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System and Method for Automatically Matching Students and Tutors Based on Derived Cognitive Styles in Massive Online Learning

IP.com Disclosure Number: IPCOM000247038D
Publication Date: 2016-Jul-28
Document File: 3 page(s) / 84K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is a method that uses cognitive styles derived from textual (and/or other forms of media) information from people’s social media profiles to match the students and the potential tutors. The system will notify the matching results to both parties.

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System and Method for Automatically Matching Students and Tutors Based on Derived Cognitive Styles in Massive Online Learning

Cognitive style is a term used in cognitive psychology to describe the way individuals think, perceive, and remember information or their preferred approach to use such information to solve problems. It is a key concept in the areas of education. One important implication is that the compatibility of cognitive styles between tutors and students significantly influences the learning outcomes.

    Recently, the massive online learning courses have become popular. However, the educational agencies running these services do not have a system or method to utilize the cognitive styles to provide matching services for their millions of users. The goal of this proposed method is to provide a system and a corresponding method for automatically matching students and tutors based on derived cognitive styles in massive online learning. The method utilizes cognitive styles derived from textual (and/or other forms of media) information from people's social media profiles. With the derived cognitive styles, the students and the potential tutors are matched. The system will notify the matching results to both parties.

    This method offers three major advantages. First, users do not need to take long surveys or provide their behavioral/biological traces. The only thing they need to do is provide the link to their social media profile and give the permission for the system to access the information during their registration session. The whole step takes no more than typing a few characteristics and a couple of mouse clickings. It is much more convenient for the users. Second, since the access to the social media profile is upon users' permission, they fully control their own privacy. Third, social media profiles are different from behavioral traces such as mobile usage data that are controlled by a few telecom operators. The educational agencies running the massive online learning applications could easily get the data with relative low cost, once they get the permission from the users. The potential of scalability is almost unlimited. The workflow of the system presented in this invention is depicted in the following figure.

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    When a student or a tutor register as a user to a massive online learning application, they will be asked whether they are willing to provide their social media accounts (e.g., Twitter* account) for identifying their cognitive styles. Then the cognitive style analysis engine will collect the accessible information from their social media profiles and calculate their cognitive styles using machine learning techniques. The derived cognitive styles will be stored in a database together with other user information. The matching engine utilizes the cognitive styles information of tutors and students (retrieved from the user database) to perform matching. Then, the matching recommendations are prese...