Building Collaborative Learning: Exploring Social Annotation in Introductory Programming

• URL: http://arxiv.org/abs/2407.10322v1
• Date: Mon, 17 Jun 2024 08:59:41 GMT
• Title: Building Collaborative Learning: Exploring Social Annotation in Introductory Programming
• Authors: Francisco Gomes de Oliveira Neto, Felix Dobslaw,
• Abstract summary: Students and teachers utilize platforms like Feedback Fruits, Perusall, and Diigo to collaboratively and discuss course materials. This approach encourages students to share their thoughts and answers with their peers, fostering a more interactive learning environment. We report the impact of Perusall on the examination results of 112 students.
• Score: 2.143931038161476
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing demand for software engineering education presents learning challenges in courses due to the diverse range of topics that require practical applications, such as programming or software design, all of which are supported by group work and interaction. Social Annotation (SA) is an approach to teaching that can enhance collaborative learning among students. In SA, both students and teachers utilize platforms like Feedback Fruits, Perusall, and Diigo to collaboratively annotate and discuss course materials. This approach encourages students to share their thoughts and answers with their peers, fostering a more interactive learning environment. We share our experience of implementing social annotation via Perusall as a preparatory tool for lectures in an introductory programming course aimed at undergraduate students in Software Engineering. We report the impact of Perusall on the examination results of 112 students. Our results show that 81% of students engaged in meaningful social annotation successfully passed the course. Notably, the proportion of students passing the exam tends to rise as they complete more Perusall assignments. In contrast, only 56% of students who did not participate in Perusall discussions managed to pass the exam. We did not enforce mandatory Perusall participation in the course. Yet, the feedback from our course evaluation questionnaire reveals that most students ranked Perusall among their favorite components of the course and that their interest in the subject has increased.

Related papers

• GPT-4 as a Homework Tutor can Improve Student Engagement and Learning Outcomes [80.60912258178045]
  We developed a prompting strategy that enables GPT-4 to conduct interactive homework sessions for high-school students learning English as a second language. We carried out a Randomized Controlled Trial (RCT) in four high-school classes, replacing traditional homework with GPT-4 homework sessions for the treatment group. We observed significant improvements in learning outcomes, specifically a greater gain in grammar, and student engagement.
  arXiv  Detail & Related papers  (2024-09-24T11:22:55Z)
• Revolutionizing Undergraduate Learning: CourseGPT and Its Generative AI Advancements [1.949927790632678]
  This paper introduces CourseGPT, a generative AI tool designed to support instructors and enhance the educational experiences of undergraduate students. Built on open-source Large Language Models (LLMs) from Mistral AI, CourseGPT offers continuous instructor support and regular updates to course materials. The paper demonstrates the application of CourseGPT using the CPR E 431 - Basics of Information System Security course as a pilot.
  arXiv  Detail & Related papers  (2024-07-25T18:02:16Z)
• Leveraging Large Language Models for Actionable Course Evaluation Student Feedback to Lecturers [6.161370712594005]
  We have 742 student responses ranging over 75 courses in a Computer Science department. For each course, we synthesise a summary of the course evaluations and actionable items for the instructor. Our work highlights the possibility of using generative AI to produce factual, actionable, and appropriate feedback for teachers in the classroom setting.
  arXiv  Detail & Related papers  (2024-07-01T13:29:55Z)
• Enhancing Student Engagement in Large-Scale Capstone Courses: An Experience Report [2.7629502923028944]
  capstone courses offer students a valuable opportunity to gain hands-on experience in software development. coordinating a capstone course, especially for a large student cohort, can be a daunting task for academic staff. We outline the iterative development and refinement of our capstone course as it grew substantially in size over a span of six consecutive sessions.
  arXiv  Detail & Related papers  (2024-04-03T23:59:35Z)
• 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)
• 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)
• 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)
• 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)
• 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)
• 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)

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.