Automated feedback on the structure of hypothesis tests

S.G. Tacoma; B.J. Heeren; J.T. Jeuring; P.H.M. Drijvers

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

Automated feedback on the structure of hypothesis tests

S.G. Tacoma, B.J. Heeren, J.T. Jeuring, P.H.M. Drijvers

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › 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	S. Isotani, A. Ogan, P. Hastings, B. McLaren, R. Luckin
Place of Publication	Cham
Publisher	Springer
Pages	281-285
Number of pages	5
ISBN (Electronic)	978-3-030-23207-8
ISBN (Print)	978-3-030-23206-1
DOIs	https://doi.org/10.1007/978-3-030-23207-8_52
Publication status	Published - Jun 2019

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	11626

Keywords

Domain reasoner
Hypothesis testing
Intelligent tutoring systems
Statistics education

Access to Document

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

TacomaHeerenJeuringDrijvers_HypothesisTestingAccepted author manuscript, 159 KB

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Tacoma, S. G., Heeren, B. J., Jeuring, J. T., & Drijvers, P. H. M. (2019). Automated feedback on the structure of hypothesis tests. In S. Isotani, 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). (Lecture Notes in Computer Science; Vol. 11626). Springer. https://doi.org/10.1007/978-3-030-23207-8_52

Tacoma, S.G. ; Heeren, B.J. ; Jeuring, J.T. et al. / 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 / S. Isotani ; A. Ogan ; P. Hastings ; B. McLaren ; R. Luckin. Cham : Springer, 2019. pp. 281-285 (Lecture Notes in Computer Science).

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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.",

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author = "S.G. Tacoma and B.J. Heeren and J.T. Jeuring and P.H.M. Drijvers",

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Tacoma, SG, Heeren, BJ, Jeuring, JT & Drijvers, PHM 2019, Automated feedback on the structure of hypothesis tests. in S Isotani, 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. Lecture Notes in Computer Science, vol. 11626, Springer, Cham, pp. 281-285. https://doi.org/10.1007/978-3-030-23207-8_52

Automated feedback on the structure of hypothesis tests. / Tacoma, S.G.; Heeren, B.J.; Jeuring, J.T. et al.
Artificial Intelligence in Education: 20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019, Proceedings, Part II. ed. / S. Isotani; A. Ogan; P. Hastings; B. McLaren; R. Luckin. Cham: Springer, 2019. p. 281-285 (Lecture Notes in Computer Science; Vol. 11626).

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

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T1 - Automated feedback on the structure of hypothesis tests

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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.

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Tacoma SG, Heeren BJ, Jeuring JT , Drijvers PHM. Automated feedback on the structure of hypothesis tests. In Isotani S, 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). doi: 10.1007/978-3-030-23207-8_52

Automated feedback on the structure of hypothesis tests

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