1. No category

Bringing Crowdlearning to School: How Can We Use CitizenScience Platforms to Promote Big Data Exploration in Science
Education?
Rivka Taub, University of Haifa, [email protected]
Dani Ben-Zvi, University of Haifa, [email protected]
Yael Kali, University of Haifa, [email protected]
Abstract: The amount and diversity of information resources that are available in the
Internet has rapidly grown over the past years along with the development of smart
technological devices. Innovative tools provide access to data, enabling the organization and
analysis of big data. This growth triggers educators to evaluate the relevance of some of the
learning strategies commonly used in schools, such as learning a subject from a single teacher,
usually referred to as an expert. This as opposed to crowdlearning, the process of learning
from others through online social spaces, websites, and activities (Sharples et al., 2013). Our
vision is to design, study and refine the learning of scientific and statistical concepts and
strategies related to a complex interdisciplinary scientific phenomenon. In particular, learners
will participate in a citizen-science project and will collect and analyze relevant big data. The
setting of such learning will integrate characteristics of crowdlearning through a situatedlearning perspective, Future Learning Spaces (FLS), and exploratory data analysis.
Keywords: big data, citizen science, crowdlearning, data science, exploratory data analysis, future
learning spaces, situated learning.
Introduction
The amount and diversity of information and communication resources has rapidly grown over the past years
along with the development of smart technological devices. One of the main characteristics of this development
refers to users of the Internet and of the smart devices who can become active participants in creating,
evaluating and revising information rather than only curating and using information created by others.
Crowdsourcing is a method by which common people, not necessarily qualified with the scientific
expertise, are called to solve a problem or carry out human intelligence tasks (Kazai, 2011). Tasks may be
related
to
science—citizens’
science
projects—or
to
other
areas,
such
as
business
(http://mystarbucksidea.force.com/) and music (Keup, 2011). Crowdsourcing projects may also provide citizens
with the opportunity to engage in spontaneous and self-directed learning from the expertise and opinions of
others, regarding innovative areas still being under exploration. Such learning is termed crowdlearning (Sharples
et al., 2013). Several projects explicitly address crowdlearning and include educational activities that enable
adult citizens or school students to take part in currently explored scientific issues. For example, MASH
(http://alpha.projectmash.org/groups/citizen-science/) is a platform where students at different ages around the
world participate in scientific inquiry projects by planning and carrying out investigations, asking questions,
collecting, analyzing, interpreting, sharing and commenting on data. The projects deal with reptiles, galaxies,
trees, and more. The platform also includes possible educational activities on the scientific concepts that the
students
are
exposed
to
while
handling
the
data.
Another
example
is
CloudSat
(http://www.cloudsat.cira.colostate.edu/) where people contribute to NASA scientists by reporting on cloud
observations. Their descriptions are later compared by scientists to pictures obtained from satellites. Additional
instructional materials include information regarding various types of clouds, how they are generated, and
possible effects on climate. Most of the instructional activities suggested in citizen-science projects focus either
on learning scientific concepts or on simple data-analysis methods. None of them focus on more complicated
analysis and inference methods of data science. We aim to develop such activities and study them.
Crowdsourcing and crowdlearning are recently studied from different perspectives, most of them are
related to the development of platforms and computational algorithms (e.g., Tarasowa, Khalili, Auer, &
Unbehauen, 2013). Few studies explore crowdlearning from the perspectives of educational theories and
strategies, and sociology (Oesterlund et al. 2014). Additionally, as much as crowdsourcing and crowdlearning
are innovative and inspiring, their use in school settings is still rare. This is not surprising and is related to a
phenomenon known as the school-society digital disconnect (Selwyn, 2006), that describes the gap students
experience between their daily interactions, which are increasingly engulfed in mobile and networked
technologies, while their in-school learning interactions are, in comparison, technologically impoverished.
Furthermore, it becomes clear that schools sometimes fail to engage students in newly required strategies that
are critical to the self-directed learning that characterizes crowdlearning, such as critical thinking, argumentation
skills, scientific literacy, and statistical literacy (Gal, 2002). There is therefore a growing need to bridge this gap
by designing curricula for a school-setting to support crowdlearning and data-based reasoning (Garfield & BenZvi, 2008) that would take advantage of current citizen-science public platforms and exploit their potential to
enhance science education using exploratory data analysis (Shaughnessy, Garfield, & Greer, 1996) with an
emphasis on big-data.
Our vision
Our vision is to create unique learning opportunities where crowdlearning is integrated into an interdisciplinary
curriculum of science and statistics education. Our aim is to design and study students' learning of scientific and
statistical concepts and ideas in an environment that supports crowdlearning and data-based reasoning of an
interdisciplinary scientific area. This learning environment will resemble out-of-school crowd and data sciences
authentic environments. In particular, it will be rich in appropriate learning technologies and enable spontaneous
learning in an online community. To do so we plan to develop a technology-enhanced learning environment that
uses a current crowdsourcing project with activities and a social infrastructure (Bielaczyc, 2006) designed to
promote a learning community that collaboratively explores a scientific phenomenon.
Design principles and theoretical background
1.
2.
3.
The learning environment Big Data Citizen Science (BDCS) will address a complex scientific
phenomenon, which will be studied through multidisciplinary lenses. Such lenses may be beneficial to
enhance conceptual understanding and problem-solving abilities. For example, studies found that
learning physics in the context of computer science promoted physics learning and enabled the students
to transfer computer-science ideas to physics contexts (Taub et al., 2015; Taub, Armoni, & Ben-Ari,
2014).
The learning environment BDCS will take place in several linked settings, of the Learning In a
NetworKed Society (LINKS) Future Learning Spaces (FLS) project. The FLS brings together cutting
edge pedagogies and a continuum of collaborative technologies into a facility that is designed to
support these purposes. This facility will be the hub that will streamline learning between various
spaces, both within our facility and outside its physical boundaries into museums, homes, and networks
around the world (Kali, Sagy, Kuflik, Mogilevsky, & Maayan-Fanar, 2014). The FLS will enable us to
explore the learning taking place in environments that are in the continuum between ambient—
naturally occurring—and designed ones (Kali et. al, 2015).
The BDCS learning environment will be inspired by the situated learning perspective with a special
focus on Socio Scientific Issues (SSI). Situated Learning refers to the nature of learning and knowing
as strongly attached to the situation in which they occur. Studies show that students’ interest in school
science declines over the years (Schreiner & Sjøberg, 2007) although they still value "real" science.
Sadler (2009) claims that one of the main distinctions between school science and “real” science stems
from the differences between the processes of knowledge acquisition. The theoretical perspective of
situated learning (Lave & Wenger, 1991) aims to deal with the perceived distinction between school
science and real science. This perspective views learning as taking place in a community of practice—
a group of people who share a craft and/or a profession (Lave & Wenger, 1991). Situated-learningbased design seeks to engage learners into activities where they learn concepts and procedures in their
authentic use, being a part of a community (Sadler, 2009).
The students will engage in exploratory data analysis activities. The studied data is big, not only by
size, but it varies in type (picture, audio, video, numbers, and text) and sources. Some data is collected
by the learners themselves, and some is retrieved from external data bases, such as scientifically
reliable sources (e.g., NASA's satellites pictures). The data-exploration activities will be inspired by the
Connections pedagogical model (Ben-zvi, Aridor, Makar, & Bakker, 2012). In the Connections Project
school students actively experience some of the processes involved in experts’ practice of data-based
enquiry by working on data scenarios, investigated by peer collaboration and classroom discussions.
Students generate and phrase the questions they wish to investigate, suggest hypotheses, collect and
analyze data, interpret the results and draw inferences (Garfield & Ben-Zvi, 2009). A central feature of
the Connections Project is the use of TinkerPlots (Konold, 2011), a statistical visualization tool that is
designed to help students develop statistical reasoning by organizing their data (ordering, stacking, and
separating data icons) and designing their own graphs.
Expected contributions
The proposed vision and project has both theoretical and practical significance. The results are expected to
enrich theories of learning sciences, citizen science and data science by providing empirical evidence
concerning their integration in a school setting. This is expected to lead to the creation of an innovative
pedagogy / learning environment which will have considerable practical implications that will generalize across
learning settings, and that may be used by both researchers and practitioners to foster citizen and data sciences
within a wide range of learning environments.
References
Ben-zvi, D., Aridor, K., Makar, K., & Bakker, A. (2012). Students ’ emergent articulations of uncertainty while
making informal statistical inferences, 44(7), 913–925. doi:10.1007/s11858-012-0420-3
Bielaczyc, K. (2006). Designing Social Infrastructure: Critical Issues in Creating Learning Environments With
Technology. Journal of the Learning Sciences, 15(3), 301–329. doi:10.1207/s15327809jls1503_1
Garfield, J., & Ben-Zvi, D. (2009). Helping students develop statistical reasoning: Implementing a statistical
reasoning learning environment. ‫‏‬. Teaching Statistics, 31(3), 72–77.
Kali, Y. et. al. (2015). Technology-Enhanced Learning Communities on a Continuum between Ambient to
Designed: What Can We Learn by Synthesizing Multiple Research Perspectives? 11th International
Conference on Computer Supported Collaborative Learning.
Kali, Y., Sagy, O., Kuflik, T., Mogilevsky, O., & Maayan-Fanar, E. (2014). Harnessing technology for
promoting undergraduate art education: A novel model that streamlines learning between classroom,
museum and home. IEEE Transactions on Learning Technologies, Online First.
Kazai, G. (2011). In Search of Quality in Crowdsourcing for Search Engine Evaluation. Advances in
Information Retrieval Lecture Notes in Computer Science, 6611, 165–176.
Keup, J. F. (2011, January 1). Computer music composition using crowdsourcing and genetic algorithms. Nova
Southeastern University. Retrieved from http://dl.acm.org/citation.cfm?id=2395339
Konold, C. (2011). TinkerPlotsTM: Dynamic data exploration. Emeryville, CA: Key Curriculum Press.
Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Learning in doing (Vol.
95). Retrieved from http://books.google.com/books?id=CAVIOrW3vYAC&pgis=1
Oesterlund, C. S., Mugar, G., Jackson, C., Hassman, K. D., & Crowston, K. (2014). Socializing the Crowd:
Learning to Talk in Citizen Science ‫‏‬. In Academy of Management Proceedings‫(‏‏‬Vol. 2014, p. 16799).
Sadler, T. (2009). Situated learning in science education: socio‐ scientific issues as contexts for practice‫‏‬. Studies
in Science Education‫‏‬, 45(1), 1–42. Retrieved from
http://www.tandfonline.com/doi/abs/10.1080/03057260802681839
Schreiner, C., & Sjøberg, S. (2007). Science education and youth’s identity construction: Two incompatible
projects? In D. Corrigan, J. Dillon, & R. Gunstone (Eds.), The re-emergence of values in science
education (pp. 231–247). Rotterdam: Sense Publisher.
Selwyn, N. (2006). Exploring the “digital disconnect” between net‐ savvy students and their schools. Learning,
Media and Technology, 31(1), 5–17. doi:10.1080/17439880500515416
Sharples, M., McAndrew, P., Weller, M., Ferguson, R., FitzGerald, E., Hirst, T., & Gaved, M. (2013).
Innovating pedagogy 2013: exploring new forms of teaching, learning and assessment, to guide educators
and policy maker.
Shaughnessy, J. M., Garfield, J., & Greer, B. (1996). Data handling. In A. J. Bishop, K. Clements, C. Keitel, J.
Kilpatrick, & C. Laborde (Eds.), International handbook of mathematics education (pp. 205–237).
Dordrecht, Netherlands: Kluwer.
Tarasowa, D., Khalili, A., Auer, S., & Unbehauen, J. (2013). CrowdLearn: Crowd-sourcing the Creation of
Highly-structured e-Learning Content.‫‏‬. CSEDU‫‏‬, 33–42.
Taub, R., Armoni, M., Bagno, E., & Ben-ari, M. (n.d.). The Effect of Computer Science on Physics Learning in
a Computational Science Environment. Computers & Education.
Taub, R., Armoni, M., & Ben-Ari, M. (2014). Abstraction as a bridging concept between computer science and
physics. In In Proceedings of the 9th Workshop in Primary and Secondary Computing Education (pp. 16–
19). Berlin Germany: ACM.
Author Biographies
Our common affiliation and its relation to crowdlearning. The authors of this proposal are affiliated with the
Learning In a NetworKed Society (LINKS) Israeli Center of Research Excellence (I-CORE). By adopting a
definition of learning as the co-creation of knowledge in technology-enhanced learning (TEL) communities, and
by bringing together a cohort of expertise within the fields of education and the social sciences, the LINKS ICORE seeks to study the types of interaction, knowledge construction and social organization that: (a) occur
spontaneously in technology-enhanced learning communities or (b) can be created by design of TEL. LINKS
refers to these, respectively, as ambient, naturally occurring, environments, and designed environments.
Specifically, in the context of crowdlearning, the authors of this proposal are interested in exploring how to
adopt ambient learning characteristics into school settings, and how to adapt current school culture, to enable the
adoption of ambient learning characteristics, such as crowdlearning, within future learning spaces (FLS).
Rivka Taub is a postdoctoral fellow at the LINKS I-CORE, at the Technologies in Education Graduate
Program, at the Faculty of Education, University of Haifa. Taub coordinated a science enrichment program for
middle and high students and is a former high-school computer-science teacher. Taub earned her PhD from the
Science Teaching Department in the Weizmann Institute of Science. Her dissertation dealt with interdisciplinary
learning of computer science, physics and mathematics in a technologically rich context—a computationalscience course. Her main research interests are on the learning emerging in interdisciplinary settings, possible
advantages and obstacles, and on ways to support such learning.
Dani Ben-Zvi is a co director of the Future Learning Spaces (FLS) project at the LINKS I-CORE. Ben-Zvi’s
research interests focus on two important aspects of human life: statistical reasoning and technology-enhanced
learning. The first refers to the kind of thinking involved in creating and evaluating data-based claims that are
used ubiquitously as means of forming credible arguments and of making decisions under uncertainty. All
citizens need nowadays to be able to engage in this kind of thinking processes and have basic statistical literacy
and numeracy skills. It should therefore be a standard ingredient of every learner's education. The second aspect,
technology, is rapidly transforming the way people communicate and collaborate, consume information and
create knowledge, learn and teach. Educational technologies can mediate and facilitate thinking about complex
domains – such as statistics, mathematics or science, making them more accessible to all learners.
Yael Kali is the director of the LINKS I-CORE, and an associate professor of technology-enhanced learning at
the Technologies in Education Graduate Program, at the Faculty of Education, University of Haifa. Her work
focuses on design, using a DBR approach, for supporting CSCL at various levels, from junior high school to
higher education. She currently serves as an Associate Editor for the journal Instructional Science. Kali has been
a faculty member at the Department of Education in Technology and Science at the Technion – Israel Institute
of Technology for seven years, and a Co-Principal Investigator at the Technology Enhanced Learning in Science
(TELS) centre, headquartered at the University of California, Berkeley. She has also served as a visiting scholar
at the Centre for Research on Computer Supported Learning & Cognition (CoCo) in the Faculty of Education
and Social Work, University of Sydney.