Feature-Aware Student knowledge Tracing (FAST) - Revision history

Yuh43 at 05:21, 4 April 2016

2016-04-04T05:21:07Z

← Older revision		Revision as of 05:21, 4 April 2016
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	Feature-Aware Student knowledge Tracing (FAST) is a novel, efficient student model created by PAWs lab and Pearson with the state-of-the-art predictive performance. It allows general, flexible features into [http://liris.cnrs.fr/~pchampin/2014/m2iade-ia2/_static/893CorbettAnderson1995.pdf Knowledge Tracing], which is the most popular student model. For the first time it unifies existed specially designed student models based on Knowledge Tracing framework with integrated advantages. We demonstrate FAST’s flexibility with examples of feature sets that are relevant to a wide audience: we haved uses features in FAST to model (i) multiple subskills, (ii) temporal Item Response Theory, and (iii) expert knowledge. Compared with Knowledge Tracing, (1) it improves up to 25% in classification performance, (2) it generates more interpretable, consistent parameters, and (3) it is 300 times faster. In a [http://adenu.ia.uned.es/workshops/pale2014/pale2014_proceedings_vol1181.pdf#page=9 follow-up study], we compared FAST to the best paper model (a single-purpose model) of the same year with favorable results while FAST is designed as a general-purpose model. The [http://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/84_EDM-2014-Full.pdf main paper] was nominated for the Best Paper Award in a top-tier conference in 2014. Both the main and the follow-up papers were cited by top researchers in the field from Carnegie Mellon University, Stanford, Cornell, ETH Zurich (etc.) with in total 34 citations since 2014 (till 04/2016).		Feature-Aware Student knowledge Tracing (FAST) is a novel, efficient student model created by PAWs lab and Pearson with the state-of-the-art predictive performance. It allows general, flexible features into [http://liris.cnrs.fr/~pchampin/2014/m2iade-ia2/_static/893CorbettAnderson1995.pdf Knowledge Tracing], which is the most popular student model. For the first time it unifies existed specially designed student models based on Knowledge Tracing framework with integrated advantages. We demonstrate FAST’s flexibility with examples of feature sets that are relevant to a wide audience: we haved uses features in FAST to model (i) multiple subskills, (ii) temporal Item Response Theory, and (iii) expert knowledge. Compared with Knowledge Tracing, (1) it improves up to 25% in classification performance, (2) it generates more interpretable, consistent parameters, and (3) it is 300 times faster. In a [http://adenu.ia.uned.es/workshops/pale2014/pale2014_proceedings_vol1181.pdf#page=9 follow-up study], we compared FAST to the best paper model (a single-purpose model) of the same year with favorable results while FAST is designed as a general-purpose model. The [http://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/84_EDM-2014-Full.pdf main paper] was nominated for the Best Paper Award in a top-tier conference in 2014. Both the main and the follow-up papers were cited by top researchers in the field from Carnegie Mellon University, Stanford, Cornell, ETH Zurich (etc.) with in total 34 citations since 2014 (till 04/2016).

			== Resources ==
	* Download the source code [http://ml-smores.github.io/fast/ here]		* Download the source code [http://ml-smores.github.io/fast/ here]
	* Read the EDM 2014 conference presentation slides [http://www.slideshare.net/huangyun/fast-presentation-48711687 here]		* Read the EDM 2014 conference presentation slides [http://www.slideshare.net/huangyun/fast-presentation-48711687 here]
	* Read the tutorial slides presented in EDM 2015 workshop [http://www.slideshare.net/huangyun/2015edm-featureaware-student-knowledge-tracing-tutorial here]		* Read the tutorial slides presented in EDM 2015 workshop [http://www.slideshare.net/huangyun/2015edm-featureaware-student-knowledge-tracing-tutorial here]

			== Publications ==
			* Gonzalez-Brenes, J. P., Huang, Y., and Brusilovsky, P. (2013) FAST: Feature-Aware Student Knowledge Tracing. In: Proceedings of NIPS 2013 Workshop on Data Driven Education, Lake Tahoe, NV, December 10, 2013, ([http://d-scholarship.pitt.edu/20353/ paper])
			*González-Brenes, J. P., Huang, Y., and Brusilovsky, P. (2014) General Features in Knowledge Tracing to Model Multiple Subskills, Temporal Item Response Theory, and Expert Knowledge. In: J. Stamper, Z. Pardos, M. Mavrikis and B. M. McLaren (eds.) Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014), London, UK, July 4-7, 2014, pp. 84-91. (First two authors contributed equally. Nominated for Best Paper Award) ([http://www.slideshare.net/huangyun/fast-presentation-48711687 presentation][http://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/84_EDM-2014-Full.pdf paper][http://www.slideshare.net/huangyun/2015edm-featureaware-student-knowledge-tracing-tutorial tutorial] [http://ml-smores.github.io/fast/ code])
			* Khajah, M. M., Huang, Y., González-Brenes, J. P., Mozer, M. C., and Brusilovsky, P. (2014) Integrating Knowledge Tracing and Item Response Theory: A Tale of Two Frameworks. In: I. Cantador, M. Chi, R. Farzan and R. Jäschke (eds.) Proceedings of Workshop on Personalization Approaches in Learning Environments (PALE 2014) at the 22th International Conference on User Modeling, Adaptation, and Personalization, UMAP 2014, Aalborg, Denmark, July 11, 2014, CEUR, pp. 7-12. (First three authors contributed equally) ([http://www.slideshare.net/huangyun/pale-public-slideshare presentation][http://ceur-ws.org/Vol-1181/pale2014_paper_01.pdf paper]).
			* Sahebi, S., Huang, Y., and Brusilovsky, P. (2014) Parameterized Exercises in Java Programming: Using Knowledge Structure for Performance Prediction. In: Proceedings of The second Workshop on AI-supported Education for Computer Science (AIEDCS) at 12th International Conference on Intelligent Tutoring Systems ITS 2014, Honolulu, Hawaii, June 6 2014. ([http://d-scholarship.pitt.edu/21915/ paper])([http://www.slideshare.net/chagh/parameterized-exercises-in-java-programming-using-knowledge-structure-for-performance-prediction presentation])
			* Huang, Y., González-Brenes, J. P., and Brusilovsky, P. (2015) The FAST toolkit for Unsupervised Learning of HMMs with Features. In: The Machine Learning Open Source Software workshop at the 32nd International Conference on Machine Learning (ICML-MLOSS 2015), Lille, France July 10, 2015. ([http://d-scholarship.pitt.edu/26043/ paper][http://mloss.org/software/view/609/ code])
			* Huang, Y., González-Brenes, J. P., Kumar, R., Brusilovsky, P. (2015) A Framework for Multifaceted Evaluation of Student Models. In: Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015), Madrid, Spain, pp. 203-210. ([http://www.educationaldatamining.org/EDM2015/uploads/papers/paper_164.pdf paper]) ([http://www.slideshare.net/huangyun/2015edm-a-framework-for-multifaceted-evaluation-of-student-models-polygon presentation])

Yuh43 at 03:40, 4 April 2016

2016-04-04T03:40:29Z

← Older revision		Revision as of 03:40, 4 April 2016
Line 2:		Line 2:

	* Download the source code [http://ml-smores.github.io/fast/ here]		* Download the source code [http://ml-smores.github.io/fast/ here]
	* Read the conference presentation slides [http://www.slideshare.net/huangyun/fast-presentation-48711687 here]		* Read the EDM 2014 conference presentation slides [http://www.slideshare.net/huangyun/fast-presentation-48711687 here]
	* Read the tutorial slides presented in EDM 2015 workshop [http://www.slideshare.net/huangyun/2015edm-featureaware-student-knowledge-tracing-tutorial here]		* Read the tutorial slides presented in EDM 2015 workshop [http://www.slideshare.net/huangyun/2015edm-featureaware-student-knowledge-tracing-tutorial here]

Yuh43 at 03:37, 4 April 2016

2016-04-04T03:37:55Z

← Older revision		Revision as of 03:37, 4 April 2016
Line 1:		Line 1:
	Feature-Aware Student knowledge Tracing (FAST) is a novel, efficient student model created by PAWs lab and Pearson with the state-of-the-art predictive performance. It allows general, flexible features into [http://liris.cnrs.fr/~pchampin/2014/m2iade-ia2/_static/893CorbettAnderson1995.pdf Knowledge Tracing], which is the most popular student model. For the first time it unifies existed specially designed student models based on Knowledge Tracing framework with integrated advantages. We demonstrate FAST’s flexibility with examples of feature sets that are relevant to a wide audience: we haved uses features in FAST to model (i) multiple subskills, (ii) temporal Item Response Theory, and (iii) expert knowledge. Compared with Knowledge Tracing, (1) it improves up to 25% in classification performance, (2) it generates more interpretable, consistent parameters, and (3) it is 300 times faster. In a [[http://adenu.ia.uned.es/workshops/pale2014/pale2014_proceedings_vol1181.pdf#page=9 follow-up study], we compared FAST to the best paper model (a single-purpose model) of the same year with favorable results while FAST is designed as a general-purpose model. The [http://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/84_EDM-2014-Full.pdf main paper] was nominated for the Best Paper Award in a top-tier conference in 2014. Both the main and the follow-up papers were cited by top researchers in the field from Carnegie Mellon University, Stanford, Cornell, ETH Zurich (etc.) with in total 34 citations since 2014 (till 04/2016).		Feature-Aware Student knowledge Tracing (FAST) is a novel, efficient student model created by PAWs lab and Pearson with the state-of-the-art predictive performance. It allows general, flexible features into [http://liris.cnrs.fr/~pchampin/2014/m2iade-ia2/_static/893CorbettAnderson1995.pdf Knowledge Tracing], which is the most popular student model. For the first time it unifies existed specially designed student models based on Knowledge Tracing framework with integrated advantages. We demonstrate FAST’s flexibility with examples of feature sets that are relevant to a wide audience: we haved uses features in FAST to model (i) multiple subskills, (ii) temporal Item Response Theory, and (iii) expert knowledge. Compared with Knowledge Tracing, (1) it improves up to 25% in classification performance, (2) it generates more interpretable, consistent parameters, and (3) it is 300 times faster. In a [http://adenu.ia.uned.es/workshops/pale2014/pale2014_proceedings_vol1181.pdf#page=9 follow-up study], we compared FAST to the best paper model (a single-purpose model) of the same year with favorable results while FAST is designed as a general-purpose model. The [http://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/84_EDM-2014-Full.pdf main paper] was nominated for the Best Paper Award in a top-tier conference in 2014. Both the main and the follow-up papers were cited by top researchers in the field from Carnegie Mellon University, Stanford, Cornell, ETH Zurich (etc.) with in total 34 citations since 2014 (till 04/2016).

	* Download the source code [http://ml-smores.github.io/fast/ here]		* Download the source code [http://ml-smores.github.io/fast/ here]
	* Read the conference presentation slides [http://www.slideshare.net/huangyun/fast-presentation-48711687 here]		* Read the conference presentation slides [http://www.slideshare.net/huangyun/fast-presentation-48711687 here]
	* Read the tutorial slides presented in EDM 2015 workshop [http://www.slideshare.net/huangyun/2015edm-featureaware-student-knowledge-tracing-tutorial here]		* Read the tutorial slides presented in EDM 2015 workshop [http://www.slideshare.net/huangyun/2015edm-featureaware-student-knowledge-tracing-tutorial here]

Yuh43 at 03:37, 4 April 2016

2016-04-04T03:37:13Z

← Older revision		Revision as of 03:37, 4 April 2016
Line 1:		Line 1:
	Feature-Aware Student knowledge Tracing (FAST) is a novel, efficient student model created by PAWs lab and Pearson with the state-of-the-art predictive performance. It allows general, flexible features ~~allows~~ into [http://liris.cnrs.fr/~pchampin/2014/m2iade-ia2/_static/893CorbettAnderson1995.pdf Knowledge Tracing], which is the most popular student model. For the first time it unifies existed specially designed student models based on Knowledge Tracing framework with integrated advantages. We demonstrate FAST’s flexibility with examples of feature sets that are relevant to a wide audience: we haved uses features in FAST to model (i) multiple subskills, (ii) temporal Item Response Theory, and (iii) expert knowledge. Compared with Knowledge Tracing, (1) it improves up to 25% in classification performance, (2) it generates more interpretable, consistent parameters, and (3) it is 300 times faster. In a [[http://adenu.ia.uned.es/workshops/pale2014/pale2014_proceedings_vol1181.pdf#page=9 follow-up study], we compared FAST to the best paper model (a single-purpose model) of the same year with favorable results while FAST is designed as a general-purpose model. The [http://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/84_EDM-2014-Full.pdf main paper] was nominated for the Best Paper Award in a top-tier conference in 2014. Both the main and the follow-up papers were cited by top researchers in the field from Carnegie Mellon University, Stanford, Cornell, ETH Zurich (etc.) with in total 34 citations since 2014 (till 04/2016).		Feature-Aware Student knowledge Tracing (FAST) is a novel, efficient student model created by PAWs lab and Pearson with the state-of-the-art predictive performance. It allows general, flexible features into [http://liris.cnrs.fr/~pchampin/2014/m2iade-ia2/_static/893CorbettAnderson1995.pdf Knowledge Tracing], which is the most popular student model. For the first time it unifies existed specially designed student models based on Knowledge Tracing framework with integrated advantages. We demonstrate FAST’s flexibility with examples of feature sets that are relevant to a wide audience: we haved uses features in FAST to model (i) multiple subskills, (ii) temporal Item Response Theory, and (iii) expert knowledge. Compared with Knowledge Tracing, (1) it improves up to 25% in classification performance, (2) it generates more interpretable, consistent parameters, and (3) it is 300 times faster. In a [[http://adenu.ia.uned.es/workshops/pale2014/pale2014_proceedings_vol1181.pdf#page=9 follow-up study], we compared FAST to the best paper model (a single-purpose model) of the same year with favorable results while FAST is designed as a general-purpose model. The [http://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/84_EDM-2014-Full.pdf main paper] was nominated for the Best Paper Award in a top-tier conference in 2014. Both the main and the follow-up papers were cited by top researchers in the field from Carnegie Mellon University, Stanford, Cornell, ETH Zurich (etc.) with in total 34 citations since 2014 (till 04/2016).

	* Download the source code [http://ml-smores.github.io/fast/ here]		* Download the source code [http://ml-smores.github.io/fast/ here]
	* Read the conference presentation slides [http://www.slideshare.net/huangyun/fast-presentation-48711687 here]		* Read the conference presentation slides [http://www.slideshare.net/huangyun/fast-presentation-48711687 here]
	* Read the tutorial slides presented in EDM 2015 workshop [http://www.slideshare.net/huangyun/2015edm-featureaware-student-knowledge-tracing-tutorial here]		* Read the tutorial slides presented in EDM 2015 workshop [http://www.slideshare.net/huangyun/2015edm-featureaware-student-knowledge-tracing-tutorial here]

Yuh43 at 03:35, 4 April 2016

2016-04-04T03:35:17Z

← Older revision		Revision as of 03:35, 4 April 2016
Line 1:		Line 1:
	Feature-Aware Student knowledge Tracing (FAST) is a novel, efficient student model created by PAWs lab and Pearson with the state-of-the-art predictive performance. It allows general, flexible features allows into [[http://liris.cnrs.fr/~pchampin/2014/m2iade-ia2/_static/893CorbettAnderson1995.pdf\|Knowledge Tracing]], which is the most popular student model. For the first time it unifies existed specially designed student models based on Knowledge Tracing framework with integrated advantages. We demonstrate FAST’s flexibility with examples of feature sets that are relevant to a wide audience: we haved uses features in FAST to model (i) multiple subskills, (ii) temporal Item Response Theory, and (iii) expert knowledge. Compared with Knowledge Tracing, (1) it improves up to 25% in classification performance, (2) it generates more interpretable, consistent parameters, and (3) it is 300 times faster. In a [[http://adenu.ia.uned.es/workshops/pale2014/pale2014_proceedings_vol1181.pdf#page=9 follow-up study], we compared FAST to the best paper model (a single-purpose model) of the same year with favorable results while FAST is designed as a general-purpose model. The [http://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/84_EDM-2014-Full.pdf main paper] was nominated for the Best Paper Award in a top-tier conference in 2014. Both the main and the follow-up papers were cited by top researchers in the field from Carnegie Mellon University, Stanford, Cornell, ETH Zurich (etc.) with in total 34 citations since 2014 (till 04/2016).		Feature-Aware Student knowledge Tracing (FAST) is a novel, efficient student model created by PAWs lab and Pearson with the state-of-the-art predictive performance. It allows general, flexible features allows into [http://liris.cnrs.fr/~pchampin/2014/m2iade-ia2/_static/893CorbettAnderson1995.pdf Knowledge Tracing], which is the most popular student model. For the first time it unifies existed specially designed student models based on Knowledge Tracing framework with integrated advantages. We demonstrate FAST’s flexibility with examples of feature sets that are relevant to a wide audience: we haved uses features in FAST to model (i) multiple subskills, (ii) temporal Item Response Theory, and (iii) expert knowledge. Compared with Knowledge Tracing, (1) it improves up to 25% in classification performance, (2) it generates more interpretable, consistent parameters, and (3) it is 300 times faster. In a [[http://adenu.ia.uned.es/workshops/pale2014/pale2014_proceedings_vol1181.pdf#page=9 follow-up study], we compared FAST to the best paper model (a single-purpose model) of the same year with favorable results while FAST is designed as a general-purpose model. The [http://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/84_EDM-2014-Full.pdf main paper] was nominated for the Best Paper Award in a top-tier conference in 2014. Both the main and the follow-up papers were cited by top researchers in the field from Carnegie Mellon University, Stanford, Cornell, ETH Zurich (etc.) with in total 34 citations since 2014 (till 04/2016).

	* Download the source code [http://ml-smores.github.io/fast/ here]		* Download the source code [http://ml-smores.github.io/fast/ here]
	* Read the conference presentation slides [http://www.slideshare.net/huangyun/fast-presentation-48711687 here]		* Read the conference presentation slides [http://www.slideshare.net/huangyun/fast-presentation-48711687 here]
	* Read the tutorial slides presented in EDM 2015 workshop [http://www.slideshare.net/huangyun/2015edm-featureaware-student-knowledge-tracing-tutorial here]		* Read the tutorial slides presented in EDM 2015 workshop [http://www.slideshare.net/huangyun/2015edm-featureaware-student-knowledge-tracing-tutorial here]

Yuh43 at 03:35, 4 April 2016

2016-04-04T03:35:04Z

← Older revision		Revision as of 03:35, 4 April 2016
Line 1:		Line 1:
	Feature-Aware Student knowledge Tracing (FAST) is a ~~new~~ student model created by PAWs lab and Pearson with the state-of-the-art predictive performance. ~~For the first time it unifies existed specially designed student models based on~~ [http://liris.cnrs.fr/~pchampin/2014/m2iade-ia2/_static/893CorbettAnderson1995.pdf Knowledge Tracing] framework with integrated advantages: ~~compared with the most popular student~~ model, ~~[http://liris~~.~~cnrs.fr/~pchampin/2014/m2iade-ia2/_static/893CorbettAnderson1995.pdf~~ Knowledge Tracing], (1) it improves up to 25% in classification performance, (2) it generates more interpretable, consistent parameters, and (3) it is 300 times faster. In a [[http://adenu.ia.uned.es/workshops/pale2014/pale2014_proceedings_vol1181.pdf#page=9 follow-up study], we compared FAST to the best paper model (a single-purpose model) of the same year with favorable results while FAST is designed as a general-purpose model. The [http://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/84_EDM-2014-Full.pdf main paper] was nominated for the Best Paper Award in a top-tier conference in 2014. Both the main and the follow-up papers were cited by top researchers in the field from Carnegie Mellon University, Stanford, Cornell, ETH Zurich (etc.) with in total 34 citations since 2014 (till 04/2016).		Feature-Aware Student knowledge Tracing (FAST) is a novel, efficient student model created by PAWs lab and Pearson with the state-of-the-art predictive performance. It allows general, flexible features allows into [[http://liris.cnrs.fr/~pchampin/2014/m2iade-ia2/_static/893CorbettAnderson1995.pdf\|Knowledge Tracing]], which is the most popular student model. For the first time it unifies existed specially designed student models based on Knowledge Tracing framework with integrated advantages. We demonstrate FAST’s flexibility with examples of feature sets that are relevant to a wide audience: we haved uses features in FAST to model (i) multiple subskills, (ii) temporal Item Response Theory, and (iii) expert knowledge. Compared with Knowledge Tracing, (1) it improves up to 25% in classification performance, (2) it generates more interpretable, consistent parameters, and (3) it is 300 times faster. In a [[http://adenu.ia.uned.es/workshops/pale2014/pale2014_proceedings_vol1181.pdf#page=9 follow-up study], we compared FAST to the best paper model (a single-purpose model) of the same year with favorable results while FAST is designed as a general-purpose model. The [http://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/84_EDM-2014-Full.pdf main paper] was nominated for the Best Paper Award in a top-tier conference in 2014. Both the main and the follow-up papers were cited by top researchers in the field from Carnegie Mellon University, Stanford, Cornell, ETH Zurich (etc.) with in total 34 citations since 2014 (till 04/2016).

	* Download the source code [http://ml-smores.github.io/fast/ here]		* Download the source code [http://ml-smores.github.io/fast/ here]
	* Read the conference presentation slides [http://www.slideshare.net/huangyun/fast-presentation-48711687 here]		* Read the conference presentation slides [http://www.slideshare.net/huangyun/fast-presentation-48711687 here]
	* Read the tutorial slides presented in EDM 2015 workshop [http://www.slideshare.net/huangyun/2015edm-featureaware-student-knowledge-tracing-tutorial here]		* Read the tutorial slides presented in EDM 2015 workshop [http://www.slideshare.net/huangyun/2015edm-featureaware-student-knowledge-tracing-tutorial here]

Yuh43 at 03:30, 4 April 2016

2016-04-04T03:30:43Z

← Older revision		Revision as of 03:30, 4 April 2016
Line 1:		Line 1:
	Feature-Aware Student knowledge Tracing (FAST) is a new student model created by PAWs lab and Pearson with the state-of-the-art predictive performance. For the first time it unifies existed specially designed student models based on [http://liris.cnrs.fr/~pchampin/2014/m2iade-ia2/_static/893CorbettAnderson1995.pdf Knowledge Tracing] framework with integrated advantages: compared with the most popular student model, [http://liris.cnrs.fr/~pchampin/2014/m2iade-ia2/_static/893CorbettAnderson1995.pdf Knowledge Tracing], (1) it improves up to 25% in classification performance, (2) it generates more interpretable, consistent parameters, and (3) it is 300 times faster. In a follow-up study, we compared FAST to the best paper model (a single-purpose model) of the same year with favorable results while FAST is designed as a general-purpose model. The [http://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/84_EDM-2014-Full.pdf main paper] was nominated for the Best Paper Award in a top-tier conference in 2014. Both the main and the ~~[http://adenu.ia.uned.es/workshops/pale2014/pale2014_proceedings_vol1181.pdf#page=9~~ follow-up] papers were cited by top researchers in the field from Carnegie Mellon University, Stanford, Cornell, ETH Zurich (etc.) with in total 34 citations since 2014 (till 04/2016).		Feature-Aware Student knowledge Tracing (FAST) is a new student model created by PAWs lab and Pearson with the state-of-the-art predictive performance. For the first time it unifies existed specially designed student models based on [http://liris.cnrs.fr/~pchampin/2014/m2iade-ia2/_static/893CorbettAnderson1995.pdf Knowledge Tracing] framework with integrated advantages: compared with the most popular student model, [http://liris.cnrs.fr/~pchampin/2014/m2iade-ia2/_static/893CorbettAnderson1995.pdf Knowledge Tracing], (1) it improves up to 25% in classification performance, (2) it generates more interpretable, consistent parameters, and (3) it is 300 times faster. In a [[http://adenu.ia.uned.es/workshops/pale2014/pale2014_proceedings_vol1181.pdf#page=9 follow-up study], we compared FAST to the best paper model (a single-purpose model) of the same year with favorable results while FAST is designed as a general-purpose model. The [http://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/84_EDM-2014-Full.pdf main paper] was nominated for the Best Paper Award in a top-tier conference in 2014. Both the main and the follow-up papers were cited by top researchers in the field from Carnegie Mellon University, Stanford, Cornell, ETH Zurich (etc.) with in total 34 citations since 2014 (till 04/2016).

	* Download the source code [http://ml-smores.github.io/fast/ here]		* Download the source code [http://ml-smores.github.io/fast/ here]
	* Read the conference presentation slides [http://www.slideshare.net/huangyun/fast-presentation-48711687 here]		* Read the conference presentation slides [http://www.slideshare.net/huangyun/fast-presentation-48711687 here]
	* Read the tutorial slides presented in EDM 2015 workshop [http://www.slideshare.net/huangyun/2015edm-featureaware-student-knowledge-tracing-tutorial here]		* Read the tutorial slides presented in EDM 2015 workshop [http://www.slideshare.net/huangyun/2015edm-featureaware-student-knowledge-tracing-tutorial here]

Yuh43: New page: Feature-Aware Student knowledge Tracing (FAST) is a new student model created by PAWs lab and Pearson with the state-of-the-art predictive performance. For the first time it unifies existe...

2016-04-04T03:29:53Z

New page: Feature-Aware Student knowledge Tracing (FAST) is a new student model created by PAWs lab and Pearson with the state-of-the-art predictive performance. For the first time it unifies existe...

New page

Feature-Aware Student knowledge Tracing (FAST) is a new student model created by PAWs lab and Pearson with the state-of-the-art predictive performance. For the first time it unifies existed specially designed student models based on [http://liris.cnrs.fr/~pchampin/2014/m2iade-ia2/_static/893CorbettAnderson1995.pdf Knowledge Tracing] framework with integrated advantages: compared with the most popular student model, [http://liris.cnrs.fr/~pchampin/2014/m2iade-ia2/_static/893CorbettAnderson1995.pdf Knowledge Tracing], (1) it improves up to 25% in classification performance, (2) it generates more interpretable, consistent parameters, and (3) it is 300 times faster. In a follow-up study, we compared FAST to the best paper model (a single-purpose model) of the same year with favorable results while FAST is designed as a general-purpose model. The [http://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/84_EDM-2014-Full.pdf main paper] was nominated for the Best Paper Award in a top-tier conference in 2014. Both the main and the [http://adenu.ia.uned.es/workshops/pale2014/pale2014_proceedings_vol1181.pdf#page=9 follow-up] papers were cited by top researchers in the field from Carnegie Mellon University, Stanford, Cornell, ETH Zurich (etc.) with in total 34 citations since 2014 (till 04/2016).

* Download the source code [http://ml-smores.github.io/fast/ here]
* Read the conference presentation slides [http://www.slideshare.net/huangyun/fast-presentation-48711687 here]
* Read the tutorial slides presented in EDM 2015 workshop [http://www.slideshare.net/huangyun/2015edm-featureaware-student-knowledge-tracing-tutorial here]