ethanporcaro/eric-fulltext-meta huggingface dataset:
Teacher Mobility in Texas: Trends in and Associations with Student, Teacher, and School Characteristics. Policy Brief
Author: Sullivan, Kate
Date: 2017-12-00
Type: Reports - Research
Subjects: Faculty Mobility,Trend Analysis,Student Characteristics,Teacher Characteristics,Institutional Characteristics,Public Schools

The goal of the study is to build a deeper understanding of teacher mobility in Texas by identifying patterns of teacher mobility. The study characterizes the movement of teachers in Texas public schools during the 2011/12 through 2015/16 school years. Analyses explore links between mobility and teacher, student, and school characteristics. In addition, this study explores the relationship between teacher mobility and school-average teacher effectiveness, as measured by the T-TESS rubric. Findings from this study provide state policymakers in Texas with updated information about the patterns and trends of mobility in the state's teaching workforce and offer a systematic baseline for monitoring and planning for future changes. The findings presented here were guided by four research questions developed with the Educator Effectiveness Alliance: (1) What are teacher mobility rates and destination proportions at the district, regional, and state levels in each school year from 2011/12 to 2015/16?; (2) What is the association between personal and professional characteristics of Texas public school teachers and their mobility behaviors?; (3) What is the association between student/school characteristics and school-level mobility rates?; and (4) What is the association between school-average rubric ratings of teachers participating in the 2014/15 T-TESS pilot and school-level mobility rates? [For the full public report provided by the Regional Educational Laboratory Southwest, &quot;Trends in Teacher Mobility in Texas and Associations with Teacher, Student, and School Characteristics,&quot; see ED578907.]
Analysis Study of Parental Choice of Education in the Millenial Era
Author: Fauzi, Ahmad
Date: 2018-00-00
Type: Journal Articles
Subjects: School Choice,Islam,Religious Schools,Parent Attitudes,Decision Making,Religious Education,Science Education,Religious Factors,Educational Quality,Muslims,Models,Barriers,Educational History,Cultural Influences,Religious Cultural Groups,Social Influences,Foreign Countries

This article discusses parents' choices about the model of Islamic education in the millennial era. This study uses the library method. The results showed that parents had several criteria in determining the choice of educational models in the millennial era, namely a). schools that provide a lot of religious material b). combining religion and science, c). giving reinforcement to monotheism, d). madrasas that have vision, mission with good management. e). madrasas that have the same status as public schools.
Adapting to Teaching and Learning during COVID-19: A Case of Islamic School&apos;s Initiative of Self-Regulated Learning
Author: Mustajab
Date: 2020-12-00
Type: Journal Articles
Subjects: Religious Schools,Islam,COVID-19,Pandemics,High School Teachers,Online Courses,School Closing,Independent Study,Barriers,Educational Technology,Foreign Countries,Self Management,Learning Motivation,Teacher Attitudes,Administrator Attitudes,Student Attitudes,Distance Education,Educational Practices,Learning Strategies,Student Evaluation

This study aims to analyze self-regulated learning in Islamic education amid the COVID-19 pandemic. This study incorporates a qualitative approach to understand self-regulated learning at Miftahul Ulum Islamic High School, East Java. The data collection techniques consist of teachers' interviews, teaching and learning observation, and documentation. The results showed that implementing self-regulated learning during the COVID-19 pandemic was carried out online through activities, analysis of learning conditions, determining the learning design, applying strategies, and evaluation. Despite challenges, online self-regulated learning facilitates Miftahul Ulum Islamic School students to participate in learning activities during the COVID-19 Pandemic.
Examining High School Career and Technical Education Programs and the Postsecondary Outcomes of Career and Technical Education Students in the Round Rock Independent School District. Policy Brief
Author: Mellor, Lynn
Date: 2021-08-00
Type: Reports - Descriptive
Subjects: High School Students,Vocational Education,Outcomes of Education,Postsecondary Education,Employment Potential,High School Graduates,Career Pathways,Wages,Educational Attainment

The policy brief describes a study done by the Regional Educational Laboratory Southwest. Round Rock Independent School District (ISD) wanted to enhance its high school career and technical education (CTE) programming to prepare CTE graduates to either enter high-wage, in-demand careers in Central Texas or continue their education after high school. However, district leaders lacked key information about the district's CTE programming and their CTE graduates' postsecondary performance. In response to a request from the Round Rock ISD, this study investigated the percentage of Round Rock ISD graduates from 2012/13 through 2017/18 who completed one or more career and technical education (CTE) programs of study. The study also examined the percentage of CTE programs of study in Round Rock ISD that were aligned with high wage, in demand career pathways in Central Texas, plus the postsecondary outcomes of these graduates who completed a program of study. Key study findings include: (1) The percentage of students who graduated with one or more CTE programs of study increased more than 10 fold across the six graduating cohorts, from 4 percent for the 2012/13 cohort to 47 percent for the 2017/18 cohort; (2) Seventy-six percent of the CTE graduates completed course requirements in the 13 programs of study aligned with regional high wage, in demand career pathways in the Central Texas labor market; and (3) More than 80 percent of the CTE graduates from each cohort enrolled in two or four year colleges or were employed within one year of high school graduation. [For the full report, &quot;Examining High School Career and Technical Education Programs and the Postsecondary Outcomes of Career and Technical Education Students in the Round Rock Independent School District,&quot; see ED608126.]
Triple Alignment: Congruency of Perceived Preschool Classroom Social Networks among Teachers, Children, and Researchers
Author: Chen, Jing
Date: 2020-00-00
Type: Reports - Research
Subjects: Social Networks,Preschool Teachers,Preschool Children,Congruence (Psychology),Researchers,Network Analysis

Classroom social networks are influential to young children's cognitive, social-emotional, and language development, but assessment and analyses of social networks are complex. Findings have been mixed regarding whether different informants (teachers, children, researchers) are congruent to their perceived classroom social networks. There is also a lack of discussion about the roles of network transformation (converting value networks into binary networks), a required data step for widely used statistical network analyses. This study addressed these issues based on network data of 16 preschool children containing 240 potential dyadic interactions collected from teacher ratings, child nominations, and researcher observations across 44 observation cycles over four school days. Results showed that the three informants were congruent in perceiving the classroom social network, while the level of congruency between the teacher-report and the researcher-report networks was the highest. Binary transformation of social networks tended to decrease the level of congruency across informants, although the level of congruency tended to be higher when more stringent binary transformation thresholds were selected. [The paper was published in &quot;Frontiers in Psychology&quot; v11 Article 1341 2020.]
Gender and Gendered Discourse in Two Online Science College Courses
Author: Henricks, Genevieve
Date: 2021-00-00
Type: Journal Articles
Subjects: Gender Differences,Language Usage,Online Courses,College Science,Astronomy,Chemistry,STEM Education,Discussion Groups,Computer Mediated Communication,Asynchronous Communication,Interpersonal Communication,Sense of Community,Discourse Communities

Discussion forums are important components of online courses because of the collaboration and community they foster, and the language used within the discussion forums may be influential in this development. In particular, studying the gendered language patterns of discussion forums can help gain insight into students' state of mind and propensity to form a community and thus ultimately may help explain men and women's differential success in online courses. This study describes the state of gendered language use in two online science courses. The results reveal that women and men do not differ in their language use along traditionally gendered lines, which holds promise for women in online courses. This means that the language that women use does not overtly mark them as female, which has the potential to help subvert the typical result of the negative outcomes associated with the female marker in science courses.
Building the Right Skills for Human Capital: Education, Skills, and Productivity in the Kyrgyz Republic. International Development in Focus
Author: Hou, Dingyong
Date: 2021-00-00
Type: Reports - Research
Subjects: Foreign Countries,Human Capital,Adults,Literacy,Numeracy,Computer Literacy,Problem Solving,Job Skills,Secondary School Teachers,Education Work Relationship,Postsecondary Education,Educational Quality,Achievement Tests,Secondary School Students,International Assessment

Building the Right Skills for Human Capital summarizes the findings from the 2019 skills survey for the adult Kyrgyz population. The skills measures used in the survey focused on literacy, numeracy, and problem solving in technology rich environments (PSTRE) and followed the same questions and approach as the Organisation for Economic Co-operation and Development's Programme for the International Assessment of Adult (PIAAC) surveys. Most jobs in the Kyrgyz Republic require regular use of reading, writing, numeracy, and information and communications technology (ICT) skills, and higher-skilled groups of people earn higher wages, suggesting that the labor market rewards higher skills. However, skills levels among the workforce are consistently low in absolute terms among varying sociodemographic groups and relative to countries that implemented PIAAC surveys. Results are not improving across cohorts, except for PSTRE. There is evidence that a substantial share of people is overschooled but underskilled. The lack of quality of education is an important driver for low skills performance. The report finds that higher levels of education are associated with higher skills levels, but even among the most educated, a large share has low skills scores, which helps explain why we find that a large share can be overeducated but underskilled for the jobs they occupy. Skill levels of secondary school teachers were also assessed. Teachers outperform the general population in both literacy and numeracy but underperform compared to professionals. Overall, one-third of teachers still have low proficiency in literacy and numeracy. With regards to ICT skills, the results suggest that nearly all secondary school teachers are currently not well equipped to impart ICT skills to their students. The book concludes with a series of policy recommendations at different levels of education, from early childhood education through life-long learning, including providing upskilling opportunities for those teachers with specific skill deficiencies.
Selective Bias: Asian Americans, Test Scores, and Holistic Admissions
Author: Carnevale, Anthony
Date: 2021-00-00
Type: Reports - Research
Subjects: Selective Admission,Asian American Students,College Admission,Pacific Americans,Racial Differences,Ethnicity,College Applicants,Scores,College Entrance Examinations,Affirmative Action,Racial Bias,Enrollment,Population Trends,Holistic Approach

Affirmative action critics argue that race-conscious admissions policies are keeping Asian American enrollment numbers unfairly low because Asian American students are held to higher admissions standards than applicants of any other race or ethnicity. &quot;Selective Bias: Asian Americans, Test Scores, and Holistic Admissions&quot; evaluates the common arguments made by them and Students for Fair Admissions, which is suing Harvard University and has lawsuits pending against the University of North Carolina and the University of Texas at Austin over their admissions practices. The report finds no strong evidence of discrimination against Asian American applicants in admissions to highly selective colleges.
Big Breakthrough on New York School Funding
Author: Lecker, Wendy
Date: 2021-00-00
Type: Reports - Evaluative
Subjects: Educational Finance,State Aid,Funding Formulas,Court Litigation,Advocacy,Low Income Students,Financial Support,School Districts,Poverty

The Foundation Aid Formula is designed to deliver more funding to New York districts with higher enrollments of low-income students who require additional resources to support their education. These higher need districts struggle to raise sufficient local dollars and rely more heavily on state aid to support their schools. As a result, the underfunding of the Foundation Aid Formula has a disproportionate impact on students in these districts. After a sustained 12-year campaign, New York advocates secured a major victory in April when lawmakers enacted a $1.4 billion increase in state aid in the FY22 State Budget, along with a commitment to fully fund the state's Foundation Aid formula by the 2023-24 school year. The $1.4 billion increase will be followed by an estimated $2.5 billion aid increase over two years to finally close the gap in the state's share of school funding required by New York's Foundation Aid Formula.
Effect of a Metacognitive Strategy Instruction on Problem Solving in Newtonian Mechanics among Vocational Higher Secondary Students
Author: Gafoor, K. Abdul
Date: 2012-07-00
Type: Journal Articles
Subjects: Grade 11,Secondary School Students,Vocational Schools,Science Instruction,Secondary School Science,Metacognition,Teaching Methods,Problem Solving,Mechanics (Physics),Science Achievement,Program Effectiveness,Scientific Concepts,Concept Formation

In view of intellectual and pedagogic challenges for students and teachers in understanding and teaching problem solving, this study reports findings on developing a strategy that encourages physics teachers to focus on a metacognitive approach to solving well-structured problems in Newtonian mechanics. It employs a pre-test post-test control group quasi-experimental design on two intact groups of 21 students each of Class 11 from vocational higher secondary school, matched one to one based on the pre-test. Although Metacognitive Strategy Instruction is found significantly contributing to problem-solving, the study recommends considering the strategy in other areas of physics and allied sciences.
Automated Claim Identification Using NLP Features in Student Argumentative Essays
Author: Wan, Qian
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Essays,Persuasive Discourse,Automation,Identification,Natural Language Processing,Documentation,Accuracy,Undergraduate Students,Computer Uses in Education

The current study explores the ability to predict argumentative claims in structurally-annotated student essays to gain insights into the role of argumentation structure in the quality of persuasive writing. Our annotation scheme specified six types of argumentative components based on the well-established Toulmin's model of argumentation. We developed feature sets consisting of word count, frequency data of key n-grams, positionality data, and other lexical, syntactic, semantic features based on both sentential and suprasentential levels. The suprasentential Random Forest model based on frequency and positionality features yielded the best results, reporting an accuracy of 0.87 and kappa of 0.73. This model will be included in an online writing assessment tool to generate feedback for student writers. [For the full proceedings, see ED615472.]
Assessing Attendance by Peer Information
Author: Deng, Pan
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Attendance Patterns,Peer Influence,Online Courses,Blended Learning,College Students,Student Interests,Student Motivation,Correlation,Grades (Scholastic),Grade Point Average,Conventional Instruction,Student Behavior,Foreign Countries,Courses

Attendance rate is an important indicator of students' study motivation, behavior and Psychological status; however, the heterogeneous nature of student attendance rates due to the course registration difference or the online/offline difference in a blended learning environment makes it challenging to compare attendance rates. In this paper, we propose a novel method called Relative Attendance Index (RAI) to measure attendance rates, which reflects students' efforts on attending courses. While traditional attendance focuses on the record of a single person or course, relative attendance emphasizes peer attendance information of relevant individuals or courses, making the comparisons of attendance more justified. Experimental results on real-life data show that RAI can indeed better reflect student engagement. [For the full proceedings, see ED615472.]
Automatic Short Answer Grading with SBERT on Out-of-Sample Questions
Author: Condor, Aubrey
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Automation,Grading,Test Items,Generalization,Models,Natural Language Processing,Artificial Intelligence,Classification

We explore how different components of an Automatic Short Answer Grading (ASAG) model affect the model's ability to generalize to questions outside of those used for training. For supervised automatic grading models, human ratings are primarily used as ground truth labels. Producing such ratings can be resource heavy, as subject matter experts spend vast amounts of time carefully rating a sample of responses. Further, it is often the case that multiple raters must come to a census before a final ground-truth rating is established. If ASAG models were developed that could generalize to out-of-sample questions, educators may be able to quickly add new questions to an auto-graded assessment without a continued manual rating process. For this project we explore various methods for producing vector representations of student responses including state-of-the-art representation methods such as Sentence-BERT as well as more traditional approaches including Word2Vec and Bag-of-words. We experiment with including previously untapped question-related information within the model input, such as the question text, question context text, scoring rubric information and a question-bundle identifier. The out-of-sample generalizability of the model is examined with both a leave-one-question-out and leave-one-bundle-out evaluation method and compared against a typical student-level cross validation. [For the full proceedings, see ED615472.]
Automatically Classifying Student Help Requests: A Multi-Year Analysis
Author: Gao, Zhikai
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Automation,Classification,Help Seeking,Computer Science Education,Computer Uses in Education,College Students

As Computer Science has increased in popularity so too have class sizes and demands on faculty to provide support. It is therefore more important than ever for us to identify new ways to triage student questions, identify common problems, target students who need the most help, and better manage instructors' time. By analyzing interaction data from office hours we can identify common patterns, and help to guide future help-seeking. My Digital Hand (MDH) is an online ticketing system that allows students to post help requests, and for instructors to prioritize support and track common issues. In this research, we have collected and analyzed a corpus of student questions from across six semesters of a CS2 with a focus on object-oriented programming course [17]. As part of this work, we grouped the interactions into five categories, analyzed the distribution of help requests, balanced the categories by Synthetic Minority Oversampling Technique (SMOTE) , and trained an automatic classifier based upon LightGBM to automatically classify student requests. We found that over 69% of the questions were unclear or barely specified. We proved the stability of the model across semesters through leave one out cross-validation and the target model achieves an accuracy of 91.8%. Finally, we find that online office hours can provide more help for more students. [For the full proceedings, see ED615472.]
Benefits of Alternative Evaluation Methods for Automated Essay Scoring
Author: Andersen, Øistein E.
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Evaluation Methods,Scoring,Essays,Computer Assisted Testing,Student Evaluation

Automated essay scoring (AES), where natural language processing is applied to score written text, can underpin educational resources in blended and distance learning. AES performance has typically been reported in terms of correlation coefficients or agreement statistics calculated between a system and an expert human examiner. We describe the benefits of alternative methods to evaluate AES systems and, more importantly, facilitate comparison between AES systems and expert human examiners. We employ these methods, together with &quot;multi-marked&quot; test data labelled by 5 expert human examiners, to guide machine learning model development and selection, resulting in models that outperform expert human examiners. We extend on previous work on a mature feature-based linear ranking perceptron model and also develop a new multitask learning neural network model built on top of a pretrained language model -- DistilBERT. Combining these two models' scores results in further improvements in performance (compared to that of each single model). [For the full proceedings, see ED615472.]
Acting Engaged: Leveraging Play Persona Archetypes for Semi-Supervised Classification of Engagement
Author: Nye, Benjamin D.
Date: 2021-00-00
Type: Reports - Research
Subjects: Learner Engagement,Profiles,Artificial Intelligence,Student Behavior,Play

Engaged and disengaged behaviors have been studied across a variety of educational contexts. However, tools to analyze engagement typically require custom-coding and calibration for a system. This limits engagement detection to systems where experts are available to study patterns and build detectors. This work studies a new approach to classify engagement patterns without expert input, by using a play persona methodology where labeled archetype data is generated by novice testers acting out different engagement patterns in a system. Domain-agnostic task features (e.g., response time to an activity, scores/correctness, task difficulty) are extracted from standardized data logs for both archetype and authentic user sessions. A semi-supervised methodology was used to label engagement; bottom-up clusters were combined with archetype data to build a classifier. This approach was analyzed with a focus on cold-start performance on small samples, using two metrics: consistency with larger full-sample cluster assignments and stability of points staying in the same cluster once assigned. These were compared against a baseline of clustering without an incrementally trained classifier. Findings on a data set from a branching multiple-choice scenario-based tutoring system indicated that approximately 52 unlabeled samples and 51 play-test labeled samples were sufficient to classify holdout sessions at 85% consistency with a full set of 145 unsupervised samples. Additionally, alignment to play persona samples for the full set matched expert labels for clusters. Use-cases and limitations of this approach are discussed. [For the full proceedings, see ED615472.]