ethanporcaro/eric-fulltext-meta huggingface dataset:
Suicide Report: A Health Risk Behavior Comparison of Montana High School Students Based on Attempted Suicide. 2019 Montana Youth Risk Behavior Survey
Author: Montana Office of Public Instruction
Date: 2019-08-00
Type: Reports - Evaluative
Subjects: Health Behavior,High School Students,Risk,National Surveys,Suicide,Gender Differences,Racial Differences,Ethnicity,At Risk Persons,Restraints (Vehicle Safety),Safety,Drinking,Weapons,School Safety,Attendance,Injuries,Bullying,Sexual Abuse,Victims,Mental Disorders,Smoking,Drug Abuse,Television Viewing,Eating Habits,Sexuality,Special Education,Physical Activity Level,Athletics,Sleep,Grades (Scholastic),Video Games,Middle School Students

The Montana Youth Risk Behavior Survey (YRBS) is administered by the Montana Office of Public Instruction every two years to students in grades 7 through 12. The purpose of the survey is 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. While all schools in Montana can participate in the survey on a volunteer basis, approximately 50 high schools are randomly selected to be included to provide the Montana statewide data to be used in other national YRBS reports. Data used in this report from the 2019 YRBS are based on a random sample survey of Montana high school students. The weighted data results contained in this report can be used to make inferences about the priority health-risk behaviors of all high school students in grades 9 through 12 in all schools in Montana. However, users should be careful in using the data since respondents in self-reported surveys may have a tendency to underreport behaviors that are socially undesirable, unhealthy, or illegal (alcohol consumption, drug use, seat belt nonuse, etc.) and overreport behaviors that are socially desirable (amount of exercise, etc.).
The Impact of the Baylor College of Medicine (BCM) 2011 and 2012 Summer Institute on Elementary and Middle-School Students&apos; Science Performance. Evaluation Report. Volume 7, Number 2
Author: Holmes, Venita
Date: 2013-09-00
Type: Reports - Research
Subjects: Program Effectiveness,Science Achievement,Summer Programs,Elementary School Students,Grade 3,Grade 4,Grade 5,Middle School Students,Grade 1,Grade 2,Grade 6,Grade 7,Grade 8,Faculty Development,Predictor Variables,Student Characteristics

The purpose of this evaluation was to measure the impact of the Baylor College of Medicine (BCM) Summer Institute on science performance of students whose teachers participated during the summers of 2011 and 2012. Paired t-tests revealed statistically significant increases in a sample of elementary students' Stanford 10 science scores at 3rd through 5th grades in 2012 and at 5th grade in 2013. For the middle-school student sample, significant increases were found at 7th and 8th grades in 2012 and 2013. Linear mixed-effects modeling revealed elementary students' previous year Stanford 10 science scores and teachers' additional hours of professional development were the strongest predictors of students' post BCM teacher participation science performance in both years. Propensity score matching yielded increases in elementary and secondary students' (State of Texas Assessments of Academic Readiness) STAAR scores following teacher participation relative to comparison group students; however, the differences were not statistically significant at p &lt; 0.05. The level of mentoring and peer support that teachers received during the school year was not measured in this evaluation and could have accounted for differences in science performance outcomes at elementary and secondary levels.
Toward Data-Driven Example Feedback for Novice Programming
Author: Zhi, Rui
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Data Use,Feedback (Response),Novices,Programming,Demonstrations (Educational),Problem Solving,Expertise,Progress Monitoring,Research Universities,Undergraduate Students,Computer Science Education

Viewing worked examples before problem solving has been shown to improve learning efficiency in novice programming. Example-based feedback seeks to present smaller, adaptive worked example steps during problem solving. We present a method for automatically generating and selecting adaptive, example-based programming feedback using historical student data. Our data-driven feature-based (DDF) example generation method automatically learns program features from data and selects example pairs based on when students complete each feature. We performed an experiment to compare three example generation methods: Student trace data, Data-Driven Features (DDF), and Expert examples. Two experts rated the quality of feedback for each generator, and they rated both the Expert and DDF example feedback as significantly more relevant to students' goals than the Student example feedback. However, there were no significant differences between the DDF and Expert examples. We compared these approaches to one that combined DDF with an Interactive Selection step (DDF-IS), where the user (in this case, an expert) selects their preferred data-driven feature before an example is selected. DDF-IS produced significantly more relevant examples than all other approaches, with significantly higher overall example quality than DDF. This suggests that our DDF approach allows more relevant examples to be selected than existing approaches, and that we may be able to leverage interactivity with the student to further improve example quality. [For the full proceedings, see ED599096.]
Insights on Variance Estimation for Blocked and Matched Pairs Designs
Author: Pashley, Nicole E.
Date: 2019-07-03
Type: Reports - Research
Subjects: Causal Models,Statistical Inference,Research Methodology,Computation,Statistical Bias,Equations (Mathematics),Matched Groups,Randomized Controlled Trials,Simulation,Error of Measurement

In the causal inference literature, evaluating blocking from a potential outcomes perspective has two main branches of work. The first focuses on larger blocks, with multiple treatment and control units in each block. The second focuses on matched pairs, with a single treatment and control unit in each block. These literatures not only provide different estimators for the standard errors of the estimated average impact, but they are also built on different sets of assumptions. Additionally, neither literature handles cases with blocks of varying size that contain singleton treatment or control units, a case which can occur with different forms of matching or post-stratifcation. Differences in the two literatures have also created some confusion regarding the benefits of blocking in general. In this paper, we first reconcile the literatures by carefully examining the performance of different estimators of treatment effect and of associated variance estimators under several different frameworks. We then use these insights to derive novel variance estimators for experiments containing blocks of different sizes. We also assess in which situations blocking is not guaranteed to reduce precision.
Measuring Students&apos; Thermal Comfort and Its Impact on Learning
Author: Jiang, Han
Date: 2019-07-00
Type: Reports - Research
Subjects: Climate,Heat,Environmental Influences,Climate Control,Measurement Techniques,Predictor Variables,Nonverbal Communication,Measurement Equipment,Learning,Learner Engagement,Foreign Countries,College Students

&quot;Thermal comfort&quot; (TC) -- how comfortable or satisfied a person is with the temperature of her/his surroundings -- is one of the key factors influencing the &quot;indoor environmental quality&quot; of schools, libraries, and offices. We conducted an experiment to explore how TC can impact students' learning. University students (n = 25) were randomly assigned to different temperature conditions in an office environment (25[degrees]C [right arrow] 30[degrees]C, or 30[degrees]C [right arrow] 25[degrees]C) that were implemented using a combination of heaters and air conditioners over a 1.25 hour session. The task of the participants was to learn from tutorial videos on three different topics, and a test was given after each tutorial. The results suggest that (1) changing the room temperature by a few degrees Celsius can stat. sig. impact students' self-reported TC; (2) the relationship between TC and learning exhibited an inverted U-curve, i.e., should be neither too uncomfortable nor too comfortable. We also explored different computer vision and sensor-based approaches to measure students' thermal comfort automatically. We found that (3) TC can be predicted automatically either from the room temperature or from an infra-red (IR) camera of the face; however, (4) TC prediction from a normal (visible-light) web camera is highly challenging, and only limited predictive power was found in the facial expression features to predict thermal comfort. [For the full proceedings, see ED599096.]
Rank-Based Tensor Factorization for Student Performance Prediction
Author: Doan, Thanh-Nam
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Data Analysis,Performance Factors,Prediction,Models,Mathematics,Knowledge Level,Problem Solving,Conceptual Tempo,Students,Learning Processes

One of the essential problems, in educational data mining, is to predict students' performance on future learning materials, such as problems, assignments, and quizzes. Pioneer algorithms for predicting student performance mostly rely on two sources of information: students' past performance, and learning materials' domain knowledge model. The domain knowledge model, traditionally curated by domain experts, maps learning materials to concepts, topics, or knowledge components that are presented in them. However, creating a domain model by manually labeling the learning material can be a difficult and time-consuming task. In this paper, we propose a tensor factorization model for student performance prediction that does not rely on a predefined domain model. Our proposed algorithm models student knowledge as a soft membership of latent concepts. It also represents the knowledge acquisition process with an added rank-based constraint in the tensor factorization objective function. Our experiments show that the proposed model outperforms state-of-the-art algorithms in predicting student performance in two real-world datasets, and is robust to hyper-parameters. [For the full proceedings, see ED599096.]
The Effect of the Baylor College of Medicine Summer Teacher Institute on HISD Elementary and Middle School Students&apos; Science Performance, 2013-2014. Evaluation Report. Volume 8, Issue 1
Author: Serrant, Ted D.
Date: 2015-01-16
Type: Reports - Research
Subjects: Program Effectiveness,Science Achievement,Summer Programs,Elementary School Students,Grade 3,Grade 4,Grade 5,Middle School Students,Grade 1,Grade 2,Grade 6,Grade 7,Grade 8,Faculty Development,Predictor Variables,Student Characteristics

The purpose of this report was to measure the effect of the Baylor College of Medicine Summer Science Institute (BCMSSI) on science performance of students whose teachers participated in the program during summer 2013. Paired t-test results showed statistically significant increases in the BCM students' Stanford 10 science performance at the third, fourth, and eighth grades. Results also showed moderate to substantively positive BCMSSCI program effect at these grade levels. Linear regression analysis indicated that students' previous year Stanford 10 science scores were the strongest predictor of science performance. Implications include additional mentoring and science support throughout the school year, and close monitoring of the instructional practices that BCMSSI teachers use in the classroom. [For the 2011-2012 report, see ED599246.]
Improving Peer Assessment Accuracy by Incorporating Relative Peer Grades
Author: Wang, Tianqi
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Peer Evaluation,Accuracy,Grades (Scholastic),Grading,Online Courses,Evaluators,Reliability,True Scores,Foreign Countries

Massive Open Online Courses (MOOCs) have become more and more popular recently. These courses have attracted a large number of students world-wide. In a popular course, there may be thousands of students. Such a large number of students in one course makes it infeasible for the instructors to grade all the submissions. Peer assessment is thus an effective paradigm that can help grade the submissions at a large scale. However, due to the variance in the ability and standard of the student graders, peer grades may be noisy and biased. Aggregating peer grades to have an accurate and fair final grade for a submission is a challenging problem because the reliability and bias degrees of graders are usually unknown in practice. To address this issue, some probabilistic models considering the graders' reliability and bias are proposed. However, due to the sparsity of peer grade observations, it is difficult for these models to estimate the accurate reliability and bias of the graders as well as the true grades of the submissions. Compared with absolute peer grades, the relative peer grades, derived from the difference between the peer grades of two submissions graded by the same grader, are less sparse and more robust to the grader's bias. Thus relative peer grades are informative and helpful in cardinal peer grading estimation whose goal is to estimate the absolute numeric grades of submissions. In this paper, we propose two new probabilistic models to help improve the accuracy of cardinal peer grading estimation using the observed relative grades among submissions. In this way, the relation between the true grades among submissions is taken into consideration when deriving the final grades. Experimental results on real MOOC peer grading datasets show that the proposed models outperform baselines and the relation of true grades among submissions indeed contributes to the improvement in the grade estimation. [For the full proceedings, see ED599096.]
Probabilistic Modeling of Peer Correction and Peer Assessment
Author: Sunahase, Takeru
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Peer Evaluation,Error Correction,Grading,Reliability,Accuracy,Probability,Models,Evaluators

Peer assessment is a promising solution for scaling up the grading of a large number of submissions. The reliability of evaluations is one of the critical issues in peer assessment; several probabilistic models have been proposed for obtaining reliable grades from peers. Peer correction is a similar framework, in which students are instructed to correct the errors in submissions from other students. &quot;Peer correction&quot; is typically performed simultaneously with peer assessment; a reviewer is instructed to correct the errors in a submission and to provide a grade to it. We observe the occasional inconsistency between a grade and the correction; for example, a reviewer provides a high grade for a submission but she corrects many errors in it. Such inconsistencies can point to unreliable reviewers. In this paper, we propose probabilistic models for peer correction, and the combination of the peer correction models and the existing peer assessment models for capturing the inconsistency to accurately estimate the reviewer reliability and the student ability. We conduct experiments using the dataset of an actual peer correction platform for language translation, and the results demonstrate that the combination of peer correction models and peer assessment models improves the accuracy of the student ability estimation. [For the full proceedings, see ED599096.]
Scholars Walk: A Markov Chain Framework for Course Recommendation
Author: Polyzou, Agoritsa
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Markov Processes,Course Selection (Students),Undergraduate Students,Decision Making,Probability,Sequential Approach,Models,Evaluation Methods,Enrollment

Course selection is a crucial and challenging problem that students have to face while navigating through an undergraduate degree program. The decisions they make shape their future in ways that they cannot conceive in advance. Available departmental sample degree plans are not personalized for each student, and personal discussion time with an academic advisor is usually limited. Data-driven methods supporting decision making have gained importance to empower student choices and scale advice to large cohorts. We propose &quot;Scholars Walk,&quot; a random-walk-based approach that captures the sequential relationships between the different courses. Based on the &quot;wisdom of the crowd&quot; and the students' prior courses, we recommend a short list of courses for next semester. Our experimental evaluation illustrates that Scholars Walk outperforms other collaborative filtering and popularity-based approaches. At the same time, our framework is very efficient, easily interpretable, while also being able to take into consideration important aspects of the educational domain. [For the full proceedings, see ED599096.]
The Baylor College of Medicine Summer Science Institute Effect on HISD Elementary- and Middle-School Students&apos; Science Performance, 2014-2015. Evaluation Report. Volume 9, Issue 2
Author: Serrant, Ted D.
Date: 2015-10-00
Type: Reports - Research
Subjects: Program Effectiveness,Science Achievement,Summer Programs,Elementary School Students,Grade 3,Grade 4,Grade 5,Middle School Students,Grade 1,Grade 2,Grade 6,Grade 7,Grade 8,Faculty Development,Predictor Variables,Student Characteristics

The purpose of this study was to measure the effect of the Baylor College of Medicine Summer Science Institute (BCMSSI) on teacher science content knowledge and students' science performance. The analysis of the effect of participation showed an increase of 253 scale score points on the 2015 State of Texas Assessments of Academic Readiness, (STAAR) science performance of eighth grade students whose teachers completed all 60 professional development (PD) hours. A higher proportion of 5th - and 8th-grade students whose teachers completed the full PD hours met Satisfactory at the Level II phase-in 1 standard and Advanced performance standard on the 2015 STAAR science test compared to their peers whose teachers did not complete the full PD hours. The results also showed statistically significant moderate to large gains in teacher content knowledge and self-efficacy at all grade levels. Teachers at all grade levels were also, overwhelmingly, positive about the science institute and agreed that the resources and materials that they received were useful. Increasing the number of teachers who complete the BCMSSI program should be considered. Continued instructional focus should be placed on improving the science performance of at-risk and economically disadvantaged students. [For the 2013-2014 report, see ED599251.]
Skills Embeddings: A Neural Approach to Multicomponent Representations of Students and Tasks
Author: Moore, Russell
Date: 2019-07-00
Type: Speeches/Meeting Papers
Subjects: Models,Knowledge Representation,Skills,Artificial Intelligence,Students,Computation,Data Analysis

Educational systems use models of student skill to inform decision-making processes. Defining such models manually is challenging due to the large number of relevant factors. We propose learning multidimensional representations (embeddings) from student activity data -- these are fixed-length real vectors with three desirable characteristics: co-location of similar students and items in a vector space; magnitude increases with skill, and that absence of a skill can be represented. Based on the Multi-component Latent Trait Model, we use a neural network with complementary trainable weights to learn these embeddings by back-propagation. We evaluate using synthetic student activity data that provides a ground truth of student skills in order to understand the impact of number of students, question items and knowledge components in the domain. We find that our data-mined parameter values can recreate the synthetic datasets up to the accuracy of the model that generated them, for domains with up to 10 simultaneously active knowledge components, which can be effectively mined using relatively small quantities of data (1000 students, 100 items). We describe a procedure to estimate the number of components in a domain, and propose a component-masking logic mechanism that improves performance on high-dimensional datasets. [For the full proceedings, see ED599096.]
Performance Level Setting Technical Report
Author: Davis, Laurie L.
Date: 2015-12-00
Type: Reports - Descriptive
Subjects: College Readiness,Career Readiness,Program Descriptions,Educational Objectives,Standard Setting,Evidence Based Practice,Educational Indicators,Behavioral Objectives,Policy Formation,Meetings,Benchmarking,College Faculty,Teacher Attitudes,Partnerships in Education,Indexing,Vocabulary,Mathematics Achievement,English,Language Arts,Elementary Secondary Education,Feedback (Response),Literacy,Advisory Committees,Consortia,Teaching Methods,Accountability,Common Core State Standards,Evaluation Methods,Summative Evaluation,Achievement Tests

This report is made up of seven chapters relating to Partnership for Assessment of Readiness for College and Careers (PARCC) performance level setting (PLS). Chapter one describes the goals of the PARCC program, content standards measured by PARCC, PARCC performance levels, and components of the PARCC system. Chapter two discusses the goals of setting performance standards, evidence-based standard setting, the PARCC performance level setting process, and changes to the performance level setting process. Chapter three provides details about the development of the performance level descriptors (PLD) that were used throughout the PLS process. Sections in this chapter discuss what performance level descriptors are, performance level descriptors and college- and career-readiness determination, approach to performance level descriptor development, and revision of the performance level descriptors. Chapter four provides details about Steps 2 and 3 of the PARCC performance level setting process, which focus on developing and conducting research studies. Chapter five provides details about Step 4 of the PARCC PLS process, which focuses on convening the pre-policy committee and developing &quot;reasonable ranges&quot; based on the PARCC research studies. Chapter six discusses step 5 of the performance level setting process, which focuses on convening performance level setting meetings. Finally, chapter seven details the process used for reviewing performance standards for reasonableness.