Outcome-Based Education: Evaluating Students' Perspectives Using Transformer

• URL: http://arxiv.org/abs/2506.17223v1
• Date: Tue, 08 Apr 2025 04:46:00 GMT
• Title: Outcome-Based Education: Evaluating Students' Perspectives Using Transformer
• Authors: Shuvra Smaran Das, Anirban Saha Anik, Md Kishor Morol, Mohammad Sakib Mahmood,
• Abstract summary: Outcome-Based Education (OBE) emphasizes the development of specific competencies through student-centered learning.<n>In this study, we reviewed the importance of OBE and implemented transformer-based models, particularly DistilBERT, to analyze an NLP dataset that includes student feedback.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Outcome-Based Education (OBE) emphasizes the development of specific competencies through student-centered learning. In this study, we reviewed the importance of OBE and implemented transformer-based models, particularly DistilBERT, to analyze an NLP dataset that includes student feedback. Our objective is to assess and improve educational outcomes. Our approach is better than other machine learning models because it uses the transformer's deep understanding of language context to classify sentiment better, giving better results across a wider range of matrices. Our work directly contributes to OBE's goal of achieving measurable outcomes by facilitating the identification of patterns in student learning experiences. We have also applied LIME (local interpretable model-agnostic explanations) to make sure that model predictions are clear. This gives us understandable information about how key terms affect sentiment. Our findings indicate that the combination of transformer models and LIME explanations results in a strong and straightforward framework for analyzing student feedback. This aligns more closely with the principles of OBE and ensures the improvement of educational practices through data-driven insights.

Related papers

• LLMs as Educational Analysts: Transforming Multimodal Data Traces into Actionable Reading Assessment Reports [6.523137821124204]
  This study investigates the use of multimodal data sources to derive meaningful reading insights.<n>We employ unsupervised learning techniques to identify distinct reading behavior patterns.<n>A large language model (LLM) synthesizes the derived information into actionable reports for educators.
  arXiv  Detail & Related papers  (2025-03-03T22:34:08Z)
• DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning [75.68193159293425]
  In-context learning (ICL) allows transformer-based language models to learn a specific task with a few "task demonstrations" without updating their parameters.<n>We propose an influence function-based attribution technique, DETAIL, that addresses the specific characteristics of ICL.<n>We experimentally prove the wide applicability of DETAIL by showing our attribution scores obtained on white-box models are transferable to black-box models in improving model performance.
  arXiv  Detail & Related papers  (2024-05-22T15:52:52Z)
• Evaluating and Optimizing Educational Content with Large Language Model Judgments [52.33701672559594]
  We use Language Models (LMs) as educational experts to assess the impact of various instructions on learning outcomes. We introduce an instruction optimization approach in which one LM generates instructional materials using the judgments of another LM as a reward function. Human teachers' evaluations of these LM-generated worksheets show a significant alignment between the LM judgments and human teacher preferences.
  arXiv  Detail & Related papers  (2024-03-05T09:09:15Z)
• C-ICL: Contrastive In-context Learning for Information Extraction [54.39470114243744]
  c-ICL is a novel few-shot technique that leverages both correct and incorrect sample constructions to create in-context learning demonstrations. Our experiments on various datasets indicate that c-ICL outperforms previous few-shot in-context learning methods.
  arXiv  Detail & Related papers  (2024-02-17T11:28:08Z)
• Learning by Self-Explaining [23.420673675343266]
  We introduce a novel workflow in the context of image classification, termed Learning by Self-Explaining (LSX) LSX utilizes aspects of self-refining AI and human-guided explanatory machine learning. Our results indicate improvements via Learning by Self-Explaining on several levels.
  arXiv  Detail & Related papers  (2023-09-15T13:41:57Z)
• From Mimicking to Integrating: Knowledge Integration for Pre-Trained Language Models [55.137869702763375]
  This paper explores a novel PLM reuse paradigm, Knowledge Integration (KI) KI aims to merge the knowledge from different teacher-PLMs, each of which specializes in a different classification problem, into a versatile student model. We then design a Model Uncertainty--aware Knowledge Integration (MUKI) framework to recover the golden supervision for the student.
  arXiv  Detail & Related papers  (2022-10-11T07:59:08Z)
• An Empirical Investigation of Commonsense Self-Supervision with Knowledge Graphs [67.23285413610243]
  Self-supervision based on the information extracted from large knowledge graphs has been shown to improve the generalization of language models. We study the effect of knowledge sampling strategies and sizes that can be used to generate synthetic data for adapting language models.
  arXiv  Detail & Related papers  (2022-05-21T19:49:04Z)
• Canvas Adoption Assessment and Acceptance of the Learning Management System on a Web-Based Platform [0.0]
  This study aims to assess student adoption of Canvas as a new learning management system and its potential as a web-based platform in the e-learning programme of the University of the East. The students perceived ease of use has a significant effect on their perceived usefulness but has no significant effects on their attitude towards the use of Canvas.
  arXiv  Detail & Related papers  (2021-01-29T01:30:55Z)
• Adversarial Infidelity Learning for Model Interpretation [43.37354056251584]
  We propose a Model-agnostic Effective Efficient Direct (MEED) IFS framework for model interpretation. Our framework mitigates concerns about sanity, shortcuts, model identifiability, and information transmission. Our AIL mechanism can help learn the desired conditional distribution between selected features and targets.
  arXiv  Detail & Related papers  (2020-06-09T16:27:17Z)
• Revisiting Meta-Learning as Supervised Learning [69.2067288158133]
  We aim to provide a principled, unifying framework by revisiting and strengthening the connection between meta-learning and traditional supervised learning. By treating pairs of task-specific data sets and target models as (feature, label) samples, we can reduce many meta-learning algorithms to instances of supervised learning. This view not only unifies meta-learning into an intuitive and practical framework but also allows us to transfer insights from supervised learning directly to improve meta-learning.
  arXiv  Detail & Related papers  (2020-02-03T06:13:01Z)

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.