Nair, Udaysankar

Ramachandran, Rahul

Word, Andrea

NASA Technical Reports Server

DATA-INTENSIVE SCIENCE MEETS INQUIRY-
DRIVEN PEDAGOGY: INTERACTIVE BIG DATA 
EXPLORATION, THRESHOLD CONCEPTS, AND 
LIMINALITY
Rahul Ramachandran*, GHRC NASA/MSFC 
rahul.ramachandran@nasa.gov 
 
Andrea Word, Udaysankar Nair 
University of Alabama in Huntsville 
AGU Fall Meeting 2014 Session ID#: 1809 
 
Identifying and Better Understanding Data Science Activities, 
Experiences, Challenges, and Gaps Areas  
https://ntrs.nasa.gov/search.jsp?R=20150000309 2019-08-31T14:24:03+00:00Z
We are more easily persuaded by the reasons we ourselves 
discover than by those which are given to us by others.”  
Blaise Pascal  
This poster outlines our ideas on how the 
combination of big data and a data exploration 
engine can potentially be used to change the way 
we teach atmospheric science 
Threshold Concepts and 
Liminality 
•  Threshold concepts in any discipline are, by their very nature, 
troublesome, irreversible, integrative, bounded, discursive, and 
reconstitutive [Perkins, 1999, 2003; Cousin, 2006; Meyer and Land, 
2003].  
•  These concepts are the deciding factors determining the extent to 
which the learner has mastered the core of the discipline and is 
prepared to work competently and creatively within it [Meyer and 
Land, 2006; Cousin, 2006].  
•  The movement of individuals from a state of ignorance of these core 
concepts to one of mastery occurs not along a linear path but through 
what Meyer and Land term liminal spaces, conceptual spaces through 
which learners move from the vaguest awareness of concepts to 
mastery, accompanied by understanding of the relevance, connectivity, 
and usefulness relative to questions and constructs in a given discipline 
[Meyer and Land, 2006].  
•  The challenges observed in traditional teaching exhibit characteristics 
typical of learners who are encountering threshold concepts but failing 
to move forward in mastering those concepts. 
o  Specifically, lack of learner engagement, reliance on mimicry rather than mastery, 
and failure to demonstrate creative and critical understanding of the concepts in 
new applications are common. 
 
Data Intensive Science 
•  Data-intensive science [Gray, 2007] represents a paradigm 
shift driven by large volumes of complex data that cannot be 
analyzed manually.  
o  Scientific discovery process is driven by knowledge extracted from 
large volumes of data. 
•  Lending itself naturally to inquiry-driven pedagogy, data-
intensive science privileges knowledge discovered through 
inductive engagement with large volumes of data rather 
than reached through traditional, deductive, hypothesis-
driven analyses.  
o  The roles of inductive, data-driven methods versus deductive, 
hypothesis-driven methods have been hotly debated in scientific 
research communities, often positioned as competitive at best and 
mutually exclusive at worst.  
•  Data- and technology-driven science can complement and 
be complemented by hypothesis-driven science in ways that 
can inform the development of best practice not only in 
research but also in education [Kell and Oliver, 2004] 
•  Data intensive methods can allow for exploration on a scale 
that is not possible in the traditional classroom with its 
typical problem sets and static, limited data samples.  
Missing Component: 
Discovery Engines 
•  A key challenge in data-intensive science is the 
development of enabling technologies to render 
tremendous volumes of a wide variety of data accessible in 
both the lab and the classroom.  
•  New “discovery engines” are needed to allow users to move 
beyond meta-data queries to explore large volumes and 
time series of multidimensional data—to discover promising 
nuggets of information, and to select and extract those 
nuggets for rigorous analyses.  
o  These discovery engines can facilitate an interactive discovery 
process whereby users can refine their queries and drill down into 
the data based on their own emerging, contextualized, scientific 
curiosity.  
o  The processes inherent in this exploring, mining, analysis, and 
discovery resonate with the engagement of learners with threshold 
concepts.  
•  As a result, these discovery engines enable the use of big 
data as a source through which to encourage learner 
engagement with and mastery of threshold concepts. 
Grand Vision 
•  Our proposed idea frames big data as a conceptual 
playground and the discovery engines as a key tool  
•  Founded on principles of inductive, experiential learning, our 
approach incorporates an iterative approach to problem 
solving that involves initial engagement with big data 
reflecting real-world situations through which learners come 
to identify relevant or promising patterns.  
•  These patterns, in turn, can be compared against additional 
real-world situations and contexts, thus allowing learners to 
follow a grounded approach to learning and knowledge 
creation through observation, formulation of hypotheses, 
and constant comparison of data across various real-world 
situations.  
•  Learners are given the opportunity to work through 
increasingly unstructured data driven challenges with access 
to relatively infinite iterations of problems through which 
they come to understand the targeted concepts. 
•  Students learn to add concern for fit, relevance, workability 
and modifiability of any generated theories to their already 
honed abilities to assess validity of claims from a traditional 
scientific perspective.  
Developing Critical Thought Processes 
Using Data Driven Exploration: An 
Example   
•  Cold water ocean regions may be triggered by intense vertical mixing of 
the ocean, a phenomenon referred to as ocean upwelling. Figure 1a shows 
three such cold regions in the Cross-Calibrated, Multi-Platform (CCMP) 
Optimally Interpolated Sea Surface Temperature (OISST) data on March 
27, 2008.  
•  Students can be asked to explain what is causing this phenomenon. 
Students will be nudged to develop critical thought processes using the 
theoretical framework to develop different hypotheses, and to use, a 
discovery engine (Polaris – prototype used here), on several years’ worth 
of satellite observations to test their hypotheses.  
•  In this example, when atmospheric conditions are right, a pressure system 
causes a low-level wind jet through gaps in coastal mountains and over 
the ocean, leading to intense ocean mixing.  
•  This can be confirmed by running simple threshold queries on the data 
exploring the sea surface temperature and surface wind for the same 
regions. Discover engine provides both a visual confirmation of such 
events (Figure 1b) and gives climatological information.  
•  We envision that in the future, exploratory exercises like this will be used 
in the classroom to help the students develop critical thought processes 
required not only linking concepts in the theoretical framework with 
observed phenomena but also gaining a firm grasp on the research 
methodology  
Processes Using Data Driven 
Exploration: An Example  
Figure 1a. OISST data coverage for three Central American sites on 
March 27, 2008 that shows a drop in sea surface temperature. 
Figure 1b. Polaris query results “discovers” regional wind jet causing the ocean upwelling. 
Results allow additional insights about the regional jet such as seasonal distribution, how 
long these events last. 
References 
•  Land, R., Cousin, G., Meyer, J.H.F. and Davies, P. (2005),Threshold concepts and troublesome 
knowledge: implications for course design and evaluation, in C. Rust (ed.), Improving Student 
Learning – equality and diversity, OxforCousin, G. (2006), in Meyer, J.H.F. and Land, R. (eds.), 
Overcoming Barriers to Student Understanding: threshold concepts and troublesome knowledge, 
London and New York: Routledge. 
•  Kell, D. B., & Oliver, S. G. (2004). Here is the evidence, now what is the hypothesis? The 
complementary roles of inductive and hypothesisdriven science in the postgenomic 
era. Bioessays, 26(1), 99-105.d: OCSLD. 
•  Meyer, J.H.F., Land, R. and Davies, P. (2006), Implications of threshold concepts for course design 
and evaluation, in Meyer, J.H.F. and Land, R. (eds.), Overcoming Barriers to Student 
Understanding: threshold concepts and troublesome knowledge, London and New York: Routledge. 
•  Meyer, J.H.F. and Land, R. (2003),Threshold concepts and troublesome knowledge: linkages to 
ways of thinking and practising, in Rust, C. (ed.), Improving Student Learning – ten years on. 
Oxford: OCSLD. 
•  Perkins, D (1999) The many faces of constructivism. Educational Leadership, 57(3), 6-11. 
•  Perkins, D (2005) The underlying game: troublesome knowledge and threshold conceptions, in 
Meyer, J.H.F and Land, R. (eds) Overcoming Barriers to Student Understanding: Threshold Concepts 
and Troublesome Knowledge.  
•  Perkins, D. N. (2006). Constructivism and troublesome knowledge. In J. H. F. Meyer & R. Land 
(Eds.), Overcoming barriers to student understanding: Threshold concepts and troublesome 
knowledge. London: Routledge.  


Data-Intensive Science Meets Inquiry-Driven Pedagogy: Interactive Big Data Exploration, Threshold Concepts, and Liminality

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