ethanporcaro/eric-fulltext-meta huggingface dataset:
NAEP Mathematics Data for Students with Disabilities. Technical Working Group Summary
Author: National Center for Special Education Research (NCSER) (ED/IES)
Date: 2020-03-18
Type: Reports - Descriptive
Subjects: National Competency Tests,Mathematics Achievement,Students with Disabilities,Testing,Access to Education,Testing Accommodations,Cognitive Processes,Data Analysis,Grade 8,Middle School Students,Mathematics Education

In March 2020, the National Center for Special Education Research (NCSER) virtually convened a group of experts to discuss gaps in research on eighth grade mathematics for students with disabilities (SWD) that could be addressed with the NAEP process data and data science techniques for this research. Invited experts also provided recommendations for researchers interested in using the NAEP process data. The discussion focused on the following topics: (1) NAEP assessment and restricted-use files, (2) NAEP administration for SWD, (3) universal design for assessment features and testing accommodations, (4) student cognition and learning in mathematics, (5) data mining using process data from digital assessments, (6) overview of the NAEP process data, (7) eighth grade mathematics and SWD, (8) common student misconceptions in eighth grade mathematics content, and (9) recommendations for conducting research using NAEP data.
Advancing Research on Students with Disabilities in Postsecondary Education. Technical Working Group Summary
Author: Nelson, Akilah Swinton
Date: 2021-12-09
Type: Reports - Descriptive
Subjects: Educational Research,Students with Disabilities,College Students,Student Experience,Student Needs,Advocacy,Black Colleges,Deafness,Learner Engagement,Academic Support Services,Intellectual Disability,College Faculty,Teacher Role,Self Determination,Intervention,Predictor Variables,Success,Accessibility (for Disabled),Transitional Programs,Student Diversity

In December 2021, the National Center for Special Education Research (NCSER) virtually convened a group of experts to discuss priorities for research and data collection on students with disabilities in postsecondary education. Invited experts represented advocates, educators, researchers, and disability service professionals. The discussion centered on five topic areas: (1) national data and current knowledge on postsecondary students with disabilities; (2) the lived postsecondary experiences of students with disabilities; (3) supporting postsecondary students with disabilities; (4) research and data priorities for the field; and (5) increasing research capacity for studying postsecondary students with disabilities. This document summarizes the presentations and discussion points as well as the panelist recommendations for IES to advance research on postsecondary students with disabilities.
Future Directions for Research to Improve Outcomes for Students with Emotional and Behavioral Disorders. Technical Working Group Meeting Summary
Author: Thompson, Ann M.
Date: 2017-11-16
Type: Reports - Descriptive
Subjects: Students with Disabilities,Emotional Disturbances,Behavior Disorders,Student Needs,Educational Policy,Elementary School Students,Secondary School Students,Educational Practices,Educational Research,Research Needs,At Risk Students,Academic Support Services,Parent Role,Transitional Programs,Disability Identification,Teacher Competencies,Systems Approach,Intervention,Barriers,Program Implementation

In November 2017, the National Center for Special Education Research in the Institute of Education Sciences convened a group of experts to discuss and provide input on research needs for improving outcomes of students with emotional and behavioral disorders (EBD). The discussion focused on four major topics of inquiry: (1) What do we know about academic, social, and emotional needs of students with EBD? (2) What do we know about policies, programs, and practices for elementary students with EBD? (3) What do we know about policies, programs, and practices for secondary students with EBD? and (4) What is needed to advance EBD research? This meeting summary covers the key points and recommendations that emerged from the discussion. [This meeting summary was prepared by Rose Li and Associates, Inc.]
Modeling and Analyzing Scorer Preferences in Short-Answer Math Questions
Author: Zhang, Mengxue
Date: 2023-00-00
Type: Speeches/Meeting Papers
Subjects: Scoring,Computer Assisted Testing,Mathematics Instruction,Mathematics Tests,Computational Linguistics,Computer Software,Evaluators,Models,Test Items,Accuracy,Case Studies,Preferences,Item Analysis,Comparative Analysis

Automated scoring of student responses to open-ended questions, including short-answer questions, has great potential to scale to a large number of responses. Recent approaches for automated scoring rely on supervised learning, i.e., training classifiers or fine-tuning language models on a small number of responses with human-provided score labels. However, since scoring is a subjective process, these human scores are noisy and can be highly variable, depending on the scorer. In this paper, we investigate a collection of models that account for the individual preferences and tendencies of each human scorer in the automated scoring task. We apply these models to a short-answer math response dataset where each response is scored (often differently) by multiple different human scorers. We conduct quantitative experiments to show that our scorer models lead to improved automated scoring accuracy. We also conduct quantitative experiments and case studies to analyze the individual preferences and tendencies of scorers. We found that scorers can be grouped into several obvious clusters, with each cluster having distinct features, and analyzed them in detail. [For the complete proceedings, see ED630829.]
Mining Detailed Course Transaction Records for Semantic Information
Author: Xu, Yinuo
Date: 2023-00-00
Type: Speeches/Meeting Papers
Subjects: Semantics,Enrollment Trends,Learning Analytics,STEM Education,Accuracy,Prediction,Dropouts,Course Selection (Students),Student Characteristics,Departments,Computer Software,State Universities,Majors (Students),Models,Student Records,Higher Education

In studies that generate course recommendations based on similarity, the typical enrollment data used for model training consists only of one record per student-course pair. In this study, we explore and quantify the additional signal present in course transaction data, which includes a more granular account of student administrative interactions with a course, such as wait-listing, enrolling, and dropping. We explore whether the additional non-enrollment records and the transaction data's chronological order play a role in providing more signal. We train skip-gram, FastText, and RoBERTa models on transaction data from five years of course taking histories. We find that the models gain moderate improvements from the extra non-enrollment records, while the chronological order of the transaction data improves the performance of RoBERTa only. The generated embeddings can also predict course features (i.e. the department, its usefulness in satisfying requirements, and whether the course is STEM) with high accuracy. Lastly, we discuss future work on the use of transaction data to predict student characteristics and train course recommender models for degree requirements. [For the complete proceedings, see ED630829.]
Optimizing Parameters for Accurate Position Data Mining in Diverse Classrooms Layouts
Author: Shou, Tianze
Date: 2023-00-00
Type: Speeches/Meeting Papers
Subjects: Classroom Design,Accuracy,Validity,Space Utilization,Learning Analytics,Recall (Psychology),Task Analysis,Classroom Communication,Inferences,Teacher Student Relationship,Motion,Proximity,Algorithms,Computer Software,Grade 7

Spatial analytics receive increased attention in educational data mining. A critical issue in stop detection (i.e., the automatic extraction of timestamped and located stops in the movement of individuals) is a lack of validation of stop accuracy to represent phenomena of interest. Next to a radius that an actor does not exceed for a certain duration to establish a stop, this study presents a reproducible procedure to optimize a range parameter for K-12 classrooms where students sitting within a certain vicinity of an inferred stop are tagged as being visited. This extension is motivated by adapting parameters to infer teacher visits (i.e., on-task and off-task conversations between the teacher and one or more students) in an intelligent tutoring system classroom with a dense layout. We evaluate the accuracy of our algorithm and highlight a tradeoff between precision and recall in teacher visit detection, which favors recall. We recommend that future research adjust their parameter search based on stop detection precision thresholds. This adjustment led to better cross-validation accuracy than maximizing parameters for an average of precision and recall (F1 = 0.18 compared to 0.09). As stop sample size shrinks with higher precision cutoffs, thresholds can be informed by ensuring sufficient statistical power in offline analyses. We share avenues for future research to refine our procedure further. Detecting teacher visits may benefit from additional spatial features (e.g., teacher movement trajectory) and can facilitate studying the interplay of teacher behavior and student learning. [For the complete proceedings, see ED630829.]
Towards Generalizable Detection of Urgency of Discussion Forum Posts
Author: Švábenský, Valdemar
Date: 2023-00-00
Type: Speeches/Meeting Papers
Subjects: Generalization,Computer Mediated Communication,MOOCs,State Universities,Rating Scales,Student Attitudes,Group Discussion,Teacher Student Relationship,Student Motivation,Models,Prediction,Learning Analytics,Data Analysis,Computational Linguistics,Sentences,Learning Processes,Natural Language Processing

Students who take an online course, such as a MOOC, use the course's discussion forum to ask questions or reach out to instructors when encountering an issue. However, reading and responding to students' questions is difficult to scale because of the time needed to consider each message. As a result, critical issues may be left unresolved, and students may lose the motivation to continue in the course. To help address this problem, we build predictive models that automatically determine the urgency of each forum post, so that these posts can be brought to instructors' attention. This paper goes beyond previous work by predicting not just a binary decision cut-off but a post's level of urgency on a 7-point scale. First, we train and cross-validate several models on an original data set of 3,503 posts from MOOCs at University of Pennsylvania. Second, to determine the generalizability of our models, we test their performance on a separate, previously published data set of 29,604 posts from MOOCs at Stanford University. While the previous work on post urgency used only one data set, we evaluated the prediction across different data sets and courses. The best-performing model was a support vector regressor trained on the Universal Sentence Encoder embeddings of the posts, achieving an RMSE of 1.1 on the training set and 1.4 on the test set. Understanding the urgency of forum posts enables instructors to focus their time more effectively and, as a result, better support student learning. [For the complete proceedings, see ED630829.]
Variational Temporal IRT: Fast, Accurate, and Explainable Inference of Dynamic Learner Proficiency
Author: Kim, Yunsung
Date: 2023-00-00
Type: Speeches/Meeting Papers
Subjects: Item Response Theory,Accuracy,Inferences,Algorithms,Learning Analytics,Psychometrics,Models

Dynamic Item Response Models extend the standard Item Response Theory (IRT) to capture temporal dynamics in learner ability. While these models have the potential to allow instructional systems to actively monitor the evolution of learner proficiency in real time, existing dynamic item response models rely on expensive inference algorithms that scale poorly to massive datasets. In this work, we propose Variational Temporal IRT (VTIRT) for fast and accurate inference of dynamic learner proficiency. VTIRT offers orders of magnitude speedup in inference runtime while still providing accurate inference. Moreover, the proposed algorithm is intrinsically interpretable by virtue of its modular design. When applied to 9 real student datasets, VTIRT consistently yields improvements in predicting future learner performance over other learner proficiency models. [For the complete proceedings, see ED630829.]
Visual Representation of Co-Authorship with GPT-3: Studying Human-Machine Interaction for Effective Writing
Author: Shibani, Antonette
Date: 2023-00-00
Type: Speeches/Meeting Papers
Subjects: Artificial Intelligence,Man Machine Systems,Writing (Composition),Collaborative Writing,Writing Processes,Visual Aids,Technology Uses in Education

With the recent release of Chat-GPT by OpenAI, the automated text generation capabilities of GPT-3 are seen as transformative and potentially systemically disruptive for higher education. While the impact on teaching and learning practices is still unknown, it is apparent that alongside risks these tools offer the potential to augment human intelligence (intelligence augmentation, or IA). However, strategies for such IA, involving partnership of tool-human, will be needed to support learning. In the context of writing, an investigation of potential approaches is needed given empirical data and studies are currently limited. We introduce a novel visual representation CoAuthorViz to examine keystroke logs from a writing assistant where writers interacted with GPT-3 writing suggestions to co-write with the machine. We demonstrate the use of our visualization by exemplifying different kinds of writing behaviour from users writing with GPT-3 support and derive metrics such as their usage of GPT-3 suggestions in relation to overall writing quality indicators. We also release the materials open source to further progress our understanding of desirable user behaviour when working with such state-of-the-art AI tools. [For the complete proceedings, see ED630829.]
Predicting Bug Fix Time in Students&apos; Programming with Deep Language Models
Author: Tsabari, Stav
Date: 2023-00-00
Type: Speeches/Meeting Papers
Subjects: College Students,Computer Science Education,Programming,Error Patterns,Coding,Error Correction,Student Behavior,Time Factors (Learning),Predictor Variables,Artificial Intelligence,Novices,Difficulty Level,Identification,Models,Language,Computer Software

Automatically identifying struggling students learning to program can assist teachers in providing timely and focused help. This work presents a new deep-learning language model for predicting &quot;bug-fix-time&quot;, the expected duration between when a software bug occurs and the time it will be fixed by the student. Such information can guide teachers' attention to students most in need. The input to the model includes snapshots of the student's evolving software code and additional meta-features. The model combines a transformerbased neural architecture for embedding students' code in programming language space with a time-aware LSTM for representing the evolving code snapshots. We evaluate our approach with data obtained from two Java development environments created for beginner programmers. We focused on common programming errors which differ in their difficulty and whether they can be uniquely identified during compilation. Our deep language model was able to outperform several baseline models that use an alternative embedding method or do not consider how the programmer's code changes over time. Our results demonstrate the added value of utilizing multiple code snapshots to predict bug-fix-time using deep language models for programming. [For the complete proceedings, see ED630829.]
Partner Keystrokes Can Predict Attentional States during Chat-Based Conversations
Author: Kuvar, Vishal
Date: 2023-00-00
Type: Speeches/Meeting Papers
Subjects: Computer Mediated Communication,Trust (Psychology),Socialization,Keyboarding (Data Entry),Prediction,Correlation,Task Analysis,Cognitive Processes,Interpersonal Communication,Data Analysis,Cooperative Learning,Attention Control,Computer Software,Foreign Countries

Computer-mediated social learning contexts have become increasingly popular over the last few years; yet existing models of students' cognitive-affective states have been slower to adopt dyadic interaction data for predictions. Here, we explore the possibility of capitalizing on the inherently social component of collaborative learning by using keystroke log data to make predictions across conversational partners (i.e., using person A's data to make prediction about if person B is mind wandering). Log files from 33 dyads (total N = 66) were used to examine: (a) how mind wandering (defined here as task-unrelated thought) during computer-mediated conversations is related to critical outcomes of the conversation (trust, likability, agreement); (b) if task-unrelated thought can be predicted by the keystrokes of one's partner; and (c) how much data is needed to make predictions by testing various window-sizes of data preceding task-unrelated thought reports. Results indicated a negative relationship between task-unrelated thought and perceptions of the conversation, suggesting that attention is an important factor during computer mediated chat conversations. Finally, in line with our hypothesis, results from mixed effects models showed that one's level of task-unrelated thought was predicted by the keystroke patterns of their conversational partner, but only using small window sizes (5s worth of data). [For the complete proceedings, see ED630829.]
Scalable and Equitable Math Problem Solving Strategy Prediction in Big Educational Data
Author: Shakya, Anup
Date: 2023-00-00
Type: Speeches/Meeting Papers
Subjects: Equal Education,Mathematics Education,Word Problems (Mathematics),Problem Solving,Prediction,Intelligent Tutoring Systems,Artificial Intelligence,Accuracy,Mastery Learning,Middle School Students

Understanding a student's problem-solving strategy can have a significant impact on effective math learning using Intelligent Tutoring Systems (ITSs) and Adaptive Instructional Systems (AISs). For instance, the ITS/AIS can better personalize itself to correct specific misconceptions that are indicated by incorrect strategies, specific problems can be designed to improve strategies and frustration can be minimized by adapting to a student's natural way of thinking rather than trying to fit a standard strategy for all. While it may be possible for human experts to identify strategies manually in classroom settings with sufficient student interaction, it is not possible to scale this up to big data. Therefore, we leverage advances in Machine Learning and AI methods to perform scalable strategy prediction that is also fair to students at all skill levels. Specifically, we develop an embedding called MVec where we learn a representation based on the mastery of students. We then cluster these embeddings with a non-parametric clustering method where we progressively learn clusters such that we group together instances that have approximately symmetrical strategies. The strategy prediction model is trained on instances sampled from these clusters. This ensures that we train the model over diverse strategies and also that strategies from a particular group do not bias the DNN model, thus allowing it to optimize its parameters over all groups. Using real world large-scale student interaction datasets from MATHia, we implement our approach using transformers and Node2Vec for learning the mastery embeddings and LSTMs for predicting strategies. We show that our approach can scale up to achieve high accuracy by training on a small sample of a large dataset and also has predictive equality, i.e., it can predict strategies equally well for learners at diverse skill levels. [For the complete proceedings, see ED630829.]
Teachers&apos; and Students&apos; Perceptions of Online Research Skills: An Exploratory Study in South Lebanese Schools
Author: Kain, Hoda
Date: 2018-00-00
Type: Journal Articles
Subjects: Foreign Countries,Middle School Students,Information Sources,Online Searching,Research Skills,Teacher Attitudes,Middle School Teachers,Student Attitudes,Grade 8,Private Schools,Plagiarism,Self Efficacy

Studies showed that middle school students misuse the Internet sources for reading and writing purposes though they are skillful at online personal communication. Eventually, this led to the emergence of copy-paste trends in students' written work. In this paper, we aimed at exploring 8th grade teachers' and students' perceptions about the factors that predict the copy-paste trend in Lebanese schools, and the extent to which these perceptions are interrelated. 172 students were surveyed and eight teachers interviewed at 3 private schools in the south of Lebanon. Results indicated that students had limited Internet skills despite their intensive Internet use and teachers lacked essential instructional practices to help students acquire basic skills to avoid plagiarism. Proposed recommendations included teachers' inservice training and curriculum review to integrate essentials of ethical writing.
SoCal Urban Wildlife Refuge Project 2014-2015: A Summary of Actions and Accomplishments in Support of the U.S. Fish &amp; Wildlife Service&apos;s Urban Wildlife Conservation Program
Author: Marshall, James
Date: 2016-01-15
Type: Reports - Descriptive
Subjects: Urban Areas,Wildlife,Conservation Education,Outdoor Education,Environmental Education,Outreach Programs,Program Effectiveness,Youth Programs,Low Income Students,Field Trips,Biodiversity

The U.S. Fish and Wildlife Service's Urban Wildlife Conservation Program invested in communicating the vital importance of wildlife conservation to urban audiences. Urban areas present a strategic opportunity to reach new audiences and expose them to the USFWS and opportunities to visit USFWS lands. Refuges close to highly populated areas provide the greatest opportunity to engage new and diverse audiences, whether through an urban refuge or an urban wildlife refuge partnership. This report highlights the work and accomplishments during the first two years of the SoCal Urban Wildlife Refuge Project--the first such competitively awarded funding to connect the 80% of city dwelling, plugged in Americans with nature, and--by extension--to values centered on conservation and environmental stewardship. This initial report highlights project partner's successes in connecting southern California communities to nature, engaging in varied and meaningful programs, and facilitating measurable benefits for the people and communities involved.