ethanporcaro/eric-fulltext-meta huggingface dataset:
Models of Youth-Adult Collaboration for Public Media. By/With/For Youth: Inspiring Next Gen Public Media Audiences. Research Brief
Author: Madden, Mary
Date: 2022-00-00
Type: Reports - Descriptive
Subjects: Mass Media,Cooperation,Adults,Design,Early Adolescents,Adolescents,Community Programs,Participatory Research

This brief is the first of a series of applied research briefs that focus on specific challenges and opportunities public media faces in its approaches to youth content and engagement. Drawing from priorities surfaced through focus group interviews with youth across the country, these briefs highlight recent research and public discussions about priorities for youth-centered design. The Next Gen Public Media initiative has observed growing interest across the public media ecosystem in developing tween- and teen-focused initiatives to develop new content, modes of engagement, and community-based programs. However, the investment of time and resources to meaningfully partner with youth can be significant, and organizations without prior experience may find it difficult to know where to start. Fortunately, the public media community is not alone in facing these challenges, and there is much to be learned from recent research and tool kits in adjacent fields. Drawing on best practices for youth participation from a wide range of academic resources, nonprofit organizations, and youth-focused government initiatives, this research brief points to evidence-based models to structure youth-adult collaborations.
Moving Up: An Action Plan to Improve Economic Opportunity for Kentucky Adults through Lifelong Learning
Author: Kentucky Council on Postsecondary Education
Date: 2022-09-00
Type: Reports - Evaluative
Subjects: Adult Education,Adult Students,Barriers,Access to Education,Organizational Change,Partnerships in Education,Educational Strategies,Economic Development,Education Work Relationship,Employment Level,Postsecondary Education,Enrollment,Educational Attainment,Poverty,Geographic Location,Race,Ethnicity,Sex

Kentucky ranks 35th in the nation in the educational attainment level of its workforce. Furthermore, at 56.3%, Kentucky has one of the lowest workforce participation rates in the nation. This depresses economic development, limits knowledge-economy job creation, curbs social and economic mobility and reduces enthusiasm for credential attainment beyond high school. To increase prosperity and well-being for our state and its people, the Council on Postsecondary Education (CPE) and Kentucky's postsecondary community are resolved to increase postsecondary opportunities for undereducated adults in both the short and long term. Adult learners often face barriers to earning their degree or credentials such as full-time employment, parenthood, poor health and housing and food insecurity. The strategies presented in this report will help lower these barriers. The plan's strategies fall under three main headings: accessibility, institutional change and partnerships. They are further categorized as occurring now (in progress or within six months), next (to be undertaken over the next 12-18 months) and later (action steps needing further collaboration and refinement, occurring in the next two to three years). The report concludes with data analyses to determine a priority population for future action.
Data-Driven Goal Setting: Searching Optimal Badges in the Decision Forest
Author: Langenhagen, Julian
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Data Analysis,Goal Orientation,Computer Software,Game Based Learning,Incentives,Higher Education,Educational Technology,Teaching Methods,Comparative Analysis,Learning Motivation,Undergraduate Students,Accounting,State Universities,Computer Games,Foreign Countries

Although badges are among the most-used game elements in gamified education, studies about their optimal features to motivate learning are scarce. How should a badge be designed to represent an incentive for a specific goal like optimal exam preparation? This study examines usage data of a higher education learning app to determine whether the used badges have the intended motivational effect. The preliminary results suggest that the badges that were initially implemented in the app have the intended effect in most cases, but the stages of the multi-level badges could be optimized. The methodological framework used in this study can be transferred to usage data of other similar learning tools. With the help of easy-to-interpret outputs of decision trees, researchers and practitioners alike can work towards an optimal badge design. [For the full proceedings, see ED623995.]
Does Practice Make Perfect? Analyzing the Relationship between Higher Mastery and Forgetting in an Adaptive Learning System
Author: Matayoshi, Jeffrey
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Guidelines,Mastery Learning,Learning Processes,Correlation,Retention (Psychology),Comparative Analysis,Integrated Learning Systems,Mathematics Instruction,Elementary School Students,Secondary School Students,College Students,Longitudinal Studies,Learning Analytics

As outlined by Benjamin Bloom, students working within a mastery learning framework must demonstrate mastery of the core prerequisite material before learning any subsequent material. Since many learning systems in use today adhere to these principles, an important component of such systems is the set of rules or algorithms that determine when a student has demonstrated mastery. A relevant issue when discussing mastery learning is its durability--in particular, we are interested in the relationship between different mastery learning thresholds and the forgetting of the learned material. As such, in this study we investigate this question using a large data set from the ALEKS adaptive learning system. Applying a quasi-experimental design, we find evidence that, while a higher mastery threshold is initially associated with a higher rate of knowledge retention, after several weeks this difference has largely disappeared. [For the full proceedings, see ED623995.]
Enhancing Stealth Assessment in Game-Based Learning Environments with Generative Zero-Shot Learning
Author: Henderson, Nathan
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Evaluation Methods,Game Based Learning,Educational Environment,Learning Strategies,Genetics,Introductory Courses,High School Students,Middle School Students

Stealth assessment in game-based learning environments has demonstrated significant promise for predicting student competencies and learning outcomes through unobtrusive data capture of student gameplay interactions. However, as machine learning techniques for student competency modeling have increased in complexity, the need for substantial data to induce such models has likewise increased. This raises scalability concerns, as capturing game interaction data is often logistically challenging yet necessary for supervised learning of student competency models. The generalizability of such models also poses significant challenges, and the performance of these models when applied to new domains or gameplay scenarios often suffers. To address these issues, we introduce a zero-shot learning approach that utilizes conditional generative modeling to generalize stealth assessment models for new domains in which prior data and competency labels may not exist. We evaluate our approach using observed student interactions with a game-based learning environment for introductory genetics. We use a conditional generative model to map latent embeddings of genetics concepts and student competencies to student gameplay patterns, enabling the generation of synthetic gameplay data associated with concepts and game levels that have not been previously introduced. Results indicate the zero-shot learning approach enhances the performance of the competency models on unseen game levels and concepts, pointing to more generalizable stealth assessment models and improved prediction of student competencies. [For the full proceedings, see ED623995.]
Guidelines for Evaluating Publicly Engaged Humanities Scholarship in Language and Literature Programs
Author: Modern Language Association of America
Date: 2022-08-00
Type: Guides - Non-Classroom
Subjects: Guidelines,Humanities,Second Language Learning,Second Language Instruction,Literature,College Second Language Programs,Departments,College Faculty,Scholarship,Ethics,Research Methodology,Bilingualism,Multilingualism,Peer Evaluation,Faculty Publishing,Evaluation Methods,Cooperation,Information Dissemination,Publications,Periodicals

The guidelines offer suggestions for departments, institutions, and faculty members in languages and literatures for valuing and assessing research in the public humanities. Because much public humanities scholarship involves engagement with communities, particularly bilingual and multilingual communities, this document places particular emphasis on the ethical questions that arise in community-engaged scholarly work. The guidelines also acknowledge genres of public humanities work that align more closely with traditional forms of humanities scholarship: research published in nonacademic venues, such as periodicals and blogs, or op-eds, lectures, and podcasts disseminated to wider audiences beyond the academy in English and other languages. Broadly, the guidance provided here is meant to engage with the fundamental questions that drive a peer review process in cases where traditional peer review may not currently be feasible and to suggest alternative modes of peer review where possible. [These guidelines were created by the Modern Language Association (MLA) Ad Hoc Committee on Valuing the Public Humanities.]
A Foot in the Door: Growth in Participation and Equity in Dual Enrollment in California. Research Brief. Volume 6, Number 7
Author: Kurlaender, Michal
Date: 2021-10-00
Type: Reports - Research
Subjects: Dual Enrollment,Equal Education,High School Students,College Credits,Community Colleges,Access to Education,Intellectual Disciplines,Racial Differences,Ethnicity,Geographic Location,Student Participation,High Schools,Public Schools,Institutional Characteristics

Dual enrollment allows high school students to take college courses and earn college credits that can provide a valuable head start toward a college degree. The practice has multiple benefits for students in both systems, improving college preparation and increasing efficiency toward completion of degrees and certificates. Many states--including California--have capitalized on these benefits by increasing high school student access to community college courses, though not all students have benefited equally. This brief builds on previous Wheelhouse research by providing a closer examination of dual enrollment growth in California. We present data about which students are participating in different types of dual enrollment in the California Community Colleges (CCC)--the primary provider of dual enrollment statewide. Matching the most recently available K-12 and CCC data, we also document how participation differs across high schools and course subjects pursued. There is cause in our findings for optimism, in that one type of dual enrollment--courses taught exclusively to high school students--is growing and appears to be increasing equity in participation. However, dual enrollment opportunities remain scarce or non-existent for many students and largely depend on the high schools they attend. [This brief was published by University of California, Davis. Wheelhouse: The Center for Community College Leadership and Research. Additional funding from the Central Valley Community Foundation.]
Ten Ways to Make Apprenticeship Central to Learning and Careers in 21st Century Pennsylvania: Policy Options to Double Apprenticeship by 2025
Author: Herzenberg, Stephen
Date: 2019-07-00
Type: Reports - Research
Subjects: Apprenticeships,Educational Policy,State Policy,Access to Education,Educational Benefits,Barriers,Labor Force Development,Secondary Education,Postsecondary Education,Educational Finance,Diversity,Public Agencies,Agency Role

Apprenticeship is a learning model and a proven strategy for (1) developing a talented and trained pipeline of workers for employers; and (2) expanding family supporting careers for individuals not pursuing a traditional four-year college track (without foreclosing their opportunities to pursue college down the road). Yet in Pennsylvania, as in states across the United States (U.S.), apprenticeship has been underutilized. In Spring 2016, Pennsylvania established the Apprenticeship and Training Office (ATO) within the Pennsylvania Department of Labor and Industry (L&amp;I). ATO established a strategic partnership with Keystone Development Partnership (KDP), a labor-management partnership with extensive experience building Industry (training) Partnerships, to expand the state's outreach capacity to employers and unions about apprenticeship The ATO also began engaging and sharing information with workforce boards across the state, elevating apprenticeships as a central Pennsylvania workforce development strategy and a key tool for expanding &quot;employer driven&quot; training. Since 2017, Pennsylvania has invested in expanding and assisting apprenticeship and pre-apprenticeship programs through grants from L&amp;I (e.g., through the new PAsmart initiative) and through the Department of Community and Economic Development (DCED). This report sketches the major challenges to the expansion of apprenticeships and the benefits of apprenticeships--which make the challenges worth overcoming. The second half of the report outlines 10 actions the ATO and Pennsylvania state government could take to grow apprenticeships and capture its benefits at a larger scale. [This report was researched and written for the Pennsylvania Apprenticeship and Training Office (ATO).]
&quot;Closing the Loop&quot; in Educational Data Science with an Open Source Architecture for Large-Scale Field Trials
Author: Fancsali, Stephen E.
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Learning Analytics,Educational Technology,Barriers,Data Analysis,Teaching Methods,Intervention,Outcomes of Education,Open Source Technology,Internet,Learning Processes,Correlation,Computer Software,Integrated Learning Systems

Ten years after the announcement of the &quot;rise of the super experiment&quot; at Educational Data Mining 2012, challenges to implementing &quot;internet scale&quot; educational experiments often persist for educational technology providers, especially when they seek to test substantive instructional interventions. Studies that deploy and test interventions, when informed by data-driven modeling, are often described as &quot;close the loop&quot; studies. Studies that close the loop attempt to link improvements in statistical and machine learning models of learning to real-world learning outcomes. After first considering challenges to internet scale experiments, we review several educational data science/mining studies that close the loop between data-driven modeling and learning outcomes. Next, we describe UpGrade, an open source architecture that, when integrated with educational technologies, helps overcome challenges to large-scale field trials (or internet scale experiments) that close the loop between data-driven work and real-world learning outcomes. In addition to describing preliminary randomized experiments that have been conducted and will soon be conducted using the architecture in two educational technology platforms, we end with a &quot;call for contributors and integrators.&quot; UpGrade contributors and integrators will be researchers and developers who seek to drive continuous, data-driven improvements in real-world settings where learning with technology occurs. [For the full proceedings, see ED623995.]
Adversarial Bandits for Drawing Generalizable Conclusions in Non-Adversarial Experiments: An Empirical Study
Author: Zhi-Han, Yang
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Algorithms,Educational Experiments,Design,Simulation,Educational Technology

Online educational technologies facilitate pedagogical experimentation, but typical experimental designs assign a fixed proportion of students to each condition, even if early results suggest some are ineffective. Experimental designs using multi-armed bandit (MAB) algorithms vary the probability of condition assignment for a new student based on prior results, placing more students in more effective conditions. While stochastic MAB algorithms have been used for educational experiments, they collect data that decreases power and increases false positive rates [22]. Instead, we propose using adversarial MAB algorithms, which are less exploitative and thus may exhibit more robustness. Through simulations involving data from 20+ educational experiments [29], we show data collected using adversarial MAB algorithms does not have the statistical downsides of that from stochastic MAB algorithms. Further, we explore how differences in condition variability (e.g., performance gaps between students being narrowed by an intervention) impact MAB versus uniform experimental design. Data from stochastic MAB algorithms systematically reduce power when the better arm is less variable, while increasing it when the better arm is more variable; data from the adversarial MAB algorithms results in the same statistical power as uniform assignment. Overall, these results demonstrate that adversarial MAB algorithms are a viable &quot;off-the-shelf&quot; solution for researchers who want to preserve the statistical power of standard experimental designs while also benefiting student participants. [For the full proceedings, see ED623995.]
Automatic Short Math Answer Grading via In-Context Meta-Learning
Author: Zhang, Mengxue
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Grading,Mathematics Instruction,Artificial Intelligence,Form Classes (Languages),Prediction,Models,Scoring,Computer Software,Generalization,Teaching Methods,Classification

Automatic short answer grading is an important research direction in the exploration of how to use artificial intelligence (AI)-based tools to improve education. Current state-of-the-art approaches use neural language models to create vectorized representations of students responses, followed by classifiers to predict the score. However, these approaches have several key limitations, including i) they use pre-trained language models that are not well-adapted to educational subject domains and/or student-generated text and ii) they almost always train one model per question, ignoring the linkage across question and result in a significant model storage problem due to the size of advanced language models. In this paper, we study the problem of automatic short answer grading for students' responses to math questions and propose a novel framework for this task. First, we use MathBERT, a variant of the popular language model BERT adapted to mathematical content, as our base model and fine-tune it on the downstream task of student response grading. Second, we use an in-context learning approach that provides scoring examples as input to the language model to provide additional context information and promote generalization to previously unseen questions. We evaluate our framework on a real-world dataset of student responses to open-ended math questions and show that our framework (often significantly) outperform existing approaches, especially for new questions that are not seen during training. [For the full proceedings, see ED623995.]
Characterizing Joint Attention Dynamics during Collaborative Problem-Solving in an Immersive Astronomy Simulation
Author: Zhou, Yiqiu
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Astronomy,Problem Solving,Science Instruction,Cooperative Learning,Learning Processes,Artificial Intelligence,Computer Simulation,Profiles,Attention Control,Student Behavior,Behavior Patterns,Peer Relationship,Classification,Task Analysis,Computer Software,Introductory Courses,Undergraduate Students

The complex and dynamic nature of collaboration makes it challenging to find indicators of productive learning and quality collaboration. This exploratory study developed a collaboration metric to capture temporal patterns of joint attention (JA) based on log files generated as students interacted with an immersive astronomy simulation using augmented reality headsets and tablets. JA is defined as the ability to coordinate attention, which thus plays an important role in collaborative problem-solving to build the common ground for knowledge co-construction. We first developed a JA metric consisting of six distinct but closely relevant states as a measure of the collaboration process. We then conducted descriptive statistics to compare frequency and temporal pattern of JA states across three learning performance groups. Our results showed that high-learning-gain groups demonstrated visual coordination behaviors more frequently and utilized this collaboration strategy in the early stage. We then investigated sequences of these JA states, focusing on one key behavior: long and consistent shared view as a proxy for collaboration. This sequential analysis revealed two different collaboration profiles: attention follow-leader and turn takers, suggesting the existence of asymmetrical participation. Our findings indicate the potential of JA metric to predict overall collaboration quality, identify undesirable collaboration behaviors, and serve as an early warning to provide just-in-time guidance. [For the full proceedings, see ED623995.]