(Meena Kharatmal and Nagarjuna)
Re-representation of knowledge in non-linear network forms such as concept mapping are known to help in meaningful learning. We refined this technique to help students develop rigour over-and-above meaningful learning, called Refined Concept Mapping. This technique focuses more on the predicate space of knowledge representation, which is necessary because models used in science deal with variable properties and relations between them rather than simple linkages between objects. The technique was developed through field studies involving teachers and students. The work is published in international concept mapping and computer science forums with focus on education. Read a full report on this project
(Amit Dakulkar and Nagarjuna)
Graphs form an important component of visualizing, analyzing, model building and interpreting data in science. Similarly in mathematics, graphs are another mode of representing mathematics, which also help in analyzing and representing functions. An analysis of school science textbooks showed that reading, constructing and making graphs, called as ‘graphicacy’, is not sufficiently addressed. Also students find it difficult to understand the relationship between the graphs and the phenomena they represent. Our research shows this important skill of reading, constructing and interpreting graphs is given almost no priority in school textbooks. Students have very few opportunities to engage with this rich topic which spans across disciplines. The connections between science and mathematics by means of graphs is missing.
To address the “poverty of graphicacy” we designed activities which engaged students in topics which span different concepts, and subjects. Constructionist learning was the context in which these activities were carried out. The design principles of these activities included ideas from the constructionism framework of Seymour Papert, studio based education, use of real world data where possible, and making the relation between concrete phenomena and their abstract representations explicit. The activities spanned a wide range of concepts and skills, such as indirect measurement, scale, statistics, experimental design and data collection, plotting, modeling, predicting, extrapolation, visualization, multiple representations, analysis etc. in order to aid learners to collect data from the real world, dataloggers (such as expEYES kit) were used. GeoGebra, a free dynamic mathematics application, helped in this visualization and interpretation of the collected data.
A very innovative use of GeoGebra was to use representations of scientific models to study the history of science. A set of four models were developed to visualize and explore the implications of geocentric and heliocentric models from the history of astronomy.
Construction of Scientific Knowledge in a Collaborative Sharing and Seeking environment, (Shraddha Gumre and Nagarjuna)
The research begun as a part of the CUBE project, it is an ongoing research study on how a group of students while maintaining simple model systems to study behavioral plasticity develop the cognitive skills required for conducting scientific research. The program used minimal equipment to demonstrate how project based science education can be done. The students learn science process skills: asking questions, testing hypotheses, designing experiments through a sustained online and offline conversations. As a part of this ongoing program, we are conducting a research study to understand how students develop quantitative model based reasoning while conducting their experimental projects. Although science is a social, collaborative inquiry, typical classroom and science laboratory settings are designed to judge learning outcomes individually. The ongoing studies demonstrate that students learn the process of doing science when they work in a collaborative, networked manner, with a heterogeneous mix of students of different cognitive levels and age groups. Such a social space was created both online and offline during the Collaboratively Understanding Biology Education (CUBE) program. It enhanced opportunities to create inscriptions, pose questions, identify variables, test hypotheses, design experiments, read published literature etc. This ongoing research study is informed and influenced by socially situated cognitive framework.
Instant Messaging as a constructionist social learning context (Rafikh Shaikh and Nagarjuna)
A collaborative framework was also found to be effective for primary school children in Rafikh’s study. In this study instant messaging (chatting) through networked computers (using OLPC) was used to facilitate numeracy and literacy skills. The study demonstrated the effective use of externalized shared memory in a collaborative and competitive (game like) setting promoting accelerated learning as well as building motivation. Using a very similar research framework as that which was adopted for Shraddha, we began to study primary school children’s mathematics learning in a constructionist social learning environment. In this ongoing study we used an instant messaging (chat) application designed on wireless-networked computers in a rural school (Khairat) for facilitating both numeracy and literacy skills. The chatting game turned out to be a highly engaging and popular activity among the primary school students. This research applies a situated cognitive constructionist framework to design and deploy studio based learning contexts in science education.
The gnowledge lab took a pioneering role in supporting the pilot implementations of the One Laptop Per Child (OLPC) project in Goa and Khairat village in Raigad district. This is being done in collaboration with Digital Bridge Foundation. Khairat is the first OLPC implementation in India. OLPC and the Sugar Learning Platform (SLP) is explicitly designed on the basis of the studio based contructionist philosophy of Symour Papert by his colleagues at MIT. SLP is produced by geeks, with several contributions from the learning students who became makers themselves, at http://sugarlabs.org/. Walter Bender a leading hacker of the SLP who visited gnowledge lab in 2012. The bottomline of this project is: learning and making can happen together. Gnowledge lab intends to transform this machine so as to rewrite the bottomline into: Learning, making and investigation can happen together in a studio environment.
Understanding Causality in Natural Selection (Dr. Abhijeet Bardapurkar and Nagarjuna)
Abhijeet Bardapurkar’s thesis “Understanding causality in natural selection: Towards the problematic of learning Darwin’s theory of evolution” is an attempt to diagnose the cause of the problem of teaching and learning the theory of evolution. The empirical results of Abhijeet’s thesis demonstrate that most students do not understand the theory. But, students do not know that they do not understand. Even the better performing students misunderstood the theory. Most teachers do not know that the student’s have misconceptions and they are least aware that they also have misconceptions. We painstakingly conducted the conceptual analysis and did identify some of the major causes. Careful analysis of the various distinctions the teacher and the student must make in order to teach and learn the theory of evolution were identified and explained with examples and elaboration. For example we distinguished between the cause of variation from the consequence of variation, individual change from heritable change; individual change from evolutionary change and their respective causes; change by transformative action from accumulation; nature’s selection from natural selection; genesis of functional structure from existence of a functional structure; etc.
(Arnab Kumar Ray, Rajiv Nair and Nagarjuna)
Based on the idea that a dependency relation (a quasi-causal relationship) between learning objectives produces a complex non-linear semantic network, a collaborative portal for building dependency networks leading to teaching-learning sequences was built. The same idea was extended to dependency relations between software components and modeled the emerging knowledge network as a complex system.
(Nagarjuna, Durgaprasad Karnam, Megha Sanyal)
Grounding Syntax through Dexterous Halted-Action-Patterns in a Layered Sensory-Motor-Network
Quite a few embodied mechanisms and cognitive architectures are being developed and discussed (Merleau-Ponty 1945; Bateson 1972; Gibson 1978; Hutchins 1995; Brooks 1991; Karmiloff-Smith 1992; Clark 1997; Thelen and Smith 1996; O’Regan, Noë 2001; Bongard and Pfiefer 2006; Tani, 2015). These non-cognitivist models give valuable insights, by integrating dynamical systems, neural networks, and distributed processing models, about the roots of experience, learning/memory. However, the semiotic aspects of cognition (Deacon 1997; Wheeler 2006; Corballis 2011) and a core feature of languages such as generative syntax (Chomsky 1988), abstract symbolic operations, and culture are weakly addressed by them. To address this gap, we propose a model (as grounded philosophical speculation) that generates syntax through dexterous Halted-Action-Patterns (HAPs) embedded in a body characterized as layered sensory-motor-network (SMN).
In this proposal, we integrate the following design principles into the existing embodied models. (a) The action patterns of the sensory-motor network are layered. (b) These layers are to be distinguished from the ’layers’ of neural network in the sense that these are based on a gradation of haltability of action-patterns at multiple zones. (c) The physical and physiological (autopoietic) action-patterns (beats e.g., heart-beat, motricity (Llinas 2001)) are mandatory and non-haltable, forming the core-sustainable layers of the SMN. (d) The haltable-action-patterns (HAPs in contrast to Fixed-Action-Patterns) are pivoted on the core-layer to get disengaged, while still being grounded in the network, making cognitively differentiating actions and experiences possible (making accessible what is available). (e) The HAPs are architectural bases of dexterity. (f) Multiple action-zones within the SMN are related in an agonistic and antagonistic manner to enable serial syntactic actions (like gesturing, naming, nesting, recursion, reflexivity, etc possible).
We discuss the implications of this architecture for furthering cognitive semiotics and cognitive robotics.