Active-Learning in the Online Environment

• URL: http://arxiv.org/abs/2004.08373v1
• Date: Thu, 2 Apr 2020 07:09:19 GMT
• Title: Active-Learning in the Online Environment
• Authors: Zahra Derakhshandeh, Babak Esmaeili
• Abstract summary: Students may suffer from feeling isolated or disconnected from the community that consists of the instructor and the learners. We propose a customized design, Tele-instruction, with useful features to enable peers and the instructor of the course to interact at their conveniences.
• Score: 2.0303656145222857
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online learning is convenient for many learners; it gives them the possibility of learning without being restricted by attending a particular classroom at a specific time. While this exciting opportunity can let its users manage their life in a better way, many students may suffer from feeling isolated or disconnected from the community that consists of the instructor and the learners. Lack of interaction among students and the instructor may negatively impact their learnings and cause adverse emotions like anxiety, sadness, and depression. Apart from the feeling of loneliness, sometimes students may come up with different issues or questions as they study the course, which can stop them from confidently progressing or make them feel discouraged if we leave them alone. To promote interaction and to overcome the limitations of geographic distance in online education, we propose a customized design, Tele-instruction, with useful features supplement to the traditional online learning systems to enable peers and the instructor of the course to interact at their conveniences once needed. The designed system can help students address their questions through the answers already provided to other students or ask for the instructor's point of view by two-way communication, similar to face-to-face forms of educational experiences. We believe our approach can assist in filling the gaps when online learning falls behind the traditional classroom-based learning systems.

Related papers

• SELFI: Autonomous Self-Improvement with Reinforcement Learning for Social Navigation [54.97931304488993]
  Self-improving robots that interact and improve with experience are key to the real-world deployment of robotic systems. We propose an online learning method, SELFI, that leverages online robot experience to rapidly fine-tune pre-trained control policies. We report improvements in terms of collision avoidance, as well as more socially compliant behavior, measured by a human user study.
  arXiv  Detail & Related papers  (2024-03-01T21:27:03Z)
• 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)
• Dynamic Diagnosis of the Progress and Shortcomings of Student Learning using Machine Learning based on Cognitive, Social, and Emotional Features [0.06999740786886534]
  Student diversity can be challenging as it adds variability in the way in which students learn and progress over time. A single teaching approach is likely to be ineffective and result in students not meeting their potential. This paper discusses a novel methodology based on data analytics and Machine Learning to measure and causally diagnose the progress and shortcomings of student learning.
  arXiv  Detail & Related papers  (2022-04-13T21:14:58Z)
• Teachable Reinforcement Learning via Advice Distillation [161.43457947665073]
  We propose a new supervision paradigm for interactive learning based on "teachable" decision-making systems that learn from structured advice provided by an external teacher. We show that agents that learn from advice can acquire new skills with significantly less human supervision than standard reinforcement learning algorithms.
  arXiv  Detail & Related papers  (2022-03-19T03:22:57Z)
• The Other Side of Black Screen: Rethinking Interaction in Synchronous Remote Learning for Collaborative Programming [0.0]
  Collaborative learning environments are crucial for learning experiential hands-on skills such as critical thinking and problem solving. In this case study, we present observations of in-person and online versions of 2 programming courses offered before and during the COVID-19 pandemic. We find that the current online video-conferencing platforms cannot foster collaborative learning among peers.
  arXiv  Detail & Related papers  (2021-11-11T01:52:12Z)
• Iterative Teacher-Aware Learning [136.05341445369265]
  In human pedagogy, teachers and students can interact adaptively to maximize communication efficiency. We propose a gradient optimization based teacher-aware learner who can incorporate teacher's cooperative intention into the likelihood function.
  arXiv  Detail & Related papers  (2021-10-01T00:27:47Z)
• Educational Robotics in Online Distance Learning: An Experience from Primary School [0.0]
  This work presents the development of an Educational Robotics activity particularly conceived for online distance learning in primary school. The devised activities are based on pen and paper approaches that are complemented by commonly used social media to facilitate communication and collaboration.
  arXiv  Detail & Related papers  (2021-05-20T12:20:44Z)
• Comparative Study of Learning Outcomes for Online Learning Platforms [47.5164159412965]
  Personalization and active learning are key aspects to successful learning. We run a comparative head-to-head study of learning outcomes for two popular online learning platforms.
  arXiv  Detail & Related papers  (2021-04-15T20:40:24Z)
• Student Barriers to Active Learning in Synchronous Online Classes: Characterization, Reflections, and Suggestions [29.903934269672572]
  This work focuses on characterizing student barriers to active learning in synchronous online environments. Through a thematic analysis, we craft a nuanced list of students' online active learning barriers within the themes of human-side, technological, and environmental barriers.
  arXiv  Detail & Related papers  (2021-04-10T21:03:15Z)
• Interaction-limited Inverse Reinforcement Learning [50.201765937436654]
  We present two different training strategies: Curriculum Inverse Reinforcement Learning (CIRL) covering the teacher's perspective, and Self-Paced Inverse Reinforcement Learning (SPIRL) focusing on the learner's perspective. Using experiments in simulations and experiments with a real robot learning a task from a human demonstrator, we show that our training strategies can allow a faster training than a random teacher for CIRL and than a batch learner for SPIRL.
  arXiv  Detail & Related papers  (2020-07-01T12:31:52Z)
• 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.