimum Wage,African Americans,Public Policy,State Policy,COVID-19,Pandemics,Salary Wage Differentials,Hispanic Americans,Hospitality Occupations,Federal Aid,Federal Legislation,Housing,Insurance

The United States and Pennsylvania economies are at a pivot point: Will we build forward better or will we build back the same? Will we make things even worse? This report revisits the policy choices that lie ahead. Most of this annual checkup on the Pennsylvania economy, the 26th &quot;State of Working Pennsylvania,&quot; presents labor market and other economic data that amount to a statement of need: Pennsylvania and the United States must, finally and firmly, enact national and state policies that make our economy work for all, and for the common good. [For the 2019 report, see ED624085.]
Addendum to the Illinois Charter School Biennial Report, 2019-20, 2020-21
Author: Illinois State Board of Education (ISBE)
Date: 2022-08-00
Type: Numerical/Quantitative Data
Subjects: Charter Schools,COVID-19,Pandemics,Annual Reports,School Districts,Federal Legislation,Enrollment Trends,Elementary Secondary Education,School Closing,Low Income Students,English Language Learners,Students with Disabilities,Comparative Analysis,Grants,College Entrance Examinations,Dropout Rate,Graduation Rate,Minority Group Students,Mathematics Achievement,Language Arts

The &quot;Illinois Charter School Biennial Report&quot; for the 2019-20 and 2020-21 school years was originally published in January 2022 but was incomplete. The COVID-19 pandemic interrupted the normal administration and reporting of statewide student performance data during this time period. More specifically, ISBE received a statewide waiver of all federally required assessments, summative designations, and reporting on accountability metrics on the Illinois Report Card for the 2019-20 school year and allowed districts to select between two assessment windows (spring or fall 2021) for the 2020-21 school year. Accordingly, only partial data was available for the 2020-21 school year at the time of this report's initial release. ISBE republished school year 2020-21 data that included districts that selected the fall 2021 testing window on July 21, 2022. This Addendum reprints the charts included in Section 5 of the original report updated with complete academic data for the 2020-21 school year. [For &quot;Illinois Charter School Biennial Report. 2019-2020, 2020-2021,&quot; see ED618454.]
State of Working Pennsylvania 2022
Author: Herzenberg, Stephen
Date: 2022-08-00
Type: Numerical/Quantitative Data
Subjects: Economic Development,Wages,Unemployment,Employment Patterns,Labor Market,Minimum Wage,African Americans,Hispanic Americans,Public Policy,State Policy,COVID-19,Pandemics,Salary Wage Differentials,Economic Climate,Labor Turnover,Corporations,Unions,Food Service

The COVID-19 pandemic brought unprecedented economic and policy challenges to the United States and other countries. Navigating out of the pandemic slowdown is another novel experience, which makes it more difficult to answer the question addressed each year in the &quot;State of Working Pennsylvania&quot;: How is the Pennsylvania economy performing from the perspective of working people? Is Pennsylvania about to enter a recession brought on by Federal Reserve interest rate hikes? Will inflation persist and erode real wages or will inflation decline quickly while real wages grow? Will the recent shrinkage of the Pennsylvania labor force--the &quot;Great Resignation&quot;--result in an enduring and long overdue increase in workers' power relative to employers in the labor market? This year's report contains sections on the following topics: (1) The State of the Pennsylvania Economy; (2) The State of the Pennsylvania Labor Market--How Great the Resignation?; (3) The State of Wage Growth in Pennsylvania; (4) The State of Pennsylvania's Low-Wage Labor Market; (5) The State of Corporate Profits and Inflation; (6) The State of Unions in Pennsylvania; and (7) Policy: Door Number One or Door Number Two. [For the 2021 report, see ED624089.]