Matching Students with Instructors

 

Summary:

In general terms, this IpLearn solution can identify instructors that meet the specific needs of a student and can identify students that fit the preferences of an instructor. Students and instructors can be automatically matched, or they can perform their own selection. Since the solution is able to work remotely over a network, the matched students and instructors can collaborate regardless of their geographical locations.

In addition, the solution can publicize feedback from students and instructors regarding their interactions and process student payments. With the solution, students and instructors anywhere in the world can more confidently and easily identify each other to work together on different subjects.

 

Features Include:

  • Identifying instructors for students and enabling students to choose instructors based on instructor information, such as subject specialties, native languages, hourly rates, references, degrees earned, work experience, availability, and preferred time frames.
  • Identifying students for instructors and enabling instructors to choose students based on student information, such as subjects the students want to learn, competency levels on subjects, native languages, preferred rates, and preferred time frames.

  • Using different rules to identify the best match and allowing students and instructors to rate the importance of different matching criteria.
  • Facilitating students’ and instructors’ initial communication, for example, to gauge their personality fit, schedule learning times, and discuss payment terms.
  • Facilitating learning sessions, for example, by establishing remote communication, providing learning materials, and monitoring the session time.

  • Managing billing and payments for instruction, based on, for example, the hourly rate of the instructor and the length of the session.

  • Allowing students and instructors to provide feedback publicly about each other after their interactions, and subsequently allowing them to comment publicly on others’ feedback, thus providing the element of personal recommendation for future users.