EIT: Earnest Insight Toolkit for Evaluating Students' Earnestness in
Interactive Lecture Participation Exercises
- URL: http://arxiv.org/abs/2311.10746v1
- Date: Tue, 31 Oct 2023 07:05:00 GMT
- Title: EIT: Earnest Insight Toolkit for Evaluating Students' Earnestness in
Interactive Lecture Participation Exercises
- Authors: Mihran Miroyan, Shiny Weng, Rahul Shah, Lisa Yan, Narges Norouzi
- Abstract summary: Earnest Insight Toolkit (EIT) is a tool designed to assess students' engagement within interactive lecture participation exercises.
Our objective is to equip educators with valuable means of identifying at-risk students for enhancing intervention and support strategies.
- Score: 2.6794462297854627
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: In today's rapidly evolving educational landscape, traditional modes of
passive information delivery are giving way to transformative pedagogical
approaches that prioritize active student engagement. Within the context of
large-scale hybrid classrooms, the challenge lies in fostering meaningful and
active interaction between students and course content. This study delves into
the significance of measuring students' earnestness during interactive lecture
participation exercises. By analyzing students' responses to interactive
lecture poll questions, establishing a clear rubric for evaluating earnestness,
and conducting a comprehensive assessment, we introduce EIT (Earnest Insight
Toolkit), a tool designed to assess students' engagement within interactive
lecture participation exercises - particularly in the context of large-scale
hybrid classrooms. Through the utilization of EIT, our objective is to equip
educators with valuable means of identifying at-risk students for enhancing
intervention and support strategies, as well as measuring students' levels of
engagement with course content.
Related papers
- Enhancing Students' Learning Process Through Self-Generated Tests [0.0]
This paper describes an educational experiment aimed at the promotion of students' autonomous learning.
The main idea is to make the student feel part of the evaluation process by including students' questions in the evaluation exams.
Questions uploaded by students are visible to every enrolled student as well as to each involved teacher.
arXiv Detail & Related papers (2024-03-21T09:49:33Z) - Evaluating Pedagogical Incentives in Undergraduate Computing: A Mixed Methods Approach Using Learning Analytics [0.0]
This paper assesses the impact of new pedagogical incentives implemented in a first-year undergraduate computing module at University College London.
We employ a mixed methods approach, combining learning analytics with qualitative data to evaluate the effectiveness of these incentives on increasing student engagement.
Our paper introduces an interpretable and actionable model for student engagement, which integrates objective, data-driven analysis with students' perspectives.
arXiv Detail & Related papers (2024-03-13T16:39:38Z) - Towards Goal-oriented Intelligent Tutoring Systems in Online Education [69.06930979754627]
We propose a new task, named Goal-oriented Intelligent Tutoring Systems (GITS)
GITS aims to enable the student's mastery of a designated concept by strategically planning a customized sequence of exercises and assessment.
We propose a novel graph-based reinforcement learning framework, named Planning-Assessment-Interaction (PAI)
arXiv Detail & Related papers (2023-12-03T12:37:16Z) - UKP-SQuARE: An Interactive Tool for Teaching Question Answering [61.93372227117229]
The exponential growth of question answering (QA) has made it an indispensable topic in any Natural Language Processing (NLP) course.
We introduce UKP-SQuARE as a platform for QA education.
Students can run, compare, and analyze various QA models from different perspectives.
arXiv Detail & Related papers (2023-05-31T11:29:04Z) - Interacting with Non-Cooperative User: A New Paradigm for Proactive
Dialogue Policy [83.61404191470126]
We propose a new solution named I-Pro that can learn Proactive policy in the Interactive setting.
Specifically, we learn the trade-off via a learned goal weight, which consists of four factors.
The experimental results demonstrate I-Pro significantly outperforms baselines in terms of effectiveness and interpretability.
arXiv Detail & Related papers (2022-04-07T14:11:31Z) - Disadvantaged students increase their academic performance through
collective intelligence exposure in emergency remote learning due to COVID 19 [105.54048699217668]
During the COVID-19 crisis, educational institutions worldwide shifted from face-to-face instruction to emergency remote teaching (ERT) modalities.
We analyzed data on 7,528 undergraduate students and found that cooperative and consensus dynamics among students in discussion forums positively affect their final GPA.
Using natural language processing, we show that first-year students with low academic performance during high school are exposed to more content-intensive posts in discussion forums.
arXiv Detail & Related papers (2022-03-10T20:23:38Z) - A literature survey on student feedback assessment tools and their usage
in sentiment analysis [0.0]
We evaluate the effectiveness of various in-class feedback assessment methods such as Kahoot!, Mentimeter, Padlet, and polling.
We propose a sentiment analysis model for extracting the explicit suggestions from the students' qualitative feedback comments.
arXiv Detail & Related papers (2021-09-09T06:56:30Z) - Seminar Learning for Click-Level Weakly Supervised Semantic Segmentation [149.9226057885554]
We propose seminar learning, a new learning paradigm for semantic segmentation with click-level supervision.
The rationale of seminar learning is to leverage the knowledge from different networks to compensate for insufficient information provided in click-level annotations.
Experimental results demonstrate the effectiveness of seminar learning, which achieves the new state-of-the-art performance of 72.51%.
arXiv Detail & Related papers (2021-08-30T17:27:43Z) - The Wits Intelligent Teaching System: Detecting Student Engagement
During Lectures Using Convolutional Neural Networks [0.30458514384586394]
The Wits Intelligent Teaching System (WITS) aims to assist lecturers with real-time feedback regarding student affect.
A CNN based on AlexNet is successfully trained and which significantly outperforms a Support Vector Machine approach.
arXiv Detail & Related papers (2021-05-28T12:59:37Z) - 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) - Relationship between Student Engagement and Performance in e-Learning
Environment Using Association Rules [9.006364242523249]
One of the challenges facing e-learning platforms is how to keep students motivated and engaged.
This paper tries to investigate the relationship between student engagement and their academic performance.
arXiv Detail & Related papers (2020-12-25T17:00:23Z)
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.