ethanporcaro/eric-fulltext-meta huggingface dataset:
The Digital Divide and Educational Equity: A Look at Students with Very Limited Access to Electronic Devices at Home. Insights in Education and Work
Author: Moore, Raeal
Date: 2018-08-00
Type: Reports - Research
Subjects: Equal Education,Access to Computers,Disadvantaged,Computer Uses in Education,Internet,College Entrance Examinations,Laptop Computers,Handheld Devices

This paper is one of a series of reports on students' access to technology. Access to technology is essential to educational success as well as workforce and community development. However, geographical, income-based, and racial/ethnic disparities in technology access persist. This &quot;digital divide&quot;--the gap between people who have sufficient knowledge of and access to technology and those who do not--can perpetuate and even worsen socioeconomic and other disparities for already underserved groups. This brief takes a closer look at one particular group: students who have access to only one device at home, a group representing 14% of all survey respondents. Taking a deeper dive into the data on students with access to only one device is important because these students may face challenges not faced by students with access to two or more devices. ACT surveyed a random sample of students who took the ACT® test as part of a national administration in April 2017. Researchers asked the students numerous questions about their access to and use of technology specifically for educational activities, both at home and in school, including the number and kinds of devices they have access to, the kind and reliability of the internet connection(s) available to them, and how often they used electronic devices for school-related activities. Access to devices and internet appears to be somewhat uneven among the ACT-tested students surveyed. Policy recommendations are to expand device access and internet among those who lack them and ensure students can access materials needed for school related activities via mobile technology.
Implementing CollegeReady™ to Promote Students&apos; Preparation for College-Level Math: Jacksonville State University Case Study. ACT Research &amp; Policy
Author: Westrick, Paul
Date: 2018-01-00
Type: Reports - Research
Subjects: College Readiness,College Freshmen,Student Placement,Remedial Mathematics,Computer Assisted Testing,College Entrance Examinations,Scores,State Universities,Test Wiseness,Mathematics Tests,Test Preparation

Almost two-thirds of students entering a community college and a third of students entering a 4-year college lack basic math and writing skills, and they often find themselves placed in developmental or remedial courses in their first year of college. Unfortunately, students placed into remedial math and English courses often have poorer educational outcomes; their retention and degree completion rates lag behind those of the students who enter college ready for college-level work. Colleges and universities have recognized this problem, and many are taking steps to help students improve their academic preparation with the goal of reducing the need for remedial course-taking. In particular, many colleges, including Jacksonville State University (JSU), have implemented EdReady -- now offered through ACT as CollegeReady -- for this very purpose. Unlike traditional placement tests, CollegeReady is a low-stakes placement system. Students can log on to the system at any time from any location and work at their own pace. If their initial CollegeReady score falls below the institution's target score, students can view study options and follow a personalized learning path to fill gaps in knowledge and skills. Using this approach, many students raise their scores and avoid remediation. In partnership with JSU, ACT researchers examined the relationship between incoming students' initial and most recent EdReady math scores with course placement decisions and math course outcomes. Preliminary findings from this study suggest that CollegeReady can help students bolster their math preparation and be successful going directly into college-level courses.
Testing Supports for English Learners: A Literature Review and Preliminary ACT Research Findings. ACT Working Paper 2018-1
Author: Moore, Joann
Date: 2018-00-00
Type: Information Analyses
Subjects: English Language Learners,Testing Accommodations,College Entrance Examinations,Evaluation Research,Scores,Research Problems,Grades (Scholastic),High School Students

In the fall of 2017, ACT began providing a limited number of supports (also known as accommodations) to English Learner (EL) students in the US taking the ACT® test. The goal of the supports is to remove construct-irrelevant variance in students' scores related to limited English proficiency and allow students to more accurately demonstrate their true proficiency on the construct being measured. This paper presents a summary of previous research on the impact of providing supports to EL students and a summary of a preliminary analysis conducted by ACT based on data from states who administered the ACT statewide, captured students' EL status, and provided accommodations to EL students. ACT's future research agenda for providing validity evidence for this initiative is also discussed.
Keeping Your Cool: Does Test Anxiety Bias Performance on the ACT? Research Report 2018-3
Author: Steedle, Jeffrey T.
Date: 2018-00-00
Type: Reports - Research
Subjects: Test Anxiety,College Entrance Examinations,Test Bias,Scores,Gender Differences,Racial Differences,Test Preparation

Debilitating test anxiety is a general threat to validity if it biases assessment scores. Moreover, if bias differs between demographic groups, anxiety also raises concerns about test fairness. This study applied structural equation modeling to investigate possible measurement bias due to anxiety on the ACT® assessment and relationships among contextual factors, anxiety, and estimated proficiency. Results indicated that ACT scores were not biased by anxiety--in general and for female, male, minority, and White examinees. Anxiety was primarily related to examinees' feelings of being unprepared for the test. Health factors such as hours of sleep and eating breakfast were related to reduced anxiety and greater test performance. Results support practical guidance for students, parents, and counselors who want to minimize the emotional and physiological symptoms of test anxiety, even if it is not likely to affect test performance.
The Use of CollegeReady to Improve Course Performance in English without the Need for Formal Remediation: A Case Study at Chattanooga State Community College. Research Report 2018-6
Author: Cruce, Ty M.
Date: 2018-00-00
Type: Reports - Research
Subjects: College Readiness,Remedial Instruction,Program Effectiveness,English Instruction,Freshman Composition,Two Year College Students,Community Colleges,College Entrance Examinations,Student Characteristics,High School Students,Grade Point Average,Reading Achievement,Academic Achievement

Many students graduate from high school under-prepared for college-level coursework, leading to a large number of students entering college who require remediation in English and mathematics. This study looked at the effectiveness of using EdReady--now offered through ACT as CollegeReady™--as a system for delivering remediation prior to college to improve students' course performance. We found that students who elected to skill-up with the product to the point where they avoided remedial coursework actually out-performed their peers with regard to their pass rate and course grades in their first college-level English composition course, providing promising support for the program. Future research should employ experimental and/or quasi-experimental designs to isolate the causal effect of CollegeReady on college success.
We the Educators: Educational Technology and the Personalisation, Standardisation, Privatisation and Datafication of Education. Literature Review
Author: Canadian Teachers' Federation
Date: 2017-00-00
Type: Information Analyses
Subjects: Educational Technology,Interdisciplinary Approach,Data Collection,Standards,Privatization,Accountability,Educational Change,Politics of Education,Individualized Instruction,Professionalism,Barriers,Neoliberalism,School Choice

Education systems around the world are now witness to a variety of educational changes and improvements, numerous social and economic disruptions, and the onset of rapid technological advances that were unimaginable in the past. Within this tsunami of change, innovative teaching and learning practices that employ emerging technologies are sweeping into schools and classrooms with the broader goal of transforming student learning. While technologies present education systems with both significant opportunities and challenges, some of the most profound developments are related to standardisation, personalisation, privatisation and the datafication of learning. To this end, Education International (EI), the Alberta Teachers' Association (ATA) and the Canadian Teachers' Federation (CTF) identified a need to explore the interdisciplinary research underpinning technology-driven datafication and its effects on teaching and learning around the world. This literature review attempts to provide a balanced view of the interdisciplinary concepts under investigation in order to inform an analysis of the converging fields of educational technology and datafication. It is part of a larger project entitled &quot;We the Educators,&quot; which brings the concepts explored in this research to life through video and animation in multiple languages. It is hoped that this project will stimulate a rich public dialogue--and greater professional scrutiny--around the relationship between the datafication of education systems and the (de)personalisation, privatisation and standardisation of student learning.
The Labor Market for Teachers under Different Pay Schemes. Research Briefs in Economic Policy. Number 140
Author: Biasi, Barbara
Date: 2018-11-28
Type: Reports - Research
Subjects: Labor Market,Teacher Salaries,Public Schools,Compensation (Remuneration),Collective Bargaining,Rewards,Teacher Effectiveness,School Districts,School District Autonomy,Value Added Models,Academic Achievement

Teachers are one of the most important inputs in the production of student achievement, and their impact persists throughout adulthood. Attracting and retaining high-quality teachers to the profession is thus a policy issue of highest importance. More attractive compensation packages are often proposed as a possible tool to achieve this goal. In most U.S. public school districts, however, teacher pay is set using rigid schedules based solely on seniority and education, with no financial rewards for effectiveness in the classroom. If allowed to set pay in a more flexible way, could school districts improve the quality of the teaching workforce? The author addresses this question by taking advantage of a reform to the collective-bargaining process for teachers in Wisconsin. [This research brief is based on Barbara Biasi, &quot;The Labor Market for Teachers under Different Pay Schemes,&quot; NBER Working Paper no. 24813, July 2018, http://www.nber.org/papers/w24813.]
Fixing the Bias in Current State K-12 Education Rankings. Policy Analysis No. 854
Author: Liebowitz, Stan J.
Date: 2018-11-13
Type: Reports - Research
Subjects: Bias,Achievement Rating,Institutional Characteristics,Reputation,Periodicals,Elementary Secondary Education,Achievement Tests,Evaluation Methods,Efficiency,Expenditures,Educational Trends,Geographic Regions,Unions,Outcomes of Education

State education rankings published by &quot;U.S. News &amp; World Report,&quot; &quot;Education Week,&quot; and others are based partly on achievement tests, which measure student learning, and partly on other factors not directly related to student learning. These rankings are aggregated in a way that provide misleading results by: (1) ignoring the substantial variation present in student populations across states; (2) including data that are inappropriate or irrelevant to the educational performance of schools; and (3) disregarding government budgetary constraints. To overcome these deficiencies, the authors created a new ranking of state education systems using demographically disaggregated achievement data and excluding less informative factors that are not directly related to learning. Using this methodology changes the order of state rankings considerably. Many states in New England and the Upper Midwest fall in the rankings, whereas many states in the South and Southwest score much higher than they do in conventional rankings. Furthermore, the authors created another set of rankings on the efficiency of education spending. In these efficiency rankings, achieving successful outcomes while economizing on education expenditures is considered better than doing so through lavish spending. These efficiency rankings cause a further increase in the rankings of southern and western states and a decline in the rankings of northern states. Lastly, the authors' regression results indicate that unionization has a powerful negative influence on educational outcomes, and that, given current spending levels, additional spending has little effect.
Who Are They Looking At? Automatic Eye Gaze Following for Classroom Observation Video Analysis
Author: Aung, Arkar Min
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Eye Movements,Classroom Observation Techniques,Video Technology,Neurological Organization,Teacher Student Relationship,Classification,Measurement,Accuracy

We develop an end-to-end neural network-based computer vision system to automatically identify &quot;where&quot; each person within a 2-D image of a school classroom is looking (&quot;gaze following&quot;), as well as &quot;who&quot; she/he is looking at. Automatic gaze following could help facilitate data-mining of large datasets of &quot;classroom observation&quot; videos that are collected routinely in schools around the world in order to understand social interactions between teachers and students. Our network is based on the architecture by [27] but is extended to predict whether each person is looking at a target inside or outside the image; and to predict not only where, but who the person is looking at. Moreover, since our focus is on classroom observation videos, we collected a dataset from scratch of publicly available classroom sessions from 70 YouTube videos and collected labels from 408 labelers who annotated a total of 17,758 gazes in 2, 263 unique image frames. Results of our experiments indicate that the proposed neural network can estimate the gaze target -- either the spatial location or the face of a person -- with substantially higher accuracy compared to several baselines. [For the full proceedings, see ED593090.]
The Long-Run Effects of Teacher Strikes: Evidence from Argentina. Research Briefs in Economic Policy. Number 123
Author: Jaume, David
Date: 2018-07-25
Type: Reports - Research
Subjects: Foreign Countries,Teacher Strikes,Elementary School Teachers,Influences,Adults,Labor Market,Elementary School Students,Socioeconomic Status,Children,Wages,Income,Gender Differences,Employment Level,Educational Attainment,At Risk Students

Teacher industrial action is a prevalent feature of public education systems across the globe. During the past few years teacher strikes have been observed in Argentina, Canada, Chile, China, France, Germany, India, Israel, Lebanon, Mexico, Russia, and the United States. A shared belief among policymakers across several of these countries is that teacher strikes disrupt learning and negatively affect student educational attainment. In some countries this sentiment has led to the enactment of legislation that severely restricts teachers' right to strike. However, despite the prevalence of teacher strikes across the globe--and the debates surrounding them--there is a lack of empirical work that credibly examines how they affect student long-run outcomes. The authors construct a new data set on teacher strikes in Argentina and use this to present the first evidence in the literature on the effect of school disruptions caused by teacher strikes on student long-run outcomes. Between 1983 and 2014 Argentina experienced 1,500 teacher strikes, with substantial variation across time and provinces, making this a particularly interesting case for the study of teacher strikes. They analyze the relationship between exposure to strikes in primary school and relevant education, labor market, and sociodemographic outcomes when the exposed students are between ages 30 and 40. They also examine whether the effects they identify carry over to the individuals' children.
Using Big Data to Sharpen Design-Based Inference in A/B Tests
Author: Sales, Adam C.
Date: 2018-07-00
Type: Reports - Research
Subjects: Courseware,Data Analysis,Causal Models,Prediction,Outcomes of Education,Evaluation Methods,Mastery Learning,Skill Development,Intelligent Tutoring Systems,Statistical Bias,Randomized Controlled Trials,Artificial Intelligence

Randomized A/B tests in educational software are not run in a vacuum: often, reams of historical data are available alongside the data from a randomized trial. This paper proposes a method to use this historical data--often highdimensional and longitudinal--to improve causal estimates from A/B tests. The method proceeds in two steps: first, fit a machine learning model to the historical data predicting students' outcomes as a function of their covariates. Then, use that model to predict the outcomes of the randomized students in the A/B test. Finally, use design-based methods to estimate the treatment effect in the A/B test, using prediction errors in place of outcomes. This method retains all of the advantages of design-based inference, while, under certain conditions, yielding more precise estimators. This paper will give a theoretical condition under which the method improves statistical precision, and demonstrates it using a deep learning algorithm to help estimate effects in a set of experiments run inside ASSISTments. [For the full proceedings, see ED593090.]
Towards a Model-Free Estimate of the Limits to Student Modeling Accuracy
Author: Chen, Binglin
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Accuracy,Probability,Student Behavior,Test Items,Models,Prediction,Item Response Theory,Error Patterns,Knowledge Level,Sequential Approach,Computer Software,Problem Solving,Computer Simulation,Difficulty Level

This paper attempts to quantify the accuracy limit of &quot;nextitem-correct&quot; prediction by using numerical optimization to estimate the student's probability of getting each question correct given a complete sequence of item responses. This optimization is performed without an explicit parameterized model of student behavior, but with the constraint that a student's likelihood of getting a problem correct only increases or remains unchanged with additional practice (i.e., no forgetting). We present results for this method for the Assistments 2009-2010 data where it suggests that there is only modest opportunity for improvement beyond the state of the art predictors. Furthermore, we describe a framework for applying this method to datasets where problems can be tagged with multiple skills and problem difficulties. Lastly, we discuss the limitations of this method, specifically its inability to give tight bounds on short sequences. [For the full proceedings, see ED593090.]
Tracking Behavioral Patterns among Students in an Online Educational System
Author: Lorenzen, Stephan
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Data Collection,Student Behavior,Elementary School Students,Foreign Countries,Online Courses,Technology Uses in Education,Integrated Learning Systems,Learning Activities

Analysis of log data generated by online educational systems is an essential task to better the educational systems and increase our understanding of how students learn. In this study we investigate previously unseen data from Clio Online, the largest provider of digital learning content for primary schools in Denmark. We consider data for 14,810 students with 3 million sessions in the period 2015-2017. We analyze student activity in periods of one week. By using non-negative matrix factorization techniques, we obtain soft clusterings, revealing dependencies among time of day, subject, activity type, activity complexity (measured by Bloom's taxonomy), and performance. Furthermore, our method allows for tracking behavioral changes of individual students over time, as well as general behavioral changes in the educational system. Based on the results, we give suggestions for behavioral changes, in order to optimize the learning experience and improve performance. [For the full proceedings, see ED593090.]
Using a Common Sense Knowledge Base to Auto Generate Multi-Dimensional Vocabulary Assessments
Author: Sharma Mittal, Ruhi
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Vocabulary Development,Language Tests,Semantics,Associative Learning,Teaching Methods,Multiple Choice Tests,Intelligent Tutoring Systems,Comparative Analysis,Evaluators,Test Items,Computer Assisted Instruction,Language Acquisition,Student Evaluation,Individualized Instruction,Young Children,Test Construction

As education gets increasingly digitized, and intelligent tutoring systems gain commercial prominence, scalable assessment generation mechanisms become a critical requirement for enabling increased learning outcomes. Assessments provide a way to measure learners' level of understanding and difficulty, and personalize their learning. There have been separate efforts in different areas to solve this by looking at different parts of the problem. This paper is a first effort to bring together techniques from diverse areas such as knowledge representation and reasoning, machine learning, inference on graphs, and pedagogy to generate automated assessments at scale. In this paper, we specifically address the problem of Multiple Choice Question (&quot;MCQ&quot;) generation for vocabulary learning assessments, specially catered to young learners (YL). We evaluate the efficacy of our approach by asking human annotators to annotate the questions generated by the system based on relevance. We also compare our approach with one baseline model and report high usability of &quot;MCQs&quot; generated by our system compared to the baseline. [For the full proceedings, see ED593090.]
Understanding Learners&apos; Opinion about Participation Certificates in Online Courses Using Topic Modeling
Author: Nanda, Gaurav
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Online Courses,Educational Certificates,Student Participation,Student Motivation,Models,Student Attitudes

This study proposes a formal multi-step methodology for qualitative assessment of topic modeling results in the context of online learner motivation to purchase Statements of Participation (SoP). We developed Latent Dirichlet Allocation (LDA) based topic models on open-ended responses of three post-course survey questions from 280 open courses offered on the FutureLearn learning platform. For qualitative assessment, we first determined the theme of the topic based on the words that constituted the topic and responses that were most strongly associated with the topic. Then, we verified the theme by comparing the topics assigned by LDA model on a test set with manual annotation. We also performed sentiment analysis to check for alignment with human judgment. Learner motivations in each theme were interpreted with the Expectancy-Value-Cost framework. Our analyses indicated that, primarily, learners were motivated to purchase the SoP based on perceptions of the utility value and financial cost of the certificate. We found that human judgment agreed with the topic model more frequently when LDA topic weights were larger. [For the full proceedings, see ED593090.]