ethanporcaro/eric-fulltext-meta huggingface dataset:
Topic Transitions in MOOCs: An Analysis Study
Author: ALSaad, Fareedah
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Online Courses,Student Diversity,Student Needs,Course Content,Models,Program Effectiveness,Markov Processes,Prediction,Sequential Approach,Lecture Method

With the emergence of MOOCs, it becomes crucial to automate the process of a course design to accommodate the diverse learning demands of students. Modeling the relationships among educational topics is a fundamental first step for automating curriculum planning and course design. In this paper, we introduce &quot;Topic Transition Map&quot; (TTM), a general structure that models the content of MOOCs at the topic level. TTMs capture the various ways instructors organize topics in their courses by modeling the transitions between topics. We investigate and analyze four different methods that can be exploited to learn the Topic Transition Map: (1) Pairwise Constrained K-Means, (2) Mixture of Unigram Language Model, (3) Hidden Markov Mixture Model, and (4) Structural Topic Model. To evaluated the effectiveness of these methods, we qualitatively compare the topic transition maps generated by each model and investigate how the Topic Transition Map can be used in three sequencing tasks: (1) determining the correct sequence, (2) predicting the next lecture, and (3) predicting the sequence of lectures. Our evaluation revealed that PCK-Means has the highest performance in the first task, HMMULM outperforms other methods in task 2, while there is no winning in task 3. [For the full proceedings, see ED615472.]
The 2019 Youth Transition Report: Outcomes for Youth and Young Adults with Disabilities
Author: Cheng, L.
Date: 2019-00-00
Type: Reports - Evaluative
Subjects: Students with Disabilities,Disabilities,Adolescents,Young Adults,Educational Attainment,Employment Level,Incidence,Postsecondary Education,Geographic Location,Symptoms (Individual Disorders),College Attendance,Graduation Rate,Adults,Military Personnel,Unemployment,Age Differences,High School Graduates,College Graduates

The Youth Transition Report presents data that underscore the gap between youth with disabilities and those without disabilities on measures of success in education and employment. While other reports present information about the working-age adult population and children with disabilities, there are no reports that specifically focus on the youth and young adult population, ages 14-24. The Institute for Educational Leadership (IEL) produced this report to address that need by creating a snapshot of the outcomes of and gaps between youth with disabilities and those without disabilities. The report also includes data on those opportunity youth whom the education and workforce systems have not adequately served and supported. This report explores descriptive status of education and employment for youth and young adults at national and state levels. This report also includes calculated data from the National Center for Education Statistics and the U.S. Department of Education's Individuals with Disabilities Education Act (IDEA) website. In future years, the annual Youth Transition Report will also include a longitudinal trend analysis of those key indicators of youth and young adults with and without disabilities.
Toward Improving Student Model Estimates through Assistance Scores in Principle and in Practice
Author: Rachatasumrit, Napol
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Models,Student Characteristics,Feedback (Response),Error Correction,Computation,Intelligent Tutoring Systems,Scores,Statistical Analysis,Cognitive Measurement,Item Response Theory,Regression (Statistics)

Student modeling is useful in educational research and technology development due to a capability to estimate latent student attributes. Widely used approaches, such as the Additive Factors Model (AFM), have shown satisfactory results, but they can only handle binary outcomes, which may yield potential information loss. In this work, we propose a new partial credit modeling approach, PC-AFM, to support multi-valued outcomes. We focus particularly on the amount of assistance, that is, the number of error feedback and hint messages, a student needs to get a problem step correct. Because errors and hint requests may not only derive from student ability, but also from non-cognitive factors (e.g., students may game the system), we first test PCAFM on synthetic data where this source of variation is not present. We confirm that PC-AFM is indeed better than AFM in recovering the true student and knowledge component (KC) parameters and even predicts student error rates better than a model fit to error rates. We then apply the approach to six real-world datasets and find that PC-AFM outperforms AFM in reliable estimation of KC parameters and produces better generalization to new students, which requires better KC estimates. However, consistent with the hypothesis that student assistance behavior is driven by motivational or meta-cognitive factors beyond their ability, we found that PC-AFM was not better in reliable estimation of student parameters nor in generalization across items, which requires accurate student estimates. We propose &quot;cross-measure cross-validation&quot; as a general method for comparing alternative measurement models for the same desired latent outcome. [For the full proceedings, see ED615472.]
The 2020 Youth Transition Report: Outcomes for Youth and Young Adults with Disabilities
Author: Cheng, L.
Date: 2020-00-00
Type: Reports - Evaluative
Subjects: Students with Disabilities,Disabilities,Adolescents,Young Adults,Educational Attainment,Employment Level,Incidence,Postsecondary Education,Geographic Location,Symptoms (Individual Disorders),College Attendance,Graduation Rate,Unemployment,High School Graduates,College Graduates,Poverty,Military Personnel

The 2020 Youth Transition Report underscores the persistent and pernicious gap between youth and young adults with disabilities and those without disabilities on measures of success in education, employment, and poverty. While data and reports exist for the working-age adult population and children with disabilities, the Institute for Educational Leadership (IEL) recognized a lack of data reports that specifically focus on the youth and young adult population in the 14-24 age group. IEL's Youth Transition Report addresses that need by presenting an annual snapshot of the outcome gaps between youth and young adults with disabilities and those without disabilities, including those opportunity youth whom the education and workforce systems have not adequately served and supported. This report consists of the disability status of the population; education, employment, and poverty indicators; and a nuanced look at the opportunity youth population. This report explores descriptive status of education and employment for youth and young adults at national and state levels. The 2020 Youth Transition Report is similar to the previous 2019 report; however, this year added a look at poverty rates for youth and young adults with disabilities and removed the comparison of employment data between youth and adults with disabilities. This report also includes calculated data from the National Center for Education Statistics. [For the 2019 report, see ED615624.]
UPreG: An Unsupervised Approach for Building the Concept Prerequisite Graph
Author: Sabnis, Varun
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Prerequisites,Fundamental Concepts,Automation,Natural Language Processing,Artificial Intelligence,Online Courses,Graphs

Today, there is a vast amount of online material for learners. However, due to the lack of prerequisite information needed to master them, a lot of time is spent in identifying the right learning content for mastering these concepts. A system that captures underlying prerequisites needed for learning different concepts can help improve the quality of learning and can save time for the learners as well. In this work, we propose an unsupervised approach, UPreG, for automatically inferring prerequisite relationships between different concepts using NLP techniques. Our approach involves extracting the concepts from unstructured texts in MOOC (Massively Open Online Courses) course descriptions, measuring semantic relatedness between the concepts and statistically inferring the prerequisite relationships between related concepts. We conducted both qualitative and quantitative studies to validate the effectiveness of our proposed approach. As there are no ground truth labels for these prerequisite relations, we conducted a user study for the evaluation of the prerequisite relations. We build the concept graph using prerequisite relations. We demonstrate few examples of the learning maps generated from the graph. The learning maps provide prerequisite information and learning paths for different concepts. [For the full proceedings, see ED615472.]
Learning Recovery and a Diverse Workforce: A Win-Win for Students Hardest Hit by COVID-19. Brief
Author: Gershenson, Seth
Date: 2021-00-00
Type: Reports - Evaluative
Subjects: COVID-19,Pandemics,School Closing,Student Needs,Disease Control,Well Being,Federal Legislation,Federal Aid,Elementary Secondary Education,Achievement Gains,Minority Group Teachers,School Districts,Evidence Based Practice,Tutoring,Extended School Day,Summer Programs,Minority Group Students,Best Practices,At Risk Students,Teacher Selection

The COVID-19 pandemic has forced school leaders to make difficult decisions about how to balance students' educational needs with the health, safety, and social-emotional well-being of students and teachers. The American Rescue Plan Act of 2021 (Rescue Plan) recently enacted by Congress offers unprecedented amounts of potentially transformative funding to elementary and secondary schools. The Rescue Plan has the potential to simultaneously spur post-pandemic learning recovery efforts and address long-standing socio-demographic gaps in academic achievement and opportunity. This brief is intended to help state and local education leaders consider how to approach this opportunity to meet a persistent problem: the need for more educators of color to serve students. To do this, the authors encourage local education agencies (LEAs) to act strategically by leveraging the findings and evidence-based practices (EBP) of two distinct research bases: (1) High-dosage tutoring, extended-day programs, and summer learning programs are effective options for learning recovery; and (2) Students benefit from exposure to a diverse, representative educator workforce.
Student Performance Prediction Using Dynamic Neural Models
Author: Delianidi, Marina
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Grade Prediction,Models,Student Experience,Cognitive Processes,Knowledge Representation

We address the problem of predicting the correctness of the student's response on the next exam question based on their previous interactions in the course of their learning and evaluation process. We model the student performance as a dynamic problem and compare the two major classes of dynamic neural architectures for its solution, namely the finite-memory Time Delay Neural Networks (TDNN) and the potentially infinite-memory Recurrent Neural Networks (RNN). Since the next response is a function of the knowledge state of the student and this, in turn, is a function of their previous responses and the skills associated with the previous questions, we propose a two-part network architecture. The first part employs a dynamic neural network (either TDNN or RNN) to trace the student knowledge state. The second part applies on top of the dynamic part and it is a multi-layer feed-forward network which completes the classification task of predicting the student response based on our estimate of the student knowledge state. Both input skills and previous responses are encoded using different embeddings. Regarding the skill embeddings we tried two different initialization schemes using (a) random vectors and (b) pretrained vectors matching the textual descriptions of the skills. Our experiments show that the performance of the RNN approach is better compared to the TDNN approach in all datasets that we have used. Also, we show that our RNN architecture outperforms the state-of-the-art models in four out of five datasets. It is worth noting that the TDNN approach also outperforms the state of the art models in four out of five datasets, although it is slightly worse than our proposed RNN approach. Finally, contrary to our expectations, we find that the initialization of skill embeddings using pretrained vectors offers practically no advantage over random initialization. [For the full proceedings, see ED615472.]
Student Strategy Prediction Using a Neuro-Symbolic Approach
Author: Shakya, Anup
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Prediction,Problem Solving,Intelligent Tutoring Systems,Learning Strategies,Models

Predicting student problem-solving strategies is a complex problem but one that can significantly impact automated instruction systems since they can adapt or personalize the system to suit the learner. While for small datasets, learning experts may be able to manually analyze data to infer student strategies, for large datasets, this approach is infeasible. We develop a Machine Learning model to predict strategies from student data. While Deep Neural Network (DNN) based methods such as LSTMs can be applied for this task, they often have long convergence times for large datasets and like several other DNN-based methods have the inherent problem of overfitting the data. To address these issues, we develop a Neuro-symbolic approach for strategy prediction, namely a model that combines strengths of symbolic AI (that can encode domain knowledge) with DNNs. Specifically, we encode relationships in the data using Markov Logic and use symmetries among these relationships to train an LSTM more efficiently. In particular, we use an importance sampling approach where we sample the training data such that for clusters/groups of symmetrical instances (instances where the strategies are likely to be symmetric), we only pick representative samples for training the model instead of using the whole group. Further, since some groups may contain more diverse strategies than the others, we adapt the importance weights based on previously observed samples. Through empirical evaluation on the KDD EDM challenge datasets, we show the scalability of our approach. [For the full proceedings, see ED615472.]
Student Practice Sessions Modeled as ICAP Activity Silos
Author: Gaweda, Adam M.
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Computer Science Education,Learning Activities,Student Behavior,Study Habits,Professional Development,Online Courses,Nontraditional Students,Learner Engagement

There are a number of novel exercise types that students can utilize while learning Computer Science, each with its own level of complexity and interaction as outlined by the ICAP Framework [10]. Some are &quot;Interactive,&quot; like solving coding problems; &quot;Constructive,&quot; like explaining code; &quot;Active,&quot; like retyping source code; and &quot;Passive,&quot; like reviewing slides. To date, there has been little research on how students vary their study and engagement habits by exercise type and when they do so. In this paper, we present our findings on student activity sequences from an online professional development course. We isolated student activities into sessions and then produced activity transition visualizations to compare the behavior of students who complete the course to those who do not. We then used multiple factor analyses to examine how students transition from one type of activity to the next. From this analysis we identified &quot;platform silos&quot; in student's work. We further expand this concept to the presence of &quot;activity silos&quot; grouping by type. We find that this siloing behavior is consistent in both completers and non-completers but is weaker for the latter group. Finally, we discuss our findings and how instructors and researchers may use this information to ensure that students show persistence through practice. [For the full proceedings, see ED615472.]
After Action Review Guide for Learning Recovery Planning
Author: Layland, Allison
Date: 2021-00-00
Type: Guides - General
Subjects: Distance Education,Blended Learning,COVID-19,Pandemics,Educational Planning,Student Needs,Needs Assessment,Outcomes of Education,Summer Programs,Extended School Year,Program Effectiveness,Documentation

The impact of a year of remote or hybrid learning on students across the nation has been significant. Early research points to the disruptions to in-person learning experienced in school year 2020-21 could result in students losing between 3 and 11 months of learning. Many students have experienced additional trauma during COVID-19 including lack of resources, unstable environments, and inequitable opportunities to learn. Planning for learning recovery must begin now to address immediate and long-term needs of students, particularly those that have been traditionally underserved. An After-Action Review (AAR) could assist schools and districts identify the lessons learned from previous summer and extended learning programs, as well as from current attempts at providing hybrid and remote learning. These lessons could then inform learning recovery strategies to implement during the summer and throughout the school year to assist students recover some of the lost learning. The information gathered through the AAR can be applied to building possible scenarios for learning recovery opportunities. [For the &quot;After Action Review Guide,&quot; see ED609070.]
A Guide to Legislative Advocacy for Youth with Disabilities: &quot;Nothing about YWD without YWD&quot;
Author: Hare, Rebecca
Date: 2007-00-00
Type: Guides - Non-Classroom
Subjects: Empowerment,Developmental Disabilities,Leadership Training,Curriculum,Youth Programs,Instructional Materials,Networks,Cooperation,Youth,Legislation,Independent Study,Legislators,Self Advocacy,Mentors,Local Government,State Government,Federal Government,Public Officials,Laws,Letters (Correspondence),Telecommunications,Mass Media,Web Sites,Advertising

The National Consortium on Leadership and Disability for Youth (NCLD-Youth) is one of 15 Youth Resource, Information, and Training Centers focused on empowering youth and emerging leaders with developmental disabilities. The NCLD-Youth is a youth-led center with partners in the District of Columbia as well as in the states of Florida and New Hampshire. NCLD-Youth strives to support and promote the next generation of leaders in the disability community through the following three objectives: (1) identify and develop high quality, disability specific curricula around the five areas of youth development and leadership; (2) test, refine, and disseminate instructional materials throughout a variety of states to build networks of national, state, and local level partnerships of peer mentors, adult advisors and councils of youth and emerging leaders; and (3) develop, train, and mentor youth and emerging young leaders with developmental disabilities in each of the three partner states to influence state and local-level youth development and leadership public policy. This guide is designed to help youth with disabilities become stronger advocates. This encompasses a wide array of issues including the basics of how a bill becomes a law, how to self-educate on the issues that are important, and how to use that information in talking with legislators. This is developed for and by young people with disabilities to be used in classroom settings, in trainings, and to better prepare young people to advocate for themselves. [This document was developed by the National Consortium on Leadership and Disability for Youth (NCLD-Youth) and printed with the support of the HSC Foundation.]
Using Guided Pathways to Build Cross-Sector Pathways Partnerships
Author: Griffin, Sarah
Date: 2021-10-00
Type: Reports - Research
Subjects: Community Colleges,Partnerships in Education,Educational Change,College School Cooperation,School Business Relationship,Disadvantaged,Labor Needs,Labor Force Development,Barriers,Models,Education Work Relationship,Employment Opportunities,Kindergarten,Elementary Secondary Education,Educational Practices,Strategic Planning,Innovation,Futures (of Society),Declining Enrollment,Outcomes of Education,Employers

Community colleges are beginning to build on guided pathways reforms in partnership with employers, universities, and K-12 schools to create cross-sector pathways to college and career opportunities for students--particularly those students traditionally underserved by higher education. The aim is to improve economic mobility for community residents and to meet the workforce needs of area employers. Based on an exploratory study at four such colleges undertaken by researchers from CCRC and WestEd, this report introduces a cross-sector pathways model and highlights emerging practices and strategies to strengthen ties between community colleges and their partners. These strengthened partnerships reduce barriers that students face as they transition between institutions and employers in their journey from high school to college and careers. The authors first discuss key challenges facing community colleges and their students today, and they illustrate how guided pathways reform efforts that emphasize cross-sector partnerships serve as a model for addressing these challenges. Drawing on examples from the study, they then outline a set of 12 innovative cross-sector pathways practices, three that occur in each of these domains: (1) within community colleges themselves; (2) between community colleges and employers; (3) between community colleges and universities; (4) and between community colleges and K-12 schools. The authors also describe strategies that community colleges are using to build mutually beneficial relationships with employers and other educational institutions. The report draws three broad conclusions: (1) Innovating at scale rather than through targeted programs--that is, establishing new practices that affect all students--creates and sustains momentum for institutional transformation; (2) Developing and sustaining innovations at scale requires changing the mindsets of faculty, staff, and partners; and (3) Implementing cross-sector pathways at scale requires community colleges to move from transactional relationships to transformational relationships focused on &quot;win-wins&quot; for the colleges and their external partners.