Adaptive Speed Tests

Daniel Bengs; Ulf Brefeld

Adaptive Speed Tests

Research output: Contributions to collected editions/works › Contributions to collected editions/anthologies › Research › peer-review

Standard

Adaptive Speed Tests. / Bengs, Daniel; Brefeld, Ulf.

Proceedings of the German Workshop on Knowledge Discovery and Machine Learning. ed. / Andreas Henrich; Hans-Christian Sperker. Bamberg : Lehrstuhl für Medieninformatik - Universität Bamberg, 2014. p. 86-90.

Research output: Contributions to collected editions/works › Contributions to collected editions/anthologies › Research › peer-review

Harvard

Bengs, D & Brefeld, U 2014, Adaptive Speed Tests. in A Henrich & H-C Sperker (eds), Proceedings of the German Workshop on Knowledge Discovery and Machine Learning. Lehrstuhl für Medieninformatik - Universität Bamberg, Bamberg, pp. 86-90. <https://www.kma.informatik.tu-darmstadt.de/fileadmin/user_upload/Group_KMA/kma_publications/speedtest.pdf>

APA

Bengs, D., & Brefeld, U. (2014). Adaptive Speed Tests. In A. Henrich, & H-C. Sperker (Eds.), Proceedings of the German Workshop on Knowledge Discovery and Machine Learning (pp. 86-90). Lehrstuhl für Medieninformatik - Universität Bamberg. https://www.kma.informatik.tu-darmstadt.de/fileadmin/user_upload/Group_KMA/kma_publications/speedtest.pdf

Vancouver

Bengs D, Brefeld U. Adaptive Speed Tests. In Henrich A, Sperker H-C, editors, Proceedings of the German Workshop on Knowledge Discovery and Machine Learning. Bamberg: Lehrstuhl für Medieninformatik - Universität Bamberg. 2014. p. 86-90

Bibtex

@inbook{daf66fa483bf464599acd55142cec6a1,

title = "Adaptive Speed Tests",

abstract = "The assessment of a person's traits such as ability is a fundamental problem in human sciences. We focus on assessments of traits that can be measured by determining the shortest time limit allowing a testee to solve simple repetitive tasks, so-called speed tests. Existing approaches for adjusting the time limit are either intrinsically nonadaptive or lack theoretical foundation. By contrast, we propose a mathematically sound framework in which latent competency skills are represented by belief distributions on compact intervals. The algorithm iteratively computes a new difficulty setting, such that the amount of belief that can be updated after feedback has been received is maximized. We provide theoretical analyses and show empirically that our method performs equally well or better than state of the art baselines in a near-realistic scenario. {\textcopyright} LWA 2013 - Lernen, Wissen and Adaptivitat, Workshop Proceedings. All rights reserved.",

keywords = "Informatics, Adaptives Testen, Empirische Untersuchung, F{\"a}higkeit, Kompetenz, Lernen , Business informatics",

author = "Daniel Bengs and Ulf Brefeld",

year = "2014",

language = "English",

pages = "86--90",

editor = "Andreas Henrich and Hans-Christian Sperker",

booktitle = "Proceedings of the German Workshop on Knowledge Discovery and Machine Learning",

publisher = "Lehrstuhl f{\"u}r Medieninformatik - Universit{\"a}t Bamberg",

address = "Germany",

}

RIS

TY - CHAP

T1 - Adaptive Speed Tests

AU - Bengs, Daniel

AU - Brefeld, Ulf

PY - 2014

Y1 - 2014

N2 - The assessment of a person's traits such as ability is a fundamental problem in human sciences. We focus on assessments of traits that can be measured by determining the shortest time limit allowing a testee to solve simple repetitive tasks, so-called speed tests. Existing approaches for adjusting the time limit are either intrinsically nonadaptive or lack theoretical foundation. By contrast, we propose a mathematically sound framework in which latent competency skills are represented by belief distributions on compact intervals. The algorithm iteratively computes a new difficulty setting, such that the amount of belief that can be updated after feedback has been received is maximized. We provide theoretical analyses and show empirically that our method performs equally well or better than state of the art baselines in a near-realistic scenario. © LWA 2013 - Lernen, Wissen and Adaptivitat, Workshop Proceedings. All rights reserved.

AB - The assessment of a person's traits such as ability is a fundamental problem in human sciences. We focus on assessments of traits that can be measured by determining the shortest time limit allowing a testee to solve simple repetitive tasks, so-called speed tests. Existing approaches for adjusting the time limit are either intrinsically nonadaptive or lack theoretical foundation. By contrast, we propose a mathematically sound framework in which latent competency skills are represented by belief distributions on compact intervals. The algorithm iteratively computes a new difficulty setting, such that the amount of belief that can be updated after feedback has been received is maximized. We provide theoretical analyses and show empirically that our method performs equally well or better than state of the art baselines in a near-realistic scenario. © LWA 2013 - Lernen, Wissen and Adaptivitat, Workshop Proceedings. All rights reserved.

KW - Informatics

KW - Adaptives Testen

KW - Empirische Untersuchung

KW - Fähigkeit

KW - Kompetenz

KW - Lernen

KW - Business informatics

M3 - Contributions to collected editions/anthologies

SP - 86

EP - 90

BT - Proceedings of the German Workshop on Knowledge Discovery and Machine Learning

A2 - Henrich, Andreas

A2 - Sperker, Hans-Christian

PB - Lehrstuhl für Medieninformatik - Universität Bamberg

CY - Bamberg

ER -

Other publications by the same author(s)

Convolutional Neural Networks

Rudolph, Y. & Brefeld, U., 01.01.2023, Sportinformatik: Modellbildung, Simulation, Datenanalyse und Visualisierung von sportbezogenen Daten. Memmert, D. (ed.). Berlin: Springer Spektrum, p. 207-215 9 p.

Research output: Contributions to collected editions/works › Chapter

Data practices in apps from Brazil: What do privacy policies inform us about?

Quadros dos Reis, V., Rabello, M. E. R., Lima, A. C., Jardim, G. P. S., Fernandes, E. R. & Brefeld, U., 10.02.2023, In: Journal on Interactive Systems. 14, 1, p. 1-8 8 p.

Research output: Journal contributions › Journal articles › Research › peer-review

Exploring the Poincaré Ellipsis

Fadel, S., Paulsen, T. & Brefeld, U., 09.2023, 20th Machine Learning on Graphs workshop.

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Hands in Focus: Sign Language Recognition Via Top-Down Attention

Sarhan, N., Wilms, C., Closius, V., Brefeld, U. & Frintrop, S., 08.10.2023, 2023 IEEE International Conference on Image Processing, ICIP 2023 - Proceedings: Proceedings. Piscataway: IEEE Electromagnetic Compatibility Society, p. 2555-2559 5 p. (Proceedings - International Conference on Image Processing, ICIP).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Masked Autoencoder Pretraining for Event Classification in Elite Soccer

Rudolph, Y. & Brefeld, U., 26.02.2024, Machine Learning and Data Mining for Sports Analytics: 10th International Workshop, MLSA 2023, Revised Selected Papers. Brefeld, U., Davis, J., Van Haaren, J. & Zimmermann, A. (eds.). Cham: Springer Nature Switzerland AG, p. 24-35 12 p. (Communications in Computer and Information Science; vol. 2035).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review