Abstract
Most approaches to student modeling assume that students’ knowledge can be represented by a large set of knowledge components that are learned independently. Knowledge components typically represent fairly small pieces of knowledge. This seems to conflict with the literature on problem solving which suggests that expert knowledge is composed of large schemas. This study compared several domain models for knowledge that is arguably composed of schemas. The knowledge is used by students to construct system dynamics models with the Dragoon intelligent tutoring system. An evaluation with 52 students showed that a relative simple domain model, that assigned one KC to each schema and schema combination, sufficed and was more parsimonious than other domain models with similarly accurate predictions.
| Original language | English (US) |
|---|---|
| Title of host publication | Artificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings |
| Publisher | Springer Verlag |
| Pages | 127-139 |
| Number of pages | 13 |
| ISBN (Print) | 9783319938424 |
| DOIs | |
| State | Published - Jan 1 2018 |
| Event | 19th International Conference on Artificial Intelligence in Education, AIED 2018 - London, United Kingdom Duration: Jun 27 2018 → Jun 30 2018 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 10947 LNAI |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 19th International Conference on Artificial Intelligence in Education, AIED 2018 |
|---|---|
| Country | United Kingdom |
| City | London |
| Period | 6/27/18 → 6/30/18 |
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ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
How should knowledge composed of schemas be represented in order to optimize student model accuracy? / Grover, Sachin; Wetzel, Jon; VanLehn, Kurt.
Artificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings. Springer Verlag, 2018. p. 127-139 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10947 LNAI).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - How should knowledge composed of schemas be represented in order to optimize student model accuracy?
AU - Grover, Sachin
AU - Wetzel, Jon
AU - VanLehn, Kurt
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Most approaches to student modeling assume that students’ knowledge can be represented by a large set of knowledge components that are learned independently. Knowledge components typically represent fairly small pieces of knowledge. This seems to conflict with the literature on problem solving which suggests that expert knowledge is composed of large schemas. This study compared several domain models for knowledge that is arguably composed of schemas. The knowledge is used by students to construct system dynamics models with the Dragoon intelligent tutoring system. An evaluation with 52 students showed that a relative simple domain model, that assigned one KC to each schema and schema combination, sufficed and was more parsimonious than other domain models with similarly accurate predictions.
AB - Most approaches to student modeling assume that students’ knowledge can be represented by a large set of knowledge components that are learned independently. Knowledge components typically represent fairly small pieces of knowledge. This seems to conflict with the literature on problem solving which suggests that expert knowledge is composed of large schemas. This study compared several domain models for knowledge that is arguably composed of schemas. The knowledge is used by students to construct system dynamics models with the Dragoon intelligent tutoring system. An evaluation with 52 students showed that a relative simple domain model, that assigned one KC to each schema and schema combination, sufficed and was more parsimonious than other domain models with similarly accurate predictions.
UR - http://www.scopus.com/inward/record.url?scp=85049364195&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049364195&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-93843-1_10
DO - 10.1007/978-3-319-93843-1_10
M3 - Conference contribution
AN - SCOPUS:85049364195
SN - 9783319938424
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 127
EP - 139
BT - Artificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings
PB - Springer Verlag
ER -