Pedanekar,Niranjan

Principal Scientist, TCS Innovation Labs - Pune (TRDDC)



Education:

  • M.S. in Mechanical Engineering, Purdue University, USA
  • B.E. in Mechanical Engineering, Government College of Engineering Pune, India

Research Interests:

  • Data Analytics
  • Data Visualization
  • Natural Language Processing
  • Learning and Education
  • Social Media

Some recent Publications

Mining Shapes of Expertise in Online Social Q&A Communities
Authors: Varun Kumar, Niranjan Pedanekar
ACM CSCW (ACM Computer Supported Cooperative Work and Social Computing) conference, 2016

Abstract: Expertise of an individual is metaphorically defined by shapes of letters such as I, T, M and hyphen, depending on her expertise in an area (depth) and the number of areas of interest (width). Industries have now started recruiting people with specific shapes of expertise. In this poster, we introduce the idea of mining shapes of user expertise in a typical online social Question and Answer (Q&A) community where expert users often answer questions posed by other users. We report observations on distribution of different shapes of expertise in a StackExchange community called Super User.

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Lights, Camera, but no Action: Exploring Affective Audio-Visual Features of Educational Videos
Authors: Abhay Doke, Niranjan Pedanekar
ACM SIGCSE (ACM Special Interest Group in Computer Science Education) conference, 2016

Abstract: Several hundred Massively Open Online Courses (MOOCs) are available for students of Computer Science (CS) across the Internet. Yet, it has been observed that students exhibit a short attention span while watching MOOC videos. At the same time, as a viewer, they are likely to watch much longer movies and even educational films. In this poster, we propose that production of MOOCs needs to borrow certain affective features from more professionally produced educational films. To support this argument, we first present the results of a limited survey indicating an affective preference of users towards educational films over video lectures from MOOCs. Taking a cue from films in general, we present an analysis of certain affective audio-visual features of educational films vis-à-vis MOOC videos. These features include visual features related to the variety of color, number of scenes and movement, and audio features related to liveliness of speech. We then use these features to classify two groups of educational videos, and conclude that MOOC-like videos often tend to lack such affective audio-visual features. We also indicate possible directions of research in educational videos based on our initial findings.

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Automatically Retrieving Explanatory Analogies from Webpages
Authors: Varun Kumar, Savita Bhat, Niranjan Pedanekar
European Conference on Information Retrieval (ECIR 2014), Published in Advances in Information Retrieval, LNCS Vol 8416, Springer 2014

Abstract: Explanatory analogies make learning complex concepts easier by elaborately mapping a target concept onto a more familiar source concept. Solutions exist for automatically retrieving shorter metaphors from natural language text, but not for explanatory analogies. In this paper, we propose an approach to find webpages containing explanatory analogies for a given target concept. For this, we propose the use of a ‘region of interest’ (ROI) based on the observation that linguistic markers and source concept often co-occur with various forms of the word ‘analogy’. We also suggest an approach to identify the source concept(s) contained in a retrieved analogy webpage. We demonstrate these approaches on a dataset created using Google custom search to find candidate web pages that may contain analogies.

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Which Hat are you Wearing Today? Enabling Perspectives while Learning Computer Science
Abhay Doke, Sys Reserach Lab, TCS, Gaurav Kumar Singh, Varun Kumar, Savita Bhat, Niranjan Pedanekar

IEEE Frontiers in Education Conference (FIE), 2014

Abstract: Computer science has a wide variety of applications in a wide variety of fields. Yet computer science education focuses primarily on the theoretician's perspective. We believe that if a variety of perspectives are brought in during learning, learners' intrinsic motivation can be increased, and learning computer science can be made more engaging and personalized. In this work-in-progress paper, we propose the concept of `hats' for augmenting computer science learning material with different perspectives. Hats are different perspectives which can be donned by the learner while learning computer science. We propose examples of hats such as Programmer, Historian, Job Seeker, Troubleshooter and Visualizer. To enable the use of hats while learning, we propose a web-based interface to augment base learning content such as textbooks with additional learning content classified under different hats. To provide scale in populating hats, we propose algorithms to automatically find content from the internet that can be categorized under different hats. We also present the results from a pilot study conducted using the hats interface.

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PUSTACK: towards an augmented, scalable and personalized interface for paper textbooks
Authors: Gandhi, Naman, et al.
Proceedings of the 11th Asia Pacific Conference on Computer Human Interaction, 2013

Abstract: In a developing country such as India, implementing personalized learning poses a challenge due to a large number of learners. Learners still tend to rely on 'authorized content' such as textbooks for their studies. Most learners use paper textbooks for the same. In this demonstration, we present PUSTACK, an interface for augmenting paper textbooks for providing personalized content in a scalable manner. We use a webcam-based setup to augment content from the physical book being read. We augment the content using 'layers' of information gathered from the Internet. We select and prioritize these layers for different learners to personalize the learning experience.

Wishful thinking: Finding Suggestions and Buy Wishes from Product Reviews
Authors: J Ramanand, Krishna Bhavsar, Niranjan Pedanekar
Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, 2010

Abstract: This paper describes methods aimed at solving the novel problem of automatically discovering 'wishes' from (English) documents such as reviews or customer surveys. These wishes are sentences in which authors make suggestions (especially for improvements) about a product or service or show intentions to purchase a product or service. Such 'wishes' are of great use to product managers and sales personnel, and supplement the area of sentiment analysis by providing insights into the minds of consumers. We describe rules that can help detect these 'wishes' from text. We evaluate these methods on texts from the electronic and banking industries.

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