Difference between revisions of "Sequencing learning with multiple representations of rational numbers (Aleven, Rummel, & Rau)"
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Contents
Learning with Multiple Reprsentations in a Complex, Realworld Domain: Intelligent Tutoring Systems for Fractions
Vincent Aleven, Nikol Rummel, and Martina Rau
Summary Table
PIs  Vincent Aleven & Nikol Rummel 
Other Contributers  Graduate Students: Martina Rau (CMU HCII) 
2008 study  N = 132 6thgrade students 
2009 study  N = 388 5th and 6thgrade students 
2010 study  N = 690 4th and 5thgrade students 
Study Start Date  September 1st, 2008 
Study End Date  August 31st, 2012 
Total Number of Students to date  N = 1210 
Total Participant Hours  ~6000 
Data available in DataShop  Dataset: Fraction Study Spring 2009 (log data only)Dataset: Mathtutor Fractions MERs Spring 2009 (revised)

Abstract
We investigate a key issue in coordinative learning, namely, how learning with multiple graphical representations should bue used to effectively support students’ conceptual understanding of fractions. In a previous experiment (Rau, Aleven, & Rummel, 2009), we demonstrated that students benefit from learning with multiple graphical representations when compared to a single graphical representation, provided that they were prompted to relate the graphical representations to the symbolic representation of fractions (e.g., 1/2). In two consecutive studies, we investigated how multiple representations should be sequenced. Prior research on contextual interference has demonstrated that interleaving different types of learning tasks can foster a deep understanding of the underlying concepts. Do the same advantages apply to interleaving representations? In future studies, we plan to investigate ways to explicitly support students in relating the different graphical representations to one another. We focus on fractions as a challenging topic area for students in which multiple representations are often used and likely to support robust learning. This research will contribute to the literature on early mathematics learning, learning with multiple representations, and learning with intelligent tutoring systems. It will also add to the portfolio of studies in the PSLC’s coordinative learning cluster.
Background & Significance
A quintessential form of coordinative learning occurs when learners work with multiple external representations (MERs) of subject matter. Accumulating evidence points towards the promise of learning with MERs (Ainsworth, Bibby, & Wood, 2002; Larkin & Simon, 1987; Seufert, 2003), and also to the need for students to make sense out of the different representations by connecting and abstracting from them (Ainsworth, 1999).
This research focuses on a difficult area of early mathematics learning: fractions. Both teachers’ experiences and research in educational psychology show that students have difficulties with fraction arithmetic and with the various representations for fractions (e.g. Brinker, 1997; Callingham & Watson, 2004; Caney & Watson, 2003; Person et al., 2004; PittaPantazi, Gray & Christou, 2004). Coordinating between MERs is regarded as a key process for learning across areas of mathematics (Kilpatrick, Swafford, & Findell, 2001; NCTM, 2000), including fractions (e.g. Kieren, 1993; Moss & Case, 1999; Martinie & BayWilliams, 2003; Thompson & Saldanha, 2003).
A number of authors have argued, based on observational studies, that MERs can lead to deeper conceptual understanding of fractions (Corwin et al., 1990; Cramer et al., 1997a, 1997b; Steiner & Stoeckling, 1997). However, we know of no experimental studies that have investigated the advantages of instruction with multiple (graphical) fraction representations over instruction that focuses on a single representation, with one exception: an in vivo experiment, in which 132 6thgrade students used four versions of CTATbuilt tutors (Rau, Aleven, & Rummel, 2009). Students learning with MERs and prompted to selfexplain performed best on a posttest and delayed posttest assessing procedural and conceptual knowledge of fractions.
At this point, however, we do not know enough about the circumstances that may influence the effectiveness of learning with multiple representations of fractions, a criticism that has been leveraged against the existing body of research on learning with MERs more generally (Ainsworth, 2006; Goldman, 2003). Learning with multiple representations is challenging. An important prerequisite for benefiting from the multiplicity of multiple graphical representations is that students conceptually understand each one of them (Ainsworth, 2006).
When designing intelligent tutoring systems that use multiple graphical representations, designers must decide how to temporally sequence the GRs. How often should the curriculum alternate between multiple graphical representations? Practice schedules are likely to impact how students understand each GR. In particular, it may matter whether items with the same attributes (e.g., task types) are practiced in a “blocked” manner (e.g., A – A – B – B) or are interleaved with practice of other item types (e.g., A – B – A – B). Research on contextual interference shows that interleaving task types leads to better learning results than blocked practice [5, 6]. A common interpretation of this finding is that interleaved practice encourages deep processing [6]. Since students cannot hold all relevant knowledge components in working memory, they must reactivate taskspecific knowledge components as they come up again in the task sequence.
The presented research investigates the effect of sequencing multiple graphical representations on students' learning of fractions.
Glossary
 Conceptual knowledge: knowledge about the rationale of a solution procedure
 Procedural knowledge: knowledge of the components of a correct procedure involving knowledge about stepbystep actions for solving problems
Research questions
 Which task attribute(s) should designers of intelligent tutoring systems interleave? Should we interleaved task types or multiple graphical representations?
 Sequencing multiple graphical representations Do students benefit most from blocked or interleaved multiple graphical representations when task types are interleaved?
Hypotheses
 We hypothesize that a mix of these two designs (i.e., an intermediate position on the continuum between highly infrequent and highly frequent switching between the representations) would be best as it allows learners to gain some experience with one representation before moving on to the next, but also facilitates making connections across representations as the (temporal) distance between representations is smaller than in the highly infrequently switching design.
 We hypothesize that gaining fluency with each of the representations is more important at the beginning of a tutoring session than towards the end. Therefore, we expect a sequence that transitions from infrequent to frequent switching between representations to be more effective than the extremes of the continuum between highly infrequent and highly infrequent switching between the representations.
2009
Dependent variables
 Previously validated pretest, immediate posttest, and delayed posttest measuring student performance on:
 Representational knowledge
 Operational knowledge
 Log data collected during tutor use, used to assess:
 Learning curves
 Time on task
 Error rates
 Hint usage
Independent Variables
 Blocked representations / interleaved topics – representations are blocked while topics are interleaved (students switch topics after every 18 problems)
 Fully interleaved representations / blocked topics – representations are highly interleaved (students switch representations after each problem) while topic types are blocked
 Moderately interleaved representations / blocked topics – representations are moderately interleaved (students switch representations after every three problems) while topic types are blocked
 Increasingly interleaved representations / blocked topics – the length of the blocks of representations is gradually reduced (at the beginning, students switch topics after every twelve problems, at the end they switch after each single problem) while topic types are blocked
2010
Dependent variables
 Previously validated pretest, immediate posttest, and delayed posttest measuring student performance on:
 Area model problems
 Number line problems
 Fraction comparison
 Proportional reasoning
 Log data collected during tutor use, used to assess:
 Learning curves
 Time on task
 Error rates
 Hint usage
Independent Variables
 Blockd representations – students switch representations after 36 problems
 Moderately interleaved representations – students switch representations after every six problems
 Fully interleaved representations –students switch representations after each problem
 Increasingly interleaved representations – the length of the blocks is gradually reduced from twelve problems at the beginning to a single problem at the end
Explanation
Data collection is still in progress.
Further Information
Connections
Annotated Bibliography
References
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