Formative assessment method of real-world learning by integrating heterogeneous elements of behavior, knowledge, and the environment

Masaya Okada, Masahiro Tada

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Real-world learning in a field is an important educational area for experience-based activities. Formative assessment by constant monitoring of the intellectual achievement of real-world learners is essential for adaptive learning support, but no assessment methodology has yet been developed. We consider a method to systematically integrate heterogeneous factors of real-world learning: learners' internal situations, their external situations, and their learning field. Then, we propose a method for formatively assessing the situation of real-world learning. The method enables us to recognize the sequence of characteristic stay behavior and the associated body posture of a learner, and to estimate the 3D location of his/her interest. The method enables the estimation of not only the learning topic that a learner is currently examining in a field but also the prospective topics that he/she should learn. Our assessment method is the basis for context-aware support to promote the emergence of new knowledge from intellectual collaboration in the world.

Original languageEnglish
Title of host publicationLAK 2014
Subtitle of host publication4th International Conference on Learning Analytics and Knowledge
PublisherAssociation for Computing Machinery
Pages1-10
Number of pages10
ISBN (Print)1595930361, 9781595930361
DOIs
Publication statusPublished - Jan 1 2014
Externally publishedYes
Event4th International Conference on Learning Analytics and Knowledge, LAK 2014 - Indianapolis, IN, United States
Duration: Mar 24 2014Mar 28 2014

Publication series

NameACM International Conference Proceeding Series

Other

Other4th International Conference on Learning Analytics and Knowledge, LAK 2014
CountryUnited States
CityIndianapolis, IN
Period3/24/143/28/14

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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