ethanporcaro/eric-fulltext-meta huggingface dataset:
A Leg Up on College: The Scale and Distribution of Community College Participation among California High School Students. Research Brief. Volume 5, Number 1
Author: Friedmann, Elizabeth
Date: 2020-01-00
Type: Reports - Research
Subjects: Dual Enrollment,High School Students,Community Colleges,Racial Differences,Ethnicity,Socioeconomic Status,Disproportionate Representation,Public Schools,Access to Education,Courses,College Credits

Research shows that dual enrollment--a practice in which high school students take college courses while they are still in high school--has multiple benefits for student success in both systems. To capitalize on those benefits, California and other states have moved in recent years to increase high school students' access to college courses. In California, the historical lack of an integrated state data system that connects information from K-12 to higher education has hampered efforts to understand the extent of dual enrollment here. The prevailing narrative has been that California lags other states and the nation in dual enrollment, which is offered in 89% of U.S. high schools, with 11% of all high school students participating nationally. This brief, released in partnership with Wheelhouse, the Center for Community College Leadership and Research, breaks new ground by matching high school and community college datasets to provide a clearer picture of college course-taking among California public high school students statewide. The analysis of course-taking for the population of students who were seniors in the 2016-17 school year--the most recent cohort for which data from both segments was available--shows that 12.6% of California high school students take college courses, a rate higher than the national average and well above what previous reports suggest for California. A closer look at the matched dataset, however, reveals significant differences in college course-taking by race and socioeconomic status. Latinx and African American students were underrepresented in community college course-taking compared to their share of overall high school students. Socioeconomically disadvantaged (SED) students were also less likely to dual enroll than their non-SED peers. The vast majority of California public high schools do not have a formal dual enrollment program, though many have at least one student enrolled in a community college course during high school. Access to an important onramp to the early college experience, and its many demonstrated benefits, is not currently available to all California high school students. [This brief was published by Wheelhouse: The Center for Community College Leadership and Research at the University of California, Davis.]
#lets-discuss: Analyzing Student Affect in Course Forums Using Emoji
Author: Blobstein, Ariel
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Computer Mediated Communication,Nonverbal Communication,Social Media,Online Courses,Group Discussion,Textbooks,Electronic Publishing,Emotional Response,Visual Aids,Student Attitudes,Prediction,Course Content,Teaching Methods,Models,Computational Linguistics,Biology,Science Instruction,State Universities,Open Source Technology,Language Usage,Classification

Emoji are commonly used in social media to convey attitudes and emotions. While popular, their use in educational contexts has been sparsely studied. This paper reports on the students' use of emoji in an online course forum in which students annotate and discuss course material in the margins of the online textbook. For this study, instructors created 11 custom emoji-hashtag pairs that enabled students to quickly communicate affects and reactions in the forum that they experienced while interacting with the course material. Example reporting includes, inviting discussion about a topic, declaring a topic as interesting, or requesting assistance about a topic. We analyze emoji usage by over 1,800 students enrolled in multiple offerings of the same course across multiple academic terms. The data show that some emoji frequently appear together in posts associated with the same paragraphs, suggesting that students use the emoji in this way to communicating complex affective states. We explore the use of computational models for predicting emoji at the post level, even when posts are lacking emoji. This capability can allow instructors to infer information about students' affective states during their &quot;at home&quot; interactions with course readings. Finally, we show that partitioning the emoji into distinct groups, rather than trying to predict individual emoji, can be both of pedagogical value to instructors and improve the predictive performance of our approach using the BERT language model. Our procedure can be generalized to other courses and for the benefit of other instructors. [For the full proceedings, see ED623995.]
Addressing Competing Objectives in Allocating Funds to Scholarships and Need-Based Financial Aid
Author: Phan, Vinhthuy
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Resource Allocation,Access to Education,Higher Education,Educational Finance,Universities,Prediction,Classification,Student Financial Aid,Academic Achievement,Financial Needs,College Attendance,Income,Educational Change,Financial Policy,Scholarships,Enrollment Management,College Freshmen,College Admission,Information Systems,State Universities

A strategy for allocating merit-based awards and need-based aid is critical to a university. Such a strategy, however, must address multiple, sometimes competing objectives. We introduce an approach that couples a gradient boosting classifier for predicting outcomes from an allocation strategy with a local search optimization algorithm, which optimizes strategies based on their expected outcomes. Unlike most existing approaches that focus strictly on allocating merit based awards, ours optimizes simultaneously the allocation of both merit-based awards and need-based aid. Further, the multi-objective optimization lets users experiment with different combinations of institution-centric and student-centric objectives to deliver outcomes that suit desired goals. With this approach, we identify multiple allocation strategies that would yield higher enrollment, revenue, or students' affordability and access to higher education than the University's existing strategy. In particular, one strategy suggests that with moderate changes to the current funding structure the University can increase students' access to higher education by more than 100%, while still maintaining a similar level of enrollment and revenue. [For the full proceedings, see ED623995.]
Admitting You Have a Problem Is the First Step: Modeling When and Why Students Seek Help in Programming Assignments
Author: Gao, Zhikai
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Computer Science Education,College Students,Programming,Coding,Help Seeking,Student Behavior

In computer science education timely help seeking during large programming projects is essential for student success. Help-seeking in typical courses happens in office hours and through online forums. In this research, we analyze students coding activities and help requests to understand the interaction between these activities. We collected student's help requests during coding assignments on two different platforms in a CS2 course, and categorized those requests into eight categories (including implementation, addressing test failures, general debugging, etc.). Then we analyzed the proportion of each type of requests and how they changed over time. We also collected student's coding status (including what part of the code changed and the frequency of commits) before they seek help to investigate if students share a similar code change behavior leading to certain type of help requests. [For the full proceedings, see ED623995.]
Grade Prediction via Prior Grades and Text Mining on Course Descriptions: Course Outlines and Intended Learning Outcomes
Author: Li, Jiawei
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Course Descriptions,Learning Analytics,Academic Achievement,Prediction,Grades (Scholastic),Academic Failure,Engineering Education,Undergraduate Students,Departments,Graphs,At Risk Students,Semantics,Syntax,Word Frequency,Computational Linguistics,Course Content,Correlation,Curriculum Development,Thinking Skills,Cognitive Ability

Academic grades in assessments are predicted to determine if a student is at risk of failing a course. Sequential models or graph neural networks that have been employed for grade prediction do not consider relationships between course descriptions. We propose the use of text mining to extract semantic, syntactic, and frequency-based features from course content. In addition, we classify intended learning outcomes according to their higher- or lower-order thinking skills. A learning parameter is then formulated to model the impact of these cognitive levels (that are expected for each course) on student performance. These features are then embedded and represented as graphs. Past academic achievements are then fused with the above features for grade prediction. We validate the performance of the above approach via datasets corresponding to three engineering departments collected from a university. Results obtained highlight that the proposed technique generates meaningful feature representations and outperforms existing methods for grade prediction. [For the full proceedings, see ED623995.]
Exploring Cultural Diversity and Collaborative Team Communication through a Dynamical Systems Lens
Author: Samadi, Mohammad Amin
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Systems Approach,Cultural Pluralism,Teamwork,Communication (Thought Transfer),Cooperation,Problem Solving,Task Analysis,Dialogs (Language),Comparative Analysis,Group Dynamics,Group Discussion,Undergraduate Students,Decision Making,Computer Mediated Communication,Integrated Learning Systems

Collaborative problem solving (CPS) is a 21st-century skill essential for learning gains, workplace success, and tackling increasingly complicated global problems. Group diversity plays a vital role during collaborative activities, especially in a digital space. Although CPS involves dynamic communication behaviors, few studies have considered the impact of cultural diversity on the complex and reoccurring discourse involved in CPS tasks. In this study, we explore team conversations during a CPS task to understand the role of cultural diversity on team communication patterns. First, we characterized team dialogues with an existing CPS framework; then used recurrence quantification analysis (RQA) to quantify group communication and capture recurrent patterns. Finally, we compared the patterns across groups with varying degrees of cultural diversity. Our results suggest that groups with higher levels of cultural diversity, compared to more homogeneous groups, had a higher number of group messages, spent more time in group discussions, and demonstrated greater convergence and complexity in communication patterns. These intricate and complicated communication patterns support the notion that cultural diversity can produce both positive and negative outcomes and may explain the perception of cultural diversity in teams as a &quot;double-edged sword&quot;. [For the full proceedings, see ED623995.]
Improving Peer Assessment with Graph Neural Networks
Author: Namanloo, Alireza A.
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Peer Evaluation,Graphs,Models,Self Evaluation (Individuals),Social Networks,Artificial Intelligence,Grading,Online Courses

Peer assessment systems are emerging in many settings, such as peer grading in large (online) classes, peer review in conferences, peer art evaluation, etc. However, peer assessments might not be as accurate as expert evaluations, thus rendering these systems unreliable. The reliability of peer assessment systems is influenced by various factors such as assessment ability of peers, their strategic assessment behaviors, and the peer assessment setup (e.g., peer evaluating group work or individual work of others). In this work, we first model peer assessment as multi-relational weighted networks that can express a variety of peer assessment setups, and can also capture conflicts of interest and strategic behaviors. Leveraging our peer assessment network model, we introduce a graph neural network which can learn assessment patterns and user behaviors to more accurately predict expert evaluations. Our extensive experiments on real and synthetic datasets demonstrate the efficacy of our approach, which outperforms a variety of peer assessment methods. [For the full proceedings, see ED623995.]
Exploring Common Trends in Online Educational Experiments
Author: Prihar, Ethan
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Educational Trends,Electronic Learning,Educational Experience,Educational Experiments,Intelligent Tutoring Systems,Randomized Controlled Trials,Skill Development,Mastery Learning,Individualized Instruction,Design

As online learning platforms become more ubiquitous throughout various curricula, there is a growing need to evaluate the effectiveness of these platforms and the different methods used to structure online education and tutoring. Towards this endeavor, some platforms have performed randomized controlled experiments to compare different user experiences, curriculum structures, and tutoring strategies in order to ensure the effectiveness of their platform and personalize the education of the students using it. These experiments are typically analyzed on an individual basis in order to reveal insights on a specific aspect of students' online educational experience. In this work, the data from 50,752 instances of 30,408 students participating in 50 different experiments conducted at scale within the online learning platform ASSISTments were aggregated and analyzed for consistent trends across experiments. By combining common experimental conditions and normalizing the dependent measures between experiments, this work has identified multiple statistically significant insights on the impact of various skill mastery requirements, strategies for personalization, and methods for tutoring in an online setting. This work can help direct further experimentation and inform the design and improvement of new and existing online learning platforms. The anonymized data compiled for this work are hosted by the Open Science Foundation and can be found at https://osf.io/59shv/. [For the full proceedings, see ED623995. For the corresponding grantee submission, see ED623511.]
Generalisable Methods for Early Prediction in Interactive Simulations for Education
Author: Cock, Jade Maï
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Science Instruction,Prediction,Models,Interaction,Vocational Education,Chemistry,Undergraduate Students,Physics,Concept Formation,Scientific Concepts,Foreign Countries,Computer Simulation,Computer Assisted Instruction

Interactive simulations allow students to discover the underlying principles of a scientific phenomenon through their own exploration. Unfortunately, students often struggle to learn effectively in these environments. Classifying students' interaction data in the simulations based on their expected performance has the potential to enable adaptive guidance and consequently improve students' learning. Previous research in this field has mainly focused on a-posteriori analyses or investigations limited to one specific predictive model and simulation. In this paper, we investigate the quality and generalisability of models for an early prediction of conceptual understanding based on clickstream data of students across interactive simulations. We first measure the students' conceptual understanding through their in-task performance. Then, we suggest a novel type of features that, starting from clickstream data, encodes both the state of the simulation and the action performed by the student. We finally propose to feed these features into GRU-based models, with and without attention, for prediction. Experiments on two different simulations and with two different populations show that our proposed models outperform shallow learning baselines and better generalise to different learning environments and populations. The inclusion of attention into the model increases interpretability in terms of effective inquiry. The source code is available on Github. [For the full proceedings, see ED623995.]
Going beyond &quot;Good Job&quot;: Analyzing Helpful Feedback from the Student&apos;s Perspective
Author: Rashid, M. Parvez
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Peer Evaluation,Feedback (Response),Natural Language Processing,Artificial Intelligence,College Students,Student Attitudes,Models

Peer assessment can be a more effective pedagogical method when reviewers provide quality feedback. But what makes feedback helpful to reviewees? Other studies have identified quality feedback as focusing on detecting problems, providing suggestions, or pointing out where changes need to be made. However, it is important to seek students' perspectives on what makes a review helpful to a reviewee. This study explores the helpfulness of feedback from students' perspectives when the feedback contained suggestions or mentioned problems or both. We applied natural language processing techniques to identify suggestions and problems mentioned in peer reviews. We also analyzed important text features that are associated with suggestions or problems detected by the peer feedback. The result showed that students are likely to find a review helpful if a suggestion is provided along with the problem mentioned in the feedback rather than simply identifying the problem. [For the full proceedings, see ED623995.]
Going Deep and Far: Gaze-Based Models Predict Multiple Depths of Comprehension during and One Week Following Reading
Author: Caruso, Megan
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Eye Movements,Reading Comprehension,Inferences,Prediction,Computer Software,Reading Processes,Models,Time Factors (Learning),Rote Learning,Accuracy,Intelligent Tutoring Systems,Teaching Methods,Undergraduate Students

What can eye movements reveal about reading, a complex skill ubiquitous in everyday life? Research suggests that gaze can reflect short-term comprehension for facts, but it is unknown whether it can measure long-term, deep comprehension. We tracked gaze while 147 participants read long, connected, informative texts and completed assessments of rote (factual) and inference comprehension (connecting ideas) while reading a text, after reading a text, after reading five texts, and after a seven-day delay. Gaze-based student-independent computational models predicted both immediate and long-term rote and inference comprehension with moderate accuracies. Surprisingly, the models were most accurate for comprehension assessed after reading all texts and predicted comprehension even after a week-long delay. This shows that eye movements can provide a lens into the cognitive processes underlying reading comprehension, including inference formation, and the consolidation of information into long-term memory, which has implications for intelligent student interfaces that can automatically detect and repair comprehension in real-time. [For the full proceedings, see ED623995.]
Improving Problem Detection in Peer Assessment through Pseudo-Labeling Using Semi-Supervised Learning
Author: Liu, Chengyuan
Date: 2022-00-00
Type: Speeches/Meeting Papers
Subjects: Peer Evaluation,Feedback (Response),Computer Mediated Communication,Teaching Methods,Artificial Intelligence,Learning Strategies,Identification,Error Patterns,Data Analysis,Form Classes (Languages),Natural Language Processing,Scores,Undergraduate Students,Accuracy,Evaluation Methods,Computer Software

An online peer-assessment system typically allows students to give textual feedback to their peers, with the goal of helping the peers improve their work. The amount of help that students receive is highly dependent on the quality of the reviews. Previous studies have investigated using machine learning to detect characteristics of reviews (e.g., Do they mention a problem, make a suggestion, or tell the student where to make a change?). Machine-learning approaches to peer-assessment evaluation are heavily reliant on labeled data to learn how to identify review characteristics. However, attaining reliable labels for those characteristics is always time-consuming and labor-intensive. In this study, we propose to apply pseudo-labeling, a semi-supervised learning-based strategy, to improve the recognition of reviews that detect problems in the reviewed work. This is done by utilizing a small, reliably labeled dataset along with a large unlabeled dataset to train a text classifier. The ultimate goal of this research is to show that for peer assessment evaluation, we can utilize both unlabeled and labeled datasets to obtain a robust auto-labeling system and thereby save much effort in labeling the data. [For the full proceedings, see ED623995.]
Housing Instability &amp; Educational Outcomes of San Mateo County Youth: New Research Shines a Light on the Impact of Homelessness on Students
Author: Stanford University, John W. Gardner Center for Youth and Their Communities (JGC)
Date: 2022-00-00
Type: Reports - Descriptive
Subjects: Housing,Homeless People,Place of Residence,Counties,Educational Policy,COVID-19,Pandemics,Longitudinal Studies,Kindergarten,Elementary Secondary Education,Identification,School Districts,Statewide Planning,Crisis Management,Information Systems,Data Collection,Equal Education,Outcomes of Education,Attendance,Low Income Students,State Policy

This study explores the ways in which local students experience housing instability--and how cross-sector strategies can better identify and serve these students. The report accompanying this brief considers a rich set of school administrative data, collected over the course of three school years prior to the COVID-19 pandemic: 2016-17 through 2018-19. Early evidence suggest the COVID-19 pandemic has and will continue to exacerbate the crisis of housing instability; some who had previously experienced housing stability are, for the first time, experiencing some form of housing instability, and others who previously experienced some form of housing instability may now face even greater or more frequent instability. The report's findings highlight the importance of strengthening systems for identifying students who are experiencing housing instability, improving data systems for recording and maintaining this information, and using such data to inform policy and practice not only in schools and districts, but throughout the county. Needs are greater than prior to the pandemic, adding to the strength of calls for integrated county- and state-wide responses to this ongoing crisis in housing and more. [For the full report, see ED624057.]