The Online Pivot: Lessons Learned from Teaching a Text and Data Mining Course in Lockdown, Enhancing online Teaching with Pair Programming and Digital Badges

• URL: http://arxiv.org/abs/2105.07847v1
• Date: Mon, 3 May 2021 09:38:26 GMT
• Title: The Online Pivot: Lessons Learned from Teaching a Text and Data Mining Course in Lockdown, Enhancing online Teaching with Pair Programming and Digital Badges
• Authors: Beatrice Alex, Clare Llewellyn, Pawel Michal Orzechowski, Maria Boutchkova
• Abstract summary: We describe the course, how we adapted it over the two pilot runs and what teaching techniques we used to improve students' learning and community building online. We discuss the lessons learned and promote the use of innovative teaching techniques applied to the digital such as digital badges and pair programming in break-out rooms for teaching Natural Language Processing courses to beginners and students with different backgrounds.
• Score: 1.477454374243818
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper we provide an account of how we ported a text and data mining course online in summer 2020 as a result of the COVID-19 pandemic and how we improved it in a second pilot run. We describe the course, how we adapted it over the two pilot runs and what teaching techniques we used to improve students' learning and community building online. We also provide information on the relentless feedback collected during the course which helped us to adapt our teaching from one session to the next and one pilot to the next. We discuss the lessons learned and promote the use of innovative teaching techniques applied to the digital such as digital badges and pair programming in break-out rooms for teaching Natural Language Processing courses to beginners and students with different backgrounds.

Related papers

• Teaching Empirical Methods at Eindhoven University of Technology [55.17240664632298]
  We discuss the challenges of teaching a course on research methods. We share our lessons learned and the do's and don'ts we learned over several iterations of teaching the course.
  arXiv  Detail & Related papers  (2024-07-05T17:14:08Z)
• Pseudo-Labeling and Contextual Curriculum Learning for Online Grasp Learning in Robotic Bin Picking [47.4409816260196]
  SSL-ConvSAC combines semi-supervised learning and reinforcement learning for online grasp learning. We demonstrate promise for improving online grasp learning on bin picking tasks using a physical 7-DoF Franka Emika robot arm with a suction gripper.
  arXiv  Detail & Related papers  (2024-03-04T21:41:27Z)
• Multimodal Classification of Teaching Activities from University Lecture Recordings [0.9790236766474201]
  We present a multimodal classification algorithm that identifies the type of activity that is being carried out at any time of the lesson. Some academic activities are more easily identifiable with the audio signal while resorting to the text transcription is needed to identify others.
  arXiv  Detail & Related papers  (2023-12-24T08:33:30Z)
• Agile Methodology in Online Learning and How It Can Improve Communication: A Case Study [0.0]
  We detail a list of techniques inspired from software engineering Agile methodologies that can be used in online teaching. We also show, by analyzing students grades, that these Agile inspired techniques probably help in the educational process.
  arXiv  Detail & Related papers  (2023-07-18T18:36:30Z)
• Multimodal Lecture Presentations Dataset: Understanding Multimodality in Educational Slides [57.86931911522967]
  We test the capabilities of machine learning models in multimodal understanding of educational content. Our dataset contains aligned slides and spoken language, for 180+ hours of video and 9000+ slides, with 10 lecturers from various subjects. We introduce PolyViLT, a multimodal transformer trained with a multi-instance learning loss that is more effective than current approaches.
  arXiv  Detail & Related papers  (2022-08-17T05:30:18Z)
• DialogID: A Dialogic Instruction Dataset for Improving Teaching Effectiveness in Online Environments [24.094249468028664]
  We present a dataset of online dialogic instruction detection, textscDialogID, which contains 30,431 effective dialogic instructions. We propose a simple yet effective adversarial training learning paradigm to improve the quality and generalization of dialogic instruction detection.
  arXiv  Detail & Related papers  (2022-06-24T02:07:12Z)
• TACo: Token-aware Cascade Contrastive Learning for Video-Text Alignment [68.08689660963468]
  A new algorithm called Token-Aware Cascade contrastive learning (TACo) improves contrastive learning using two novel techniques. We set new state-of-the-art on three public text-video retrieval benchmarks of YouCook2, MSR-VTT and ActivityNet.
  arXiv  Detail & Related papers  (2021-08-23T07:24:57Z)
• Teaching with Commentaries [108.62722733649542]
  We propose a flexible teaching framework using commentaries and learned meta-information. We find that commentaries can improve training speed and/or performance. commentaries can be reused when training new models to obtain performance benefits.
  arXiv  Detail & Related papers  (2020-11-05T18:52:46Z)
• Designing learning experiences for online teaching and learning [0.0]
  SUTD adopts various student-centric teaching and learning teaching methods and approaches. This means that our graduate/under instructors have to be ready to teach using these student student-centric teaching and learning pedagogies. I share my experiences of redesigning this teaching course that is typically conducted face-to-face to a synchronous online course.
  arXiv  Detail & Related papers  (2020-10-26T07:03:49Z)
• ClassCode: An Interactive Teaching and Learning Environment for Programming Education in Classrooms [7.156054045963555]
  We present ClassCode, a web-based environment tailored to programming education in classrooms. Students can take online tutorials prepared by instructors at their own pace. They can then deepen their understandings by performing interactive coding exercises interleaved within tutorials. ClassCode tracks all interactions by each student, and summarizes them to instructors. This serves as a progress report, facilitating instructors to provide additional explanations in-situ or revise course materials.
  arXiv  Detail & Related papers  (2020-01-22T18:28:16Z)
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer [64.22926988297685]
  Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP) In this paper, we explore the landscape of introducing transfer learning techniques for NLP by a unified framework that converts all text-based language problems into a text-to-text format.
  arXiv  Detail & Related papers  (2019-10-23T17:37:36Z)

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.