ethanporcaro/eric-fulltext-meta huggingface dataset:
Race, Late Bloomers and First-Year GPA: Predicting beyond the Freshman Year
Author: Gayles, Jonathan
Date: 2012-09-00
Type: Journal Articles
Subjects: Grade Point Average,Race,Racial Differences,Predictive Validity,Standardized Tests,College Entrance Examinations,Minority Group Students,Regression (Statistics),African American Students,White Students,Asian American Students,Hispanic American Students,Graduation Rate,Time to Degree,Honors Curriculum,High School Students,College Graduates

First-year grade point average (FYGPA) is an oft-referenced outcome criterion for assessments of the predictive validity of a variety of admissions mechanisms. Unfortunately, few studies examine the relationship between FYGPA and long-term outcomes such as graduating grade point average and six-year graduation. Influenced by Wilson's (1980) late-bloomer hypothesis, this paper examines the relationship between FYGPA and graduating grade point average, honors graduation and six-year graduation. Further, the possibility of a racial differential in this relationship is examined. Using simple linear regression and logistic regression in addition to Zar's (1999) formula for comparing slopes, FYGPA is determined to bear a strong relationship with each of the dependent variables across race.
The Nexus between the Above-Average Effect and Cooperative Learning in the Classroom
Author: Breneiser, Jennifer E.
Date: 2012-12-00
Type: Journal Articles
Subjects: Cooperative Learning,Self Evaluation (Individuals),Performance,Group Activities,Accuracy,Academic Achievement,Undergraduate Students,Self Concept,Student Surveys

The present study examines the above-average effect (Chambers &amp; Windschitl, 2004; Moore &amp; Small, 2007) in assessments of task performance. Participants completed self-estimates of performance and group estimates of performance, before and after completing a task. Participants completed a task individually and in groups. Groups were self-selected by participants, or randomly assigned by the researchers. Previous research examined the above-average effect in performance self-estimates for individuals, but little has been done examining the above-average effect in group performance. Results indicated robust above-average effects for both individual and group estimates of performance, and these effects were not limited by group type. Furthermore, above-average effects were observed for estimates of performance both before and after completion of the task, suggesting that participants were not more accurate in their post-task estimates. In addition to these data, results of a group-work survey administered to participants are disseminated, suggesting some practical applications for group work in the college classroom setting.
Multisite Studies and Scaling up in Educational Research
Author: Harwell, Michael
Date: 2012-12-00
Type: Journal Articles
Subjects: Educational Research,Research Design,Public Health,Nursing,Models,Theories

A scale-up study in education typically expands the sample of students, schools, districts, and/or practices or materials used in smaller studies in ways that build in heterogeneity. Yet surprisingly little is known about the factors that promote successful scaling up efforts in education, in large part due to the absence of empirically supported theories of scaling up. A literature for scale-up studies in education is growing but is years away from providing research-supported practices in planning and conducting these studies. Following the suggestion of Schneider and McDonald (2006) to import relevant knowledge from other fields into the scale-up literature in education, this paper examines the multisite public health and nursing literature in search of a multidisciplinary knowledge base that can inform scaling up efforts in education. Five strategies and practices identified in these literatures as critical to scaling up success are described.
Field Experience + Inclusive ECE Classrooms = Increased Preservice Teacher Efficacy in Working with Students with Developmental Delays or Disabilities
Author: Atiles, Julia T.
Date: 2012-12-00
Type: Journal Articles
Subjects: Field Experience Programs,Inclusion,Preservice Teachers,Preservice Teacher Education,Self Efficacy,Developmental Delays,Developmental Disabilities,Undergraduate Students,Self Concept Measures,Correlation,Teaching Skills,Classroom Environment,Classroom Techniques,Elementary School Curriculum

The current study examined whether field placements within an inclusive classroom are associated with improved preservice teacher's efficacy when working with children with developmental delays or disabilities. Study participants were 165 undergraduate students enrolled in primary teacher education classes at a Midwestern university. Participants responded to a modified version of the Teacher's Sense of Efficacy Scale. A significant positive correlation between preservice teachers' efficacy of working with children with developmental delays or disabilities and their inclusive field experiences was found. Findings in this study stress the need for undergraduate early childhood education programs to utilize diverse, highly inclusive classrooms in their students' field experiences; not only in the hopes of increasing efficacy, but to also develop the skills and dispositions valued by our profession.
Assessing Student Teaching Experiences: Teacher Candidates&apos; Perceptions of Preparedness
Author: Lee, Joohi
Date: 2012-12-00
Type: Journal Articles
Subjects: Teaching Experience,Student Teaching,Student Teacher Attitudes,Career Readiness,Program Effectiveness,Early Childhood Education,Pretests Posttests,Student Surveys,Pedagogical Content Knowledge,Lesson Plans,Classroom Techniques,Family Involvement,Professional Development

The purpose of this study is to investigate the effectiveness of student teaching experiences by measuring teacher candidates' perceptions of their preparedness. The participants were 130 teacher candidates who had completed their student teaching as part of a program preparing them to teach children in pre-K through grade 4. Teacher candidates responded to the survey by recalling their before and after student teaching experiences. A paired t-test was calculated to determine statistical mean differences before and after student teaching on five categories: a) pedagogical content knowledge, b) planning and preparation for instruction, c) classroom management, d) promoting family involvement, and e) professionalism. Mean differences of all of the paired items between pre-and post survey were shown to be statistically significant on all five categories.
Investigating Social Desirability Bias in Student Self-Report Surveys
Author: Miller, Angie L.
Date: 2012-09-00
Type: Journal Articles
Subjects: Social Desirability,National Surveys,Learner Engagement,Student Behavior,Correlation,Measures (Individuals),Benchmarking,Scores,Regression (Statistics),Effect Size,Self Evaluation (Individuals),Cooperative Learning,Teacher Student Relationship,Academic Standards,Enrichment Activities,Social Development,Individual Development,Educational Environment,Campuses,Student Attitudes,College Freshmen,College Seniors,Grades (Scholastic),Educational Experience

The frequent use of student self-report surveys in higher education calls into question the possibility of social desirability having an unwanted influence on responses. This research explores the potential presence of social desirability bias with the National Survey of Student Engagement (NSSE), a widely used assessment of student behaviors. Correlations between a short social desirability scale and NSSE benchmarks, subscales, and selected items suggest that the majority of scores have no significant relationship with a measure of social desirability. A series of regression models controlling for demographic variables produce similar results. Effect sizes and estimates of explained variance are also discussed.
The Benefits of Latin?
Author: Holliday, Lisa R.
Date: 2012-09-00
Type: Journal Articles
Subjects: Educational Benefits,Latin,Second Language Learning,Grammar,English,Transfer of Training,Writing Skills,Cognitive Ability,Second Language Instruction,Role,Elementary Secondary Education,Vocabulary Development,Educational History,Surveys,Validity

Classicists have long claimed that the study of Latin has benefits that exceed knowledge of the language itself, and in the current economic times, these claims are made with urgency. Indeed, many contend that Latin improves English grammar and writing skills, cognitive abilities, and develops transferable skills necessary for success in the sciences. In and of itself, the study of Latin seems to be a topic of concern primarily within Classics departments. However, given the broad claims that have been made about the benefits of Latin for educational development, it is useful to investigate the role of Latin within elementary and high school curriculums as it relates to learning. Does the study of Latin improve cognitive abilities and English skills, including grammar and vocabulary? In order to address this question, this article does the following: (1) Surveys the historical background of the debate, beginning with the 1921-1924 study by the American Classical League (ACL); (2) Considers the claims tested in the ACL study, using modern research to assess the validity of the tests; and (3) Considers the possible benefits of Latin in light of the surveyed research. There is evidence to support that the study of Latin has several benefits, including increased English vocabulary and grammatical knowledge. However, research does not indicate that Latin is unique in this regard. Regarding cognitive abilities and performance in other disciplines such as mathematics, the majority of research does not indicate that Latin gives students any advantage in these areas. That being said, Latin, like other foreign languages, still has much to offer students.
The Role of CLEAR Thinking in Learning Science from Multiple-Document Inquiry Tasks
Author: Griffin, Thomas D.
Date: 2012-00-00
Type: Journal Articles
Subjects: Middle School Students,Logical Thinking,Thinking Skills,Science Process Skills,Science Instruction,Secondary School Science,Personality Traits,Individual Differences,Measures (Individuals),Inquiry,Task Analysis,Earth Science,Climate,Reading Skills,Grade 7,Urban Schools,Correlation,Regression (Statistics)

The main goal for the current study was to investigate whether individual differences in domain-general thinking dispositions might affect learning from multiple-document inquiry tasks in science. Middle school students were given a set of documents and were tasked with understanding how and why recent patterns in global temperature might be different from what has been observed in the past from those documents. Understanding was assessed with two measures: an essay task and an inference verification task. Domain-general thinking dispositions were assessed with a Commitment to Logic, Evidence, and Reasoning (CLEAR) thinking scale. The measures of understanding were uniquely predicted by both reading skills and CLEAR thinking scores, and these effects were not attributable to prior knowledge or interest. The results suggest independent roles for thinking dispositions and reading ability when students read to learn from multiple-document inquiry tasks in science.
Interdisciplinary Studies and the Real World: A Practical Rationale for and Guide to Postgraduation Evaluation and Assessment
Author: Brooks, Benjamin
Date: 2012-00-00
Type: Journal Articles
Subjects: Interdisciplinary Approach,College Graduates,Graduate Students,Graduate Study,Undergraduate Students,Student Evaluation,Educational Assessment,Data Collection,Data Analysis,Outcomes of Education,Satisfaction,Career Choice,Career Development,Teacher Effectiveness,Accountability,Academic Achievement,Program Evaluation,Institutional Mission,Student Educational Objectives,Measures (Individuals),Measurement Objectives

This article is an outgrowth of the evaluation and assessment process in a large interdisciplinary studies (IDS) program at West Virginia University. We argue for the importance of collecting and assessing quantitative and qualitative data concerning learning outcomes, student satisfaction, career choice and development, and graduate education after students have graduated from an undergraduate IDS program. While there are difficulties inherent in developing an assessment instrument and gathering quality data, we contend that this process ultimately yields valuable insights that can improve student learning, teacher quality, and programmatic design. Moreover, a comprehensive postgraduate tool can provide evaluative data directly relevant to critics' demands for higher education to implement greater accountability measures and demonstrate programmatic achievement. To assist other IDS programs, this article includes a practical guide to navigating the difficulties in developing a postgraduation evaluation and assessment tool and carrying out a plan for collecting data on a large scale.
Putting ECD into Practice: The Interplay of Theory and Data in Evidence Models within a Digital Learning Environment
Author: Rupp, Andre A.
Date: 2012-00-00
Type: Journal Articles
Subjects: Computer Networks,Evidence Based Practice,Design,Instructional Design,Theory Practice Relationship,Models,Information Retrieval,Data Processing,Measurement,Troubleshooting,Computer Interfaces,Bayesian Statistics,Electronic Learning,Case Studies,Simulated Environment,Visualization,Computer Software,Engineering,Data Analysis,Multivariate Analysis,Psychometrics,Classification

In this paper we describe the development and refinement of &quot;evidence rules&quot; and &quot;measurement models&quot; within the &quot;evidence model&quot; of the &quot;evidence-centered design&quot; (ECD) framework in the context of the &quot;Packet Tracer&quot; digital learning environment of the &quot;Cisco Networking Academy.&quot; Using &quot;Packet Tracer&quot; learners design, configure, and troubleshoot computer networks within an interactive interface. This leads to &quot;product data,&quot; which result from the students' final submitted network configurations, and &quot;process data,&quot; which are log file entries detailing how they got to the final configurations. We discuss how an iterative cycle of empirical analyses and discussions with subject-matter experts is essential for identifying and accumulating evidence about skill profiles of learners and their development. We present results from descriptive, exploratory, and confirmatory diagnostic modeling analyses for both data types, which required bringing to bear a diversity of tools from multivariate statistics, modern psychometrics, and educational data mining. We close the paper with a discussion of the implications of this work for evidence-based argumentation guided by ECD principles within digital learning environments more generally.
Leveraging Educational Data Mining for Real-Time Performance Assessment of Scientific Inquiry Skills within Microworlds
Author: Gobert, Janice D.
Date: 2012-00-00
Type: Journal Articles
Subjects: Data Analysis,Inquiry,Science Process Skills,Student Evaluation,Models,Scaffolding (Teaching Technique),Performance Based Assessment,Science Tests,Computer Assisted Testing,Middle School Students

We present &quot;Science Assistments,&quot; an interactive environment, which assesses students' inquiry skills as they engage in inquiry using science microworlds. We frame our variables, tasks, assessments, and methods of analyzing data in terms of &quot;evidence-centered design.&quot; Specifically, we focus on the &quot;student model,&quot; the &quot;task model,&quot; and the &quot;evidence model&quot; in the &quot;conceptual assessment framework.&quot; In order to support both assessment and the provision of scaffolding, the environment makes inferences about student inquiry skills using models developed through a combination of text replay tagging [cf. Sao Pedro et al. 2011], a method for rapid manual coding of student log files, and &quot;educational data mining.&quot; Models were developed for multiple inquiry skills, with particular focus on detecting if students are testing their articulated hypotheses, and if they are designing controlled experiments. Student-level cross-validation was applied to validate that this approach can automatically and accurately identify these inquiry skills for new students. The resulting detectors also can be applied at run-time to drive scaffolding intervention.
Design and Discovery in Educational Assessment: Evidence-Centered Design, Psychometrics, and Educational Data Mining
Author: Mislevy, Robert J.
Date: 2012-00-00
Type: Journal Articles
Subjects: Educational Assessment,Psychometrics,Evidence,Computer Games,Computer Simulation,Video Games,Computer Assisted Testing,Technological Advancement,Technology Uses in Education,Blended Learning,Information Technology,Data Analysis,Data Processing,Pattern Recognition,Program Design,Program Implementation,Computer Networks,Item Response Theory

&quot;Evidence-centered design&quot; (ECD) is a comprehensive framework for describing the conceptual, computational and inferential elements of educational assessment. It emphasizes the importance of articulating inferences one wants to make and the evidence needed to support those inferences. At first blush, ECD and &quot;educational data mining&quot; (EDM) might seem in conflict: structuring situations to evoke particular kinds of evidence, versus discovering meaningful patterns in available data. However, a dialectic between the two stances increases understanding and improves practice. We first introduce ECD and relate its elements to the broad range of digital inputs relevant to modern assessment. We then discuss the relation between EDM and psychometric activities in educational assessment. We illustrate points with examples from the &quot;Cisco Networking Academy&quot;, a global program in which information technology is taught through a blended program of face-to-face classroom instruction, an online curriculum, and online assessments.
On Instructional Utility, Statistical Methodology, and the Added Value of ECD: Lessons Learned from the Special Issue
Author: Nelson, Brian
Date: 2012-00-00
Type: Journal Articles
Subjects: Statistical Analysis,Value Added Models,Educational Assessment,Program Design,Program Implementation,Electronic Learning,Educational Environment,Evidence,Psychometrics,Data Analysis,Case Studies

This special issue of &quot;JEDM&quot; was dedicated to bridging work done in the disciplines of &quot;educational and psychological assessment&quot; and &quot;educational data mining&quot; (EDM) via the assessment design and implementation framework of &quot;evidence-centered design&quot; (ECD). It consisted of a series of five papers: one conceptual paper on ECD, three applied case studies that use ECD and EDM tools, and one simulation study that relies on ECD for its design and EDM for its implementation. In this reflection piece we discuss some of the key lessons that we have learned from the articles in this special issue with respect to the instructional utility of the digital learning environments, the nature of the statistical methodologies used, and the added value of the ECD framework for the work conducted in these projects.
Using the ECD Framework to Support Evidentiary Reasoning in the Context of a Simulation Study for Detecting Learner Differences in Epistemic Games
Author: Sweet, Shauna J.
Date: 2012-00-00
Type: Journal Articles
Subjects: Epistemology,Computer Games,Educational Games,Simulation,Nonparametric Statistics,Statistical Analysis,Network Analysis,Data Analysis,Differences,Identification,Design,Evidence Based Practice,Student Evaluation,Logical Thinking,Land Use

The &quot;evidence-centered design&quot; (ECD) framework is a powerful tool that supports careful and critical thinking about the identification and accumulation of evidence in assessment contexts. In this paper, we demonstrate how the ECD framework provides critical support for designing simulation studies to investigate statistical methods within an ill-defined methodological domain like games-based assessment. We discuss the design and selected findings of a complex simulation study to investigate the utility of statistics derived from a non -parametric method called &quot;epistemic network analysis&quot;; this method is used in practice for the analysis of real data from a suite of digital learning environments called &quot;epistemic games.&quot; We present findings that show that one marginal ENA statistic, the &quot;weighted density&quot; statistic from social network analysis, has some ability to detect differences among a variety of simulated learners when they play different types of simulated games. Detection strength ranged from weak to strong, depending jointly on game design characteristics and the types of learners being compared. Our work illustrates the complex challenges of how best to describe, justify, and evaluate design decisions for simulation studies in the context of games-based assessment.
Identifying Key Features of Student Performance in Educational Video Games and Simulations through Cluster Analysis
Author: Kerr, Deirdre
Date: 2012-00-00
Type: Journal Articles
Subjects: Evidence Based Practice,Design,Academic Achievement,Educational Games,Video Games,Simulation,Multivariate Analysis,Error Patterns,Learning Strategies,Data Collection,Data Analysis,Middle School Students,Grade 6,Grade 7,Grade 8,Algebra,Accuracy

The assessment cycle of &quot;evidence-centered design&quot; (ECD) provides a framework for treating an educational video game or simulation as an assessment. One of the main steps in the assessment cycle of ECD is the identification of the key features of student performance. While this process is relatively simple for multiple choice tests, when applied to log data from educational video games or simulations it becomes one of the most serious bottlenecks facing researchers interested in implementing ECD. In this paper we examine the utility of cluster analysis as a method of identifying key features of student performance in log data stemming from educational video games or simulations. In our study, cluster analysis was able to consistently identify key features of student performance in the form of solution strategies and error patterns across levels, which contained few extraneous actions and explained a sufficient amount of the data.
Evidence-Centered Design for Diagnostic Assessment within Digital Learning Environments: Integrating Modern Psychometrics and Educational Data Mining
Author: Rupp, André A.
Date: 2012-00-00
Type: Journal Articles
Subjects: Electronic Learning,Psychometrics,Educational Environment,Educational Technology,Educational Assessment,Evidence,Case Studies,Data Analysis,Data Processing,Multivariate Analysis,Program Implementation,Program Design

In recent years the educational community has increasingly embraced digital technologies for the purposes of developing alternative learning environments, providing diagnostic feedback, and fostering the development of so-called 21st-century skills. This special issue is dedicated to bridging recent work from the disciplines of educational and psychological assessment and educational data mining (EDM) via the assessment design and implementation framework of &quot;evidence-centered design&quot; (ECD). It consists of a series of five papers: one conceptual paper on ECD, three applied case studies that use ECD and EDM tools, and one simulation study that relies on ECD for its design and EDM for its implementation. In this introduction, we discuss the underlying rationales for the special issue in more detail, provide a short introduction to ECD, and describe the focus of the five selected papers.