The Effect of an Intelligent Tutor on Performance on Specific Posttest Problems
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| Title: | The Effect of an Intelligent Tutor on Performance on Specific Posttest Problems |
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| Language: | English |
| Authors: | Sales, Adam, Prihar, Ethan, Heffernan, Neil, Pane, John F. |
| Source: | International Educational Data Mining Society. 2021. |
| Availability: | International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/ |
| Peer Reviewed: | Y |
| Page Count: | 10 |
| Publication Date: | 2021 |
| Document Type: | Speeches/Meeting Papers Reports - Research |
| Education Level: | Elementary Secondary Education |
| Descriptors: | Intelligent Tutoring Systems, Academic Achievement, Educational Technology, Algebra, Item Response Theory, Hierarchical Linear Modeling, Program Effectiveness, Pretests Posttests, Achievement Tests, Elementary Secondary Education, Standardized Tests |
| Assessment and Survey Identifiers: | TerraNova Multiple Assessments |
| Abstract: | This paper drills deeper into the documented effects of the Cognitive Tutor Algebra I and ASSISTments intelligent tutoring systems by estimating their effects on specific problems. We start by describing a multilevel Rasch-type model that facilitates testing for differences in the effects between problems and precise problem-specific effect estimation without the need for multiple comparisons corrections. We find that the effects of both intelligent tutors vary between problems-- the effects are positive for some, negative for others, and undeterminable for the rest. Next we explore hypotheses explaining why effects might be larger for some problems than for others. In the case of ASSISTments, there is no evidence that problems that are more closely related to students' work in the tutor displayed larger treatment effects. [For the full proceedings, see ED615472.] |
| Abstractor: | As Provided |
| Entry Date: | 2021 |
| Accession Number: | ED615618 |
| Database: | ERIC |
| Abstract: | This paper drills deeper into the documented effects of the Cognitive Tutor Algebra I and ASSISTments intelligent tutoring systems by estimating their effects on specific problems. We start by describing a multilevel Rasch-type model that facilitates testing for differences in the effects between problems and precise problem-specific effect estimation without the need for multiple comparisons corrections. We find that the effects of both intelligent tutors vary between problems-- the effects are positive for some, negative for others, and undeterminable for the rest. Next we explore hypotheses explaining why effects might be larger for some problems than for others. In the case of ASSISTments, there is no evidence that problems that are more closely related to students' work in the tutor displayed larger treatment effects. [For the full proceedings, see ED615472.] |
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