Research on development of learning advice system using answer process analysis as teacher data

2017 Fiscal Year Final Research Report

• Project/Area Number: 15K12169
• Research Category: Grant-in-Aid for Challenging Exploratory Research
• Allocation Type: Multi-year Fund
• Research Field: Learning support system
• Research Institution: Nagoya Institute of Technology (2016-2017); Kyushu University (2015)
• Principal Investigator: Hayashi Atsuhiro 名古屋工業大学, 工学(系)研究科(研究院), 教授 (70189637)
• Research Collaborator: Tatsuoka Kikumi K. 元Columbia University ; Tatsuoka Curtis Case Western Reserve University
• Project Period (FY): 2015-04-01 – 2018-03-31
• Keywords: 学習診断 / 問題分析 / 解答過程解析 / Task Analysis / 学習達成度 / 教育評価 / 機械学習

Outline of Final Research Achievements

Rule Space Method (RSM) is a technique developed in the domain of the cognitive science. It starts from the use of an incidence matrix Q that characterizes the underlying cognitive processes and knowledge (Attribute) involved in each Item. It is a grasping method of each examinee's mastered/non-mastered learning level (Knowledge State, KS) from item response patterns, and a list of all the possible KSs can be generated algorithmically by applying Boolean Algebra to the incidence matrix Q. But the task analysis that is the work to find some suitable attributes for each item is quite hard.
We have found that RSM and NNM are similarities between the results from the two approaches, and moreover they have complementary characteristics when applied in practice. So, in this research, we discuss the comparisons of both approaches by focusing on the structure of the NNM and of KSs in the RSM. And we show an application result for a reasoning test.

Free Research Field

教育工学