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

• Assessment of Personalized Learning in Immersive and Intelligent Virtual Classroom on Student Engagement [5.982610439839458]
  The study aims to provide insights into how personalized learning approaches can enhance student participation, motivation, and academic performance. The eye movement paradigm has the potential to assess student engagement and promote better educational outcomes.
  arXiv  Detail & Related papers  (2025-01-14T06:49:22Z)
• Real-time classification of EEG signals using Machine Learning deployment [0.0]
  This study proposes a machine learning-based approach for predicting the level of students' comprehension with regard to a certain topic. A browser interface was introduced that accesses the values of the system's parameters to determine a student's level of concentration on a chosen topic.
  arXiv  Detail & Related papers  (2024-12-27T08:14:28Z)
• 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 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)

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.