ethanporcaro/eric-fulltext-meta huggingface dataset:
Higher Education&apos;s Next Great Challenge: Ensuring Full Inclusion for Students with Disabilities
Author: Hill, Eve
Date: 2020-06-00
Type: Guides - Non-Classroom
Subjects: Inclusion,Students with Disabilities,College Students,Barriers,Social Bias,Attitudes toward Disabilities,Student Recruitment,Compliance (Legal),Accessibility (for Disabled),Academic Persistence,Access to Computers,Civil Rights Legislation,Federal Legislation,College Admission,Student Needs,Academic Accommodations (Disabilities),Animals,Testing Accommodations,School Culture,School Policy,School Safety,Technology Uses in Education,Educational Technology,Accountability

Disability-diversity on college campuses is no longer optional--it is an expectation. Students entering college today have grown up with the ADA and they have witnessed inclusion and mainstreaming of students with disabilities their entire lives. The most powerful barriers to full inclusion include stigmas about students with disabilities, negatives attitudes, technology barriers, and lack of understanding among campus administrators, faculty, and staff. Becoming an inclusive community takes work at all levels--from the top administration and Board of Trustees, to faculty and staff, to students. This new guide outlines specific actions that all institutions can take to create an inclusive environment for students whether in-person or online. Encouraging a more diverse student and alumni population benefits the entire learning establishment while preparing all students for a future workforce that is fully inclusive.
Grouping Source Code by Solution Approaches--Improving Feedback in Programming Courses
Author: Höppner, Frank
Date: 2021-00-00
Type: Reports - Research
Subjects: Coding,Classification,Programming,Computer Science Education,Semantics,Novices,Problem Solving,Instruction,Programming Languages

Various similarity measures for source code have been proposed, many rely on edit- or tree-distance. To support a lecturer in quickly assessing live or online exercises with respect to &quot;approaches taken by the students,&quot; we compare source code on a more abstract, semantic level. Even if novice student's solutions follow the same idea, their code length may vary considerably -- which greatly misleads edit and tree distance approaches. We propose an alternative similarity measure based on &quot;variable usage paths&quot; (VUP), that is, we use the way how variables are used in the code to elaborate code similarity. The final stage of the measure involves a matching of variables in functions based on how the variable is used by the instructions. A preliminary evaluation on real data is presented. [For the full proceedings, see ED615472.]
Publications Output: U.S. Trends and International Comparisons. Science &amp; Engineering Indicators 2020. NSB-2020-6
Author: White, Karen
Date: 2019-12-17
Type: Information Analyses
Subjects: Publications,Periodicals,Conferences (Gatherings),Scientific Research,International Cooperation,Research Utilization,Foreign Countries,Journal Articles,Research Reports,Citations (References)

This report presents data on peer-reviewed S&amp;E journals and conference proceedings reflecting the rapidly expanding volume of research activity, the involvement and scientific capabilities different countries, and the expanding research ecosystem demonstrated through international collaborations. Publication output grew about 4% annually over the past 10 years. China and India grew more than the world average, while the United States and European Union grew less than the world average. Research papers from the United States and EU countries had higher impact scores. International collaborations have increased over the past 10 years. [SRI International, Center for Innovation Strategy and Policy assisted with report preparation.]
Embedding Navigation Patterns for Student Performance Prediction
Author: Loginova, Ekaterina
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Navigation (Information Systems),Academic Achievement,Grade Prediction,Integrated Learning Systems,Online Courses,Learning Strategies,Secondary School Students,Data Analysis

Predicting academic performance using trace data from learning management systems is a primary research topic in educational data mining. An important application is the identification of students at risk of failing the course or dropping out. However, most approaches utilise past grades, which are not always available and capture little of the student's learning strategy. The end-to-end models we implement predict whether a student will pass a course using only navigational patterns in a multimedia system, with the advantage of not requiring past grades. We experiment on a dataset containing coarse-grained action logs of more than 100,000 students participating in hundreds of short course. We propose two approaches to improve the performance: a novel encoding scheme for trace data, which reflects the course structure while remaining flexible enough to accommodate previously unseen courses, and unsupervised embeddings obtained with an autoencoder. To provide insight into model behaviour, we incorporate an attention mechanism. Clustering the vector representations of student behaviour produced by the proposed methods shows that distinct learning strategies specific to low- and high-achievers are extracted. [For the full proceedings, see ED615472.]
Fair-Capacitated Clustering
Author: Quy, Tai Le
Date: 2021-00-00
Type: Reports - Research
Subjects: Cluster Grouping,Artificial Intelligence,Mathematics,Computer Uses in Education,Grouping (Instructional Purposes),Foreign Countries,Secondary School Students,College Students

Traditionally, clustering algorithms focus on partitioning the data into groups of similar instances. The similarity objective, however, is not sufficient in applications where a &quot;fair-representation&quot; of the groups in terms of protected attributes like gender or race, is required for each cluster. Moreover, in many applications, to make the clusters useful for the end-user, a &quot;balanced cardinality&quot; among the clusters is required. Our motivation comes from the education domain where studies indicate that students might learn better in diverse student groups and of course groups of similar cardinality are more practical e.g., for group assignments. To this end, we introduce the &quot;fair-capacitated clustering problem&quot; that partitions the data into clusters of similar instances while ensuring cluster fairness and balancing cluster cardinalities. We propose a two-step solution to the problem: (1) we rely on fairlets to generate minimal sets that satisfy the fair constraint; and (2) we propose two approaches, namely hierarchical clustering and partitioning-based clustering, to obtain the fair-capacitated clustering. Our experiments on three educational datasets show that our approaches deliver well-balanced clusters in terms of both fairness and cardinality while maintaining a good clustering quality. [For the full proceedings, see ED615472.]
Experimental Evaluation of Similarity Measures for Educational Items
Author: Cechák, Jaroslav
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Measurement,Proximity,Grammar,English (Second Language),Second Language Instruction,Generalization,Data Collection,Test Items,Language Tests,Foreign Countries

Measuring similarity of educational items has several applications in the development of adaptive learning systems, and previous research has already proposed a wide range of similarity measures. In this work, we provide an experimental evaluation of selected similarity measures using a large dataset. The used items are alternate-choice questions for the practice of English grammar for second language learners; the dataset contains thousands of items and over 10 million student answers. Our results provide warnings about the generalizability of results presented in EDM works: (1) the results vary significantly between knowledge components and (2) the size of available data is an important factor. [For the full proceedings, see ED615472.]
Prevalence of Learning Difficulties among School Students in Kerala
Author: Abdul Gafoor, K.
Date: 2015-00-00
Type: Journal Articles
Subjects: Foreign Countries,Incidence,Students with Disabilities,Learning Disabilities,Intellectual Disability,Visual Impairments,Autism,Pervasive Developmental Disorders,Attention Deficit Hyperactivity Disorder,Physical Disabilities,Inclusion,Special Education,Institutional Characteristics,Student Needs,Teacher Attitudes

This study estimates the percentage of incidence of students who are categorised as Learning Disabled in schools of Kerala, along with estimating studying the prevalence of students categorised by schools as mentally Retarded (MR), Visually Impaired (VI), Autism, ADHD and Orthopedically Impaired and investigates perceived difficulties of schools in facilitating the inclusive education of these children. The sample is 31 government and government-aided schools drawn by simple random procedure from Kozhikode (n=14) and Malappuram (n=17) districts, covering 26382 students. There is variation among schools in the prevalence of students with LD and other impairments. In a school of an average size of 850, LD and other categories of SEN have near 40 students. In general, teachers categorise five per cent of students as belonging to SEN categories, half them being learning disabled (2.23%) and half belonging to MR, VI, Autism, ADHD, HI, and OI (2.53%). The incidence of SEN children assessed by this study surely is less than what can be expected in the population, as the study considered a share of the relevant age group who attend the school only. Implications are added.
Tennessee Higher Education Fact Book: 2020-2021
Author: Tennessee Higher Education Commission, Tennessee Student Assistance Corporation (THEC/TSAC)
Date: 2021-00-00
Type: Reports - Evaluative
Subjects: COVID-19,Pandemics,Higher Education,Enrollment,Public Colleges,Private Colleges,Technical Institutes,Proprietary Schools,Community Colleges,Full Time Equivalency,Withdrawal (Education),Two Year College Students,College Students,Gender Differences,Racial Differences,Ethnicity,Age Differences,Student Financial Aid,Federal Aid,Grants,Part Time Students,Full Time Students,Majors (Students),College Entrance Examinations,Federal Legislation,Student Loan Programs,College Transfer Students,Remedial Instruction,School Holding Power,Graduation,Academic Degrees,Graduation Rate,Job Placement,College Graduates,Scores,Licensing Examinations (Professions),Student Satisfaction,Educational Finance,College Programs,Accreditation (Institutions),College Admission,College Faculty,Teacher Salaries,Tuition,Fees,Student Costs,Family Income,State Aid,Debt (Financial),Loan Default,Scholarships,Funding Formulas,State Programs,Academic Persistence,Quality Assurance,Financial Support

This year's &quot;Tennessee Higher Education Fact Book&quot; consists of five sections and an appendix of additional resources and terminology. The five main sections of the report include: (1) Student Participation, Fall 2020; (2) Student Success, Academic Year 2019-20; (3) Academic and Fiscal Trends, Academic Year 2019-20; (4) Quality Assurance Funding, 2019-20; and (5) Outcomes Funding Formula, Academic Year 2019-20. Each of the main sections provides data on Student Participation, Student Success, Academic and Fiscal Trends, Quality Assurance Funding, and Outcomes-Based Funding. Each section begins with either an introductory overview or a summary of key takeaways. Unlike previous iterations of the report, static institutional profiles are not included as a distinct section of the report. Instead, a separate dynamic institutional profile dashboard has been developed to coincide with the release of the Fact Book. Once available, the dashboard may be accessed on the Fact Book landing page (https://www.tn.gov/thec/research/fact-book.html). Also new this year, the sections on Student Participation and Student Success contain new tables that disaggregate data by race and ethnicity to a greater extent than in prior reports. COVID-19 presented unprecedented and unpredictable circumstances for Tennessee postsecondary education. Many of the tables and figures presented in this report may not be comparable to prior years due to the impact of the COVID-19 pandemic. [For &quot;Tennessee Higher Education Fact Book: 2019-20,&quot; see ED608921.]
Early Prediction of Conceptual Understanding in Interactive Simulations
Author: Cock, Jade
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Prediction,Concept Formation,Scientific Concepts,Physics,Active Learning,Inquiry,Learning Analytics,Interaction,Computer Simulation,Classification,College Freshmen

Interactive simulations allow students to independently explore scientific phenomena and ideally infer the underlying principles through their exploration. Effectively using such environments is challenging for many students and therefore, adaptive guidance has the potential to improve student learning. Providing effective support is, however, also a challenge because it is not clear how effective inquiry in such environments looks like. Previous research in this area has mostly focused on grouping students with similar strategies or identifying learning strategies through sequence mining. In this paper, we investigate features and models for an early prediction of conceptual understanding based on clickstream data of students using an interactive Physics simulation. To this end, we measure students' conceptual understanding through a task they need to solve through their exploration. Then, we propose a novel pipeline to transform clickstream data into predictive features, using latent feature representations and interaction frequency vectors for different components of the environment. Our results on interaction data from 192 undergraduate students show that the proposed approach is able to detect struggling students early on. [For the full proceedings, see ED615472.]
Effects of Algorithmic Transparency in Bayesian Knowledge Tracing on Trust and Perceived Accuracy
Author: Williamson, Kimberly
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Bayesian Statistics,Learning Processes,Computer Software,Learning Analytics,Trust (Psychology),Accuracy,Markov Processes,Participant Characteristics,Surveys,College Entrance Examinations,Attitude Measures

Knowledge tracing algorithms such as Bayesian Knowledge Tracing (BKT) can provide students and teachers with helpful information about their progress towards learning objectives. Despite the popularity of BKT in the research community, the algorithm is not widely adopted in educational practice. This may be due to skepticism from users and uncertainty over how to explain BKT to them to foster trust. We conducted a pre-registered 2x2 survey experiment (n=170) to investigate attitudes towards BKT and how they are affected by verbal and visual explanations of the algorithm. We find that ostensible learners prefer BKT over a simpler algorithm, rating BKT as more trustworthy, accurate, and sophisticated. Providing verbal and visual explanations of BKT improved confidence in the learning application, trust in BKT and its perceived accuracy. Findings suggest that people's acceptance of BKT may be higher than anticipated, especially when explanations are provided. [For the full proceedings, see ED615472.]
Investigating the Validity of Methods Used to Adjust for Multiple Comparisons in Educational Data Mining
Author: Matayoshi, Jeffrey
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Statistical Analysis,Validity,Classification,Hypothesis Testing,Methods,Data Analysis,Educational Research

Research studies in Educational Data Mining (EDM) often involve several variables related to student learning activities. As such, it may be necessary to run multiple statistical tests simultaneously, thereby leading to the problem of multiple comparisons. The Benjamini-Hochberg (BH) procedure is commonly used in EDM research to address this issue, and it has proven to be a useful method. However, the main limitation of the procedure is that it requires the statistical tests to either be independent or satisfy certain dependency conditions. The Benjamini-Yekutieli (BY) procedure is an alternative that can be applied under arbitrary dependence assumptions, but this extra flexibility comes with a loss of statistical power; hence, the BH procedure is preferred whenever it can be properly applied. Based on these considerations, in this work we employ simulation studies to assess the validity of the BH procedure in two scenarios common to EDM research. The first scenario considers the evaluation and comparison of different classification models--such an analysis might occur, for instance, during the model tuning and validation stage of a study. Then, in the second scenario we look at experiments involving the study of state transitions in sequential data, examples of which occur in affect dynamics research. We find that the BH procedure performs as expected when used with simulated classification model predictions; however, when applied to simulated sequential data, it does not perform at the expected level. Based on these results, as well as previous studies evaluating the BH and BY methods, we discuss the appropriate usage of these procedures for the scenarios under examination. [For the full proceedings, see ED615472.]
Knowing &quot;When&quot; and &quot;Where&quot;: Temporal-ASTNN for Student Learning Progression in Novice Programming Tasks
Author: Mao, Ye
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Programming,Computer Science Education,Learning Processes,Learning Analytics,Programming Languages,Computer Software,Short Term Memory,Models,Classification,Teaching Methods,Networks,Natural Language Processing,Coding,Task Analysis,Evaluation Methods,Novices,Instructional Effectiveness

As students learn how to program, both their programming code and their understanding of it evolves over time. In this work, we present a general data-driven approach, named &quot;Temporal-ASTNN&quot; for modeling student learning progression in open-ended programming domains. Temporal-ASTNN combines a novel neural network model based on abstract syntactic trees (AST), named ASTNN, and Long-Short Term Memory (LSTM) model. ASTNN handles the &quot;linguistic&quot; nature of student programming code, while LSTM handles the &quot;temporal&quot; nature of student learning progression. The effectiveness of ASTNN is first compared against other models including a state-of-the-art algorithm, Code2Vec across two programming domains: iSnap and Java on the task of program classification (&quot;correct&quot; or &quot;incorrect&quot;). Then the proposed temporal-ASTNN is compared against the original ASTNN and other temporal models on a challenging task of student success early prediction. Our results show that Temporal-ASTNN can achieve the best performance with only the first 4-minute temporal data and it continues to outperform all other models with longer trajectories. [For the full proceedings, see ED615472.]
Learning Expert Models for Educationally Relevant Tasks Using Reinforcement Learning
Author: MacLellan, Christopher J.
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Models,Learning Activities,Relevance (Education),Reinforcement,Teaching Methods,Mathematics Education,Artificial Intelligence,Computer Simulation

There has been great progress towards Reinforcement Learning (RL) approaches that can achieve expert performance across a wide range of domains. However, researchers have not yet applied these models to learn expert models for educationally relevant tasks, such as those taught within tutoring systems and educational games. In this paper we explore the use of Proximal Policy Optimization (PPO) [25] for learning expert models for tutoring system tasks. We explore two alternative state and action space representations for this RL approach in the context of two intelligent tutors (a fraction arithmetic tutor and a multicolumn addition tutor). We compare the performance of these models to a computational model of learning built using the Apprentice Learner architecture. To evaluate these models, we look at whether they achieve mastery and how many training opportunities they take to do so. Our analysis shows that at least one PPO model is able to successfully achieve mastery within both tutors, suggesting that RL models might be successfully applied to learn expert models for educationally relevant tasks. We find that the Apprentice model also achieves mastery, but requires substantially less training (thousands of times less examples) than PPO. Finally, we find that there is an interaction between the PPO representation and task (one representation is better for one tutor and the other representation is better for the other tutor), suggesting that the design of the state and action representations for RL is important for success. Our work showcases the promise of RL for expert model discovery in educationally relevant tasks and highlights limitations and challenges that need further research to overcome. [For the full proceedings, see ED615472.]
Learning Student Program Embeddings Using Abstract Execution Traces
Author: Cleuziou, Guillaume
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Programming,Computer Science Education,Electronic Learning,Data Analysis

Improving the pedagogical effectiveness of programming training platforms is a hot topic that requires the construction of fine and exploitable representations of learners' programs. This article presents a new approach for learning program embeddings. Starting from the hypothesis that the function of a program, but also its &quot;style&quot;, can be captured by analyzing its execution traces, the &quot;code2aes2vec&quot; method proceeds in two steps. A first step generates abstract execution sequences (AES) from both predefined test cases and abstract syntax trees (AST) of the submitted programs. The &quot;doc2vec&quot; method is then used to learn condensed vector representations (embeddings) of the programs from these AESs. Experiments performed on real data sets shows that the embeddings generated by &quot;code2aes2vec&quot; efficiently capture both the semantics and the style of the programs. Finally, we show the relevance of the program embeddings thus generated on the task of automatic feedback propagation as a proof of concept. [For the full proceedings, see ED615472.]