Experience of Teaching Data Visualization using Project-based Learning
- URL: http://arxiv.org/abs/2111.04428v1
- Date: Thu, 21 Oct 2021 16:47:34 GMT
- Title: Experience of Teaching Data Visualization using Project-based Learning
- Authors: Dietrich Kammer, Elena Stoll, Adam Urban
- Abstract summary: We show which input was provided when necessary for students to achieve their goals.
We discuss and compare the tools we found useful for students to accomplish their goals.
- Score: 0.3437656066916039
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We report our experience in two installations of a course on data
visualization that featured project-based learning. Given the rationale of this
approach, we show which input was provided when necessary for the students to
achieve their goals. We also discuss and compare the tools we found useful for
students to accomplish their goals. Fitting project-based learning into the
standard schedule of a semester at University is a particular challenge,
because it is hard for students to devote longer periods of time when working
on their projects. Furthermore, online learning was a challenge to effectively
perform group activities and work. We address didactic considerations, our
course structure, tools, and student projects. Finally, we draw conclusions on
the results and improvements in the course structure.
Related papers
- FlashHack: Reflections on the Usage of a Micro Hackathon as an Assessment Tool in a Machine Learning Course [0.0]
Group project-based learning is an increasingly popular form of experiential learning in CS education.
To tackle these issues, we introduced FlashHack: a monitored, incremental, in-classroom micro Hackathon.
Our results indicate high student engagement and satisfaction, alongside simplified assessment processes for instructors.
arXiv Detail & Related papers (2024-10-07T11:21:11Z) - Integrating HCI Datasets in Project-Based Machine Learning Courses: A College-Level Review and Case Study [0.7499722271664147]
This study explores the integration of real-world machine learning (ML) projects using human-computer interfaces (HCI) datasets in college-level courses.
arXiv Detail & Related papers (2024-08-06T23:05:15Z) - Tool Learning with Large Language Models: A Survey [60.733557487886635]
Tool learning with large language models (LLMs) has emerged as a promising paradigm for augmenting the capabilities of LLMs to tackle highly complex problems.
Despite growing attention and rapid advancements in this field, the existing literature remains fragmented and lacks systematic organization.
arXiv Detail & Related papers (2024-05-28T08:01:26Z) - Explainable Few-shot Knowledge Tracing [48.877979333221326]
We propose a cognition-guided framework that can track the student knowledge from a few student records while providing natural language explanations.
Experimental results from three widely used datasets show that LLMs can perform comparable or superior to competitive deep knowledge tracing methods.
arXiv Detail & Related papers (2024-05-23T10:07:21Z) - CLOVA: A Closed-Loop Visual Assistant with Tool Usage and Update [69.59482029810198]
CLOVA is a Closed-Loop Visual Assistant that operates within a framework encompassing inference, reflection, and learning phases.
Results demonstrate that CLOVA surpasses existing tool-usage methods by 5% in visual question answering and multiple-image reasoning, by 10% in knowledge tagging, and by 20% in image editing.
arXiv Detail & Related papers (2023-12-18T03:34:07Z) - Transitioning a Project-Based Course between Onsite and Online. An
Experience Report [1.2584276673531931]
We present an investigation regarding the challenges faced by student teams across four consecutive iterations of a team-focused, project-based course in software engineering.
The studied period includes the switch to fully online activities in the spring of 2020, and covers the return to face-to-face teaching two years later.
Students reported that the effective use of collaborative tools eased team organization and communication while online.
arXiv Detail & Related papers (2023-08-28T07:37:20Z) - Semi-Supervised Lifelong Language Learning [81.0685290973989]
We explore a novel setting, semi-supervised lifelong language learning (SSLL), where a model learns sequentially arriving language tasks with both labeled and unlabeled data.
Specially, we dedicate task-specific modules to alleviate catastrophic forgetting and design two modules to exploit unlabeled data.
Experimental results on various language tasks demonstrate our model's effectiveness and superiority over competitive baselines.
arXiv Detail & Related papers (2022-11-23T15:51:33Z) - A Machine Learning system to monitor student progress in educational
institutes [0.0]
We propose a data driven approach that makes use of Machine Learning techniques to generate a classifier called credit score.
The proposal to use credit score as progress indicator is well suited to be used in a Learning Management System.
arXiv Detail & Related papers (2022-11-02T08:24:08Z) - Modular Framework for Visuomotor Language Grounding [57.93906820466519]
Natural language instruction following tasks serve as a valuable test-bed for grounded language and robotics research.
We propose the structuring of language, acting, and visual tasks into separate modules that can be trained independently.
arXiv Detail & Related papers (2021-09-05T20:11:53Z) - Deeper Learning By Doing: Integrating Hands-On Research Projects Into a
Machine Learning Course [3.553493344868414]
This paper describes the organization of our project-based machine learning courses.
In addition to incorporating project-based learning in our courses, we aim to develop project-based learning components aligned with real-world tasks.
arXiv Detail & Related papers (2021-07-28T23:41:27Z) - 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)
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.