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
N1 - Funding Information:
Acknowledgments. This research was supported by NSF IIS-1628782, NSF IIS-1123823, ONR N00014-13-C-0029, ONR N00014-12-C-0643 and US Army, W911NF-04-D-0005, Delivery Order No. 0041.
Funding Information:
This research was supported by NSF IIS-1628782, NSF IIS1123823, ONR N00014-13-C-0029, ONR N00014-12-C-0643 and US Army, W911NF-04-D-0005, Delivery Order No. 0041.
Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
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.
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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
A2 - Mavrikis, Manolis
A2 - Penstein Rosé, Carolyn
A2 - McLaren, Bruce
A2 - Hoppe, H. Ulrich
A2 - Luckin, Rose
A2 - Porayska-Pomsta, Kaska
A2 - du Boulay, Benedict
A2 - Martinez-Maldonado, Roberto
PB - Springer Verlag
T2 - 19th International Conference on Artificial Intelligence in Education, AIED 2018
Y2 - 27 June 2018 through 30 June 2018
ER -