NetSciEd 2024: Satellite Symposium on Network Science and Education @ NetSci 2024

NetSciEd 2024: The Symposium on Network Science and Education will be held on Monday June 17th, 2024, at the NetSci 2024 Conference, following the previous editions. 

NetSciEd 2024 is a perfect venue to discuss anything related to network science and education, including educational activities to teach/learn network science and applications of network science to understand, model, and improve educational systems and practices. Teachers, students and education researchers are welcome to participate, and we look forward to further discussions about developing a network science curriculum for K-12 students.


PROGRAM:

NetSciEd 2024 will take place on Monday June 17th, 2024.  See below for details of the program:

Agenda NetSciEd 2024

REGISTRATION: 

Attendees should formally register through the Conference's website: https://netsci2024.com/en/participate/registration 


CALL FOR CONTRIBUTIONS:

Contributed oral presentations will be 15 minutes long in total (including both talk and Q & A). If you are interested in presenting at NetSciEd 2024, please submit a brief abstract to Evelyn Panagakou by April 20th, 2024 (deadline extended).   Your abstract should:

* Include the title of your presentation, the list of authors and their affiliations, and the contact information (e-mail address) of the corresponding author.

* Include a summary of your presentation (up to 300 words).

* Be formatted as a single PDF file (maximum 2 pages including figures/tables, if any).


TOPICS OF DISCUSSION:

Topics to be discussed at the Satellite include but are not limited to:


INVITED TALKS:

Many junior scholars in network science aim to pursue long-term careers in academia. The transition into a stable academic position often involves a shift in professional focus towards teaching. In this talk, I'll share some reflections on this shift in focus. My reflections are based on my personal experience after two years on the tenure track at a small liberal arts college (SLAC) in the US. My primary aims are to help early-career network scientists learn about the distinctive features of SLAC jobs; decide whether or not to pursue them; and prepare themselves for the job market and the first several years. Towards these aims, I'll briefly describe about how I navigated the job market; how I developed my teaching skills; how I've approached the incorporation of teaching as a major part of my professional identity; and how I'm thinking about my evolving research program at this stage in my career.
(This talk is primarily aimed at junior scholars who are thinking about their next academic career stage, as well as their graduate and postdoctoral mentors.)

Engaging students in research opportunities plays an important role in their educational experience, and your institution may be looking for such contributions to your teaching portfolio. In this talk I’ll share my experience including students in network science research at a small liberal arts college. I’ll share how I’ve recruited and prepared students, how I’ve picked research projects, and what types of outcomes we’ve had.

Over the last several years, algorithms have become a pervasive part of daily life, and increasing attention has been paid to their ethics and societal effects. Harmful impacts of algorithms include racial bias in criminal sentencing recommendations, privacy violations in product recommendations, and gender bias in document search. While potentially-harmful network algorithms have received less attention, they, too, exist, in applications such as friendship recommendation and network sampling. As research in this area has blossomed, so too has the desire to incorporate such topics into the classroom. At universities across the world, this has been accomplished partly through the addition of courses on so-called fair machine learning. However, while such courses are valuable, there are a few drawbacks. First, these are typically electives, and so only a fraction of students are exposed to the material. Second, teaching the material in this way may have the effect of “silo”-ing the content, and students may not fully appreciate the need to consider such topics across the entire spectrum of what they do. However, it is important for students to see such considerations as baked into their field, rather than a separate and distinct part. For these reasons, I have begun development of ethics-related modules that simultaneously advance a course with respect to core topics while also incorporating technical content related to ethics. Some of these modules are network algorithm-specific; others are not. This project is in its early stages and my developed modules have not yet been used in the classroom. In this presentation, I will begin by giving an overview of existing work in this area, and then discuss my initial attempts at creating modules relating to network science.

Many research positions, resources in education of today are getting more reachable thanks to technologies. Here we will speak about analysis of case of the network of researchers (www.lewibo.org), who are involved in crossborder education, which have led to not just knowledge exchange (on network theory, epidemics spreading) and also collection of needs in various  areas of the world including remote locations.


CONTRIBUTED TALKS:

We are a student and teacher at a small undergraduate liberal arts college who will share experiences in undergraduate network science classrooms and research. Bates College began offering a Network Theory and Analysis Course in Spring 2019. The course was co-designed by a Mathematician and a Digital Humanist and is heavily project-based, with students taking on an individual or team research project at the end. Eric had an individual interest in complex systems and took the course in 2022. That interest was further sparked in the Networks course and pandemic conditions allowed for a subsequent flexible remote research position. In a duoethnography form with an open opportunity for the audience to direct questions, we will reflect on our journey together as Network Science researchers.

Traditional education engages all students with the same topics, at the same time, and at the same pace. Such a system leaves some students behind and fails to challenge others. An alternative system provides an environment in which professionals engage in lifelong learning complementing their formal education, while balancing personal and professional tasks since learning is personalized, adaptive, succinct and at their fingertips. This flexible environment is learner-centric, where content is recommended to meet their individual learning outcomes while building on their unique set of interests and existing skills. We propose a curated environment modeled as a Network of Knowledge that organizes an existing repository of micro-modules (short PDFs, videos, code, PowerPoint, simulations, examples, exercises, etc.) The network can evolve over time as authors add new content to the repository. This environment supports non-traditional methods where learners are presented with guided learning that allows for the new content to be incorporated and discoverable by learners or instructors. This environment enables the creation of personalized and adaptive learning paths to enhance education by filling in a learner’s exact missing gaps (‘just in time’ learning) while building on each learner’s knowledge and experiences using adaptable targeted content. This contrasts the linear, one-size-fits-all approach to traditional classroom and many current online approaches that cater to many students and include all possible topics that may be covered (’just in case’ learning). Furthermore, our network construct not only encourages a collaborative approach to education by different content authors, but also collaboration by different instructors who can harness the network for their own course design, including through automated methods. We present concrete examples of content curated in this format and how that supports personalized learning. From our experience teaching these courses and student feedback, we also show the potential of encouraging lifelong learning [2].
[1] Ralucca Gera, D’Marie BartoIf, Michelle L Isenhour, and Simona Tick. CHUNK: Curated heuristic using a network of knowledge. In The Fifth International Conference on Human and Social Analytics (HUSO’19), 2019.
[2] Ralucca Gera, Mark Reith, D’Marie BartoIf, Simona Tick, and Akrati Saxena. A vision of personalized education using network science: Co-developing a dynamic network of knowledge. In accepted in International Conference on Artificial Intelligence in Education (AIED’23), 2023.
[3] Mark Reith and Matt Dever. AVOLVE. https://avolve.apps.dso.mil. Accessed: 2023-08-08.

Increased interest in asynchronous and hybrid learning and a desire to move away from a “one-size-fits-all” classroom mentality has brought more attention to the wide availability of learning content, which exists as online exposition, podcasts, videos, and assessments. We represent this content as a Network of Knowledge, whose structure and organization enable recommender systems and machine learning algorithms to identify personalized content to present to individual learners. This has applications in both asynchronous settings as well as in providing individualized content to bolster “flipped” face-to-face classroom settings. However, such a network of content is not only expansive, but constantly growing as authors and expositors continue to create new material. Our proposed framework, as shown in Figure 1, surrounds a multimodal network of content, with nodes that represent primarily content as well as their associated authors and modalities. Weighted edges represent relationships between nodes with topics in common, and connects content with their authors and modality. In this research, we present an example network of available WWW linear algebra content. We explore the network evolution as content is added algorithmically using YouTube keyword tags and examine effects on the overall network topology. Our algorithm adopts a repeatable methodology for developing a growing corpus of learning content that a recommender system may present to each learner. As the number of edges has the potential to grow exponentially, we propose ways to mitigate these effects on network scalability. We present areas of continued and future exploration, including the use of semantic similarity instead of keyword similarity to represent weighted edges [2].
[1] Paolo J Singh. Developing a framework for a temporal Network of Knowledge to facilitate adaptive, personalized learning. PhD thesis, Monterey, CA; Naval Postgraduate School, in progress.
[2] Paolo J Singh, Scott Adams, Joel Hunter, Rachel Kenagy, Michael Piscopo, and Ralucca Gera. Growth of temporal networks representing a network of knowledge, in progress.


PUBLICATION OF A JOURNAL SPECIAL ISSUE:

Invited and contributing speakers are invited to submit a full paper based on their presentations to the NetSciEd 2024 special issue of the Northeast Journal of Complex Systems (NEJCS). NEJCS is an open-access journal which does not require any APCs.


ORGANIZERS:

Catherine Cramer (UCSD, USA,  cbcramer@ucsd.edu)

Ralucca Gera (Naval Postgraduate School, USA, rgera@nps.edu)

Evelyn (Evangelia) Panagakou (Northeastern University, USA, e.panagakou@northeastern.edu — Main Contact)

Mason A. Porter (UCLA, USA, mason@math.ucla.edu)

Hiroki Sayama (Binghamton University, USA, sayama@binghamton.edu)

Stephen Uzzo (National Museum of Mathematics, USA, uzzo@momath.org)


Copyright - NetSciEd 2024