ethanporcaro/eric-fulltext-meta huggingface dataset:
Career Pathways in Career and Technical Education. Policy Brief
Author: Cielinski, Anna
Date: 2019-01-00
Type: Reports - Descriptive
Subjects: Career Development,Vocational Education,Adult Education,Labor Force Development,Low Income Groups,Literacy,Community Colleges,Human Services,Federal Legislation,Secondary Education,Postsecondary Education,Employment Qualifications,Job Skills,Federal Aid,State Aid,Data Analysis,Minority Group Students,At Risk Students,Alignment (Education),Educational Finance,Educational Legislation,Higher Education,Technical Education

During the early 2000s, state and local leaders developed career pathways programs in an effort to redesign education and workforce development systems to better meet the education and employment needs of low-income, educationally underprepared adults. These career pathways increase individual prosperity and regional competitiveness by aligning adult education, community colleges, human services, and workforce development efforts. Adults with low incomes or low literacy levels are a target population in the career pathways movement. Following the reauthorization of the Carl D. Perkins Act (Perkins V) in 2018, three federal laws include a shared definition of career pathways: Perkins V, the Workforce Innovation and Opportunity Act (WIOA), and the Higher Education Act (HEA). Career pathways support people in obtaining a secondary and postsecondary credential, provide participants with adequate career counselling, establish integrated design and delivery, and ensure labor market value. Perkins V implementation offers an opportunity for states to better align programs of study and career pathways to ensure that more learners can get high-quality education and training that meets their needs and helps them move into good jobs. Recommendations for state leaders include the following: (1) Use federal State Leadership Funds, as explicitly permitted in the law, to integrate and align programs of study with career pathways to best serve low-income adults (now in &quot;special populations&quot;) and other target populations; (2) Use data focusing on special populations and communities of color to answer the state plan question on how states will align programs of study and career pathways to the needs of the state, regional, and local labor market; and (3) Encourage local uses of funds, as specifically permitted under the law, to pursue coordination with other education and workforce development programs and initiatives, including career pathways and sector partnerships developed under WIOA.
Does Pupil Transportation Close the School Quality Gap? Evidence from New York City. Research Report. Updated
Author: Cordes, Sarah A.
Date: 2019-02-00
Type: Reports - Research
Subjects: Transportation,Urban Schools,Public Schools,Correlation,Educational Quality,Elementary School Students,School Choice,Racial Differences,Ethnicity,Hispanic American Students,African American Students,Eligibility,Student Characteristics,Geographic Location,Institutional Characteristics,Limited English Speaking,Gender Differences,Poverty,Attendance Patterns,Charter Schools,Special Schools,White Students,Asian American Students

Using uniquely detailed individual-level data on transportation provided to all New York City public school students, we examine the relationship between transportation use and school quality for elementary school students (K-5). We find that students who attend choice schools are more likely to use transportation (either school bus or free public transportation) and to attend better schools than their zoned schools. Among students who attend choice schools, those who use transportation attend significantly better schools than their peers attending nearby choice schools, with bus riders experiencing the largest gains in school quality. Further, transportation appears to play a particularly important role for black and Hispanic bus riders, who are 30 to 40 percentage points more likely to attend significantly better schools than their same-race peers who attend choice schools but do not use transportation. These results point to the importance of pupil transportation in allowing urban children to access good schools.
A Coupled User Clustering Algorithm for Web-Based Learning Systems
Author: Niu, Ke
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Web Based Instruction,Student Needs,User Needs (Information),Mathematics,Computation,Cluster Grouping,Educational Technology,Information Systems,Models,Learning Processes,Data Collection,Accuracy

User clustering algorithms have been introduced to analyze users' learning behaviors and help to provide personalized learning guides in traditional Web-based learning systems. However, the explicit and implicit coupled interactions, which means the correlations between user attributes generated from learning actions, are not considered in these algorithms. Much significant and useful information which can positively affect clustering accuracy is neglected. To solve the above issue, we proposed a coupled user clustering algorithm for Wed-based learning systems. It respectively takes into account intra-coupled and inter-coupled relationships of learning data, and utilizes Taylor-like expansion to represent their integrated coupling correlations. The experiment result demonstrates the outperformance of the algorithm in terms of efficiently capturing correlations of learning data and improving clustering accuracy. [For the full proceedings, see ED592609.]
A Comparative Analysis of Techniques for Predicting Student Performance
Author: Bydžovská, Hana
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Prediction,Academic Achievement,Grades (Scholastic),Information Retrieval,Data Analysis,College Students,Social Behavior,Student Behavior,Classification,Accuracy,Course Content,Foreign Countries

The problem of student final grade prediction in a particular course has recently been addressed using data mining techniques. In this paper, we present two different approaches solving this task. Both approaches are validated on 138 courses which were offered to students of the Faculty of Informatics of Masaryk University between the years of 2010 and 2013. The first approach is based on classification and regression algorithms that search for patterns in study-related data and also data about students' social behavior. We prove that students' social behavior characteristics improve prediction for a quarter of courses. The second approach is based on collaborative filtering techniques. We predict the final grades based on previous achievements of similar students. The results show that both approaches reached similar average results and can be beneficially utilized for student final grade prediction. The first approach reaches significantly better results for courses with a small number of students. In contrary, the second approach achieves significantly better results for mathematical courses. We also identified groups of courses for which we are not able to predict the grades reliably. Finally, we are able to correctly identify half of all failures (that constitute less than a quarter of all grades) and predict the final grades only with the error of one degree in the grade scale. [For the full proceedings, see ED592609.]
A Comparison of Automatic Teaching Strategies for Heterogeneous Student Populations
Author: Clement, Benjamin
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Intelligent Tutoring Systems,Markov Processes,Models,Teaching Methods,Decision Making,Learning Processes,Individual Differences,Individualized Instruction,Problem Solving,Computation

Online planning of good teaching sequences has the potential to provide a truly personalized teaching experience with a huge impact on the motivation and learning of students. In this work we compare two main approaches to achieve such a goal, POMDPs that can find an optimal long-term path, and Multi-armed bandits that optimize policies locally and greedily but that are computationally more efficient while requiring a simpler learner model. Even with the availability of data from several tutoring systems, it is never possible to have a highly accurate student model or one that is tuned for each particular student. We study what is the impact of the quality of the student model on the final results obtained with the two algorithms. Our hypothesis is that the higher flexibility of multi-armed bandits in terms of the complexity and precision of the student model will compensate for the lack of longer term planning featured in POMDPs. We present several simulated results showing the limits and robustness of each approach and a comparison of heterogeneous populations of students. [For the full proceedings, see ED592609.]
Teacher Turnover in Maine: Analysis of Staffing Patterns from 2005-06 to 2016-17
Author: Morris, Lisa
Date: 2018-04-00
Type: Reports - Research
Subjects: Faculty Mobility,Teacher Persistence,Educational Trends,Public Schools,Public School Teachers,Teacher Characteristics,Teacher Shortage,Teaching Experience,Teacher Retirement,School Districts,Teacher Supply and Demand,Rural Urban Differences,Teaching Conditions,Predictor Variables,Work Environment,Profiles,Teacher Recruitment,Beginning Teachers,Teacher Education,Urban Schools,Rural Schools,Suburban Schools,Teacher Salaries,Tables (Data),School Size,Poverty

There have long been anecdotal reports that some Maine districts have difficulty filling vacancies and retaining teachers. This is a common lament for schools in rural areas, and for schools across the state in hiring teachers for certain subject areas-namely math, science, special education, and foreign languages. Current policy initiatives in Maine such as the push for proficiency-based high school diplomas are raising the stakes for schools to employ high-quality teachers in all content areas. There is a concern that schools facing persistent teacher shortages may struggle to provide a comprehensive educational program, resulting in inequitable learning opportunities for their students. To further investigate the empirical evidence behind these anecdotal reports, the Joint Standing Committee on Education and Cultural Affairs commissioned this study of the Maine Education Policy Research Institute as part of its 2017-18 work plan. This report addresses the following questions: (1) Teacher profiles: what are the age, experience and education profiles of Maine' teachers overall, and how do school-level teacher profiles vary by school size, poverty level, and rurality? (2) What are Maine's state wide teacher retention and turnover rates? How has turnover changed overtime? (3) Do school retention and turnover rates vary by school characteristics (size, poverty level, locale, average salary and teacher demographic profile)? (4) Who stays and who leaves: what factors (individual, job-related, or school) are associated with teacher retention and turnover? (5) Who moves: what are the changes in salary and other work conditions (salary, school type, etc.) associated with job-to-job moves? Findings are summarized with respect to implications for Maine policy and practice, with a particular emphasis on current policy questions in the 128th Legislative Session. While Maine's overall turnover rates were lower than the national average, they nonetheless present an ongoing challenge for schools. Several of the report conclusions lead to areas for potential policy interventions as well as questions for possible further exploration. Policy implications are categorized and provided in the report according to their area of challenge. [This report was published by the Maine Education Policy Research Institute in the Center for Education Policy, Applied Research, and Evaluation (CEPARE).]
A Nonlinear State Space Model for Identifying At-Risk Students in Open Online Courses
Author: Wang, Feng
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Identification,At Risk Students,Online Courses,Large Group Instruction,Potential Dropouts,Prediction,Probability,Accuracy,Dropout Characteristics,Models,Classification

How to identify at-risk students in open online courses has received increasing attention, since the dropout rate is unexpectedly high. Most prior studies have focused on using machine learning techniques to predict student dropout based on features extracted from students' learning activity logs. However, little work has viewed the dropout prediction problem as a sequence classification problem in the consideration that the dropout probability of a student at the current time step can be likely dependent on her/his engagement at the previous time step. Therefore, in this paper, we propose a nonlinear state space model to solve this problem. We show how students' latent states at different time steps can be learned via this model, and demonstrate its outperforming prediction accuracy relative to related methods through experiment. [For the full proceedings, see ED592609.]
Automatic Assessment of Constructed Response Data in a Chemistry Tutor
Author: Crossley, Scott
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Intelligent Tutoring Systems,Chemistry,Natural Language Processing,High School Students,Science Tests,Responses,Accuracy,Scoring

This study introduces the Constructed Response Analysis Tool (CRAT), a freely available tool to automatically assess student responses in online tutoring systems. The study tests CRAT on a dataset of chemistry responses collected in the ChemVLab+. The findings indicate that CRAT can differentiate and classify student responses based on semantic overlap with student input and indices related to word frequency, text content, and lexical sophistication. Overall, the findings suggest that more accurate student responses show greater overlap with the content learned, include more academic function words, contain greater content that is descriptive, and includes more specific and familiar words. [For the full proceedings, see ED592609.]
Assessing Student-Generated Design Justifications in Engineering Virtual Internships
Author: Rus, Vasile
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Engineering Education,Internship Programs,Computer Simulation,Models,Experiments,Artificial Intelligence,Man Machine Systems,Comparative Analysis

Engineering virtual internships are simulations where students role play as interns at fictional companies, working to create engineering designs. To improve the scalability of these virtual internships, a reliable automated assessment system for tasks submitted by students is necessary. Therefore, we propose a machine learning approach to automatically assess student generated textual design justifications in two engineering virtual internships, &quot;Nephrotex&quot; and &quot;RescuShell.&quot; To this end, we compared two major categories of models: domain expert-driven vs. general text analysis models. The models were coupled with machine learning algorithms and evaluated using 10-fold cross validation. We found no quantitative differences among the two major categories of models, domain expert-driven vs. general text analysis, although there are major qualitative differences as discussed in the paper. [This paper was funded in part by the Office of the Vice Chancellor for Research and Graduate Education at the University of Wisconsin-Madison.] [For the full proceedings, see ED592609.]
SNAP and Students: Food Assistance Can Support College Success
Author: Welton, Carrie
Date: 2019-01-00
Type: Reports - Descriptive
Subjects: Welfare Services,Food,Nutrition,Federal Programs,College Students,Low Income Students,Academic Achievement,Eligibility,Welfare Recipients

Postsecondary education is often out of reach for students with low incomes, many of whom receive little or no financial support from their family for school and who have significant unmet need, even after financial aid. Students of color (approximately 40 percent of all college students) face additional systemic barriers that hinder access and completion, contributing to less successful outcomes in higher education. For these reasons today's students require robust supports that promote their ability to attain a postsecondary credential leading to a living-wage job. The federal Supplemental Nutrition Assistance Program (SNAP, formerly food stamps) provides nutrition assistance to low-income individuals and families. SNAP is critical because food insecurity is one of the greatest threats to health, wellbeing, and academic success for low-income college students. The graphic in this document illustrates how--once meeting the regular eligibility requirements--students can qualify for SNAP through one of these additional criteria.
Aim Low: Correlation-Based Feature Selection for Model-Based Reinforcement Learning
Author: Shen, Shitian
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Correlation,Selection,Methods,Intelligent Tutoring Systems,Artificial Intelligence,Models,College Science,Physics,College Students

We explored a series of feature selection methods for model-based Reinforcement Learning (RL). More specifically, we explored four common correlation metrics and based on them, we proposed the fifth one named Weighed Information Gain (WIG). While much existing correlation-based feature selection methods mostly explored high correlation by default, we explored two options: &quot;High&quot; vs. &quot;Low.&quot; The former selects the next feature that has the highest correlation measure with existing selected ones while the latter selects the one with the lowest correlations. The 10 correlation-based methods were compared against previous feature selection methods for model-based RL across several datasets collected from two vastly different intelligent tutoring systems. Our results showed that the 10 correlation-based methods significantly outperform all other methods across all datasets. Among the five correlation metrics, WIG performed best. Surprisingly, for each of correlation metrics, the low option significantly outperform its high correlation peer and thus it suggests that low correlation-based feature selection methods are more effective for model-based RL than high ones. [For the full proceedings, see ED592609.]
Acting the Same Differently: A Cross-Course Comparison of User Behavior in MOOCs
Author: Gelman, Ben
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Large Group Instruction,Online Courses,Student Behavior,Learner Engagement,Potential Dropouts,Student Participation,Use Studies,Intervention,Time on Task,Homework,Time Management,Time Factors (Learning),Dropouts,Learning Experience

Recent studies of MOOCs demonstrate their ability to reach a large number of users, but also caution against the high rate of dropout. Some have looked closely at MOOC participation in order to better understand how and when users start to disengage, and, if they remain engaged, in what activities they participate. Most of this prior work relies heavily on descriptive statistics or clustering methodologies to highlight basic user participation characteristics. In this paper, we adapt NMF to provide a multi-dimensional view of user participation. We use log data to create a bottom-up understanding of user participation, and identify five basic behaviors associated with participants' use of content and their engagement with assessment. Furthermore, we do a cross-course analysis across four courses and find that these five behaviors are present in all courses. Interestingly, users' participation patterns - how they engage in these five behaviors - vary across courses even when the course topics are similar. Our methodology can be applied to other data sets, and findings from this work can assist in interventions to help users successfully accomplish their learning goals. [For the full proceedings, see ED592609.]
An Ensemble Method to Predict Student Performance in an Online Math Learning Environment
Author: Stapel, Martin
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Teaching Methods,Academic Achievement,Electronic Learning,Mathematics Instruction,Educational Environment,Accuracy,Predictor Variables,Concept Mapping,Graphs,Mathematical Concepts,Knowledge Representation,Information Retrieval,Foreign Countries,Data Processing

The number of e-learning platforms and blended learning environments is continuously increasing and has sparked a lot of research around improvements of educational processes. Here, the ability to accurately predict student performance plays a vital role. Previous studies commonly focused on the construction of predictors tailored to a formal course. In this paper we relax this constraint, leveraging domain knowledge and combining a knowledge graph representation with activity scopes based on sets of didactically feasible learning objectives. Specialized scope classifiers are then combined to an ensemble to robustly predict student performance on learning objectives independently of the student's individual learning setting. The final ensemble's accuracy trumps any single classifier tested. [For the full proceedings, see ED592609.]
Automatic Gaze-Based Detection of Mind Wandering during Narrative Film Comprehension
Author: Mills, Caitlin
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Attention,Eye Movements,Identification,Automation,Films,Comprehension,Artificial Intelligence,Accuracy,Undergraduate Students

Mind wandering (MW) reflects a shift in attention from task-related to task-unrelated thoughts. It is negatively related to performance across a range of tasks, suggesting the importance of detecting and responding to MW in real-time. Currently, there is a paucity of research on MW detection in contexts other than reading. We addressed this gap by using eye gaze to automatically detect MW during narrative film comprehension, an activity that is used across a range of learning environments. In the current study, students self-reported MW as they watched a 32.5-minute commercial film. Students' eye gaze was recorded with an eye tracker. Supervised machine learning models were used to detect MW using global (content-independent), local (content-dependent), and combined global+local features. We achieved a student-independent score (MW F[subscript 1]) of 0.45, which reflected a 29% improvement over a chance baseline. Models built using local features were more accurate than the global and combined models. An analysis of diagnostic features revealed that MW primarily manifested as a breakdown in attentional synchrony between eye gaze and visually salient areas of the screen. We consider limitations, applications, and refinements of the MW detector. [For the full proceedings, see ED592609.]
Back to the Basics: Bayesian Extensions of IRT Outperform Neural Networks for Proficiency Estimation
Author: Wilson, Kevin H.
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Item Response Theory,Bayesian Statistics,Computation,Artificial Intelligence,Computer Assisted Instruction,Prediction,Student Reaction,Accuracy

Estimating student proficiency is an important task for computer based learning systems. We compare a family of IRT-based proficiency estimation methods to Deep Knowledge Tracing (DKT), a recently proposed recurrent neural network model with promising initial results. We evaluate how well each model predicts a student's future response given previous responses using two publicly available and one proprietary data set. We find that IRT-based methods consistently matched or outperformed DKT across all data sets at the finest level of content granularity that was tractable for them to be trained on. A hierarchical extension of IRT that captured item grouping structure performed best overall. When data sets included non-trivial autocorrelations in student response patterns, a temporal extension of IRT improved performance over standard IRT while the RNN-based method did not. We conclude that IRT-based models provide a simpler, better-performing alternative to existing RNN-based models of student interaction data while also affording more interpretability and guarantees due to their formulation as Bayesian probabilistic models. [For the full proceedings, see ED592609.]