Interleaved practice in multi-dimensional learning tasks: Which dimension should we interleave?

Rau, Martina A.; Aleven, Vincent; Rummel, Nikol

doi:10.1016/j.learninstruc.2012.07.003

Cited by 56 publications

(54 citation statements)

References 64 publications

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“…We contrasted four conditions that differed only in the degree to which, in the tutor's problem sets, the graphical representations where blocked or interleaved. In accordance with the results from our earlier experiment (Rau et al 2013a), we consistently used a moderately interleaved practice schedule of task types across all conditions. Furthermore, our goal was to investigate students' learning gains from working with the Fractions Tutor by comparing their performance on equivalent pretests, posttests, and delayed posttests.…”

Section: Classroom Experiment: Effects Of Practice Schedulesmentioning

confidence: 53%

“…Our prior research showed that multiple graphical representations lead to better learning than a single graphical representation (Rau et al 2009(Rau et al , 2013c. We further showed that when a choice has to be made to interleave either task types or graphical representations, we should interleave task types (Rau et al 2013a). However, our prior work did not address the question of whether interleaving graphical representations of fractions leads to better learning than blocking graphical representations.…”

Section: Multiple Graphical Representations Of Fractionsmentioning

confidence: 71%

“…We found these prompts to be effective in an earlier experimental study (Rau et al 2009). Second, the Fractions Tutor moderately interleaves task types, building on our earlier finding that interleaving task types leads to better learning than blocking task types (Rau et al 2013a). We use this moderately interleaved sequence of task types consistently across the different sequences of multiple graphical representations contrasted in the present experiment.…”

Section: The Fractions Tutormentioning

confidence: 98%

“…Task types differ in terms of the problem-solving procedure (or action sequence, in the case of motor tasks; memory trace in the case of fact retrieval or vocabulary learning) a given problem involves, whereas graphical representations differ in terms of the concepts they invoke. In our prior research (Rau et al 2013a) we compared interleaving of task types to interleaving of representations (i.e., interleaving task types while blocking graphical representations versus interleaving graphical representations while blocking task types). We found that when the choice is to interleave one while blocking the other, the choice should be made to interleave task types.…”

Section: Multiple Graphical Representations Of Fractionsmentioning

confidence: 99%

“…The advantage of interleaved practice has been attributed to two kinds of processes that play a role in deep, cognitive processing of the learning material (Rau et al 2013a). First, interleaved practice schedules require learners to frequently reactivate the knowledge needed to solve each learning task (de Croock et al 1998;Magill 1983, 1985): when tasks are presented in an interleaved sequence, the required knowledge has to be retrieved more frequently from long-term memory-it cannot be kept in memory from one task to the next if there is not overlap in requisite knowledge.…”

Section: Multiple Graphical Representations Of Fractionsmentioning

confidence: 99%

See 4 more Smart Citations

How Should Intelligent Tutoring Systems Sequence Multiple Graphical Representations of Fractions? A Multi-Methods Study

Rau

Aleven

Rummel

et al. 2013

Int J Artif Intell Educ

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Providing learners with multiple representations of learning content has been shown to enhance learning outcomes. When multiple representations are presented across consecutive problems, we have to decide in what sequence to present them. Prior research has demonstrated that interleaving tasks types (as opposed to blocking them) can foster learning. Do the same advantages apply to interleaving representations? We addressed this question using a variety of research methods. First, we conducted a classroom experiment with an intelligent tutoring system for fractions. We compared four practice schedules of multiple graphical representations: blocked, fully interleaved, moderately interleaved, and increasingly interleaved. Based on data from 230 4th and 5th-grade students, we found that interleaved practice leads to better learning outcomes than blocked practice on a number of measures. Second, we conducted a think-aloud study to gain insights into the learning mechanisms underlying the advantage of interleaved practice. Results show that students make connections between representations only when explicitly prompted to do so (and not spontaneously). This finding suggests that reactivation, rather than abstraction, is the main mechanism to account for the advantage of interleaved practice. Third, we used methods derived from Bayesian knowledge tracing to analyze tutor log data from the classroom experiment. Modeling latent measures of students' learning rates, we find higher learning rates for interleaved practice than for blocked practice. This finding extends prior research on practice schedules, which shows that interleaved practice (compared to blocked practice) impairs students' problem-solving performance during the practice phase when using raw performance measures such as error rates. Our findings have implications for the design of multi-representational learning materials and for research on adaptive practice schedules in intelligent tutoring systems.

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Section: Classroom Experiment: Effects Of Practice Schedulesmentioning

confidence: 53%

Section: Multiple Graphical Representations Of Fractionsmentioning

confidence: 71%

Section: The Fractions Tutormentioning

confidence: 98%

Section: Multiple Graphical Representations Of Fractionsmentioning

confidence: 99%

Section: Multiple Graphical Representations Of Fractionsmentioning

confidence: 99%

See 3 more Smart Citations

How Should Intelligent Tutoring Systems Sequence Multiple Graphical Representations of Fractions? A Multi-Methods Study

Rau

Aleven

Rummel

et al. 2013

Int J Artif Intell Educ

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show abstract

A systematic review of interleaving as a concept learning strategy

Firth

Rivers

Boyle

2021

Review of Education

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A systematic review was conducted into the effect of interleaving the order of examples of concepts in terms of both memory of items and transfer to new items. This concept has important implications for how and when teachers present examples in the classroom. A total of 26 studies met the inclusion criteria; a subset of 17 studies (with 32 constituent datasets) formed the basis of a meta‐analysis, and the remainder were analysed within a narrative review. Memory (as tested by presenting studied items from a learned category) showed an interleaving benefit with effect sizes (Hedges’ g) of up to 0.65, and transfer (as tested by presenting novel items from a learned category) a benefit with effect sizes of up to 0.66. Interleaving was found to be of greatest use when differences between items are subtle, and the benefit extended to both art‐ and science‐based items, with implication for practitioner decisions over how and when to apply the technique. It also extended to delayed tests. The review revealed that the literature is dominated by laboratory studies of university undergraduates, and the need for future school‐based research using authentic classroom tasks is outlined.

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Using Machine Learning to Overcome the Expert Blind Spot for Perceptual Fluency Trainings

Rau

Sen

Zhu

2019

Lecture Notes in Computer Science

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Interleaved practice in multi-dimensional learning tasks: Which dimension should we interleave?

Cited by 56 publications

References 64 publications

How Should Intelligent Tutoring Systems Sequence Multiple Graphical Representations of Fractions? A Multi-Methods Study

How Should Intelligent Tutoring Systems Sequence Multiple Graphical Representations of Fractions? A Multi-Methods Study

A systematic review of interleaving as a concept learning strategy

Using Machine Learning to Overcome the Expert Blind Spot for Perceptual Fluency Trainings

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