Bringing Social Computing to Secondary School Classrooms
- URL: http://arxiv.org/abs/2401.09591v1
- Date: Wed, 17 Jan 2024 20:40:51 GMT
- Title: Bringing Social Computing to Secondary School Classrooms
- Authors: Kianna Bolante, Kevin Chen, Quan Ze Chen, Amy Zhang
- Abstract summary: Social computing topics are rarely touched upon in existing middle and high school curricula.
We develop lessons covering how social computing relates to the topics of Data Management, Encrypted Messaging, Human-Computer Interaction Careers, Machine Learning and Bias, Misinformation, and Online Behavior.
We find that 81.13% of students expressed greater interest in the content of our lessons compared to their interest in STEM overall.
- Score: 9.217508954179563
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Social computing is the study of how technology shapes human social
interactions. This topic has become increasingly relevant to secondary school
students (ages 11--18) as more of young people's everyday social experiences
take place online, particularly with the continuing effects of the COVID-19
pandemic. However, social computing topics are rarely touched upon in existing
middle and high school curricula. We seek to introduce concepts from social
computing to secondary school students so they can understand how computing has
wide-ranging social implications that touch upon their everyday lives, as well
as think critically about both the positive and negative sides of different
social technology designs.
In this report, we present a series of six lessons combining presentations
and hands-on activities covering topics within social computing and detail our
experience teaching these lessons to approximately 1,405 students across 13
middle and high schools in our local school district. We developed lessons
covering how social computing relates to the topics of Data Management,
Encrypted Messaging, Human-Computer Interaction Careers, Machine Learning and
Bias, Misinformation, and Online Behavior. We found that 81.13% of students
expressed greater interest in the content of our lessons compared to their
interest in STEM overall. We also found from pre- and post-lesson comprehension
questions that 63.65% learned new concepts from the main activity. We release
all lesson materials on a website for public use. From our experience, we
observed that students were engaged in these topics and found enjoyment in
finding connections between computing and their own lives.
Related papers
- Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions [67.60397632819202]
Building socially-intelligent AI agents (Social-AI) is a multidisciplinary, multimodal research goal.
We identify a set of underlying technical challenges and open questions for researchers across computing communities to advance Social-AI.
arXiv Detail & Related papers (2024-04-17T02:57:42Z) - Socially Responsible Computing in an Introductory Course [2.7426067696238468]
Given the potential for technology to inflict harm and injustice on society, it is imperative that we cultivate a sense of social responsibility among our students.
We piloted an introductory Java programming course in which activities engaging students in ethical and socially responsible considerations were integrated across modules.
The data from the class suggests that the students found the inclusion of the social context in the technical assignments to be more motivating and expressed greater agency in realizing social change.
arXiv Detail & Related papers (2024-01-02T16:52:50Z) - Social World Knowledge: Modeling and Applications [2.9417848476446364]
Social world knowledge is a key ingredient in effective communication and information processing by humans and machines alike.
We introduce SocialVec, a framework for eliciting low-dimensional entity embeddings from the social contexts in which they occur in social networks.
Similar to word embeddings which facilitate tasks that involve text semantics, we expect the learned social entity embeddings to benefit multiple tasks of social flavor.
arXiv Detail & Related papers (2023-06-28T15:25:30Z) - Understanding and improving social factors in education: a computational
social science approach [0.0]
Computational social scientists can creatively advance this emerging research frontier.
This article briefly discusses recent studies of learning through large-scale digital platforms.
We believe computational social scientists can creatively advance this emerging research frontier.
arXiv Detail & Related papers (2023-01-13T15:40:07Z) - Coordinated Science Laboratory 70th Anniversary Symposium: The Future of
Computing [80.72844751804166]
In 2021, the Coordinated Science Laboratory CSL hosted the Future of Computing Symposium to celebrate its 70th anniversary.
We summarize the major technological points, insights, and directions that speakers brought forward during the symposium.
Participants discussed topics related to new computing paradigms, technologies, algorithms, behaviors, and research challenges to be expected in the future.
arXiv Detail & Related papers (2022-10-04T17:32:27Z) - Disadvantaged students increase their academic performance through
collective intelligence exposure in emergency remote learning due to COVID 19 [105.54048699217668]
During the COVID-19 crisis, educational institutions worldwide shifted from face-to-face instruction to emergency remote teaching (ERT) modalities.
We analyzed data on 7,528 undergraduate students and found that cooperative and consensus dynamics among students in discussion forums positively affect their final GPA.
Using natural language processing, we show that first-year students with low academic performance during high school are exposed to more content-intensive posts in discussion forums.
arXiv Detail & Related papers (2022-03-10T20:23:38Z) - PHASE: PHysically-grounded Abstract Social Events for Machine Social
Perception [50.551003004553806]
We create a dataset of physically-grounded abstract social events, PHASE, that resemble a wide range of real-life social interactions.
Phase is validated with human experiments demonstrating that humans perceive rich interactions in the social events.
As a baseline model, we introduce a Bayesian inverse planning approach, SIMPLE, which outperforms state-of-the-art feed-forward neural networks.
arXiv Detail & Related papers (2021-03-02T18:44:57Z) - Over a Decade of Social Opinion Mining [1.0152838128195467]
This systematic review focuses on the evolving research area of Social Opinion Mining.
Natural language can be understood in terms of the different opinion dimensions, as expressed by humans.
Future research directions are presented, whereas further research and development has the potential of leaving a wider academic and societal impact.
arXiv Detail & Related papers (2020-12-05T17:59:59Z) - Social Interactions Clustering MOOC Students: An Exploratory Study [57.822523354358665]
Comments were categorized based on how students interacted with them, e.g., how a student's comment received replies from peers.
Statistical modelling and machine learning were used to analyze comment categorization, resulting in 3 strong and stable clusters.
arXiv Detail & Related papers (2020-08-10T09:32:38Z) - Mathematical Foundations for Social Computing [21.041093050431183]
Social computing encompasses the mechanisms through which people interact with computational systems.
In June 2015, we brought together roughly 25 experts in related fields to discuss the promise and challenges of establishing mathematical foundations for social computing.
This document captures several of the key ideas discussed.
arXiv Detail & Related papers (2020-07-07T17:50:27Z) - I Know Where You Are Coming From: On the Impact of Social Media Sources
on AI Model Performance [79.05613148641018]
We will study the performance of different machine learning models when being learned on multi-modal data from different social networks.
Our initial experimental results reveal that social network choice impacts the performance.
arXiv Detail & Related papers (2020-02-05T11:10:44Z)
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.