All Together Now: Teachers as Research Partners in the Design of Search
Technology for the Classroom
- URL: http://arxiv.org/abs/2105.03708v1
- Date: Sat, 8 May 2021 14:25:44 GMT
- Title: All Together Now: Teachers as Research Partners in the Design of Search
Technology for the Classroom
- Authors: Emiliana Murgia, Monica Landoni, Theo Huibers, Maria Soledad Pera
- Abstract summary: We showcase the value of involving teachers in all aspects related to the design of search tools for the classroom.
We share insights on the role of the experts-in-the-loop, i.e., teachers who provide the connection between search tools and students.
- Score: 3.7448613209842967
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: In the classroom environment, search tools are the means for students to
access Web resources. The perspectives of students, researchers, and industry
practitioners lead the ongoing research debate in this area. In this article,
we argue in favor of incorporating a new voice into this debate: teachers. We
showcase the value of involving teachers in all aspects related to the design
of search tools for the classroom; from the beginning till the end. Driven by
our research experience designing, developing, and evaluating new tools to
support children's information discovery in the classroom, we share insights on
the role of the experts-in-the-loop, i.e., teachers who provide the connection
between search tools and students. And yes, in our case, always involving a
teacher as a research partner.
Related papers
- Youth as Advisors in Participatory Design: Situating Teens' Expertise in Everyday Algorithm Auditing with Teachers and Researchers [0.0]
We situate youth as advisors to a group of high school computer science teacher- and researcher-designers creating learning activities.
Specifically, we explore algorithm auditing as a potential entry point for youth and adults to critically evaluate generative AI algorithmic systems.
arXiv Detail & Related papers (2025-04-09T18:27:17Z) - Representational Alignment Supports Effective Machine Teaching [81.19197059407121]
We integrate insights from machine teaching and pragmatic communication with the literature on representational alignment.
We design a supervised learning environment that disentangles representational alignment from teacher accuracy.
arXiv Detail & Related papers (2024-06-06T17:48:24Z) - A Design Space for Intelligent and Interactive Writing Assistants [55.9780345526642]
We explore five aspects of writing assistants: task, user, technology, interaction, and ecosystem.
Within each aspect, we define dimensions (i.e., fundamental components of an aspect) and codes (i.e., potential options for each dimension) by systematically reviewing 115 papers.
Our design space aims to offer researchers and designers a practical tool to navigate, comprehend, and compare the various possibilities of writing assistants.
arXiv Detail & Related papers (2024-03-21T04:03:16Z) - Conversational Agents and Children: Let Children Learn [3.5674815260438764]
We discuss the need to design, develop, and deploy (conversational) agents that can guide children in their quest for online resources.
We argue that agents should "let children learn" and should be built to take on a teacher-facilitator function.
arXiv Detail & Related papers (2023-02-23T14:12:03Z) - ARDIAS: AI-Enhanced Research Management, Discovery, and Advisory System [24.42822218256954]
ARDIAS is a web-based application that aims to provide researchers with a full suite of discovery and collaboration tools.
ARDIAS currently allows searching for authors and articles by name and gaining insights into the research topics of a particular researcher.
With the aid of AI-based tools, ARDIAS aims to recommend potential collaborators and topics to researchers.
arXiv Detail & Related papers (2023-01-25T13:30:10Z) - A Pilot Study on Teacher-Facing Real-Time Classroom Game Dashboards [0.0]
We present the results of a participatory design process for a teacher-facing, real-time game data dashboard.
We analyze post-gameplay survey and interview data to understand teachers' experiences with the tool.
arXiv Detail & Related papers (2022-10-17T20:44:07Z) - 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) - 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) - CASTing a Net: Supporting Teachers with Search Technology [3.325250482015064]
We argue for the need to broaden the research focus to include teachers and how search technology can aid them.
In particular, we share how furnishing a behind-the-scenes portal for teachers can empower them by providing a window into the spelling, writing, and concept connection skills of their students.
arXiv Detail & Related papers (2021-05-07T18:13:49Z) - Are Top School Students More Critical of Their Professors? Mining
Comments on RateMyProfessor.com [83.2634062100579]
Student reviews and comments on RateMyProfessor.com reflect realistic learning experiences of students.
Our study proves that student reviews and comments contain crucial information and can serve as essential references for enrollment in courses and universities.
arXiv Detail & Related papers (2021-01-23T20:01:36Z) - A New Neural Search and Insights Platform for Navigating and Organizing
AI Research [56.65232007953311]
We introduce a new platform, AI Research Navigator, that combines classical keyword search with neural retrieval to discover and organize relevant literature.
We give an overview of the overall architecture of the system and of the components for document analysis, question answering, search, analytics, expert search, and recommendations.
arXiv Detail & Related papers (2020-10-30T19:12:25Z) - 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.