Effects of Non-Cognitive Factors on Post-Secondary Persistence of Deaf
Students: An Agent-Based Modeling Approach
- URL: http://arxiv.org/abs/2006.12624v1
- Date: Mon, 22 Jun 2020 21:11:56 GMT
- Title: Effects of Non-Cognitive Factors on Post-Secondary Persistence of Deaf
Students: An Agent-Based Modeling Approach
- Authors: Marie Alaghband, Ivan Garibay
- Abstract summary: Post-secondary education persistence is the likelihood of a student remaining in post-secondary education.
We consider four non-cognitive factors: having clear goals, social integration, social skills, and academic experience, which influence the departure decision of deaf students.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Post-secondary education persistence is the likelihood of a student remaining
in post-secondary education. Although statistics show that post-secondary
persistence for deaf students has increased recently, there are still many
obstacles obstructing students from completing their post-secondary degree
goals. Therefore, increasing the persistence rate is crucial to increase
education and work goals for deaf students. In this work, we present an
agent-based model using NetLogo software for the persistence phenomena of deaf
students. We consider four non-cognitive factors: having clear goals, social
integration, social skills, and academic experience, which influence the
departure decision of deaf students. Progress and results of this work suggest
that agent-based modeling approaches promise to give better understanding of
what will increase persistence.
Related papers
- Bayesian Causal Forests for Longitudinal Data: Assessing the Impact of Part-Time Work on Growth in High School Mathematics Achievement [0.0]
We introduce a longitudinal extension of Bayesian Causal Forests.
This model allows for the flexible identification of both individual growth in mathematical ability and the effects of participation in part-time work.
Results reveal the negative impact of part time work for most students, but hint at potential benefits for those students with an initially low sense of school belonging.
arXiv Detail & Related papers (2024-07-16T17:18:33Z) - Student Data Paradox and Curious Case of Single Student-Tutor Model: Regressive Side Effects of Training LLMs for Personalized Learning [25.90420385230675]
The pursuit of personalized education has led to the integration of Large Language Models (LLMs) in developing intelligent tutoring systems.
Our research uncovers a fundamental challenge in this approach: the Student Data Paradox''
This paradox emerges when LLMs, trained on student data to understand learner behavior, inadvertently compromise their own factual knowledge and reasoning abilities.
arXiv Detail & Related papers (2024-04-23T15:57:55Z) - Digital Distractions from the Point of View of Higher Education Students [0.0]
The aim of this study was to identify the main digital distractions from the point of view of students.
Students considered digital distractions to have a significant impact on their performance in lab sessions.
Professors should implement strategies to raise students' awareness of the significant negative effects of digital distractions on their performance.
arXiv Detail & Related papers (2024-02-04T19:28:20Z) - Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning
from Human Feedback [55.78118035358662]
Reinforcement learning from human feedback serves as a crucial bridge, aligning large language models with human and societal values.
We have identified that the reward model often finds shortcuts to bypass its intended objectives.
We propose an innovative solution, applying the Product-of-Experts technique to separate reward modeling from the influence of sequence length.
arXiv Detail & Related papers (2023-10-08T15:14:39Z) - Sensitivity, Performance, Robustness: Deconstructing the Effect of
Sociodemographic Prompting [64.80538055623842]
sociodemographic prompting is a technique that steers the output of prompt-based models towards answers that humans with specific sociodemographic profiles would give.
We show that sociodemographic information affects model predictions and can be beneficial for improving zero-shot learning in subjective NLP tasks.
arXiv Detail & Related papers (2023-09-13T15:42:06Z) - Can Language Models Teach Weaker Agents? Teacher Explanations Improve
Students via Personalization [84.86241161706911]
We show that teacher LLMs can indeed intervene on student reasoning to improve their performance.
We also demonstrate that in multi-turn interactions, teacher explanations generalize and learn from explained data.
We verify that misaligned teachers can lower student performance to random chance by intentionally misleading them.
arXiv Detail & Related papers (2023-06-15T17:27:20Z) - Distantly-Supervised Named Entity Recognition with Adaptive Teacher
Learning and Fine-grained Student Ensemble [56.705249154629264]
Self-training teacher-student frameworks are proposed to improve the robustness of NER models.
In this paper, we propose an adaptive teacher learning comprised of two teacher-student networks.
Fine-grained student ensemble updates each fragment of the teacher model with a temporal moving average of the corresponding fragment of the student, which enhances consistent predictions on each model fragment against noise.
arXiv Detail & Related papers (2022-12-13T12:14:09Z) - Explainable Student Performance Prediction With Personalized Attention
for Explaining Why A Student Fails [0.5607676459156788]
We propose a novel Explainable Student performance prediction method with Personalized Attention (ESPA)
BiLSTM architecture extracts the semantic information in the paths with specific patterns.
The ESPA consistently outperforms the other state-of-the-art models for student performance prediction.
arXiv Detail & Related papers (2021-10-15T08:45:43Z) - The Challenges of Assessing and Evaluating the Students at Distance [77.34726150561087]
The COVID-19 pandemic has caused a strong effect on higher education institutions with the closure of classroom teaching activities.
This short essay aims to explore the challenges posed to Portuguese higher education institutions and to analyze the challenges posed to evaluation models.
arXiv Detail & Related papers (2021-01-30T13:13:45Z) - Stimuli-Sensitive Hawkes Processes for Personalized Student
Procrastination Modeling [1.6822770693792826]
Student procrastination and cramming for deadlines are major challenges in online learning environments.
Previous attempts on dynamic modeling of student procrastination suffer from major issues.
We introduce a new personalized stimuli-sensitive Hawkes process model (SSHP) to predict students' next activity times.
arXiv Detail & Related papers (2021-01-29T22:07:07Z) - Dual Policy Distillation [58.43610940026261]
Policy distillation, which transfers a teacher policy to a student policy, has achieved great success in challenging tasks of deep reinforcement learning.
In this work, we introduce dual policy distillation(DPD), a student-student framework in which two learners operate on the same environment to explore different perspectives of the environment.
The key challenge in developing this dual learning framework is to identify the beneficial knowledge from the peer learner for contemporary learning-based reinforcement learning algorithms.
arXiv Detail & Related papers (2020-06-07T06:49:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.