Automated feedback on the structure of hypothesis tests

Sietske Tacoma; Bastiaan Heeren; Johan Jeuring; Paul Drijvers

doi:10.1007/978-3-030-23207-8_52

Automated feedback on the structure of hypothesis tests

Sietske Tacoma^*, Bastiaan Heeren, Johan Jeuring, Paul Drijvers

^*Corresponding author for this work

Research Line Learning (part of LIRS program)

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

Abstract

Hypothesis testing is a challenging topic for many students in introductory university statistics courses. In this paper we explore how automated feedback in an Intelligent Tutoring System can foster students’ ability to carry out hypothesis tests. Students in an experimental group (N = 163) received elaborate feedback on the structure of the hypothesis testing procedure, while students in a control group (N = 151) only received verification feedback. Immediate feedback effects were measured by comparing numbers of attempted tasks, complete solutions, and errors between the groups, while transfer of feedback effects was measured by student performance on follow-up tasks. Results show that students receiving elaborate feedback solved more tasks and made fewer errors than students receiving only verification feedback, which suggests that students benefited from the elaborate feedback.

Original language	English
Title of host publication	Artificial Intelligence in Education
Subtitle of host publication	20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019, Proceedings, Part II
Editors	Seiji Isotani, Eva Millán, Amy Ogan, Peter Hastings, Bruce McLaren, Rose Luckin
Place of Publication	Cham
Publisher	Springer
Chapter	52
Pages	281-285
Number of pages	5
ISBN (Electronic)	9783030232078
ISBN (Print)	9783030232061
DOIs	https://doi.org/10.1007/978-3-030-23207-8_52
Publication status	Published - 21 Jun 2019
Event	20th International Conference on Artificial Intelligence in Education - Palmer House Hilton Hotel, Chicago, United States Duration: 25 Jun 2019 → 29 Jun 2019 https://caed-lab.com/aied2019/

Publication series

Series	Lecture Notes in Computer Science
Volume	11626

Conference

Conference	20th International Conference on Artificial Intelligence in Education
Abbreviated title	AIED 2019
Country	United States
City	Chicago
Period	25/06/19 → 29/06/19
Internet address	https://caed-lab.com/aied2019/

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Keywords

Domain reasoner
Hypothesis testing
Intelligent tutoring systems
Statistics education

Cite this

Tacoma, S., Heeren, B., Jeuring, J., & Drijvers, P. (2019). Automated feedback on the structure of hypothesis tests. In S. Isotani, E. Millán, A. Ogan, P. Hastings, B. McLaren, & R. Luckin (Eds.), Artificial Intelligence in Education: 20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019, Proceedings, Part II (pp. 281-285). Springer. Lecture Notes in Computer Science, Vol.. 11626 https://doi.org/10.1007/978-3-030-23207-8_52

Tacoma, Sietske ; Heeren, Bastiaan ; Jeuring, Johan ; Drijvers, Paul. / Automated feedback on the structure of hypothesis tests. Artificial Intelligence in Education: 20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019, Proceedings, Part II. editor / Seiji Isotani ; Eva Millán ; Amy Ogan ; Peter Hastings ; Bruce McLaren ; Rose Luckin. Cham : Springer, 2019. pp. 281-285 (Lecture Notes in Computer Science, Vol. 11626).

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abstract = "Hypothesis testing is a challenging topic for many students in introductory university statistics courses. In this paper we explore how automated feedback in an Intelligent Tutoring System can foster students{\textquoteright} ability to carry out hypothesis tests. Students in an experimental group (N = 163) received elaborate feedback on the structure of the hypothesis testing procedure, while students in a control group (N = 151) only received verification feedback. Immediate feedback effects were measured by comparing numbers of attempted tasks, complete solutions, and errors between the groups, while transfer of feedback effects was measured by student performance on follow-up tasks. Results show that students receiving elaborate feedback solved more tasks and made fewer errors than students receiving only verification feedback, which suggests that students benefited from the elaborate feedback.",

keywords = "Domain reasoner, Hypothesis testing, Intelligent tutoring systems, Statistics education",

author = "Sietske Tacoma and Bastiaan Heeren and Johan Jeuring and Paul Drijvers",

year = "2019",

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doi = "10.1007/978-3-030-23207-8_52",

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series = "Lecture Notes in Computer Science",

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editor = "Seiji Isotani and Mill{\'a}n, {Eva } and Amy Ogan and Peter Hastings and Bruce McLaren and Rose Luckin",

booktitle = "Artificial Intelligence in Education",

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Tacoma, S, Heeren, B , Jeuring, J & Drijvers, P 2019, Automated feedback on the structure of hypothesis tests. in S Isotani, E Millán, A Ogan, P Hastings, B McLaren & R Luckin (eds), Artificial Intelligence in Education: 20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019, Proceedings, Part II. Springer, Cham, Lecture Notes in Computer Science, vol. 11626, pp. 281-285, 20th International Conference on Artificial Intelligence in Education, Chicago, United States, 25/06/19. https://doi.org/10.1007/978-3-030-23207-8_52

Automated feedback on the structure of hypothesis tests. / Tacoma, Sietske; Heeren, Bastiaan ; Jeuring, Johan; Drijvers, Paul.

Artificial Intelligence in Education: 20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019, Proceedings, Part II. ed. / Seiji Isotani; Eva Millán; Amy Ogan; Peter Hastings; Bruce McLaren; Rose Luckin. Cham : Springer, 2019. p. 281-285 (Lecture Notes in Computer Science, Vol. 11626).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

TY - GEN

T1 - Automated feedback on the structure of hypothesis tests

AU - Tacoma, Sietske

AU - Heeren, Bastiaan

AU - Jeuring, Johan

AU - Drijvers, Paul

PY - 2019/6/21

Y1 - 2019/6/21

N2 - Hypothesis testing is a challenging topic for many students in introductory university statistics courses. In this paper we explore how automated feedback in an Intelligent Tutoring System can foster students’ ability to carry out hypothesis tests. Students in an experimental group (N = 163) received elaborate feedback on the structure of the hypothesis testing procedure, while students in a control group (N = 151) only received verification feedback. Immediate feedback effects were measured by comparing numbers of attempted tasks, complete solutions, and errors between the groups, while transfer of feedback effects was measured by student performance on follow-up tasks. Results show that students receiving elaborate feedback solved more tasks and made fewer errors than students receiving only verification feedback, which suggests that students benefited from the elaborate feedback.

AB - Hypothesis testing is a challenging topic for many students in introductory university statistics courses. In this paper we explore how automated feedback in an Intelligent Tutoring System can foster students’ ability to carry out hypothesis tests. Students in an experimental group (N = 163) received elaborate feedback on the structure of the hypothesis testing procedure, while students in a control group (N = 151) only received verification feedback. Immediate feedback effects were measured by comparing numbers of attempted tasks, complete solutions, and errors between the groups, while transfer of feedback effects was measured by student performance on follow-up tasks. Results show that students receiving elaborate feedback solved more tasks and made fewer errors than students receiving only verification feedback, which suggests that students benefited from the elaborate feedback.

KW - Domain reasoner

KW - Hypothesis testing

KW - Intelligent tutoring systems

KW - Statistics education

U2 - 10.1007/978-3-030-23207-8_52

DO - 10.1007/978-3-030-23207-8_52

M3 - Conference article in proceeding

SN - 9783030232061

T3 - Lecture Notes in Computer Science

SP - 281

EP - 285

BT - Artificial Intelligence in Education

A2 - Isotani, Seiji

A2 - Millán, Eva

A2 - Ogan, Amy

A2 - Hastings, Peter

A2 - McLaren, Bruce

A2 - Luckin, Rose

PB - Springer

CY - Cham

T2 - 20th International Conference on Artificial Intelligence in Education

Y2 - 25 June 2019 through 29 June 2019

ER -

Tacoma S, Heeren B , Jeuring J, Drijvers P. Automated feedback on the structure of hypothesis tests. In Isotani S, Millán E, Ogan A, Hastings P, McLaren B, Luckin R, editors, Artificial Intelligence in Education: 20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019, Proceedings, Part II. Cham: Springer. 2019. p. 281-285. (Lecture Notes in Computer Science, Vol. 11626). https://doi.org/10.1007/978-3-030-23207-8_52

Access to Document

10.1007/978-3-030-23207-8_52