Connecting Adaptive Perceptual Learning and Signal Detection Theory in Skin Cancer Screening.

Combining perceptual learning techniques with adaptive learning algorithms has been shown to accelerate the development of expertise in medical and STEM learning domains (Kellman & Massey, 2013; Kellman, Jacoby, Massey & Krasne, 2022). Virtually all adaptive learning systems have relied on simple accuracy data that does not take into account response bias, a problem that may be especially consequential in multi-category perceptual classifications. We investigated whether adaptive perceptual learning in skin cancer screening can be enhanced by incorporating signal detection theory (SDT) methods that separate sensitivity from criterion. SDT-style concepts were used to alter sequencing, and separately to define mastery (category retirement). SDT retirement used a running d' estimate calculated from a recent window of trials based on hit and false alarm rates. Undergraduate participants used a Skin Cancer PALM (perceptual adaptive learning module) to learn classification of 10 cancerous and readily-confused non-cancerous skin lesion types. Four adaptive conditions varied either the type of adaptive sequencing (standard vs. SDT) or retirement criteria (standard vs. SDT). A non-adaptive control condition presented didactic instruction on dermatologic screening in video form, including images, classification schemes, and detailed explanations. All adaptive conditions robustly outperformed the non-adaptive control in both learning efficiency and fluency (large effect sizes). Between adaptive conditions, SDT retirement criteria produced greater learning efficiency than standard, accuracy-based mastery criteria at both immediate and delayed posttests (medium effect sizes). SDT sequencing and standard adaptive sequencing did not differ. SDT enhancements to adaptive perceptual learning procedures have potential to enhance learning efficiency.

Comparisons in Adaptive Perceptual Category Learning.

Recent work suggests that learning perceptual classifications can be enhanced by combining single item classifications with adaptive comparisons triggered by each learner's confusions. Here, we asked whether learning might work equally well using all comparison trials. In a face identification paradigm, we tested single item classifications, paired comparisons, and dual instance classifications that resembled comparisons but required two identification responses. In initial results, the comparisons condition showed evidence of greater efficiency (learning gain divided by trials or time invested). We suspected that this effect may have been driven by easier attainment of mastery criteria in the comparisons condition, and a negatively accelerated learning curve. To test this idea, we fit learning curves and found data consistent with the same underlying learning rate in all conditions. These results suggest that paired comparison trials may be as effective in driving learning of multiple perceptual classifications as more demanding single item classifications.

Comparing Adaptive and Random Spacing Schedules during Learning to Mastery Criteria.

Adaptive generation of spacing intervals in learning using response times improves learning relative to both adaptive systems that do not use response times and fixed spacing schemes (Mettler, Massey & Kellman, 2016). Studies have often used limited presentations (e.g., 4) of each learning item. Does adaptive practice benefit learning if items are presented until attainment of objective mastery criteria? Does it matter if mastered items drop out of the active learning set? We compared adaptive and non-adaptive spacing under conditions of mastery and dropout. Experiment 1 compared random presentation order with no dropout to adaptive spacing and mastery using the ARTS (Adaptive Response-time-based Sequencing) system. Adaptive spacing produced better retention than random presentation. Experiment 2 showed clear learning advantages for adaptive spacing compared to random schedules that also included dropout. Adaptive spacing performs better than random schedules of practice, including when learning proceeds to mastery and items drop out when mastered.

Adaptive vs. Fixed Spacing of Learning Items: Evidence from Studies of Learning and Transfer in Chemistry Education.

Spacing presentations of learning items across time improves memory relative to massed schedules of practice - the well-known spacing effect. Spaced practice can be further enhanced by adaptively scheduling the presentation of learning items to deliver customized spacing intervals for individual items and learners. ARTS - Adaptive Response-time-based Sequencing (Mettler, Massey, & Kellman 2016) determines spacing dynamically in relation to each learner's ongoing speed and accuracy in interactive learning trials. We demonstrate the effectiveness of ARTS when applied to chemistry nomenclature in community college chemistry courses by comparing adaptive schedules to fixed schedules consisting of continuously expanding spacing intervals. Adaptive spacing enhanced the efficiency and durability of learning, with learning gains persisting after a two-week delay and generalizing to a standardized assessment of chemistry knowledge after 2-3 months. Two additional experiments confirmed and extended these results in both laboratory and community college settings.

The Synergy of Passive and Active Learning Modes in Adaptive Perceptual Learning.

Adaptive learning systems that generate spacing intervals based on learner performance enhance learning efficiency and retention (Mettler, Massey & Kellman, 2016). Recent research in factual learning suggests that initial blocks of passive trials, where learners observe correct answers without overtly responding, produce greater learning than passive or active trials alone (Mettler, Massey, Burke, Garrigan & Kellman, 2018). Here we tested whether this passive + active advantage generalizes beyond factual learning to perceptual learning. Participants studied and classified images of butterfly genera using either: 1) Passive Only presentations, 2) Passive Initial Blocks followed by active, adaptive scheduling, 3) Passive Initial Category Exemplar followed by active, adaptive scheduling, or 4) Active Only learning. We found an advantage for combinations of active and passive presentations over Passive Only or Active Only presentations. Passive trials presented in initial blocks showed the best performance, paralleling earlier findings in factual learning. Combining active and passive learning produces greater learning gains than either alone, and these effects occur for diverse forms of learning, including perceptual learning.

A comparison of adaptive and fixed schedules of practice.

Understanding and optimizing spacing during learning is a central topic for research in learning and memory and has substantial implications for real-world learning. Spacing memory retrievals across time improves memory relative to massed practice-the well-known spacing effect. Most spacing research has utilized fixed (predetermined) spacing intervals. Some findings indicate advantages of expanding over equal spacing (e.g., Landauer & Bjork, 1978); however, evidence is mixed (e.g., Karpicke & Roediger, 2007), and the field has lacked an integrated explanation. Learning may instead depend on interactions of spacing with an underlying variable of learning strength that varies for learners and items, and it may be better optimized by adaptive adjustments of spacing to learners' ongoing performance. Two studies investigated an adaptive spacing algorithm, Adaptive Response-Time-based Sequencing or ARTS (Mettler, Massey & Kellman, 2011) that uses response-time and accuracy to generate spacing. Experiment 1 compared adaptive scheduling with fixed schedules having either expanding or equal spacing. Experiment 2 compared adaptive schedules to 2 fixed "yoked" schedules that were copied from adaptive participants, equating average spacing across conditions. In both experiments, adaptive scheduling outperformed fixed conditions at immediate and delayed tests of retention. No evidence was found for differences between expanding and equal spacing. Yoked conditions showed that learning gains were due to adaptation to individual items and learners. Adaptive spacing based on ongoing assessments of learning strength yields greater learning gains than fixed schedules, a finding that helps to understand the spacing effect theoretically and has direct applications for enhancing learning in many domains. (PsycINFO Database Record

Perceptual learning modules in mathematics: enhancing students' pattern recognition, structure extraction, and fluency.

Learning in educational settings emphasizes declarative and procedural knowledge. Studies of expertise, however, point to other crucial components of learning, especially improvements produced by experience in the extraction of information: perceptual learning (PL). We suggest that such improvements characterize both simple sensory and complex cognitive, even symbolic, tasks through common processes of discovery and selection. We apply these ideas in the form of perceptual learning modules (PLMs) to mathematics learning. We tested three PLMs, each emphasizing different aspects of complex task performance, in middle and high school mathematics. In the MultiRep PLM, practice in matching function information across multiple representations improved students' abilities to generate correct graphs and equations from word problems. In the Algebraic Transformations PLM, practice in seeing equation structure across transformations (but not solving equations) led to dramatic improvements in the speed of equation solving. In the Linear Measurement PLM, interactive trials involving extraction of information about units and lengths produced successful transfer to novel measurement problems and fraction problem solving. Taken together, these results suggest (a) that PL techniques have the potential to address crucial, neglected dimensions of learning, including discovery and fluent processing of relations; (b) PL effects apply even to complex tasks that involve symbolic processing; and (c) appropriately designed PL technology can produce rapid and enduring advances in learning.