ethanporcaro/eric-fulltext-meta huggingface dataset:
2019 Montana Youth Risk Behavior Survey High School Results and 2019 Comparative Tables for: Grades 7-8; American Indian Students on or near a Reservation; American Indian Students in Urban Schools; Nonpublic Accredited Schools; Alternative Schools; Students with Disabilities
Author: Montana Office of Public Instruction
Date: 2019-00-00
Type: Reports - Research
Subjects: High School Students,Health Behavior,National Surveys,Risk,At Risk Students,Student Surveys,State Surveys,Health Education,Incidence,Tables (Data),Trend Analysis,Safety,Violence,Suicide,Smoking,Drinking,Drug Use,Sexuality,Pregnancy,Body Weight,Eating Habits,Physical Activities,Head Injuries,Sleep,Grades (Scholastic),Special Education,Injuries,Dating (Social),Aggression,Bullying,Marijuana,Dietetics,Food,Homeless People

The Youth Risk Behavior Survey (YRBS) is an epidemiologic surveillance system that was established by the U.S. Centers for Disease Control and Prevention (CDC) to help monitor the prevalence of behaviors that not only influence youth health, but also put youth at risk for the most significant health and social problems that can occur during adolescence and adulthood. To monitor priority health-risk behaviors in each of these six categories and obesity and asthma among youth and young adults, CDC developed the YRBS. The YRBS has been conducted biennially since 1991 in Montana. The purpose of the YRBS is to assist educators and health professionals in determining the prevalence of the aforementioned health-risk behaviors among youth. This report describes the results of the survey and the methods used to conduct the survey. The results will be used to focus the continuing development of statewide comprehensive health education and to reduce those health behaviors that place Montana youth at risk. Survey results are presented in the following parts: (1) Introduction; (2) Survey Methods; (3) Frequency Distributions; (4) Point of Interest (POI) Graphs - Gender, Grade Level, and Race; (5) Trend Analysis Tables (2009-2019); and (6) Comparative Tables by Student Population. [Additional funding for YRBS was provided by the Montana Board of Crime Control, Montana Department of Public Health and Human Services, Billings Area Indian Health Service, and Montana Department of Transportation. For the Summary Report, see ED599100.]
Longitudinal Study of External Validity of the PARCC Performance Levels: Phase 1 Report
Author: Steedle, Jeffrey
Date: 2017-03-01
Type: Reports - Research
Subjects: College Readiness,Career Readiness,Test Validity,Longitudinal Studies,Mathematics Tests,Language Tests,College Entrance Examinations,Benchmarking,Algebra,Geometry,Grade 9,Grade 10,Grade 11,Aptitude Tests,High School Students,Writing Tests,Reading Tests

On the Partnership for Assessment of Readiness for College and Careers (PARCC) assessments, the attainment of performance level 4 is intended to indicate college readiness or being &quot;on track&quot; to college and career readiness. Students who achieve Level 4 should have a 0.75 probability of attaining at least a C in entry-level, credit-bearing, post-secondary coursework. This report describes the first phase of a two-part longitudinal external validity study of claims about the alignment of PARCC Level 4 to college readiness. The performance level setting (PLS) process was designed to help students, parents, teachers and policymakers understand how students performed relative to the content standards for college and career readiness. In this study, PARCC Level 4 was compared to college readiness benchmarks on external assessments. There are straightforward connections between PARCC Level 4 on capstone assessments (Grade 11 ELA/L and Algebra II) and the SAT and ACT benchmarks because they are all intended to indicate college readiness for students approaching high school graduation. However, a study examining only these connections is limited by available data. Namely, not all PARCC states administered capstone assessments in 2014-2015. In states that did, many students who took Algebra II were tenth graders, and such students would not have taken the SAT or ACT by the time of this study in 2016.
Seizing the Opportunity: Educating Students with Disabilities in Charter Schools. Executive Summary
Author: Lake, Robin
Date: 2019-10-00
Type: Reports - Descriptive
Subjects: Students with Disabilities,Charter Schools,Special Education,Individualized Education Programs,Teacher Student Relationship,School Effectiveness,School Culture,Student Needs,Regular and Special Education Relationship,Inclusion,Educational Strategies

This is the executive summary for the report, &quot;Seizing the Opportunity: Educating Students with Disabilities in Charter Schools.&quot; Only about 20 percent of students with disabilities score at proficient levels on state assessments. Fewer than 70 percent earn a high school diploma within four years. Charter schools, which operate with enhanced flexibility and accountability for results, are well-positioned to find new, creative solutions to these challenges. They have shown promise in using creativity to tackle other complex education problems, such as preparing more students from underserved backgrounds to succeed in college. Some charter schools are using their flexibility and focus on results to reach every child with a disability, but more schools should do this. The report is the first step in developing an evidence base about how charter schools meet the needs of unique learners, how they can improve on this work, and what aspects of chartering as a governance model support or impede their ability to do so. Researchers spent 12 months studying charter schools that are getting results educating children with disabilities. The report describes the norms and practices at work in schools where they commonly observed students with disabilities showing academic success, parents expressing confidence in the school, and teachers feeling supported and successful with their students with disabilities. They also found that effective education for these students depends on an integrated set of school-wide principles that are set in action, reinforced, and sustained by intentional organizational systems and structures. [For the full report, see ED599101.]
A Comparison of Automated Scale Short Form Selection Strategies
Author: Raborn, Anthony W.
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Psychometrics,Measures (Individuals),Mathematics,Heuristics,Simulation,Test Validity,Test Reliability,Factor Analysis,Factor Structure,Test Length,Goodness of Fit

Short forms of psychometric scales have been commonly used in educational and psychological research to reduce the burden of test administration. However, it is challenging to select items for a short form that preserve the validity and reliability of the scores of the original scale. This paper presents and evaluates multiple automated methods for scale short form creation based on metaheuristic optimization algorithms that incorporate validity criteria based on internal structure and relationships with other variables. The ant colony optimization (ACO) algorithm, tabu search (TS), simulated annealing (SA) and genetic algorithm (GA) are examined using confirmatory factor analysis (CFA) of scales with one factor, three factor, and bi-factor factorial structure. The results indicate that SA created short forms with best model fit for scales with one and three factor structures, but ACO was able to obtain highest reliability. For scales with bi-factor structure, SA provide short forms with best model fit, but TS obtained highest reliability. Overall, the SA algorithm is recommended because it produced consistently best model fit and reliability that was only slightly lower than the ACO or TS algorithms. [For the full proceedings, see ED599096.]
A Better Cold-Start for Early Prediction of Student At-Risk Status in New School Districts
Author: Coleman, Chad
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Prediction,At Risk Students,Predictor Variables,Elementary Secondary Education,School Districts,Data Collection,Models,High School Graduates,Graduation,Dropouts,Data Analysis,Student Characteristics,Socioeconomic Status,Racial Differences,Ethnicity,Graduation Rate,Grade Point Average,Attendance,Student Evaluation,Student Behavior

Determining which students are at risk of poorer outcomes -- such as dropping out, failing classes, or decreasing standardized examination scores -- has become an important area of research and practice in both K-12 and higher education. The detectors produced from this type of predictive modeling research are increasingly used in early warning systems to identify which students are at risk and intervene to support better outcomes. In K-12, it has become common practice to re-build and validate these detectors, district-by-district, due to different data semantics and risk factors for students in different districts. As these detectors become more widely used, however, it becomes desirable to also apply detectors in school districts without sufficient historical data to build a district-specific model. Novel approaches that can address the complex data challenges a new district presents are critical for extending the benefits of early warning systems to all students. Using an ensemble-based algorithm, we evaluate a model averaging approach that can generate a useful model for previously-unseen districts. During the ensembling process, our approach builds models for districts that have a significant amount of historical records and integrates them through averaging. We then use these models to generate predictions for districts suffering from high data missingness. Using this approach, we are able to predict student-at-risk status effectively for unseen districts, across a range of grade ranges, and achieve prediction goodness comparable to previously published models predicting at-risk. [For the full proceedings, see ED599096.]
Academic Performance Estimation with Attention-based Graph Convolutional Networks
Author: Hu, Qian
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Academic Achievement,Attention,Prior Learning,Prediction,Data Collection,Graphs,Grades (Scholastic),Undergraduate Students,At Risk Students

Student's academic performance prediction empowers educational technologies including academic trajectory and degree planning, course recommender systems, early warning and advising systems. Given a student's past data (such as grades in prior courses), the task of student's performance prediction is to predict a student's grades in future courses. Academic programs are structured in a way that prior courses lay the foundation for future courses. The knowledge required by courses is obtained by taking multiple prior courses, which exhibits complex relationships modeled by graph structures. Traditional methods for student's performance prediction usually neglect the underlying relationships between multiple courses; and how students acquire knowledge across them. In addition, traditional methods do not provide interpretation for predictions needed for decision making. In this work, we propose a novel attentionbased graph convolutional networks model for student's performance prediction. We conduct extensive experiments on a real-world dataset obtained from a large public university. The experimental results show that our proposed model outperforms state-of-the-art approaches in terms of grade prediction. The proposed model also shows strong accuracy in identifying students who are at-risk of failing or dropping out so that timely intervention and feedback can be provided to the student. [For the full proceedings, see ED599096.]
Active Learning for Student Affect Detection
Author: Yang, Tsung-Yen
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Active Learning,Measurement Techniques,Intelligent Tutoring Systems,Educational Technology,Technology Uses in Education,Affective Behavior,Observation,Computation,Psychological Patterns

&quot;Sensor-free&quot; detectors of student affect that use only student activity data and no physical or physiological sensors are cost-effective and have potential to be applied at large scale in real classrooms. These detectors are trained using student affect labels collected from human observers as they observe students learn within intelligent tutoring systems (ITSs) in real classrooms. Due to the inherent diversity of student activity and affect dynamics, observing the affective states of some students at certain times is likely to be more informative to the affect detectors than observing others. Therefore, a carefully-crafted observation schedule may lead to more meaningful observations and improved affect detectors. In this paper, we investigate whether active (machine) learning methods, a family of methods that adaptively select the next most informative observation, can improve the efficiency of the affect label collection process. We study several existing active learning methods and also propose a new method that is ideally suited for the problem setting in affect detection. We conduct a series of experiments using a real-world student affect dataset collected in real classrooms deploying the ASSISTments ITS. Results show that some active learning methods can lead to high-quality affect detectors using only a small number of highly informative observations. We also discuss how to deploy active learning methods in real classrooms to improve the affect label collection process and thus sensor-free affect detectors. [For the full proceedings, see ED599096.]
DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills
Author: Choffin, Benoît
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Intervals,Scheduling,Repetition,Memorization,Memory,Time Perspective,Models,Skill Development,Retention (Psychology),Sequential Approach,Instructional Materials,Mastery Learning,Computer Assisted Instruction,Learning Strategies,Drills (Practice)

Spaced repetition is among the most studied learning strategies in the cognitive science literature. It consists in temporally distributing exposure to an information so as to improve long-term memorization. Providing students with an adaptive and personalized distributed practice schedule would benefit more than just a generic scheduler. However, the applicability of such adaptive schedulers seems to be limited to pure memorization, e.g. flashcards or foreign language learning. In this article, we first frame the research problem of optimizing an adaptive and personalized spaced repetition scheduler when memorization concerns the application of underlying multiple skills. To this end, we choose to rely on a student model for inferring knowledge state and memory dynamics on any skill or combination of skills. We argue that no knowledge tracing model takes both memory decay and multiple skill tagging into account for predicting student performance. As a consequence, we propose a new student learning and forgetting model suited to our research problem: DAS3H builds on the additive factor models and includes a representation of the temporal distribution of past practice on the skills involved by an item. In particular, DAS3H allows the learning and forgetting curves to differ from one skill to another. Finally, we provide empirical evidence on three real-world educational datasets that DAS3H outperforms other state-of-the-art EDM models. These results suggest that incorporating both item-skill relationships and forgetting effect improves over student models that consider one or the other. [For the full proceedings, see ED599096.]
A Human-Machine Hybrid Peer Grading Framework for SPOCs
Author: Han, Yong
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Peer Evaluation,Grading,Online Courses,Computer Assisted Testing,Man Machine Systems,Bayesian Statistics,True Scores

Peer-grading is commonly adopted by instructors as an effective assessment method for MOOCs (Massive Open Online Courses) and SPOCs (Small Private online course). For solving the problems brought by varied skill levels and attitudes of online students, statistical models have been proposed to improve the fairness and accuracy of peer-grading. However, these models fail to deliver accurate inference in the SPOCs scenario because affinity among students may seriously affect the objectivity and reliability of students in the peer-assessment process. To address this problem, this paper proposes a human-machine hybrid peer-grading framework, including an automatic grader to ensure reasonable peer grades before the Bayesian models are utilized to infer the true scores. This framework can significantly eliminate the severely biased grades by those undutiful students, and thus improve the accuracy of the true-score estimation in the Bayesian peer-grading models. Both simulated and real peer-grading datasets in our experiments demonstrate the effectiveness of this new framework for SPOCs. [For the full proceedings, see ED599096.]
Active Learning of Strict Partial Orders: A Case Study on Concept Prerequisite Relations
Author: Liang, Chen
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Active Learning,Mathematical Concepts,Serial Ordering,Logical Thinking,Inquiry,Search Strategies,Information Retrieval

Strict partial order is a mathematical structure commonly seen in relational data. One obstacle to extracting such type of relations at scale is the lack of large scale labels for building effective data-driven solutions. We develop an active learning framework for mining such relations subject to a strict order. Our approach incorporates relational reasoning not only in finding new unlabeled pairs whose labels can be deduced from an existing label set, but also in devising new query strategies that consider the relational structure of labels. Our experiments on concept prerequisite relations show our proposed framework can substantially improve the classification performance with the same query budget compared to other baseline approaches. [For the full proceedings, see ED599096.]
A Multivariate Elo-Based Learner Model for Adaptive Educational Systems
Author: Abdi, Solmaz
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Models,Foreign Countries,College Students,Multivariate Analysis,Predictive Validity,Computer Assisted Instruction

The Elo rating system has been recognised as an effective method for modelling students and items within adaptive educational systems. The existing Elo-based models have the limiting assumption that items are only tagged with a single concept and are mainly studied in the context of adaptive testing systems. In this paper, we introduce a multivariate Elo-based learner model that is suitable for the domains where learning items can be tagged with multiple concepts, and investigate its fit in the context of adaptive learning. To evaluate the model, we first compare the predictive performance of the proposed model against the standard Elo-based model using synthetic and public data sets. Our results from this study indicate that our proposed model has superior predictive performance compared to the standard Elo-based model, but the difference is rather small. We then investigate the fit of the proposed multivariate Elo-based model by integrating it into an adaptive learning system which incorporates the principles of open learner models (OLMs). The results from this study suggest that the availability of additional parameters derived from multivariate Elo-based models have two further advantages: guiding adaptive behaviour for the system and providing additional insight for students and instructors. [For the full proceedings, see ED599096.]
Application of Hidden Markov Models to Quantify the Impact of Enrollment Patterns on Student Performance
Author: Boumi, Shahab
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Markov Processes,Enrollment,College Students,Full Time Students,Part Time Students,Academic Achievement,Data Analysis

Simplified categorizations have often led to college students being labeled as full-time or part-time students. However, at many universities student enrollment patterns can be much more complicated, as it is not uncommon for students to alternate between full-time and part-time enrollment each semester based on finances, scheduling, or family needs. While prior research has established that full-time students maintain better outcomes than their part-time counterparts, little study has examined the impact of mixed enrollment patterns on academic outcomes. In this paper, we apply a Hidden Markov Model to identify students' enrollment strategies according to three different categories: part-time, full-time, and mixed enrollment. According to the enrollment classification we investigate and compare the academic performance outcomes of each group. Analysis of data collected from the University of Central Florida from 2008 to 2017 indicates that mixed enrollment students are closer in performance to full-time students, than part-time students. More importantly, during their part-time semesters, mixed-enrollment students significantly outperform part-time students. Such a finding suggests that increased engagement through the occasional full-time enrollment leads to better overall outcomes. [For the full proceedings, see ED599096.]
Affect Detection in Home-Based Educational Software for Young Children
Author: Smeets, Roger
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Computer Software,Young Children,Evaluation Methods,Psychological Patterns,Affective Behavior,Emotional Response,Foreign Countries,Preschool Children,Kindergarten,Elementary School Students,Computer Assisted Testing

Research on automated affect detection in educational software using play log data has shown promising results. Yet most studies use classroom-based software designed for adolescents or adults. In this paper, we aim to detect affection in an online educational platform primarily aimed at home use by young children. This presents two challenges: we have to rely on a self-report instrument of affect that users can utilize at home, and we have to make sure that this instrument is properly understood by children. To this end, we developed and validated an emoticon-based self-report instrument to derive ground-truth labels of four emotions: Joy, frustration, confusion, and boredom. Training a number of different classifiers for automated affect detection yields promising results, in particular for detecting joy and frustration. [For the full proceedings, see ED599096.]
Affective State Prediction in a Mobile Setting Using Wearable Biometric Sensors and Stylus
Author: Wampfler, Rafael
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Affective Behavior,Prediction,Problem Solving,Mathematics,Handheld Devices,Educational Technology,Technology Uses in Education,Classification,Measurement Equipment,Physiology,Undergraduate Students,Feedback (Response)

The role of affective states in learning has recently attracted considerable attention in education research. The accurate prediction of affective states can help increase the learning gain by incorporating targeted interventions that are capable of adjusting to changes in the individual affective states of students. Until recently, most work on the prediction of affective states has relied on expensive and stationary lab devices that are not well suited for classrooms and everyday use. Here, we present an automated pipeline capable of accurately predicting (AUC up to 0.86) the affective states of participants solving tablet-based math tasks using signals from low-cost mobile bio-sensors. In addition, we show that we can achieve a similar classification performance (AUC up to 0.84) by only using handwriting data recorded from a stylus while students solved the math tasks. Given the emerging digitization of classrooms and increased reliance on tablets as teaching tools, stylus data may be a viable alternative to bio-sensors for the prediction of affective states. [For the full proceedings, see ED599096.]