CIMEL project home page

CIMEL is an acronym for Constructive, collaborative Inquiry-based Multimedia E-learning.

The following project goals are slightly updated from our original proposal to the National Science Foundation (submitted June 1, 2000 and granted October 1, 2000, Grant # EIA-0087977):

  • To design a multimedia framework for constructive, collaborative, inquiry-based learning, with multiple tracks, for introductory and upper level computer science courses, for students with diverse learning styles.
  • To design, implement and evaluate content in three areas representing a cross-section of computer science curricula: a course introducing computer science (CS0/CS1) and upper level or introductory graduate level courses in Software Engineering (OOSE). The prototype contains a multimedia material on Software Engineering plus material under development for the introductory course. Material for the introductory course complements a CS0/CS1 textbook under development, The Universal Computer: Introducing Computer Science with Multimedia (2003, available from the authors).
  • To design, implement and evaluate algorithms for text data mining that will assist students and researchers in discovering emerging trends in topics germane to the OOSE and PL courses. A reference librarian persona (agent) will help students learn how to use a hot topics data mining engine.
  • To design, implement and evaluate a collaborative user interface in which personae seamlessly connect students to human instructors and librarians via networking technologies.

Definitions:

  • Constructive learning goes beyond learning by receiving knowledge, to learning by building systems, with immediate, visual feedback.
  • Collaborative learning encourages students to interact with instructors and librarians, via both live links and remote-controlled "show me" sessions or by reviewing a multimedia FAQ of recorded "show me" sessions.
  • Inquiry-based learning guides the student into pursuing exploratory research in a community of students and scholars.
  • Text data mining algorithms perform automatic analysis of textual information by partitioning collections of text into topical knowledge domains. The approach traces these topical domains over time to detect emerging trends in conceptual content. 'Hot topics' detection will draw from high quality collections of technical literature, such as Compendex and INSPEC.
  • A collaborative user interface will implement a protocol to track mouse pointer position, mouse actions and interface events. If a human instructor or librarian is not available, a corresponding persona will be able to help the learner select from a library of "show me" tutorials.

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