Titlebook: Artificial Intelligence in Education; 22nd International C Ido Roll,Danielle McNamara,Vania Dimitrova Conference proceedings 2021 Springer

并置

0302-9743 e in Education, AIED 2021, held in Utrecht, The Netherlands, in June 2021.*.The 40 full papers presented together with 76 short papers, 2 panels papers, 4 industry papers, 4 doctoral consortium, and 6 workshop papers were carefully reviewed and selected from 209 submissions. The conference provides

Geyser

Matteo Moscatelli,Donatella Bramanti the proposed framework uses IRT to average prediction scores from various AES models while considering the characteristics of each model for evaluation of examinee ability. This study demonstrates that the proposed framework provides higher accuracy than individual AES models and simple averaging methods.

aspersion

Detlev Lück,Eric D. Widmer,Vida Česnuitytėiveness and efficiency of these systems. This challenge has been cited by a number of researchers as one of the most important for the field of AIED. In this paper, we discuss existing progress towards resolving this challenge, break down five sub-challenges, and propose how to address the sub-challenges.

配偶

https://doi.org/10.1007/978-3-030-71169-6bserved systematicity in machine error, namely, that cases with low estimated reading accuracy are harder to score correctly for fluency. We show that the method yields an improved performance, including on out-of-domain data.

Noctambulant

Integration of Automated Essay Scoring Models Using Item Response Theory the proposed framework uses IRT to average prediction scores from various AES models while considering the characteristics of each model for evaluation of examinee ability. This study demonstrates that the proposed framework provides higher accuracy than individual AES models and simple averaging methods.

合法

Badger

Exploiting Structured Error to Improve Automated Scoring of Oral Reading Fluencybserved systematicity in machine error, namely, that cases with low estimated reading accuracy are harder to score correctly for fluency. We show that the method yields an improved performance, including on out-of-domain data.

BAIL

Diane S. Lauderdale,Jen-Hao Chenrovided vocabulary lists, we compare them to the vocabulary needed by 37 Syrian refugees living in Lebanon and Germany. We show that the vocabulary provided by the Cambridge English List and Duolingo has low usefulness and low efficiency and discuss future directions for personal vocabulary recommendations.

ALIAS

Diane S. Lauderdale,Jen-Hao Chenaper, we introduce Social Coherence (SC), another marker of collaboration, and our analysis shows that WC-GCMS is sensitive to the SC level of group discourse, further validating the potency of the metric.

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