Towards Sharing Student Models Across Learning Systems

被引：3

作者：

Baker, Ryan S. ^{[1
]}

McLaren, Bruce M. ^{[2
]}

Hutt, Stephen ^{[1
]}

Richey, J. Elizabeth ^{[2
]}

Rowe, Elizabeth ^{[3
]}

Almeda, Ma Victoria ^{[3
]}

Mogessie, Michael ^{[2
]}

Al Andres, Juliana M. ^{[1
]}

机构：

[1] Univ Penn, Grad Sch Educ, Philadelphia, PA 19104 USA

[2] Carnegie Mellon Univ, Human Comp Interact Inst, Pittsburgh, PA 15213 USA

[3] TERC, EdGE, Cambridge, MA USA

来源：

ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT II | 2021年 / 12749卷

关键词：

Student model sharing; AIED system integration; BLAP;

D O I：

10.1007/978-3-030-78270-2_10

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Modern AIED systems develop sophisticated and multidimensional models of students. However, what is learned about students in one system-their skills, behaviors, and affect-is not carried over to other systems that could benefit students by using the information, potentially reducing both the effectiveness and efficiency of these systems. This challenge has been cited by a number of researchers as one of the most important for the field of AIED. In this paper, we discuss existing progress towards resolving this challenge, break down five sub-challenges, and propose how to address the sub-challenges.

引用

页码：60 / 65

页数：6

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