How should knowledge composed of schemas be represented in order to optimize student model accuracy?

Sachin Grover; Jon Wetzel; Kurt VanLehn

doi:10.1007/978-3-319-93843-1_10

How should knowledge composed of schemas be represented in order to optimize student model accuracy?

Sachin Grover, Jon Wetzel, Kurt VanLehn

Computing, Informatics and Decision Systems Engineering, School of (CIDSE)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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
Editors	Manolis Mavrikis, Carolyn Penstein Rosé, Bruce McLaren, H. Ulrich Hoppe, Rose Luckin, Kaska Porayska-Pomsta, Benedict du Boulay, Roberto Martinez-Maldonado
Publisher	Springer Verlag
Pages	127-139
Number of pages	13
ISBN (Print)	9783319938424
DOIs	https://doi.org/10.1007/978-3-319-93843-1_10
State	Published - 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

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-93843-1_10

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Grover, S., Wetzel, J., & VanLehn, K. (2018). How should knowledge composed of schemas be represented in order to optimize student model accuracy? In M. Mavrikis, C. Penstein Rosé, B. McLaren, H. U. Hoppe, R. Luckin, K. Porayska-Pomsta, B. du Boulay, & R. Martinez-Maldonado (Eds.), Artificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings (pp. 127-139). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10947 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-93843-1_10

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. ed. / Manolis Mavrikis; Carolyn Penstein Rosé; Bruce McLaren; H. Ulrich Hoppe; Rose Luckin; Kaska Porayska-Pomsta; Benedict du Boulay; Roberto Martinez-Maldonado. 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

Grover, S, Wetzel, J & VanLehn, K 2018, How should knowledge composed of schemas be represented in order to optimize student model accuracy? in M Mavrikis, C Penstein Rosé, B McLaren, HU Hoppe, R Luckin, K Porayska-Pomsta, B du Boulay & R Martinez-Maldonado (eds), Artificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10947 LNAI, Springer Verlag, pp. 127-139, 19th International Conference on Artificial Intelligence in Education, AIED 2018, London, United Kingdom, 6/27/18. https://doi.org/10.1007/978-3-319-93843-1_10

Grover S, Wetzel J , VanLehn K. How should knowledge composed of schemas be represented in order to optimize student model accuracy? In Mavrikis M, Penstein Rosé C, McLaren B, Hoppe HU, Luckin R, Porayska-Pomsta K, du Boulay B, Martinez-Maldonado R, editors, 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)). https://doi.org/10.1007/978-3-319-93843-1_10

Grover, Sachin ; Wetzel, Jon ; VanLehn, Kurt. / How should knowledge composed of schemas be represented in order to optimize student model accuracy?. Artificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings. editor / Manolis Mavrikis ; Carolyn Penstein Rosé ; Bruce McLaren ; H. Ulrich Hoppe ; Rose Luckin ; Kaska Porayska-Pomsta ; Benedict du Boulay ; Roberto Martinez-Maldonado. Springer Verlag, 2018. pp. 127-139 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{fa83c10b317349cf82a1eeaf2629ca56,

title = "How should knowledge composed of schemas be represented in order to optimize student model accuracy?",

abstract = "Most approaches to student modeling assume that students{\textquoteright} 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.",

author = "Sachin Grover and Jon Wetzel and Kurt VanLehn",

note = "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.; 19th International Conference on Artificial Intelligence in Education, AIED 2018 ; Conference date: 27-06-2018 Through 30-06-2018",

year = "2018",

doi = "10.1007/978-3-319-93843-1_10",

language = "English (US)",

isbn = "9783319938424",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "127--139",

editor = "Manolis Mavrikis and {Penstein Ros{\'e}}, Carolyn and Bruce McLaren and Hoppe, {H. Ulrich} and Rose Luckin and Kaska Porayska-Pomsta and {du Boulay}, Benedict and Roberto Martinez-Maldonado",

booktitle = "Artificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings",