True: You could find the answer using that rule. Formula: ______________ False: To use that rule you’d need to know __________________________
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Table 3
Verbal utterances segmented into complex propositions
Transcribed Utterances Segmented Utterances Because I was thinking it’s the
same as this one – oh, maybe not, though. So this is 41.8 and this is 63, and DAC, CB are – ABC, are - so this is 104.8, right there, so that means that, that means that B is equal to DCB. And so that means that this angle, CB whatever, is 104.8 because they’re corresponding, I think, and then – so you have 180 minus, 180 minus 104.8.
Because I was thinking it’s the same as this one – // oh, maybe not, though. //
So this is 41.8 and this is 63, // and DAC, CB are – ABC, are- // so this is 104.8, right there, //
so that means that, that means that B is equal to DCB.// And so that means that this angle, CB whatever, is 104.8 because they’re corresponding, I think, //
Table 4
Verbal code families, codes, descriptions, and examples
Family Code Description Example
Self-
explanation
Shallow
The student explains reasoning using only a shallow basis, such as: 1) using the numbers 180 or 90 without geometry rationale; 2) using the same measure of other known or solved angles; or 3) using the same rules as the previous angles/problems solved.
And I’m just going to guess 41, since BAR is 41.
Elaboration The student evidences use of prior knowledge without focusing on specific actions, operators, or geometry terms.
So this angle we know is 66.5. [indicating previously solved angle] Goal
Operator
The student names a (sub)goal and explicitly names an operator which will lead to this (sub)goal.
so I’m going to take 41 degrees minus 35.4 to find the new angle Principle-
based
The student focuses on one specific geometry principle, what it means, or how it does/does not apply.
So this has to add 180, since it's a triangle.
Diagram Explanation
Diagram Reading
The student reads off information from the diagram, usually as an angle or line or other geometry figure.
XCT- Diagram
Interaction
The student uses mouse to indicate interaction with diagram or diagram features but does not make a complete explanation.
Uh, that side, that side, and that side.
Diagram Explanation
The student identifies diagram components as being specific geometry elements or diagram features as having specific relationships.
This line here is the transversal because it connects two parallel lines to create these angles.
Monitoring
Positive
Monitoring The student indicates knowledge or understanding.
Okay, that makes sense. Negative
Monitoring
The student indicates confusion or a lack of knowledge about the content or about what to do.
So I’m sort of stuck. Error
Monitoring
The student acknowledges s/he has made a mistake and/or describes the mistake made.
and I was reading it wrong, um, when I tried to put the answer in. Progress
Monitoring
The student reflects on progress made (e.g., summarizes completed work); includes moving on to the next problem.
So that problem is done.
3
Table 4 (cont.)
Family Code Description Example
Planning
Glossary
Planning The student voices plans to use the glossary.
so I’m gonna check the glossary. Hint
Planning The student plans to seek a hint.
So I need a hint and I just click here. Subgoal
Planning
The student mentions which aspect of the task s/he is working on/will work on next.
So now to find the measure of angle IRL.
Techniques
Mathematics Procedures
The student talks about the mathematical procedures being used without explaining geometry components or referring to a goal.
so I’m just gonna go ahead and put 180 minus 72.6 minus 72.6.
Numerical Answer
The student states the numerical measurement only, without explanation, analysis, or elaboration.
Um, so this has to be 35.5. Principle-
Naming (Given)
The principle “given” is attributed, whether as a single word or in a longer narrative.
It’s given.
Principle- Naming (NOT Given)
One of the geometry principles is being named but no actual self- explanation occurs.
Interior angle same side.
Reading / Paraphrasing
Glossary The student reads or paraphrases (part of) the clarifications in the Glossary text.
"Corresponding, equilateral, linear pair."
Hint The student reads or paraphrases (part of) the hints. Includes hints that pop up to offer help to student.
"You can find the measure of NIE using the linear pair postulate.” Problem
Statement
The student reads or paraphrases the problem or given information presented.
"Find the measure of angle IRL."
Tutor Interaction
Feedback Response
The student acknowledges system feedback. Yes. And that is right.
Narration The student narrates his or her noncontent-related interactions with the tutor.
Now I’ll hit enter.
Tutor
The student comments on the tutor interface, the appearance of the problems on screen (e.g., question mark placement or
highlighting), or anything related to superficial aspects of the tutor.
It's waiting.
CHAPTER 3
RESULTS
Analyses Learning Assessments
Previous research found a learning benefit for students who highlighted diagram elements to justify geometry problem-solving. Thus, we used pre- and posttest
assessments to determine if the effect would be replicated. Analyses for the practiced problem-solving component of pre-, post-, and delayed posttests were conducted using a repeated measures multivariate analysis of variance (RM-MANOVA). A mixed design was used where the between-subjects factor was Cognitive Tutor version (visual explanations condition vs. control condition). The within-subjects factor was test time (pretest, posttest, and delayed posttest). Dependent measures included performance on the skills practiced in the tutor: angle measures and geometry principle names. These scores were calculated as percent correct.
Because posttests were administered immediately after the intervention as well as 1 week later, analyses for transfer items were conducted using a repeated measures multivariate analysis of variance (RM-MANOVA). The between-subjects factor was Cognitive Tutor version, and the within-subjects factor was test time (posttest vs.
delayed posttest). Dependent measures included performance on visual and explanation transfer items during the posttest and delayed posttest.
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Self-explanations
An important research question was how highlighting relevant diagram elements might influence students’ processes of self-explanation. To examine potential differences in overall verbal output as well as potential differences in self-explanation patterns, a multivariate analysis of variance (MANOVA) was performed. The independent variable was condition, and the dependent variables included the total number of utterances, the percent of total utterances for each code family, and the percent of family utterances for each individual code. To control for overall verbosity, the numbers of coded utterances in each code families were examined as percent of total utterances. To examine patterns within each code family, individual codes were examined as a percentage of their respective code families. Five codes were excluded from the individual and family code analyses because of low relevance or low incidence. Principle-naming of given rules was excluded from the analysis because it meant that students simply were noting that they had used “given” information to fill in angle measures provided by the tutor at the
beginning of each problem. Similarly, problem statement reading was excluded since all students generally read the problem statements to begin each problem and seldom referred back to them. The entire tutor code family was excluded because its codes tended to be very low in quantity across all participants, and tutor-specific utterances were not theoretically meaningful to the purpose of the study (i.e., these comments refer to design features rather than domain-based thinking about tutored content).
Self-explanations and Learning Assessments
How do specific self-explanations contribute to retention and transfer? To answer this question, we examined the relationship between significant diagram codes and learning measures. Bivariate correlations were calculated to examine the relationship between individual explanation codes and practiced and transfer scores from both the
43
posttest and delayed posttest assessments. To control for overall verbosity, correlations used the percent of total utterances for individual codes.
For all analyses, main effects and interactions of the independent variables were examined and conclusions were based on a standard alpha level of .05.
Findings
Learning Assessments Practiced Items
The Geometry Cognitive Tutor has been shown to produce significant learning gains (Aleven & Koedinger, 2002; Butcher & Aleven, 2008); and, as expected, the RM- MANOVA showed a significant main effect for test time (F(4, 15) = 11.97, p < .001). There was no significant effect for condition (F < 1), and no interaction between condition and test time (F < 1). Univariate tests showed that the test time effect was significant for both angle measures (F(2, 36) = 22.13, p < .001) and for naming geometry rules (F(2, 36) = 23.62, p < .001). A pairwise comparison (with Bonferroni’s adjustment) found that overall learning gains on practiced items were significant between the pretest and posttest (Mdiff = .35, p < .001) and between the pretest and delayed posttest (Mdiff = .30, p < .001); the changes between posttest and delayed posttest were not significant (Mdiff = .04, p > .99).). Means and standard deviations are shown in Table 5.
Transfer Items
On items of transfer (visual transfer and explanation transfer), the RM-MANOVA showed there was no significant main effect for condition (F(2, 17) = 1.02, p = .38) or test time (F(2, 17) = 1.36, p = .28), and no interaction between condition and test time (F < 1). See Table 6 for means and standard deviations.
44
Self-explanations
In terms of overall verbal output, the two conditions were similar. Means and standard deviations for family codes as percent of total utterances are listed in Table 7. Overall, the MANOVA showed no significant effect for condition (F(6, 13) = 2.12, p = .12). Within the univariate analyses, some dependent variables did not differ significantly between conditions while others did. Total utterances (the total number of coded
utterances) showed no main effect of condition (F(1, 18) = 2.80, p = .11). The code families that did not differ significantly by condition included: self-explanations (F < 1); monitoring statements (F < 1); and, planning statements (F < 1). The reading code family showed a nonsignificant trend (F(1, 18) = 4.16, p = .06) with participants in the visual explanations condition reading slightly less (M = 17.1, SD = 4.2) than the control condition (M = 21.3, SD = 5.0). Reading utterances for this analysis included the reading of glossary
information or hints. Two code families showed significant differences in the percentage of total utterances by condition: diagram explanations, and technique utterances. The percent of diagram explanations produced by participants was higher in the visual explanations condition (M = 24.9, SD = 6.9) than in the control condition (M = 18.3, SD = 4.9; F(1, 18) = 6.12, p = .02). Diagram explanation statements included diagram reading, diagram interactions, and diagram explanations. A significant effect was also seen for the percent of techniques described in utterances (F(1, 18) = 4.9, p = .04): participants in the control condition talked about techniques more frequently (M = 11.6, SD = 3.1) than students in the visual explanations condition (M = 8.7, SD = 2.5). Techniques consisted of problem-solving strategies that were not explained or tied into geometry principles (e.g., stating unexplained mathematical operations or single numerical answers, and mentioning principle names without elaboration).
Overall, condition did not have a significant effect on the distribution of individual codes within code families (F(1,18) = 70.9, p = .09). However, four codes differed
45
significantly between conditions (see Table 8 for means and standard deviations for individual codes). For the visual explanations condition, subgoal planning utterances represented a higher proportion of the planning code family (M = .67, SD = .14) than seen for the control condition (M = .48, SD = .13; F(1, 18) = 9.45, p < .01). Subgoal planning consisted of students referring to the part of the problem they planned to work on next. The visual explanations condition also produced a higher proportion of
numerical answers (M = .46, SD = .16) in the technique family than the control condition (M = .30, SD = .10; F(1, 18) = 6.84, p = .02); the numerical answer technique was used to code utterances stating angle measurements without elaboration. For a visualization of the distribution of individual codes within each family by condition, see Figures 12-17.
The two other individual codes found to differ between conditions belonged to the diagram explanations code family. As noted earlier, the diagram explanations code family included specific codes that indicate how deeply the learner was utilizing the visual information: diagram reading, diagram interactions, and diagram explanations. Diagram reading consisted only of reading from the diagram and was considered a shallow utterance; diagram explanations were considered deep utterances, consisting of more complete explanations of how geometry principles applied to the diagram. Diagram interactions consisted of more content than diagram reading but less than a complete explanation. Students prompted to highlight diagram elements in the tutor did not differ from the control condition in their proportion of diagram interactions (F < 1). However, as seen in Figure 13, students who were prompted to highlight diagram elements produced a higher proportion of deep diagram explanations (M = .48, SD = .16) than students in the control condition (M = .28, SD = .15; F(1,18) = 8.78, p < .01). Conversely, students who were prompted to highlight diagram elements produced a smaller proportion of shallow diagram reading (M = .14, SD = .08) than students in the control condition (M = .34, SD = .22; F(1,18) = 6.63, p = .02).
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Self-explanations and Learning Assessments
Regarding the relationship between diagram explanation family codes and learning outcomes, the percent of total utterances coded as diagram reading was inversely related to performance on nearly all practiced and transfer items on the posttest and delayed posttest (see Table 9). No correlations between deep diagram explanations and learning outcomes reached significance at an alpha of .05. However, there was a slight – though nonsignificant – pattern to the correlations across the assessments. All learning outcome measures on the posttest and delayed posttest correlated negatively with the percent of diagram reading codes and all but one correlated positively with the percent of diagram explanation codes.
Because of the trend differentiating the overall quantity of reading utterances between conditions, the relationship between reading family codes and learning outcomes was also explored. The reading category was comprised of two different sources of information (reading hints vs. reading glossary items). Whereas students who focus on reading and considering glossary items are working more generatively in the system, an overreliance on hints has been shown to be a shallow strategy. Thus, correlations between posttests and delayed posttest items and the percent of total utterances made up by reading from these specific types of information sources were investigated. Overall, reading is negatively correlated to performance on the posttest and delayed posttest; however, these correlations are not significant. Although both
conditions were similar in the proportions of their reading belonging to these two sources, hint reading made up a majority of reading for both the control condition (M = 62.3, SD = 29.8) as well as the visual explanations condition (M = 68.7, SD = 30.4). The percent of total utterances coded as glossary reading was positively correlated with every learning outcome; however, only its correlation with performance on posttest practiced items reached significance at an alpha of .05 (r = .589, p < .01). Meanwhile,
47
the percent of total utterances coded as hint reading was significantly, negatively
correlated with every learning outcome. See Table 10 for the magnitude and significance level of these correlations.
Figure 12. The percent of total utterances coded in the self-explanation code family by condition (left); and, the distribution of individual codes within the self-explanation code family by condition (right).
0 5 10 15 20 25 30 % of Tot al U tt er ances
Visual Explanations Control
0 20 40 60 80 100
Shallow Elaboration Goal-operator Principle-based
% of Fam ily U tt er ances
Visual Explanations Control
Overall Self-explanation Utterances
Distribution of Self-explanation Utterances
4
Figure 13. The percent of total utterances coded in the diagram explanation code family by condition (left); and, the distribution of individual codes within the diagram explanation code family by condition (right).
0 5 10 15 20 25 30 % of Tot al U tt er ances
Visual Explanations Control
0 20 40 60 80 100 Diagram Reading Diagram Interaction Diagram Explanation % of Fam ily U tt er ances
Visual Explanations Control
Overall Diagram Explanation Utterances
Distribution of Diagram Explanation Utterances
4
Figure 14. The percent of total utterances coded in the monitoring code family by condition (left); and, the distribution of individual codes within the monitoring code family by condition (right).
0 5 10 15 20 25 30 % of Tot al U tt er ances
Visual Explanations Control
0 20 40 60 80 100
Positive Negative Error Progress
% of Fam ily U tt er ances
Visual Explanations Control
Overall Monitoring Utterances Distribution of Monitoring Utterances
Figure 15. The percent of total utterances coded in the planning code family by condition (left); and, the distribution of individual codes within the planning code family by condition (right).
0 5 10 15 20 25 30 % of Tot al U tt er ances
Visual Explanations Control
0 20 40 60 80 100
Glossary Hint Subgoal
% of Fam ily U tt er ances
Visual Explanations Control
Overall Planning Utterances Distribution of Planning Utterances
5
Figure 16. The percent of total utterances coded in the techniques code family by condition (left); and, the distribution of individual codes within the techniques code family by condition (right).
0 5 10 15 20 25 30 % of Tot al U tt er ances
Visual Explanations Control
0 20 40 60 80 100
Numerical Answer Mathematics Principle-naming
% of Fam ily U tt er ances
Visual Explanations Control
Overall Technique Utterances Distribution of Technique Utterances
5
Figure 17. The percent of total utterances coded in the reading code family by condition (left); and, the distribution of individual codes within the reading code family by condition (right).
0 5 10 15 20 25 30 % of Tot al U tt er ances
Visual Explanations Control
0 20 40 60 80 100 Glossary Hints % of Fam ily U tt er ances
Visual Explanations Control
Overall Reading Utterances Distribution of Reading Utterances
5
Table 5
Percent Correct Means (and Standard Deviations) for Practiced Items
Visual Explanations (n=10) Control (n=10)
DV
Pretest Posttest
Delayed
Posttest Pretest Posttest
Delayed Posttest Angle Measures 43.3 (26.9) 74.3 (29.2) 72.9 (24.7) 48.9 (23.5) 87.1 (21.8) 80.0 (23.5) Geometry Rule Names 2.2 (4.7) 31.4 (28.4) 34.3 (28.7) 0 (0) 47.1 (41.5) 45.7 (39.2) Table 6
Percent Correct Means (and Standard Deviations) for Transfer Items
Visual Explanations (n=10) Control (n=10)
DV Posttest Delayed Posttest Posttest Delayed Posttest
Visual Items 70.0 (14.0) 69.5 (15.3) 72.5 (11.2) 71.5 (14.0)
Explanation Items 51.4 (36.9) 61.4 (28.8) 67.9 (30.7) 73.6 (29.4)
5
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Table 7
Percent of Total Utterances (and Standard Deviations) for Code Families Code Family Visual Explanations
(n=10) Control (n=10) Self-explanations 13.6 (4.7) 13.4 (3.2) Diagram Explanations 24.9 (6.9) 18.3 (4.9) Monitoring 23.4 (7.6) 23.4 (7.0) Planning 12.3 (4.5) 12.0 (5.1) Techniques 8.7 (2.5) 11.6 (3.1) Reading 17.1 (4.2) 21.3 (5.0) Table 8
Percent (and Standard Deviations) of Code Family for Individual Codes
Code Family Code
Visual Explanations (n=10) Control (n=10) Self- explanation Shallow 62.3 (13.1) 56.6 (16.7) Elaboration 23.1 (10.0) 26.2 (10.7) Goal-operator 6.1 (6.5) 3.8 (4.6) Principle-based 8.5 (8.3) 13.4 (7.7) Diagram Explanation Diagram Reading 14.1 (8.8) 33.7 (22.4) Diagram Interaction 37.8 (11.6) 37.9 (14.7) Diagram Explanation 48.2 (15.6) 28.2 (14.6) Monitoring Positive Monitoring 32.2 (12.7) 38.5 (11.3) Negative Monitoring 50.1 (12.6) 46.8 (13.2) Error Monitoring 9.4 (6.3) 7.2 (6.1) Progress Monitoring 8.3 (5.9) 7.2 (4.1) Planning Glossary Planning 8.8 (8.7) 13.8 (10.0) Hint Planning 24.6 (13.0) 38.2 (17.7) Subgoal Planning 66.8 (14.4) 47.7 (13.3) Techniques Mathematics Procedures 32.8 (16.6) 40.8 (9.0) Numerical Answer 45.8 (16.0) 30.0 (10.4)
Principle-naming (NOT Given) 21.3 (10.8) 29.1 (16.2) Reading /
Paraphrasing
Glossary 31.3 (30.4) 37.8 (29.9)
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Table 9
Correlations between percent of diagram reading and diagram explanation and the posttest and delayed posttest learning assessments
Posttest Delayed Posttest
Practiced Visual Transfer Explanation Transfer Practiced Visual Transfer Explanation Transfer Diagram Reading -.526* -.482* -.504* -.573** -.506* -.397 Diagram Explanations .118 .302 -.051 .062 .151 .116
** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).
Table 10
Correlations between overall reading, proportion of glossary reading, and proportion of hint reading and the posttest and delayed posttest learning assessments
Posttest Delayed Posttest
Practiced Visual Transfer Explanation Transfer Practiced Visual Transfer Explanation Transfer Overall Reading -.107 -.166 -.083 -.239 -.283 -.228 Glossary Reading .589** .432 .384 .423 .401 .377 Hint Reading -.686** -.574** -.457* -.624** -.637** -.567** ** Correlation is significant at the 0.01 level (2-tailed).
CHAPTER 4
DISCUSSION
Does Highlighting Diagram Elements Result in Improved