Spatial thinking spans numerous disciplines and perspectives, so there is a need for courses to address spatial thinking from multiple perspectives. However, developing online and/or blended courses that effectively teach spatial thinking topics can be particularly challenging.
This workshop aims to assist educators with developing interdisciplinary courses on spatial thinking in the following ways:
Lightning Talks: Workshop participants will provide their perspectives on teaching spatial thinking from their discipline’s perspective in 5-minute lightning talks. See call details below.
Group Discussions: Workshop participants will be broken into smaller groups for break-out group discussions, following the lightning talks. These break-out group discussions will be designed to foster discussions between individuals with differing perspectives. These discussions, along with a final large group discussion at the end of the workshop, will allow workshop attendees to learn more about discipline(s) that they are unfamiliar with, and to share educational resources from their own discipline(s).
Online Learning Platform: After the workshop, the online learning platform will continue to be made available to workshop participants. This will allow for participants-and the whole spatial cognition community and beyond-to make use of the learning materials in their own blended learning courses.
Call for Lightning Talk Presenters: If you want to share your perspective on teaching spatial thinking using online and/or blended methods, by giving a lightning talk, please submit a short paper (link to EasyChair submissions TBA). Short papers should be a maximum of 6-pages (including references and figures) and follow the Springer LNCS formatting style (http://www.springer.com/computer/lncs?SGWID=0-164-7-72376-0). Note that papers not adhering to the style guidelines or the page limits will be rejected without review. Manuscripts will be reviewed by at least two members of the program committee and/or expert panel. At least one author of each accepted paper must be present at the workshop to give the lightning talk. We plan to make the workshop proceedings available on our online learning platform, and publish the workshop proceedings with CEUR-WS.
Topics of Interest: Short papers, and their accompanying lightning talks, must focus on some aspect of teaching spatial thinking and provide resources for teaching an interdisciplinary spatial thinking course. Examples of topics include: educational resources/tools/recommendations, major spatial thinking topics, major spatial thinking findings, recommended readings, and/or future directions. The author(s) field(s) may include any discipline that deals with spatial thinking. Disciplines include, but are not limited to: Psychology, Geography, GIScience, Geoscience, Education, Informatics, Linguistics, Neuroscience, Architecture, and Art.
○ Bio: Tomi Kauppinen is a project leader and docent at the Aalto University School of Science in Finland. He holds a habilitation (2014) in geoinformatics from the University of Muenster (WWU) in Germany, and a title of docent (2014) and a Ph.D. (2010) in media technology from the Aalto University. From April 2014 to September 2014 he was appointed as the Cognitive Systems Substitute Professor at the University of Bremen in Germany, and since 2015 he is a Privatdozent at WWU. His transdisciplinary research investigates the roles of information networks, information visualisation, spatial information and online learning.
● Heather Burte (email@example.com), Department of Psychology, Tufts University
○ Bio: Heather Burte is a post-doctoral scholar at Tufts University, and received her PhD from the University of California, Santa Barbara. Her work connects cognitive and educational psychology to investigate individual differences in spatial thinking, specifically in navigation and STEM learning.
Tomi Kauppinen, a blog post for his keynote on “How to manage and share Spatiotemporal Research Data? Supporting learning and reproducibility online via Linked Open Science.” at the The 3rd LEARN workshop on Research Data Management, “Make research data management policies work”, organized by the EU-funded project, LEARN(Leaders Activating Research Networks), Helsinki, June 28th, 2015.
Why to manage and share research data?
With open data taking on and also open access (to publications), the big question remains: where is open science? I argue that for open science to really fly we need both the
open research data = data used or produced by scientific efforts
open accessible methods = methods in publications made reproducible
But how to do this? By whom, where and when? Essentially, first we need to answer the “why” questions – i.e. figuring out the excellent incentives – and then the other important questions (who, what, where, when, how) will naturally follow.
The “why” question calls us to think about the
Incentives for a researcher to open their data. Thus: why would a researcher open his/her data for others? Is it enough that many journals (e.g. PLOS One, see our article as an example) now require data to be available?
Incentives for funders and research managers to request opening of research data. Thus: why would decision-makers ask for the open data?
Incentives for the society to ask for open data. Thus: why is it useful to have open research data?
Learning as the key term to answer the why questions
If we look at these why-questions there is an interesting answer that covers all of them. The answer to create incentives for opening research data and enabling reproducibility includes a key term, that is, learning.
Interestingly, learning largely happens via reproducing existing efforts (just think about all the text books and their numerous examples with enumerated steps for reproducing success).
Thus if we manage to reach the learning layer, the reproducibility will follow.
Now let us get back to our “why” questions, and start from the “why” question number 3: what if we agree that the society at large wants to learn about what science produces (like educating citizens to be well-informed about the world, educating students to be masters in their fields or educating companies to develop new systems and explore growth options)?
The society calls for better ways to to support learning, and preferably online as we are now living in the connected world.
Now we get an answer also for the “why” question number 2: the funders and managers act as the representatives of the society and listen for the requirements. Decision-makers are already in many countries requiring data to be managed and open (for instance NSF in USA with their requirement for the Data Management Plan). However, as reported just recently by an expert group for the European Open Science Cloud there is still ” an alarming lack of reproducibility of current published research”. Thus after carefully listening the society decision-makers should increasingly ask for the learning and reproducibility layers as a prerequisite for positive funding agreements.
Incentivizing researchers via learning and communication settings
Now the last but not least “why” question number 1 concerning our researcher. Clearly, the availability of funding creates an incentive for the researcher to support reproducing of the research, and thus a proper research data management allowing to do so. However, there is a bigger and better answer to the why question. Science is communication and so is learning. If we allow the researcher to move from the rather tedious task of “just research data management” to be able to allow others to learn (students, citizens, company people) how to in fact reproduce interesting research settings the picture is suddenly quite completely different.
Indeed, many researchers are also teachers and look for excellent ways for communicating what they feel is important for students to learn about. By creating a culture-shift towards online learning and reproducibility by utilizing excellent research data we thus create big incentives for researchers to engage themselves in proper research data management.
Let us check some examples
As for examples there is the LODUM – Linked Open Data University of Münster project where we showed how to create the data infrastructure and the learning layer. The data created as part of LODUM has been in use by not only many student projects but also by new funded projects. As an example below is a visualization showing the amount of publications by university buildings (Keßler and Kauppinen, 2012).
Clearly creating of useful research data management schemes via opening linked data online calls for a culture-shift from traditional paper-as-the-end-result kind of publishing. To answer this call, Linked Open Science is an approach to enable interconnecting of scientific assets for allowing reproducibility and learning to happen.
Linked Open Science?
Linked Open Science (Kauppinen and de Espindola 2011) builds on the four key elements:
Linked Data: Input data, results and provenance information are published and archived using the Linked Data principles.
OpenSource and Web-based Environments: Methods are written for publication in open source environments.
Cloud Computing: The execution of methods and access to various resources are provided using the Cloud Computing approach.
Creative Commons: CC Licensing is in use to provide the legal and technical infrastructure for scientific assets.
This allows for creating of greater reproducibility environments where students and researchers can learn and explore new questions. In the context of complex phenomena such as the Brazilian Amazon Rainforest one can ask: How to link ecological, economical and social data? (Kauppinen et al. 2014) What related processes can we evidence about the Brazilian Amazon Rainforest by interacting with visualizations? (Bartoschek et al. 2013). For this, tutorials of LinkedScience.org support online learning.
How does science work?
Further on, by studying scientific assets that are interconnected according to the Linked Science approach, it could perhaps be possible to find interesting laws about how science itself works. For instance, lately we analyzed data on 100 000 participations of scientists in conferences to reveal the associative nature of conference participation (Smiljanić, Chatterjee, Kauppinen, Mitrović Dankulov 2016). See below a figure made to illustrate the idea in a visual way, and thus support learning about the research finding.
We need to focus on why-questions to find true incentives for different parties (researchers, decision-makers, citizens) to do and require proper research data management
As we discussed, learning is a great incentive as it requires good communication, and in essence often also reproducibility built on research data
Linked Open Science is an approach to interconnect scientific assets and to support reproducibility and learning
There is a big potential for research on understanding how science itself works by analyzing the traces left by researchers and scientific assets they produce.
Modelling revealed that the probability of participating in the same conference again increases in relation to previous regular participation.
Researchers at Aalto University, Institute of Physics Belgrade and the Saha Institute in Kolkata have used a computational model to prove that participants make a more favourable decision to participating in scientific conferences the more often they have previously participated in the conference. The likelihood to participate grows regardless of the qualities of the conference, like its location, size or specialization.
“This first result opens up a novel, very rich research field. It will be interesting to study whether the same behavior can be discovered in other types of participations as well. Further on, the research agenda can include studying what kinds of actions increase community feelings and thus get people to participate. Our model can be used to research and understand participation phenomena, and perhaps can be used as a basis for new community building methods”, says docent Tomi Kauppinen from the Aalto University.
The researchers collaborated to analyse data from six scientific conferences of different sizes and programmes, held in different locations. The data comprised approximately 100 000 individual participation details covering a period of up to 30 years. The calculations are based on the so called Pólya Urn model, which is a probability theory based model used to make quantitative analysis of large sets of data. The result of the study was recently published in the scientific PLOS ONE journal.
“Modelling revealed that the probability of a researcher participating in the same conference again increases in relation to previous regular participation, and reduces when participation is irregular,’ explains the person responsible for the modelling, Marija Mitrović Dankulov from the Institute of Physics Belgrade. ‘The outcome is a fairly obvious one, but community inclusiveness, the common factor that we perceived, is apparent in all conference participation, and for the first time we were able to show this with the help of modelling.”
The result is in line with the so-called power law, which is a common physical law that is realised in many natural phenomena like the sizes of earthquakes or moon craters. Further on, also man-made phenomena like word frequencies in most languages follow the power law.
Digital information provides physicists and data scientists interested in societal phenomena and other researchers with immense possibilities to model social phenomena. The researchers already have thoughts about future research topics.
“It will be interesting to study whether our model can explain participation patterns of events organized both in physical places and online. Further on, by studying scientific assets that are interconnected according to the Linked Science approach, it could perhaps be possible to find interesting laws about how science works beyond these participation laws”, says Tomi Kauppinen.
Two of the co-authors Marija Mitrović Dankulov and Arnab Chatterjee, Saha Institute, are alumni of Aalto University.
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