Computational Education using Latent Structured Prediction

Tanja Käser, Alexander Schwing, Tamir Hazan, Markus Gross
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:540-548, 2014.

Abstract

Computational education offers an important add-on to conventional teaching. To provide optimal learning conditions, accurate representation of students’ current skills and adaptation to newly acquired knowledge are essential. To obtain sufficient representational power we investigate suitability of general graphical models and discuss adaptation by learning parameters of a log-linear distribution. For interpretability we propose to constrain the parameter space a-priori by leveraging domain knowledge. We show the benefits of general graphical models and of regularizing the parameter space by evaluation of our models on data collected from a computational education software for children having difficulties in learning mathematics.

Cite this Paper

BibTeX
@InProceedings{pmlr-v33-kaser14, title = {{Computational Education using Latent Structured Prediction}}, author = {Käser, Tanja and Schwing, Alexander and Hazan, Tamir and Gross, Markus}, booktitle = {Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics}, pages = {540--548}, year = {2014}, editor = {Kaski, Samuel and Corander, Jukka}, volume = {33}, series = {Proceedings of Machine Learning Research}, address = {Reykjavik, Iceland}, month = {22--25 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v33/kaser14.pdf}, url = {https://proceedings.mlr.press/v33/kaser14.html}, abstract = {Computational education offers an important add-on to conventional teaching. To provide optimal learning conditions, accurate representation of students’ current skills and adaptation to newly acquired knowledge are essential. To obtain sufficient representational power we investigate suitability of general graphical models and discuss adaptation by learning parameters of a log-linear distribution. For interpretability we propose to constrain the parameter space a-priori by leveraging domain knowledge. We show the benefits of general graphical models and of regularizing the parameter space by evaluation of our models on data collected from a computational education software for children having difficulties in learning mathematics.} }
Endnote
%0 Conference Paper %T Computational Education using Latent Structured Prediction %A Tanja Käser %A Alexander Schwing %A Tamir Hazan %A Markus Gross %B Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2014 %E Samuel Kaski %E Jukka Corander %F pmlr-v33-kaser14 %I PMLR %P 540--548 %U https://proceedings.mlr.press/v33/kaser14.html %V 33 %X Computational education offers an important add-on to conventional teaching. To provide optimal learning conditions, accurate representation of students’ current skills and adaptation to newly acquired knowledge are essential. To obtain sufficient representational power we investigate suitability of general graphical models and discuss adaptation by learning parameters of a log-linear distribution. For interpretability we propose to constrain the parameter space a-priori by leveraging domain knowledge. We show the benefits of general graphical models and of regularizing the parameter space by evaluation of our models on data collected from a computational education software for children having difficulties in learning mathematics.
RIS
TY - CPAPER TI - Computational Education using Latent Structured Prediction AU - Tanja Käser AU - Alexander Schwing AU - Tamir Hazan AU - Markus Gross BT - Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics DA - 2014/04/02 ED - Samuel Kaski ED - Jukka Corander ID - pmlr-v33-kaser14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 33 SP - 540 EP - 548 L1 - http://proceedings.mlr.press/v33/kaser14.pdf UR - https://proceedings.mlr.press/v33/kaser14.html AB - Computational education offers an important add-on to conventional teaching. To provide optimal learning conditions, accurate representation of students’ current skills and adaptation to newly acquired knowledge are essential. To obtain sufficient representational power we investigate suitability of general graphical models and discuss adaptation by learning parameters of a log-linear distribution. For interpretability we propose to constrain the parameter space a-priori by leveraging domain knowledge. We show the benefits of general graphical models and of regularizing the parameter space by evaluation of our models on data collected from a computational education software for children having difficulties in learning mathematics. ER -
APA
Käser, T., Schwing, A., Hazan, T. & Gross, M.. (2014). Computational Education using Latent Structured Prediction. Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 33:540-548 Available from https://proceedings.mlr.press/v33/kaser14.html.

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