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Table 1: Student and Tutor Characteristics in Human-Human Speech and Text Conditions

in A Comparison of Tutor and Student Behavior in Speech Versus Text Based Tutoring
by Carolyn P. Rose, Diane Litman, Dumisizwe Bhembe, Kate Forbes, Scott Silliman, Ramesh Srivastava, Kurt Vanlehn 2003
"... In PAGE 6: ... We hope to have an analysis cov- ering all of our data in both conditions by the time of the workshop. As shown in Table1 , analysis of the data that has been collected and transcribed to date is already show- ing interesting differences between the ITSPOKE (spo- ken) and WHY2-ATLAS (text) corpora of human-human dialogues. The #trns columns show mean and standard deviation for the total number of turns taken by the stu- dents or tutor in each problem dialogue, while the next pair of columns show the mean and standard deviation for the total number of words spoken or typed by the stu- dents or tutor (#wds) in each problem dialogue.... ..."
Cited by 6

Table 3. Performance Measures when interacting with human tutors

in Natural Language Generation for Intelligent Tutoring Systems: a case study
by unknown authors
"... In PAGE 7: ... Unfortunately, when we collected our naturalistic data, we did not have students take the post-test. However, performance measures were automatically collected, and they are reported in Table3 (as in Table 2, measures other than reading times are cumulative across the three problems). If we compare Tables 2 and 3, it is apparent that when interacting with a human tutor,... ..."

Table 3: Tutor mediation for Peer Tutoring

in Scaffolding Group Learning in a Collaborative Networked Environment
by Amy S. Wu , Rob Farrell, Mark K. Singley 2002
"... In PAGE 8: ... Information was directed from the tutor to the student, just as we saw in the Expert Tutoring condition. As shown in Table3 , our data set demonstrates that a peer, with a lower average number of interactions, intervenes slightly less than a teacher. The average number of interactions between the teacher and the student was 10.... ..."
Cited by 3

Table 3. Main elements of interactivity in Cognitive Tutors

in Exploring the Assistance Dilemma in Experiments with Cognitive Tutors
by Kenneth R. Koedinger, Vincent Aleven
"... In PAGE 18: ... This tough challenge for tutor designers is discussed further below. Interactivity 4: Knowledge component assessment and mastery learning The third main form of interactivity in Cognitive Tutors (see Table3 ) is the mastery learning method. It represents a form of assistance giving that involves choosing problems for students to solve as opposed to students choosing problem themselves.... In PAGE 19: ... Implications The studies presented above provide strong evidence for the effectiveness of Cognitive Tutors over other forms of instruction, including typical classroom instruction. They also support the main interactive features listed in Table3 (yes/no feedback, on-demand hints, and mastery learning). With respect to the information giving/withholding dimension, the results of the studies on yes/no feedback, feedback content, and hint timing consistently indicate that giving information after a problem-solving step is better than withholding it.... ..."

Table 7: Example hint sequence in the Area unit of the Geometry Cognitive Tutor

in unknown title
by unknown authors 1998
"... In PAGE 30: ... This data set was collected in a different school in a different year, compared to the data from the Angles unit presented earlier. The hint sequences in the Area unit, illustrated in Table7 , have a different underlying hint plan, compared to those in the Angles unit. First, the hint sequences in the Table 7: Example hint sequence in the Area unit of the Geometry Cognitive Tutor... ..."
Cited by 3

Table 7: Example hint sequence in the Area unit of the Geometry Cognitive Tutor

in Towards Computer-Based Tutoring of Help-Seeking Skills Aleven
by Vincent Aleven, Bruce M. Mclaren, Kenneth R. Koedinger 1998
"... In PAGE 31: ... This data set was collected in a different school in a different year, compared to the data from the Angles unit presented earlier. The hint sequences in the Area unit, illustrated in Table7 , have a different underlying hint plan, compared to those in the Angles unit. First, the hint sequences in the Table 7: Example hint sequence in the Area unit of the Geometry Cognitive Tutor... ..."
Cited by 3

Table 3. The Summary of Human Tutoring Sessions K30 - K38

in An Analysis of Multiple Tutoring Protocols
by Byung-in Cho, Joel A. Michel, Martha W. Evens
"... In PAGE 7: ...75 - 3 0.33 - CVP: Centrifuge Procedure TPR: Alpha-adrenergic Procedure Table3 summarizes sessions K30 - K38, which are the input to the rule induction program. = means the transcript does not have the stage data.... ..."

Table 3. The Summary of Human Tutoring Sessions K30 - K38

in 1 An Analysis of Multiple Tutoring Protocols 1
by Byung-in Cho, Joel A. Michael, Allen A. Rovick, Martha W. Evens
"... In PAGE 7: ...75 - 30.3 - CVP: Centrifuge Procedure TPR: Alpha-adrenergic Procedure Table3 summarizes sessions K30 - K38, which are the input to the rule induction program. = means the transcript does not have the stage data.... ..."

Table 1. Data on Pseudo Tutor Development and Instructional Use (in Minutes)

in Opening the Door to Non-Programmers: Authoring Intelligent Tutor Behavior by Demonstration
by Kenneth R. Koedinger, Vincent Aleven, Neil Heffernan, Bruce Mclaren, Matthew Hockenberry 2004
"... In PAGE 8: ... o The Language Learning: Classroom Project: Four students in a Language Tech- nologies course at CMU used the Pseudo Tutor technology to each build two prototype Pseudo Tutors related to language learning. In order to estimate the development time to instructional time ratio, we asked the authors on each project, after they had completed a set of Pseudo Tutors, to estimate the time spent on design and development tasks and the expected instructional time of the resulting Pseudo Tutors (see Table1 ). Design time is the amount of time spent selecting and researching problems, and structuring those problems on paper.... In PAGE 11: ... Pseudo Tutor authoring opens the door to new developers who have limited programming skills. While the Pseudo Tutor development time estimates in Table1 compare favorably to past estimates for intelligent tutor development, they must be considered with caution. Not only are the these estimates rough, there are differences in the quality of the tutors produced where most Pseudo Tutors to date have been ready for initial lab testing (alpha versions) and past Cognitive tutors have been ready for extensive classroom use (beta+ versions).... ..."
Cited by 22

Table 1. Data on Pseudo Tutor Development and Instructional Use (in Minutes)

in Opening the Door to Non-Programmers: Authoring Intelligent Tutor Behavior by Demonstration
by Kenneth R. Koedinger, Vincent Aleven, Neil Heffernan, Bruce McLaren, Matthew Hockenberry
"... In PAGE 8: ... o The Language Learning: Classroom Project: Four students in a Language Tech- nologies course at CMU used the Pseudo Tutor technology to each build two prototype Pseudo Tutors related to language learning. In order to estimate the development time to instructional time ratio, we asked the authors on each project, after they had completed a set of Pseudo Tutors, to estimate the time spent on design and development tasks and the expected instructional time of the resulting Pseudo Tutors (see Table1 ). Design time is the amount of time spent selecting and researching problems, and structuring those problems on paper.... In PAGE 11: ... Pseudo Tutor authoring opens the door to new developers who have limited programming skills. While the Pseudo Tutor development time estimates in Table1 compare favorably to past estimates for intelligent tutor development, they must be considered with caution. Not only are the these estimates rough, there are differences in the quality of the tutors produced where most Pseudo Tutors to date have been ready for initial lab testing (alpha versions) and past Cognitive tutors have been ready for extensive classroom use (beta+ versions).... ..."
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