TutorSpace: At-Scale Personalized Learning

TutorSpace: At-Scale Personalized Learning


Education is one of the highest priority spends for households in many developed and developing countries and a significant part of this spending goes in the higher education space. In India, the spending has increased consistently in last two decades at an annual growth rate of nearly 21% in both rural and urban areas. In parallel, national governments are also increasing budgetary allocations in the education sector.

The growth of Massive Open Online Courses (MOOCs) are considered as one of the biggest revolution in education in last several decades. One significant impact of the MOOC phenomenon is that they have accelerated the widespread availability of quality Open Educational Resources (OER). XRCI believes that technology-enabled MOOCs and OERs can be utilized to provide personalized educational experience based on students’ background, their learning behaviour, and performance. XRCI is working towards building personalized recommendation systems that automatically create such customized video and/or text-based content.

XRCI’s ed-tech solutions are pilot ready. Please write to om.deshmukh@xerox.com to discuss pilot opportunities in further detail.}

Video Link: https://dl.dropboxusercontent.com/u/5152939/TutorSpace_BR16916.mp4

Instructional Videos as Next-gen Textbooks
Instructional videos are set to be the next-generation textbooks. Humans are efficient in non-linearly skimming through a textbook to estimate temporal flow of concepts. Textbooks also provide a ‘table of content’ and an ‘index’ of key concepts discussed in the book for easy and to-the-point navigation.But, video content by its very nature is very difficult to visualize and consume. On the other hand, video lectures contain two extra features beyond the lingual information: paralingual and extra-lingual information. We analyse the video content to automatically generate a topic-based table of content and a list of key words that capture the important concepts discussed in the video. The topics in the table of content and the keywords are hyperlinked with the video for ease of navigation. The lingual, paralingual and extra-lingual information is analysed to automatically generate video-pages analogous to the individual pages of a multi-page text document. For more information, please have a look at following papers.

Teaching Style Analysis
Any instructional content (textbook, tutorial, video lecture) is judged not just on the content but also on the style of delivery. More engaging content leads to better understanding and higher retention rates. In this research, we are focused on analysing the content for level-of-difficulty and concept-density but (in the case of instructional videos) also the speaking and teaching styles of the lecturer and how these dimensions impact the learnability.

Concept Linking
Concept maps and knowledge maps, often used as learning materials, enable users to recognize important concepts and the relationships between them. Concept maps can be used to provide adaptive learning guidance for learners such as path systems for curriculum sequencing to improve the effectiveness of the learning process. Generation of concept maps typically involve domain experts, which makes it costly. We are building a framework for discovering concepts and their relationships, such as prerequisites and relatedness, by analysing content from textual sources such as a textbook and multimedia sources such as instructional videos.

Dynamic and Personalized Assessment
One of the challenges with instructional videos is that, while they provide the audio-visual feel of a classroom lecture, they lack the dynamic flavour of a classroom. Our research efforts are focused on generating automatic question answering sessions based on the student’s learning pattern and the feedback received by the instructor. The questions can be a mix of questions that need open-ended free-text answers as well as Multiple Choice Questions (MCQs) which have a deep motivation from the Item Response Theory (IRT) research. Our research efforts also involve expanding IRT to questions beyond the 1-step objective questions. Automatic evaluation of these free-form answers and quantifying student understanding is also an active area of research.

Mobile-enabled Learning
India is the third largest smartphone market and has an annual growth of about 163% as compared to the global growth of 39%. Mobile devices such as phones and tablets has potential to enable anytime anywhere learning. As part of education efforts @ XRCI, we are leveraging mobile devices to deliver personalized education materials to the students in K-12 as well as higher education space. Today’s mobile phones provide multitude of mobile sensors such as accelerometer, touch screen, location etc. We are using these sensors to capture user's interactions with educational content with the goal of enhancing the overall learning experience.