Enhancing Students' Learning Process Through Self-Generated Tests

• URL: http://arxiv.org/abs/2403.15488v1
• Date: Thu, 21 Mar 2024 09:49:33 GMT
• Title: Enhancing Students' Learning Process Through Self-Generated Tests
• Authors: Marcos Sánchez-Élez, Inmaculada Pardines, Pablo García, Guadalupe Miñana, Sara Román, Margarita Sánchez, José L. Risco-Martín,
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The use of new technologies in higher education has surprisingly emphasized students' tendency to adopt a passive behavior in class. Participation and interaction of students are essential to improve academic results. This paper describes an educational experiment aimed at the promotion of students' autonomous learning by requiring them to generate test type questions related to the contents of the course. The main idea is to make the student feel part of the evaluation process by including students' questions in the evaluation exams. A set of applications running on our university online learning environment has been developed in order to provide both students and teachers with the necessary tools for a good interaction between them. Questions uploaded by students are visible to every enrolled student as well as to each involved teacher. In this way, we enhance critical analysis skills, by solving and finding possible mistakes in the questions sent by their fellows. The experiment was applied over 769 students from 12 different courses. Results show that the students who have actively participated in the experiment have obtained better academic performance.

Related papers

• The Perceived Learning Behaviors and Assessment Techniques of First-Year Students in Computer Science: An Empirical Study [0.0]
  Students believe that in-person instruction is the most effective way to learn. For evaluation methods, there is a preference for practical and written examinations.
  arXiv  Detail & Related papers  (2024-05-10T08:45:32Z)
• 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)
• Virtual learning environment is pleasure or pressure [0.0]
  The research paper discussed the students opinions on online learning and virtual classroom learning. The research mainly focused on the students perception, the comfort and discomfort of e-learning. The study revealed that over 50 percent of the students are having excellent knowledge of digital skills.
  arXiv  Detail & Related papers  (2022-10-19T08:33:55Z)
• 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)
• Real-time Attention Span Tracking in Online Education [0.0]
  This paper intends to provide a mechanism that uses the camera feed and microphone input to monitor the real-time attention level of students during online classes. We propose a system that uses five distinct non-verbal features to calculate the attention score of the student during computer based tasks and generate real-time feedback for both students and the organization.
  arXiv  Detail & Related papers  (2021-11-29T17:05:59Z)
• TecCoBot: Technology-aided support for self-regulated learning [52.77024349608834]
  Self-study activities can increase the degree of activity and the contribution of self-study activities to the achievement of learning outcomes. Especially in times of a global pandemic, self-study activities are increasingly executed at home, where students already use technology-enhanced materials, processes, and digital platforms.
  arXiv  Detail & Related papers  (2021-11-23T13:50:21Z)
• 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)
• ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback [54.142719510638614]
  In this paper, we frame the problem of providing feedback as few-shot classification. A meta-learner adapts to give feedback to student code on a new programming question from just a few examples by instructors. Our approach was successfully deployed to deliver feedback to 16,000 student exam-solutions in a programming course offered by a tier 1 university.
  arXiv  Detail & Related papers  (2021-07-23T22:41:28Z)
• Fostering Student Engagement in a Mobile Formative Assessment System for High-School Economics [0.76146285961466]
  The main purpose of the presented tool is to better support the learning aims of the participants and to increase their engagement in the learning process. The experiments demonstrated that the presented tool is usable; it motivates the students and improves their understanding.
  arXiv  Detail & Related papers  (2021-06-21T08:15:49Z)
• Peer-inspired Student Performance Prediction in Interactive Online Question Pools with Graph Neural Network [56.62345811216183]
  We propose a novel approach using Graph Neural Networks (GNNs) to achieve better student performance prediction in interactive online question pools. Specifically, we model the relationship between students and questions using student interactions to construct the student-interaction-question network. We evaluate the effectiveness of our approach on a real-world dataset consisting of 104,113 mouse trajectories generated in the problem-solving process of over 4000 students on 1631 questions.
  arXiv  Detail & Related papers  (2020-08-04T14:55:32Z)
• Neural Multi-Task Learning for Teacher Question Detection in Online Classrooms [50.19997675066203]
  We build an end-to-end neural framework that automatically detects questions from teachers' audio recordings. By incorporating multi-task learning techniques, we are able to strengthen the understanding of semantic relations among different types of questions.
  arXiv  Detail & Related papers  (2020-05-16T02:17:04Z)

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.