ethanporcaro/eric-fulltext-meta huggingface dataset:
Higher Education in Focus 2019: North Dakota. Essential Performance Indicators for Midwestern States
Author: Midwestern Higher Education Compact
Date: 2020-00-00
Type: Reports - Evaluative
Subjects: Student Evaluation,Higher Education,Educational Attainment,Educational Indicators,Postsecondary Education,Employment Qualifications,Labor Market,Occupations,Racial Differences,Ethnicity,Academic Degrees,High School Graduates,High School Students,Income,Preschool Education,Enrollment,Grade 8,Mathematics Achievement,Reading Achievement,Science Achievement,Graduation Rate,College Entrance Examinations,Core Curriculum,College Readiness,College Attendance,College Credits,Young Adults,Adults,College Transfer Students,Public Colleges,College Graduates,Time to Degree,Student Financial Aid,Grants,Family Income,Paying for College,Two Year Colleges,Colleges,Student Loan Programs,Debt (Financial),State Aid,Tuition,Educational Finance,Expenditure per Student,Institutional Characteristics,Low Income Students,Federal Aid,Elementary Secondary Education

In the United States, approximately 65 percent of all jobs in 2020 will require some level of postsecondary education, and the demand will reach 72 percent in North Dakota. This report seeks to inform public discourse on higher education by providing key performance indicators relevant to the goal of improving educational attainment for a healthy economy and society. Performance indicators are categorized within five areas: (1) Preparation; (2) Participation; (3) Completion; (4) Affordability; and (5) Finance. Several indicators provide the national and Midwestern Higher Education Compact (MHEC) regional values as well as the median of the top five states in the nation as possible benchmarks. Performance indicators are disaggregated by race/ethnicity and family income when data are available.
Higher Education in Focus 2019: Wisconsin. Essential Performance Indicators for Midwestern States
Author: Midwestern Higher Education Compact
Date: 2020-00-00
Type: Reports - Evaluative
Subjects: Student Evaluation,Higher Education,Educational Attainment,Educational Indicators,Postsecondary Education,Employment Qualifications,Labor Market,Occupations,Racial Differences,Ethnicity,Academic Degrees,High School Graduates,High School Students,Income,Preschool Education,Enrollment,Grade 8,Mathematics Achievement,Reading Achievement,Science Achievement,Graduation Rate,College Entrance Examinations,Core Curriculum,College Readiness,College Attendance,College Credits,Young Adults,Adults,College Transfer Students,Public Colleges,College Graduates,Time to Degree,Student Financial Aid,Grants,Family Income,Paying for College,Two Year Colleges,Colleges,Student Loan Programs,Debt (Financial),State Aid,Tuition,Educational Finance,Expenditure per Student,Institutional Characteristics,Low Income Students,Federal Aid,Elementary Secondary Education

In the United States, approximately 65 percent of all jobs in 2020 will require some level of postsecondary education, and the demand will reach 62 percent in Wisconsin. This report seeks to inform public discourse on higher education by providing key performance indicators relevant to the goal of improving educational attainment for a healthy economy and society. Performance indicators are categorized within five areas: (1) Preparation; (2) Participation; (3) Completion; (4) Affordability; and (5) Finance. Several indicators provide the national and Midwestern Higher Education Compact (MHEC) regional values as well as the median of the top five states in the nation as possible benchmarks. Performance indicators are disaggregated by race/ethnicity and family income when data are available.
Higher Education in Focus 2019: Indiana. Essential Performance Indicators for Midwestern States
Author: Midwestern Higher Education Compact
Date: 2020-00-00
Type: Reports - Evaluative
Subjects: Student Evaluation,Higher Education,Educational Attainment,Educational Indicators,Postsecondary Education,Employment Qualifications,Labor Market,Occupations,Racial Differences,Ethnicity,Academic Degrees,High School Graduates,High School Students,Income,Preschool Education,Enrollment,Grade 8,Mathematics Achievement,Reading Achievement,Science Achievement,Graduation Rate,College Entrance Examinations,Core Curriculum,College Readiness,College Attendance,College Credits,Young Adults,Adults,College Transfer Students,Public Colleges,College Graduates,Time to Degree,Student Financial Aid,Grants,Family Income,Paying for College,Two Year Colleges,Colleges,Student Loan Programs,Debt (Financial),State Aid,Tuition,Educational Finance,Expenditure per Student,Institutional Characteristics,Low Income Students,Federal Aid,Elementary Secondary Education

In the United States, approximately 65 percent of all jobs in 2020 will require some level of postsecondary education, and the demand will reach 62 percent in Indiana. This report seeks to inform public discourse on higher education by providing key performance indicators relevant to the goal of improving educational attainment for a healthy economy and society. Performance indicators are categorized within five areas: (1) Preparation; (2) Participation; (3) Completion; (4) Affordability; and (5) Finance. Several indicators provide the national and Midwestern Higher Education Compact (MHEC) regional values as well as the median of the top five states in the nation as possible benchmarks. Performance indicators are disaggregated by race/ethnicity and family income when data are available.
Higher Education in Focus 2019: Kansas. Essential Performance Indicators for Midwestern States
Author: Midwestern Higher Education Compact
Date: 2020-00-00
Type: Reports - Evaluative
Subjects: Student Evaluation,Higher Education,Educational Attainment,Educational Indicators,Postsecondary Education,Employment Qualifications,Labor Market,Occupations,Racial Differences,Ethnicity,Academic Degrees,High School Graduates,High School Students,Income,Preschool Education,Enrollment,Grade 8,Mathematics Achievement,Reading Achievement,Science Achievement,Graduation Rate,College Entrance Examinations,Core Curriculum,College Readiness,College Attendance,College Credits,Young Adults,Adults,College Transfer Students,Public Colleges,College Graduates,Time to Degree,Student Financial Aid,Grants,Family Income,Paying for College,Two Year Colleges,Colleges,Student Loan Programs,Debt (Financial),State Aid,Tuition,Educational Finance,Expenditure per Student,Institutional Characteristics,Low Income Students,Federal Aid,Elementary Secondary Education

In the United States, approximately 65 percent of all jobs in 2020 will require some level of postsecondary education, and the demand will reach 71 percent in Kansas. This report seeks to inform public discourse on higher education by providing key performance indicators relevant to the goal of improving educational attainment for a healthy economy and society. Performance indicators are categorized within five areas: (1) Preparation; (2) Participation; (3) Completion; (4) Affordability; and (5) Finance. Several indicators provide the national and Midwestern Higher Education Compact (MHEC) regional values as well as the median of the top five states in the nation as possible benchmarks. Performance indicators are disaggregated by race/ethnicity and family income when data are available.
Higher Education in Focus 2019: Minnesota. Essential Performance Indicators for Midwestern States
Author: Midwestern Higher Education Compact
Date: 2020-00-00
Type: Reports - Evaluative
Subjects: Student Evaluation,Higher Education,Educational Attainment,Educational Indicators,Postsecondary Education,Employment Qualifications,Labor Market,Occupations,Racial Differences,Ethnicity,Academic Degrees,High School Graduates,High School Students,Income,Preschool Education,Enrollment,Grade 8,Mathematics Achievement,Reading Achievement,Science Achievement,Graduation Rate,College Entrance Examinations,Core Curriculum,College Readiness,College Attendance,College Credits,Young Adults,Adults,College Transfer Students,Public Colleges,College Graduates,Time to Degree,Student Financial Aid,Grants,Family Income,Paying for College,Two Year Colleges,Colleges,Student Loan Programs,Debt (Financial),State Aid,Tuition,Educational Finance,Expenditure per Student,Institutional Characteristics,Low Income Students,Federal Aid,Elementary Secondary Education

In the United States, approximately 65 percent of all jobs in 2020 will require some level of postsecondary education, and the demand will reach 74 percent in Minnesota. This report seeks to inform public discourse on higher education by providing key performance indicators relevant to the goal of improving educational attainment for a healthy economy and society. Performance indicators are categorized within five areas: (1) Preparation; (2) Participation; (3) Completion; (4) Affordability; and (5) Finance. Several indicators provide the national and Midwestern Higher Education Compact (MHEC) regional values as well as the median of the top five states in the nation as possible benchmarks. Performance indicators are disaggregated by race/ethnicity and family income when data are available.
Course Recommendation for University Environments
Author: Ma, Boxuan
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Course Selection (Students),College Students,Online Courses,Student Attitudes,Decision Making,Student Interests,Grade Prediction,Mathematics

Recommending courses to students is a fundamental and also challenging issue in the traditional university environment. Not exactly like course recommendation in MOOCs, the selection and recommendation for higher education is a non-trivial task as it depends on many factors that students need to consider. Although many studies on this topic have been proposed, most of them only focus either on historical course enrollment data or on models of predicting course outcomes to give recommendation results, regardless of multiple reasons behind course selection behavior. To address such a challenge, we first conduct a survey to show the underlying characteristic of the course selection of university students. According to the survey results, we propose a hybrid course recommendation framework based on multiple features. Our experimental result illustrates that our method outperforms other approaches. Also, our framework is easier to interpret, scrutinize, and explain than conventional black-box methods for course recommendation. [For the full proceedings, see ED607784.]
Analyzing Student Procrastination in MOOCs: A Multivariate Hawkes Approach
Author: Yao, Mengfan
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Large Group Instruction,Online Courses,Student Behavior,Study Habits,Time Factors (Learning),Time Management,Interaction,Instructional Materials,Computer Science Education,Learning Activities,Assignments,Models,Grades (Scholastic)

Student procrastination, as the voluntary delay of intended work despite expecting to be worse off for the delay, is an important factor with potentially negative consequences in student well-being and learning. In online educational settings such as Massive Open Online Courses (MOOCs), the effect of procrastination is considered to be even more prevalent and detrimental, as online courses are often self-paced and self-directed, where higher levels of self-regulated learning are expected from the students. Past research has mainly described students' procrastination by either static time-related measures (e.g. averaged starting time over all assignments per student), or by temporal models' parameters, under the assumptions that student activities take place at a constant rate (e.g. Homogeneous Poisson models), and that student interactions with one learning material are independent of interactions with another. In this work, we propose to consider the interdependence between the students' temporal activities while modeling their sequences in a continuous time scale. To this end, we propose to model the interaction sequence between each student and each course module, i.e. each module-student pair, as Multi-dimensional Hawkes processes, which not only capture the relationship between students' learning activities and their exogenous stimuli such as assignment deadlines, but also capture the endogenous responses within and between types of learning materials. Our experiments show that not only there exists dependencies between students' historical activities and the future ones when different types of learning materials are involved, such dependencies also provide meaningful interpretations in terms of students' procrastination behaviors. Furthermore, our findings show that in addition to association with delay, the parameters learned by multi-dimensional Hawkes processes provide more procrastination-related information and can improve our explanation of student grades. [For the full proceedings, see ED607784.]
A Dataset of Learnersourced Explanations from an Online Peer Instruction Environment
Author: Bhatanagar, Sameer
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Peer Teaching,Electronic Learning,Educational Technology,Web Based Instruction,Homework,Logical Thinking,Data Collection,Persuasive Discourse,STEM Education,Peer Evaluation,Biology,Chemistry,Physics,College Science,College Students

Online &quot;Peer Instruction&quot; has become prevalent in many &quot;flipped classroom&quot; settings, yet little work has been done to examine the content students generate in such a learning environment. This study characterizes a dataset generated by an open-source, web-based homework system that prompts students to first answer questions, and then provide explanations of their reasoning. Of particular interest in this dataset, is that students are also prompted to evaluate a subset of peer explanations based on how convincing they are, as part of the Peer Instruction learning script. Since these student &quot;votes&quot; are then used in the selection of what is shown to future learners, we cast this as an instance of &quot;learnersourcing,&quot; a paradigm that presents new research opportunities for the Learning Analytics community. This study characterizes a dataset from one &quot;Peer Instruction&quot; tool, that includes not only the student generated answers and explanations, but this novel &quot;vote&quot; attribute, which aims to capture how convincing each explanation is to other learners. The dataset includes longitudinal observations of student responses over the course of a semester, following groups from three STEM disciplines. The data is made available to interested researchers. [For the full proceedings, see ED607784.]
Are You Really a Team Player? Profiling of Collaborative Problem Solvers in an Online Environment
Author: Forsyth, Carol
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Teamwork,Profiles,Problem Solving,Cooperative Learning,Online Courses,College Students,Electronics,Group Dynamics

Collaborative problem solving (CPS) is considered a necessary skill for students and workers in the 21st century as the advent of technology requires more and more people to frequently work in teams. In the current study, we employed theoretically-grounded data mining techniques to identify four profiles of collaborative problem solvers interacting with an online electronics task. The profiles were created based on 11 theoretically-grounded CPS skills defined a priori. The resulting four profiles correlated in expected directions with in-task performance and had interesting relationships with external measures associated with prior knowledge and CPS skills. These results inform and partially replicate findings from our previous research using a similar approach on a smaller dataset. Implications and comparisons between the two studies will be discussed. [For the full proceedings, see ED607784.]
A Quantitative Machine Learning Approach to Master Students Admission for Professional Institutions
Author: Zhao, Yijun
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Artificial Intelligence,College Admission,Masters Programs,Professional Education,College Applicants,Prediction,Computer Science Education,Graduate Students,Statistical Analysis

Accurately predicting which students are best suited for graduate programs is beneficial to both students and colleges. In this paper, we propose a quantitative machine learning approach to predict an applicant's potential performance in the graduate program. Our work is based on a real world dataset consisting of MS in CS [Master of Science in Computer Science] students in the College of Computer and Information Science program at Northeastern University. We address two challenges associated with our task: subjectivity in the data due to change of admission committee membership from year to year and the shortage of training data. Our experimental results demonstrate an effective predictive model that could serve as a Focus of Attention (FOA) tool for an admission committee. [For the full proceedings, see ED607784.]
School Improvement Grant Funded Academic Programs: A Quantitative Correlational Analysis Study
Author: Tiller, Claude John, Jr.
Date: 2020-08-00
Type: Dissertations/Theses - Doctoral Dissertations
Subjects: Federal Legislation,Federal Aid,Educational Finance,Grants,Educational Improvement,Educational Innovation,Achievement Gap,Academic Achievement,Urban Schools,School Districts,Mathematics Instruction,Reading Instruction,Intervention,Scores,High School Students,College Entrance Examinations,Mathematics Tests,Reading Tests

In 2009 President Barack Obama signed into law the American Recovery and Reinvestment Act (Dragoset, et al., 2017). The Recovery Act served as the financial umbrella which helped to allocate $3 billion to fund School Improvement Grants to support states and school districts in championing original, innovative programs as well as to hire staff to narrow the academic achievement gap (Dragoset et al., 2017). The problem was the limited research on the extent to which math and reading intervention programs funded by the School Improvement Grant have impacted academic achievement. The purpose of this quantitative correlational analysis study was to determine if a correlation exists between the inclusion of School Improvement Grant funded academic programs and Scholastic Assessment Test scores in math and reading in a large urban school district in a northern U.S. State.
Auto Generation of Diagnostic Assessments and Their Quality Evaluation
Author: Dhavala, Soma
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Automation,Diagnostic Tests,Test Construction,Test Items,Item Banks,Evaluation Criteria,Item Response Theory

A good diagnostic assessment is one that can (i) discriminate between students of different abilities for a given skill set, (ii) be consistent with ground truth data and (iii) achieve this with as few assessment questions as possible. In this paper, we explore a method to meet these objectives. This is achieved by selecting questions from a question database and assembling them to create a diagnostic test paper according to a given configurable policy. We consider policies based on multiple attributes of the questions such as discrimination ability and behavioral parameters, as well as a baseline policy. We develop metrics to evaluate the policies and perform the evaluation using historical student attempt data on assessments conducted on an online learning platform, as well as on a pilot test on the platform administered to a subset of users. We are able to estimate student abilities 40% better with a diagnostic test as compared to baseline policy, with questions derived from a larger dataset. Further, empirical data from a pilot gave an 18% higher spread, denoting better discrimination, for our diagnostic test compared to the baseline test. [For the full proceedings, see ED607784.]
As College Students and Parents Demand Robust COVID-19 Response, University Finances Suffer. Policy Brief
Author: Mikula, Andrew
Date: 2020-05-00
Type: Reports - Descriptive
Subjects: COVID-19,Pandemics,Higher Education,College Students,Educational Finance,Distance Education,School Closing,Paying for College,Online Courses,Student Financial Aid,Federal Legislation,Federal Aid,Grants,Resource Allocation,Foreign Students

The COVID-19 crisis has all but upended higher education's business model, given the high susceptibility of college campuses to viral outbreaks. Since a vaccine will likely not be available until 2021, universities could be facing up to three consecutive semesters of remote learning and reduced revenue. Going forward, more students may choose to forego or delay their education if schools convert to online models to keep the virus at bay. The federal CARES Act provides some $12.5 billion in relief to colleges and universities, but advocates say it won't be enough, citing added financial aid burdens from families facing joblessness and difficulty recruiting international students subject to travel bans. Others have lamented the bailout of large, elite universities with exorbitant endowments while a small, private college in Wisconsin has already permanently closed due to COVID-19. The report includes a chart that shows the allocations from the federal government under the CARES Act for the largest colleges and universities in Massachusetts.
U.S. Department of Education FY 2012 Annual Performance Report and FY 2014 Annual Performance Plan
Author: Department of Education (ED), Office of the Chief Financial Officer (OCFO)
Date: 2013-04-10
Type: Reports - Descriptive
Subjects: Public Agencies,Federal Government,Strategic Planning,Institutional Mission,Administrative Organization,Postsecondary Education,Vocational Education,Adult Education,Elementary Secondary Education,Early Childhood Education,Equal Education,Educational Improvement,Organizational Objectives,Performance,Federal Legislation

The United States Department of Education's &quot;Annual Performance Report&quot; (APR) for fiscal year (FY) 2012 and &quot;Annual Performance Plan&quot; (APP) for fiscal year (FY) 2014 provide information on the current status of the performance of the Department's strategic goals for its &quot;FY 2011-14 Strategic Plan.&quot; This is the second year in which the Department has tracked the progress of the measures for its strategic goals under this Strategic Plan. While there are substantial trend data for many of its national measures of success, the Department has established a number of new measures with new baselines under its &quot;FY 2011-14 Strategic Plan.&quot; This year, the Department has consolidated its &quot;FY 2012 Annual Performance Report&quot; and the &quot;FY 2014 Annual Performance Plan&quot; in an effort to provide more complete and meaningful data on its past performance and its efforts to improve performance in coming fiscal years. The report is organized into the following sections: (1) Mission and Organizational Structure; (2) Overview; (3) Summary of Performance Results; (4) Performance Management Initiatives at the Department; (5) Performance Details; and (6) Appendices. [For the &quot;U.S. Department of Education Strategic Plan for Fiscal Years 2011-2014,&quot; see ED529722.]