Automated feedback on the structure of hypothesis tests

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-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 languageEnglish
Title of host publicationArtificial Intelligence in Education
Subtitle of host publication20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019, Proceedings, Part II
EditorsSeiji Isotani, Eva Millán, Amy Ogan, Peter Hastings, Bruce McLaren, Rose Luckin
Place of PublicationCham
PublisherSpringer
Chapter52
Pages281-285
Number of pages5
ISBN (Electronic)9783030232078
ISBN (Print)9783030232061
DOIs
Publication statusPublished - 21 Jun 2019
Event20th International Conference on Artificial Intelligence in Education - Palmer House Hilton Hotel, Chicago, United States
Duration: 25 Jun 201929 Jun 2019
https://caed-lab.com/aied2019/

Publication series

SeriesLecture Notes in Computer Science
Volume11626

Conference

Conference20th International Conference on Artificial Intelligence in Education
Abbreviated titleAIED 2019
CountryUnited States
CityChicago
Period25/06/1929/06/19
Internet address

Fingerprint

student
hypothesis testing
university statistics
testing procedure
Group
ability
performance

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). Cham: 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’ 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 = "Sietske Tacoma and Bastiaan Heeren and Johan Jeuring and Paul Drijvers",
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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 proceedingConference article in proceedingAcademicpeer-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

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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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KW - Statistics education

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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