Workshop at AIED 2005
12th International Conference on Artificial Intelligence in Education
Sequencing Vocabulary Instruction: Artificial vs. Real Users
There are various widely researched strategies that appear to be helpful in some, but not necessarily all vocabulary learning situations. However, an early report suggested that an extremely simple strategy, in which only the ordering of the material presented is varied, might have very substantial effects on learning and recall. These observations have been used as the basis of many subsequent developments, but rarely been subject to rigorous examination and replication. We have recently been examining both the theoretical foundation, and the practical implementation, of this latter approach. In this paper we present a comparison of data obtained using virtual users, operating in accordance with the underlying theory of memory, with the earlier experimental data obtained with real users.
to a Histogram-Based Student Modeling Approach to Facilitate
In this paper we
describe our intended approach to student modeling for language tutoring in
the context of a project titled "Teaching and
Learning Linguistically Complex Languages", recently funded
by the United States Department of Education under the Title VI International
Research and Studies Program. The
project aims to support foreign language learning and to enhance cross-cultural
understanding by producing substantive textual and lexical learning materials
and computer-based instructional tools that aid learners in reading authentic
materials in languages that present special difficulties for reading. The specific goals of the project are:
Speech Recognition to Construct a Student Model for a
Intelligent Tutoring Systems derive much of their power from having a student model that describes the learner's competencies. However, constructing a student model is challenging for computer tutors that use automated speech recognition (ASR) as input, due to inherent inaccuracies in ASR. We describe two extremely simplified models of developing word decoding skills and explore whether there is sufficient information in ASR output to determine which model fits student performance better, and under what circumstances is one model preferable to another. The two models described are a lexical model that assumes students learn words as whole-unit chunks, and a grapheme-to-phoneme (G->P) model that assumes students learn the individual letter-to-sound mappings that compose the words. We use the data collected by the ASR to show that the G->P model better describes student performance than the lexical model. We then determine which model performs better under what conditions. On one hand, the G->P model better correlates with student performance data when a student is older, more proficient at grapheme-to-phoneme mappings, or when the word is more difficult to read/spell. On the other hand, the lexical model better correlates with student performance data when the student has seen the word more times.
Using speech recognition to model children's reading skill development
Intelligent computer tutors can derive much of their power from having a student model that describes the learner's competencies. However, constructing a student model is challenging for computer tutors that use automated speech recognition (ASR) as input. This paper reports using ASR output from a computer tutor for reading to compare two models of how students learn to read words: a model that assumes students learn words as whole-unit chunks, and a model that assumes students learn the individual letter->sound mappings that make up words. We use the data collected by the ASR to show that a model of letter->sound mappings better describes student performance. We then compare using the student model and the ASR, both alone and in combination, to predict which words the student will read correctly, as scored by a human transcriber. Surprisingly, majority class has a higher classification accuracy than the ASR. However, we demonstrate that the ASR output still has useful information, and that classification accuracy is not a good metric for this task, and the Area Under Curve (AUC) of ROC curves is a superior scoring method. The AUC of the student model is statistically reliably better (0.670 vs. 0.550) than that of the ASR, which in turn is reliably better than majority class. These results show that ASR can be used to compare theories of how students learn to read words, and modeling individual learner's proficiencies may enable improved speech recognition.
Student Knowledge in an Oral
Guided oral reading has been shown to have positive pedagogical value;
our Reading Companion provides a shared reading experience in which
students read on-screen books aloud guided by the Companion, which offers
scaffolded modeling of expert skill and feedback based on speech recognition.
A student model is maintained for each student. This model tracks student
performance and decoding knowledge in terms of decoding/pronunciation
generalizations and surface word features.
MAC: Adaptive, perception-based speech remediation s/w for mobile devices
In this paper, we present a mobile adaptive computer assisted language learning (MAC) software aimed to help Japanese-English speakers in perceptually distinguishing the non-native /r/ vs. /l/ English phonemic contrast with a view to improving their own English pronunciation in this regard. The software is adaptive and more practice is given for the learner on contrasts that are most difficult for them, and the learners themselves choose their level of adaptation. MAC is implemented in Java (J2ME), allowing the software to be used on a wide range of mobile devices including most recent mobile phones. This allows the application to be used anywhere and anytime, on a device that the learner probably already owns.