ethanporcaro/eric-fulltext-meta huggingface dataset:
Measuring Gameplay Affordances of User-Generated Content in an Educational Game
Author: Hicks, Drew
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Affordances,Educational Games,Creative Activities,Video Games,Programming,Computer Uses in Education

Level creation is a creative game-play exercise that resembles problem-posing, and has shown to be engaging and helpful for players to learn about the game's core mechanic. However, in user-authoring environments, users often create levels without considering the game's objective, or with entirely different objectives in mind, resulting in levels which fail to afford the core gameplay mechanic. This poses a bigger threat to educational games, because the core gameplay is aligned with the learning objectives. Therefore, such levels fail to provide any opportunity for players to practice the skills the game is designed to teach. To address this problem, we designed and compared three versions of level creators in a programming game -- Freeform, Programming, and Building-Block. Our results show that a simple-to-use building-block editor can guarantee levels that contain some affordances, but an editor designed to use the same core mechanic as gameplay results in the highest-quality levels. [For the full proceedings, see ED592609.]
Pattern Mining Uncovers Social Prompts of Conceptual Learning with Physical and Virtual Representations
Author: Rau, Martina A.
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Visual Aids,Manipulative Materials,STEM Education,Concept Formation,High School Students,Cues,Chemistry,Scientific Concepts,Metacognition,Secondary School Science,Intelligent Tutoring Systems,Teacher Role,Behavior Patterns,Cooperative Learning

To succeed in STEM, students need to connect visual representations to domain-relevant concepts, which is a difficult task for them. Prior research shows that physical representations (that students manipulate with their hands) and virtual representations (that they manipulate on a computer) have complementary advantages for conceptual learning. Further, physical and virtual representations are often embedded into different social classroom practices. Thus, to optimally combine these representation modes, we need to understand what social events prompt students to connect representations to concepts, and if different representation modes afford different social prompts. A multiple-case study with 12 high-school students addresses this question. Student pairs worked with physical and virtual representations of chemistry. Frequent patterns obtained from discourse data show that students incrementally co-construct concept-representation connections, and that instructor prompts are key triggers of these connections for both representation modes. Meta-cognitive statements serve as important prompts in the absence of an instructor when students work with virtual representations. I discuss implications for interventions that combine physical and virtual representations. [For the full proceedings, see ED592609.]
Head Start Children&apos;s Developmental Progress and Kindergarten Experiences. Research Brief. OPRE Report 2017-71
Author: Aikens, Nikki
Date: 2017-09-00
Type: Reports - Research
Subjects: Early Intervention,Child Development,Young Children,Kindergarten,Preschool Education,Hispanic American Students,African American Students,White Students,English (Second Language),Spanish Speaking,At Risk Students,Socioeconomic Influences,One Parent Family,Parent Background,Educational Attainment,Family Income,Poverty,Cognitive Development,Social Development,Emotional Development,Child Health,Physical Education,Institutional Characteristics,Vocabulary Development,Educational Environment,Teacher Characteristics

This brief focuses on Head Start children's developmental progress and kindergarten experiences, drawing on data from the 2009 cohort of the Head Start Family and Child Experiences Survey (FACES 2009). The focal population is children who entered Head Start for the first time in fall 2009, completed one or two years of the program, and were attending kindergarten in spring 2011 or 2012. Head Start children still participating in the study during kindergarten are a diverse group. At program entry, 39 percent of the children are Hispanic/Latino, 32 percent are African American, and 21 percent are white. Thirty percent of them live in households where a language other than English is the primary language spoken to them. Spanish is by far the most prevalent non-English language, spoken as the primary home language to 27 percent of the children. Families of many Head Start children face a number of socioeconomic risks. At program entry, 47 percent of children live in single-parent households, 37 percent live with mothers who have less than a high school education, and 64 percent live in households whose total income is below the federal poverty threshold. This brief addresses two central research questions: (1) What are Head Start children's cognitive, social-emotional, and health and physical outcomes from Head Start entry to the spring of kindergarten? and (2) What are the characteristics of the schools and classrooms Head Start children attend during kindergarten? Relevant methodological information is summarized at the end of this brief.
On Generalizability of MOOC Models
Author: Kidzinsk, Lukasz
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Generalizability Theory,Online Courses,Large Group Instruction,Models,Goodness of Fit,Accuracy,College Students,Cohort Analysis,Statistical Bias,Statistical Analysis,Sample Size

The big data imposes the key problem of generalizability of the results. In the present contribution, we discuss statistical tools which can help to select variables adequate for target level of abstraction. We show that a model considered as over-fitted in one context can be accurate in another. We illustrate this notion with an example analysis experiment on the data from 13 university Massive Online Open Courses (MOOCs). We discuss statistical tools which can be helpful in the analysis of generalizability of MOOC models. [For the full proceedings, see ED592609.]
Helping Students Succeed by Strengthening the Carl D. Perkins Career and Technical Education Act. Hearing before the Committee on Education and the Workforce, U.S. House of Representatives, One Hundred Fourteenth Congress, Second Session (May 17, 2016). Serial Number 114-48
Author: US House of Representatives. Committee on Education and the Workforce
Date: 2017-00-00
Type: Legal/Legislative/Regulatory Materials
Subjects: Educational Legislation,Federal Legislation,Technical Education,Vocational Education,Hearings,Outcomes of Education,Educational Change,Academic Achievement,Educational Improvement,Labor Force Development,Equal Education,Federal Government,Education Work Relationship,Industry,Job Skills,Skill Development,School Business Relationship,STEM Education,Success,Alignment (Education),Work Experience Programs,Credentials,Secondary Education,Postsecondary Education

This document records testimony from a hearing held to examine ways to help students succeed by strengthening the Carl D. Perkins Career and Technical Education Act. Member statements were presented by: (1) Honorable John Kline, Chairman, Committee on Education and the Workforce; and (2) Honorable Robert C. Scott, Ranking Member, Committee on Education and the Workforce, U.S. House of Representatives. Witness statements were presented by: (1) Honorable Tim Kaine, a Representative in Congress from the State of Virginia; (2) Paul Tse, Project Manager, Shapiro and Duncan, Inc.; (3) Jason Bates, Manager, Toyota-Bodine Aluminum, Inc.; and (4) Monty Sullivan, President, Louisiana Community and Technical College System. Additional submissions were presented by: (1) Honorable Marcia L. Fudge, a Representative in Congress from the State of Ohio: Letter dated May 3, 2016, from Partnership for 21st Century Learning P21; and Letter dated May 16, 2016, from the National Education Association (NEA); (2) Honorable Glenn Thompson, a Representative in Congress from the State of Pennsylvania: Letter dated May 16, 2016, from Opportunity America; and Recommendations Perkins Career and Technical Education Act; (3) Chairman Kline on behalf of Congressman Will Hurd, questions submitted for the record to: Mr. Bates and Dr. Sullivan; (4) Honorable Michael D. Bishop, a Representative in Congress from the state of Michigan, questions submitted to Dr. Sullivan; (5) Honorable Elise Stefanik, a Representative in Congress from the state of New York, questions submitted to: Mr. Bates and Mr. Tse; and (6) Response to questions submitted: Mr. Bates, Dr. Sullivan, and Mr. Tse.
Investigating Difficult Topics in a Data Structures Course Using Item Response Theory and Logged Data Analysis
Author: Fouh, Eric
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Item Response Theory,Data Analysis,Mathematics,Intelligent Tutoring Systems,Data,Comparative Analysis,Models,College Students,Mathematics Instruction

We present an analysis of log data from a semester's use of the OpenDSA eTextbook system with the goal of determining the most difficult course topics in a data structures course. While experienced instructors can identify which topics students most struggle with, this often comes only after much time and effort, and does not provide real-time analysis that might benefit an intelligent tutoring system. Our factors included the fraction of wrong answers given by student, results from Item Response Theory, and the rate of model answer and hint use by students. We grouped exercises by topic covered to yield a list of topics associated with the harder exercises. We found that a majority of these exercises were related to algorithm analysis topics. We compared our results to responses given by a sample of experienced instructors, and found that the automated results match the expert opinions reasonably well. We investigated reasons that might explain the over-representation of algorithm analysis among the difficult topics, and hypothesize that visualizations might help to better present this material. [For the full proceedings, see ED592609.]
How Good Is Popularity? Summary Grading in Crowdsourcing
Author: Li, Haiying
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Peer Acceptance,Electronic Publishing,Collaborative Writing,Grading,Scoring,Natural Language Processing,Semantics,Scores,Grades (Scholastic),Reading Comprehension,Readability,Writing Improvement,Teaching Methods,Reading Instruction

In this paper, we applied the crowdsourcing approach to develop an automated popularity summary scoring, called wild summaries. In contrast, the golden standard summaries generated by one or more experts are called expert summaries. The innovation of our study is to compute LSA (Latent Semantic Analysis) similarities between target summary and wild summaries rather than expert summaries. We called this method CLSAS, i.e., crowdsourcing-based LSA similarity. We evaluated CLSAS by comparing it with other approaches, Coh-Metrix language and discourse features and LIWC psychometric word measures. Results showed that CLSAS alone could explain 19% of human summary score, which was equivalent to the variance explained by dozens of language and discourse features and/or the word features. Results also showed that adding language and/or word features to CLSAS increased small additional correlations. Findings imply that crowdsourcing-based LSA similarity approach is a promising method for automated summary assessment. [For the full proceedings, see ED592609.]
Providing More Students a Pathway to Success by Strengthening Career and Technical Education. Hearing before the Subcommittee on Early Childhood, Elementary, and Secondary Education, Committee on Education and the Workforce, U.S. House of Representatives, One Hundred Fifteenth Congress, First Session (February 28, 2017). Serial Number 115-7
Author: US House of Representatives. Committee on Education and the Workforce
Date: 2017-00-00
Type: Legal/Legislative/Regulatory Materials
Subjects: Vocational Education,Access to Education,Federal Legislation,Educational Legislation,Educational Improvement,Labor Force Development,Partnerships in Education,Education Work Relationship

This document records testimony from a hearing before the Subcommittee on Early Childhood, Elementary, and Secondary Education that was held to discuss strengthening career and technical education (CTE) and the need to reform, improve, and modernize current law so that more students can experience the power of this important education. By strengthening CTE policies, there is an opportunity to ensure the law reflects the current realities facing students, workers, and employers today. It is an important opportunity, one that allows more Americans to not only seize opportunities in the workforce, but helps them excel in the high-skilled jobs that exist today. Member statements were presented by: (1) Honorable Jared Polis, Ranking Member, Subcommittee on Early Childhood, Elementary, and Secondary Education; and (2) Honorable Todd Rokita, Chairman, Subcommittee on Early Childhood, Elementary, and Secondary Education. Witness statements were presented by: (1) Janet Global, Director of Career and Technical Education, Canyons School District; (2) Glenn E. Johnson, Manufacturing Workforce Development Leader, BASF Corporation; (3) Mimi Lufkin, Chief Executive Officer, National Alliance for Partnerships in Equity (NAPE); and (4) Mike Rowe, CEO, Mikeroweworks Foundation. Additional submissions were presented by: (1) Honorable Lou Barletta, a Representative in Congress from the State of Pennsylvania: Letter dated March 21, 2017, from International Code Council; (2) Mr. Johnson: Chart--Workforce Development Pipeline; (3) Mr. Polis: The CTE Equation in Tennessee; (4) Mr. Rowe: CompTIA--Change in TECH Act; (5) Chairman Rokita: Article--How Congress can help millions of Americans achieve career success; and (6) Honorable Glenn Thompson, a Representative in Congress from the State of Tennessee: Letter dated February 24, 2017, from Data Quality Campaign (DQC).
Modeling the Influence of Format and Depth during Effortful Retrieval Practice
Author: Maass, Jaclyn K.
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Memory,Test Format,Cues,Cognitive Processes,Test Items,Difficulty Level,Recall (Psychology),Multiple Choice Tests

This research combines work in memory, retrieval practice, and depth of processing research. This work aims to identify how the format and depth of a retrieval practice item can be manipulated to increase the effort required to successfully recall or formulate an answer, with the hypothesis that if the effort required to answer an item is increased there will be more benefit to learning. This hypothesis stems from work on desirable difficulties and the effortful retrieval hypothesis. Our data source was an experiment that used a 2 (question depth: factual, applied) x 2 (answer format: multiple choice, short answer) between-subjects design to investigate the effects of these conditions on retrieval practice performance. The experiment was delivered online though Mechanical Turk (n = 178). A logistic regression predicting performance during practice indicates that participants get more (in terms of an increase in future predicted success) from successful retrievals of items that fall within the more difficult level of both the format and depth factors (i.e., short answer and applied). There is also some support that the benefit from multiple choice items may be increased by asking deeper, more applied questions. The application of these results to scheduling effective practice is discussed. [This paper was supported by the University of Memphis's Institute for Intelligent Systems. For the full proceedings, see ED592609.]
Modeling Interactions across Skills: A Method to Construct and Compare Models Predicting the Existence of Skill Relationships
Author: Botelho, Anthony F.
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Prerequisites,Relationship,Models,Comparative Analysis,Prediction,Accuracy,Mathematics Skills,Electronic Learning

The incorporation of prerequisite skill structures into educational systems helps to identify the order in which concepts should be presented to students to optimize student achievement. Many skills have a causal relationship in which one skill must be presented before another, indicating a strong skill relationship. Knowing this relationship can help to predict student performance and identify prerequisite arches. Skill relationships, however, are not directly measurable; instead, the relationship can be estimated by observing differences of student performance across skills. Such methods of estimation, however, seem to lack a baseline model to compare their effectiveness. If two methods of estimating the existence of a relationship yield two different values, which is the more accurate result? In this work, we propose a method of comparing models that attempt to measure the strength of skill relationships. With this method, we begin to identify those student-level covariates that provide the most accurate models predicting the existence of skill relationships. Focusing on interactions of performance across skills, we use our method to construct models to predict the existence of five strongly-related and five simulated poorly-related skill pairs. Our method is able to evaluate several models that distinguish these differences with significant accuracy gains over a null model, and provides the means to identify that interactions of student mastery provide the most significant contributions to these gains in our analysis. [For the full proceedings, see ED592609.]
Establishing Achievable Goals: Recommendations for Improved Goal-Setting Under the Every Student Succeeds Act
Author: Regenstein, Elliot
Date: 2018-12-00
Type: Reports - Research
Subjects: Goal Orientation,Accountability,Educational Legislation,Federal Legislation,Educational Quality,Academic Achievement,Educational Innovation,School Districts,Reading Achievement,College Readiness,Career Readiness,Low Achievement,Student Needs,Educational Improvement,Standardized Tests,Federal Government,State Standards,Elementary Secondary Education,Mathematics Achievement

Since 2002 the federal government has required states to have accountability systems that measure the quality of schools. When the No Child Left Behind (NCLB) Act ushered in the accountability era nationwide, it did so with the ambitious goal of ensuring that by 2014 &quot;all children . . . reach, at a minimum, proficiency on challenging state academic achievement standards and state academic assessments.&quot; That goal was not achieved. In 2015 the federal government hit the reset button on its accountability approach with the passage of the Every Student Succeeds Act (ESSA). The ESSA gives states flexibility to set their own goals and gives states greater flexibility in measuring progress toward those goals. This paper examines how the federal ESSA offers new opportunities to improve teaching and learning in Illinois. It also examines how current state goals match up with growth and proficiency rates in the nation's most successful schools. [Co-written with Foresight Law + Policy and the Center for Urban Education Leadership at the University of Illinois at Chicago.]
Mining Behaviours of Students in Autograding Submission System Logs
Author: McBroom, Jessica
Date: 2016-00-00
Type: Speeches/Meeting Papers
Subjects: Data Collection,Student Behavior,Academic Achievement,Correlation,At Risk Students,Computer Science,Automation,Grading,Student Evaluation,Feedback (Response),Data Analysis,Grades (Scholastic),Programming,College Students,Foreign Countries

Effective mining of data from online submission systems offers the potential to improve educational outcomes by identifying student habits and behaviours and their relationship with levels of achievement. In particular, it may assist in identifying students at risk of performing poorly, allowing for early intervention. In this paper we investigate different methods of following the development of student behaviour throughout the semester using online submission system data, and different approaches to analysing this development. We demonstrate the application of these methods to data from a junior computer science course (N=494) and discuss their usefulness in understanding the common behavioural strategies of students in this course and how these develop over time. Finally, we draw links between behaviour in weekly coding tasks and student performance in the final exam and discuss whether these methods could be applicable midway through the semester. [For the full proceedings, see ED592609.]
The Scale Development Study on Foreign Language Speaking Self-Efficacy Perception
Author: Ocak, Gürbüz
Date: 2018-00-00
Type: Journal Articles
Subjects: Likert Scales,Second Language Learning,Speech Communication,Self Efficacy,College Second Language Programs,Second Language Instruction,Factor Analysis,Correlation,Item Analysis,Validity,Goodness of Fit,Foreign Countries,Undergraduate Students,Student Attitudes,Self Evaluation (Individuals)

The aim of this study is to develop a five point likert scale called 'Foreign Language Speaking Self-Efficacy'. A draft scale which includes 38 items has been given to 493 students studying in Afyon Kocatepe University, Foreign Languages School in Turkey. The principal component analysis has been employed in factor analysis of the scale to examine the structural validity. As a result of the analysis 14 items have been excluded and remaining 24 items have been grouped under 3 factors. The total explained variance is 57,428. The Cronbach's Alpha value is 0.944 that indicates the internal consistency of the scale. Furthermore, item-total and item-remaining correlations are significant (p&lt;0.001) and item discrimination tested by t-test to the bottom and top 27% is also found significant. After exploratory factor analysis, confirmatory factor analysis has been carried out and goodness of fit indexes are seen either acceptable or close to the acceptable values (RMSEA= 0,74; CFI=0,89; RMR=0,61; GFI=0,82; AGFI=0,78; NNFI=0,87).
The Examination of Respect Level of Undergraduate Students for Differences According to Various Variables
Author: Karafil, Burcu
Date: 2018-00-00
Type: Journal Articles
Subjects: Foreign Countries,Undergraduate Students,Prosocial Behavior,Individual Differences,Gender Differences,Departments,Second Language Instruction,Scores

The purpose of this descriptive study is to examine respect level of the undergraduate students for differences according to the various variables such as gender, department and foreign language preparation class education. The participants of the study consisted of 242 students studying at Afyon Kocatepe University, Turkey in the spring term of 2016-2017 academic year. Simple random sampling method was used in selecting the participants. To collect the data, a &quot;Respect of Difference Survey&quot; developed by Öksüz and Güven (2012) was used. The survey consists of three subscales of 'informational', 'social categories' and 'value' differences. In the analyses, firstly, descriptive statistics such as frequencies, means, and standard deviations were computed to display the subjects' overall responses to the respect level for differences. Secondly, t-test was conducted in order to determine whether there were any significant differences in respect level of undergraduate students for respect in terms of gender, department and prep class education variables. It has been found that undergraduate students have a high respect level for differences.