ethanporcaro/eric-fulltext-meta huggingface dataset:
Student Subtyping via EM-Inverse Reinforcement Learning
Author: Yang, Xi
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Grouping (Instructional Purposes),Learning Strategies,Artificial Intelligence,Learning Analytics,College Students,Cluster Grouping

In the learning sciences, heterogeneity among students usually leads to different learning strategies or patterns and may require different types of instructional interventions. Therefore, it is important to investigate student subtyping, which is to group students into subtypes based on their learning patterns. Subtyping from complex student learning processes is often challenging because of the information heterogeneity and temporal dynamics. Various inverse reinforcement learning (IRL) algorithms have been successfully employed in many domains for inducing policies from the trajectories and recently has been applied for analyzing students' temporal logs to identify their domain knowledge patterns. IRL was originally designed to model the data by assuming that all trajectories have a &quot;single&quot; pattern or strategy. Due to the heterogeneity among students, their strategies can vary greatly and the design of traditional IRL may lead to suboptimal performance. In this paper, we applied a novel expectation-maximization IRL (EM-IRL) to extract heterogeneous learning strategies from sequential data collected from three simulation environments and real-world longitudinal students' logs. Experiments on simulation environments showed that EM-IRL can successfully identify different policies from the heterogeneous sequences with different strategies. Furthermore, experimental results from our educational dataset showed that EM-IRL can be used to obtain different student subtypes: a &quot;learning-oriented&quot; subtype who learned the material as much as possible regardless of the time in that they spent significantly more time than the other two subtypes and learned significantly; an &quot;efficient-oriented&quot; subtype who learned efficiently in that they not only learned significantly but also spent less time than the first subtype; a &quot;no learning&quot; subtype who spent less amount of time than first subtype and failed to learn. [For the full proceedings, see ED607784.]
Towards Accurate and Fair Prediction of College Success: Evaluating Different Sources of Student Data
Author: Yu, Renzhe
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Prediction,Accuracy,College Students,Success,Data,Integrated Learning Systems,Student Surveys,Predictive Validity,Disadvantaged,Learning Analytics,Bias

In higher education, predictive analytics can provide actionable insights to diverse stakeholders such as administrators, instructors, and students. Separate feature sets are typically used for different prediction tasks, e.g., student activity logs for predicting in-course performance and registrar data for predicting long-term college success. However, little is known about the overall utility of different data sources across prediction tasks and the fairness of their predictions with respect to different subpopulations. Using data from over 2,000 college students at a large public university, we examined the utility of institutional data, learning management system (LMS) data, and survey data for accurately and fairly predicting short-term and long-term student success. We found that institutional data and LMS data both have decent predictive power, but survey data shows very little predictive utility. Combining institutional data with LMS data leads to even higher accuracy than using either alone. In terms of fairness, using institutional data consistently underestimates historically disadvantaged student subpopulations more than their peers, whereas LMS data tend to overestimate some of these groups more often. Combining the two data sources does not fully neutralize the biases and still leads to high rates of underestimation among disadvantaged groups. Moreover, algorithmic biases affect not only demographic minorities but also students with acquired disadvantages. These analyses serve to inform more cost-effective and equitable use of student data for predictive analytics applications in higher education. [For the full proceedings, see ED607784.]
Whose Truth Is the &quot;Ground Truth&quot;? College Admissions Essays and Bias in Word Vector Evaluation Methods
Author: Arthurs, Noah
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: College Admission,Essays,Evaluation Methods,Natural Language Processing,Social Bias,Ethics,Semantics,Low Income Students,Public Colleges,Family Income

Word vectors are widely used as input features in natural language processing (NLP) tasks. Researchers have found that word vectors often encode the biases of society, and steps have been taken towards debiasing the vectors themselves. However, little has been said about the fairness of the methods used to evaluate the quality of vectors. Analogical and word similarity tasks are commonplace, but both rely on purportedly ground truth statements about the semantic relationships between words (e.g. &quot;man is to woman as king is to queen&quot;). These analogies look reasonable when only taking into account the literal meanings of words, but two issues arise: (1) people don't always use words in a literal sense, and (2) the same word may be used differently by different groups of people. In this paper, we split a dataset of over 800,000 college admissions essays into quartiles based on reported household income (RHI) and train sets of word vectors on each quartile. We then test these sets of vectors on common intrinsic evaluation tasks. We find that vectors trained on the essays of higher income students encode more of each task's target semantic relationships than vectors trained on the essays of lower income students. These results hold even when controlling for word frequency. We conclude that the tasks themselves are biased towards the writing of higher income students, and we challenge the notion that there exist ground truth semantic relationships that word vectors must encode in order to be useful. [For the full proceedings, see ED607784.]
The NAEP EDM Competition: On the Value of Theory-Driven Psychometrics and Machine Learning for Predictions Based on Log Data
Author: Zehner, Fabian
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Data Analysis,Competition,Classification,Prediction,Psychometrics,Statistical Analysis,Reaction Time,Efficiency,National Competency Tests,Response Style (Tests)

The &quot;2nd Annual WPI-UMASS-UPENN EDM Data Mining Challenge&quot; required contestants to predict efficient testtaking based on log data. In this paper, we describe our theory-driven and psychometric modeling approach. For feature engineering, we employed the Log-Normal Response Time Model for estimating latent person speed, and the Generalized Partial Credit Model for estimating latent person ability. Additionally, we adopted an n-gram feature approach for event sequences. For training a multi-label classifier, we distinguished inefficient test takers who were going too fast and those who were going too slow, instead of using the provided binary target label. Our best-performing ensemble classifier comprised three sets of low-dimensional classifiers, dominated by test-taker speed. While our classifier reached moderate performance, relative to competition leaderboard, our approach makes two important contributions. First, we show how explainable classifiers could provide meaningful predictions if results can be contextualized to test administrators who wish to intervene or take action. Second, our re-engineering of test scores enabled us to incorporate person ability into the estimation. However, ability was hardly predictive of efficient behavior, leading to the conclusion that the target label's validity needs to be questioned. The paper concludes with tools that are helpful for substantively meaningful log data mining. [For the full proceedings, see ED607784.]
Reopening of Schools in the 2020-2021 School Year: Parent Survey Results. Research Brief. Volume 1907
Author: Hernandez, Vanessa Gonzalez
Date: 2020-06-00
Type: Reports - Research
Subjects: Public Schools,School Districts,COVID-19,Pandemics,School Closing,Parent Attitudes,Online Courses,Distance Education,Conventional Instruction,Blended Learning,Employed Parents,School Schedules,Student Characteristics,Prevention,Health Promotion,Hygiene,Recess Breaks,Student Needs,Instructional Program Divisions,Preschool Education,Elementary Secondary Education,Student Transportation,Attendance,Racial Differences,Ethnicity,Socioeconomic Status

As the state and city begin to reopen following the COVID-19 pandemic, Miami-Dade County Public Schools (M-DCPS) conducted an online Districtwide survey of parents of students who currently attend and who plan to attend M-DCPS during the 2020-2021 school year. The Reopening of Schools Parent Survey gathered information from parents regarding their preferences and attitudes toward the reopening of schools. Over 100,000 respondents completed the survey, representing over 155,000 students who will be attending M-DCPS in 2020-2021. Results from the survey indicate a nearly equal preference for the three attendance options presented to the parents (physical, distance learning, or combination). The strongest contributor to attendance preference was anticipated parental work arrangement. Moreover, parents indicated a clear preference for their children attending school all days of the week in shifts or on alternate days. These results continue to hold even when considering grade level and other students characteristics such as ESE, ELL, and gifted. Most parents agreed with a series of preventative measures M-DCPS is considering for physical attendance including temperature checks, washing hands, wearing face masks, eating meals in classrooms, and one-way movement in school buildings; less support was found for the elimination of outdoor recess. While attitudes and preferences might change as more sectors of the country begin to reopen and other developments with COVID-19 occur, the overwhelming response to this survey provided a large representative sample that is similar to the District as a whole. Survey results revealed an understanding of parent choices, including their desire to have the same option for all children in the household regardless of grade level and having their options closely tied to their work arrangement.
Need for Sleep: Causes and Consequences of Insufficient Sleep in Adolescents. Can Delaying School Start Times Help? Information Capsule. Volume 1902
Author: Gonzalez Hernandez, Vanessa
Date: 2020-00-00
Type: Reports - Research
Subjects: Sleep,Fatigue (Biology),School Schedules,Educational Change,Attendance,Academic Achievement,Student Needs,High School Students,Middle School Students,Student Behavior,Accidents,Puberty

Delaying school start times may help in alleviating some of the negative effects of the lack of sleep among adolescents. There appears to be a consensus among various researchers that later school start times are associated with an increase in total sleep time in adolescents, although some researchers find that such an effect might be fleeting. In addition, later school start times are associated with better behavioral health, decreased tardiness, and a decreased rate of vehicle crashes for school-aged drivers. Findings on the effects of delayed school start times on student academic performance appear to be inconsistent.
2020 Cost of Recruiting an Undergraduate Student Report: Benchmarking Data for Four-Year Colleges and Universities
Author: Ruffalo Noel Levitz (RNL)
Date: 2020-00-00
Type: Reports - Research
Subjects: Undergraduate Students,Student Recruitment,Costs,College Admission,Enrollment Rate,School Personnel,Enrollment Management,Benchmarking,Budgets,Resource Allocation,College Transfer Students,Foreign Students,Public Colleges,Private Colleges

What does it cost to recruit an undergraduate student? How many front-line staff do campuses have for each new undergraduate? Where do campuses expect to focus their budget resources? &quot;The 2020 Cost of Recruiting an Undergraduate Student Report&quot; provides findings from a survey of campus enrollment managers and admissions directors. This report explores key metrics on recruitment and marketing, including: (1) metrics for first-time-in-college undergraduates, transfer students, and international students; (2) data on staffing levels related to recruitment; and (3) survey results on budget priorities and allocations. This report has data from 66 private and public four-year institutions and provides useful insights on how much campuses spend on their recruitment efforts. [For the previous report &quot;2018 Cost of Recruiting an Undergraduate Student Report: Benchmarking Data for Recruitment and Admissions Costs and Enrollment Staffing Levels for Four-Year Colleges and Universities, Based on a Poll of Campus Officials,&quot; see ED608075.]
Process Based Analysis on Scientific Inquiry Tasks Using Large-Scale National Assessment Dataset
Author: Gong, Tao
Date: 2020-07-00
Type: Speeches/Meeting Papers
Subjects: Science Experiments,Time,Inquiry,Scores,High Achievement,Low Achievement,Science Process Skills,National Competency Tests,Science Tests,Grade 8,Grade 12,Middle School Students,High School Students

This paper investigates differences in students having various scores when designing controlled experiments in two types of scientific inquiry tasks (a fair test and an exhaustive test). We measure temporal features of preparation time and execution time, which reflect respectively the process of question understanding and answer planning and that of executing the control-of-variables strategy in answer formulation. We also measure mean execution time per answering event to reflect the efficiency of answering events. Results show that: in the fair test, the full score students showed less execution time than the lowest score ones; in the exhaustive test, the full score students showed more execution time than the lowest score ones; but in both tests, the high-performing students had less mean execution time than the low-performing ones. These results reveal that despite test differences, students who appropriately apply the control-of-variables strategy in these tests are more goal-directed and efficient in planning and executing response strategies than those who fail to do so. This study provides process-based features and large-scale evidence of scientific inquiry practice in in educational assessment. [For the full proceedings, see ED607784.]
2018 Cost of Recruiting an Undergraduate Student Report: Benchmarking Data for Recruitment and Admissions Costs and Enrollment Staffing Levels for Four-Year Colleges and Universities, Based on a Poll of Campus Officials
Author: Ruffalo Noel Levitz (RNL)
Date: 2018-00-00
Type: Reports - Research
Subjects: Undergraduate Students,Student Recruitment,Costs,College Admission,Enrollment Rate,School Personnel,Enrollment Management,Benchmarking,Budgets,Resource Allocation,College Transfer Students,Foreign Students,Public Colleges,Private Colleges

This report offers information on how much institutions are spending on marketing and recruitment compared to their peers, including key benchmarks for return-on-investment, budgeting, resource allocations, recruiting transfer and international students, and more. The report provides separate findings for private vs. public institutions and includes recommendations on how to use the information to evaluate spending. Among the findings: (1) four-year private institutions spend 3x more than four-year public institutions to recruit a single student; (2) nearly one in three four-year privates said they had a budget decrease for the 2017-18 year, compared to just 7 percent of four-year publics; and (3) while 61 percent of public institutions said their allocations for traditional marketing would remain the same, nearly 1 in 4 publics said it would decrease. Contrast that with 61 percent of campuses saying their digital marketing would increase.
2018-2019 Overview of M-DCPS&apos; Academic Performance. Information Capsule. Volume 1901
Author: Tirado, Andrea
Date: 2019-10-00
Type: Reports - Research
Subjects: Achievement Tests,Standardized Tests,State Standards,Public Schools,Academic Achievement,Reading Tests,Writing Tests,Mathematics Tests,Achievement Gains,Science Tests,Algebra,Geometry,Achievement Gap,African American Students,Hispanic American Students,White Students,Middle School Students,High School Students,Biology,Civics,History Instruction,Elementary School Students

This Information Capsule answers the most frequently asked questions about Miami-Dade County Public Schools' (M-DCPS) academic performance during the 2018-2019 school year. M-DCPS earned a District Performance Grade of &quot;A&quot; in 2019. The percentage of M-DCPS schools earning a School Performance Grade of &quot;A&quot; remained stable from 2018 to 2019. No traditional M-DCPS school or charter school received a grade of &quot;F.&quot; From 2018 to 2019, M-DCPS students' performance on the Florida Standards Assessments (FSA) English Language Arts (ELA) and Mathematics improved at the majority of grade levels tested. Performance on the FCAT 2.0 Science decreased at both grade levels tested. Across all grade levels, the percentage of first-time test takers scoring at the higher Achievement Levels increased on the two FSA End-of-Course (EOC) assessments (Algebra 1 and Geometry) from 2018 to 2019. Similarly, the percentage of first-time test takers scoring at the higher Achievement Levels increased on the three Next Generation Sunshine State Standards (NGSSS) EOC assessments (Biology 1, Civics, and U.S. History). From 2018 to 2019, the District made progress reducing the achievement gap between Black and White students and between Hispanic and White students at selected grade levels on the FSA and FCAT 2.0 assessments. The gap between Black and White students and between Hispanic and White students was also reduced on two of the three NGSSS EOC exams (U.S History and Civics) for all grades and on Biology 1 for grade 10.
Shortages and Inequities in the Philadelphia Public School Teacher Workforce
Author: Pierce, Jill C.
Date: 2020-08-00
Type: Reports - Research-practitioner Partnerships
Subjects: Elementary Secondary Education,Public Schools,Teacher Shortage,Teacher Recruitment,Teacher Persistence,Minority Group Students,Teacher Student Ratio,Teacher Certification,Minority Group Teachers,Teacher Salaries,Urban Schools,Charter Schools,Alternative Teacher Certification,Racial Bias,Ethnicity,Teaching Experience,Public School Teachers

Educators are centrally important in the fight for racial justice and access to high-quality education. This has never been more true than in the age of the COVID-19 pandemic, as the public acknowledges the crucial role that our public school teachers play in the well-being of our children and of our society at large. Yet our teacher workforce is under duress. As has been true across the country, Philadelphia's public schools faced teacher shortages even before the pandemic. COVID-19 is likely to result in further contraction of the teacher preparation pipeline, and in higher rates of attrition as teachers exit their professions due to physical or mental health stressors. And while we know that all students-particularly students of color-benefit from a diverse teaching force, the number of teachers of color is shrinking as well. When taken together, these are troubling trends. This latest brief -- the fourth in RFA and PERC's Back to School series -- draws on data from the Common Core of Data, Civil Rights Data Collection, and other state and federal sources to provide an overview of the pre-pandemic status of the teaching workforce in Philadelphia's 320 district and charter public schools, and provides some recommendations for recruiting and retaining a diverse and well-prepared teaching force. Key findings: (1) Philadelphia would need over 1,500 additional teachers just to reach the state average student/teacher ratio; (2) Black and Hispanic students in district and charter schools are more likely to be taught by teachers who do not hold any certifications, are not certified in their subject areas (particularly math and science), or are inexperienced; (3) Fewer persons of color are entering teacher preparation programs statewide, likely exacerbating the dearth of teachers of color in Philadelphia; and (4) Teachers in Philadelphia are paid less, on average, than teachers in most of the city's bordering school districts, even though Philadelphia's students have among the highest needs. The brief concludes with &quot;implications and recommendations&quot; of strategies to recruit and retain a qualified, more diverse teaching workforce, many of which are already underway in the School District. [For Brief 3, &quot;Back on Track: How Off-Track Ninth Graders Progressed in Later Years of High School, Class of 2017 and 2018,&quot; see ED608081.]
E-Expectations® of Transfer Students: A 2020 Trend Report
Author: Ruffalo Noel Levitz (RNL)
Date: 2020-00-00
Type: Numerical/Quantitative Data
Subjects: College Transfer Students,College Choice,Student Recruitment,Information Sources,Communication (Thought Transfer),Expectation,Influences,College Bound Students,Web Sites,Advertising,Student Attitudes

Nearly 40 percent of college students are transfers. They approach the college search process much differently than traditional students, choosing a college much more quickly and often having a more focused and specific reason for choosing a new school. How can your campus meet their needs, engage more transfers, and increase your enrollment of this student population? The &quot;E-Expectations® of Transfer Students&quot; report includes insights on key topics such as: (1) The most influential resources transfer students use in their searches; (2) How transfers initiate communication with a campus; (3) The most important content and features on college websites; (4) What transfers find most frustrating about college websites…and how they will react; and (5) How many have seen and clicked on digital ads. [This report is a joint research project from Ruffalo Noel Levitz, Cappex, OmniUpdate + Destiny Solutions, and PLEXUSS. For &quot;How to Amplify Digital Engagement of High School Students during the College Search Process. 2019 E-Expectations® Trend Report,&quot; see ED608185.]
Strategies for Reducing Student Absenteeism in Philadelphia
Author: Comly, Rachel
Date: 2020-08-00
Type: Reports - Research-practitioner Partnerships
Subjects: Attendance,Attendance Patterns,Truancy,Educational Strategies,Educational Policy,State Policy,Barriers,Elementary Secondary Education,Community Involvement,Data Use,Family Involvement,Interpersonal Relationship,Student Transportation,Community Coordination,Family School Relationship

As many districts in Pennsylvania pivot to hybrid or virtual models of education in response to Covid-19, it is hard to think about what &quot;attendance&quot; means or how to track it. Yet attendance is more important than ever. Students need to stay engaged to continue their learning and to access the myriad supports that districts are offering. And we know that chronic absenteeism leads to lower performance and increases the likelihood of dropout. How can we keep students coming to school? Increased communication with parents and timely use of data to identify and overcome the barriers to attendance are more important than ever--especially in districts with already high rates of absenteeism. [This is Brief 1 of the Back to School series.]