Teaching Creativity Using a Realistic Multi-User Operation: Packet Tracer

• URL: http://arxiv.org/abs/2101.02009v1
• Date: Sun, 27 Dec 2020 01:56:26 GMT
• Title: Teaching Creativity Using a Realistic Multi-User Operation: Packet Tracer
• Authors: Muhammad Khairul Ezad Bin Sulaiman
• Abstract summary: Multi-user capabilities in Packet Tracer provide an incentive for immersive realistic learning to increase the efficiency of teaching and learning in the networking class. The multi-user functionality that uses the one-to-many remote relay server concept allows lecturers to remotely control and test many students at one time.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Multi-user capabilities in Cisco Packet Tracer provide an incentive for immersive realistic learning to increase the efficiency of teaching and learning in the networking class. It is difficult to evaluate the configuration skills of the students in the classroom, particularly during laboratory class, while teaching networking course, the most problems for lecturers. The multi-user functionality that uses the one-to-many remote relay server concept allows lecturers to remotely control and test many students at one time. The key purpose of this paper is to incorporate creativity in teaching by using realistic multi-user practises in the networking class. In this study, the multiuser operation was developed and used via an existing LAN link in the classroom. The focus of the operation is the simple configuration of network equipment such as routers and switches. This practise was used during the laboratory session, encouraging lecturers to establish relationships based on a large audience. The professor is able to evaluate the success of the students during the execution of the activity and to ensure that each student engages in the activity. As a result of introducing this approach, the curiosity of students in technical subjects can be improved and student success can be conveniently tracked on the part of the lecturer. Lecturers are able to monitor the configuration capabilities of students closely to ensure that each student engages in the laboratory class. The lecturer will train students not only to develop their configuration and troubleshooting abilities by using this teaching form, but also to accelerate the pace at which students need to fix network problems.

Related papers

• Awaking the Slides: A Tuning-free and Knowledge-regulated AI Tutoring System via Language Model Coordination [52.20542825755132]
  We develop Slide2Lecture, a tuning-free and knowledge-regulated intelligent tutoring system. It can effectively convert an input lecture slide into a structured teaching agenda consisting of a set of heterogeneous teaching actions. For teachers and developers, Slide2Lecture enables customization to cater to personalized demands.
  arXiv  Detail & Related papers  (2024-09-11T16:03:09Z)
• Learn to Teach: Improve Sample Efficiency in Teacher-student Learning for Sim-to-Real Transfer [5.731477362725785]
  We propose a sample efficient learning framework termed Learn to Teach (L2T) that recycles experience collected by the teacher agent. We show that a single-loop algorithm can train both the teacher and student agents under both Reinforcement Learning and Inverse Reinforcement Learning contexts.
  arXiv  Detail & Related papers  (2024-02-09T21:16:43Z)
• 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)
• Augmenting Knowledge Distillation With Peer-To-Peer Mutual Learning For Model Compression [2.538209532048867]
  Mutual Learning (ML) provides an alternative strategy where multiple simple student networks benefit from sharing knowledge. We propose a single-teacher, multi-student framework that leverages both KD and ML to achieve better performance.
  arXiv  Detail & Related papers  (2021-10-21T09:59:31Z)
• 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)
• 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)
• Point Adversarial Self Mining: A Simple Method for Facial Expression Recognition [79.75964372862279]
  We propose Point Adversarial Self Mining (PASM) to improve the recognition accuracy in facial expression recognition. PASM uses a point adversarial attack method and a trained teacher network to locate the most informative position related to the target task. The adaptive learning materials generation and teacher/student update can be conducted more than one time, improving the network capability iteratively.
  arXiv  Detail & Related papers  (2020-08-26T06:39:24Z)
• Interactive Knowledge Distillation [79.12866404907506]
  We propose an InterActive Knowledge Distillation scheme to leverage the interactive teaching strategy for efficient knowledge distillation. In the distillation process, the interaction between teacher and student networks is implemented by a swapping-in operation. Experiments with typical settings of teacher-student networks demonstrate that the student networks trained by our IAKD achieve better performance than those trained by conventional knowledge distillation methods.
  arXiv  Detail & Related papers  (2020-07-03T03:22:04Z)
• 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.