Improving Students’ Learning With Effective Learning Techniques

Many students are being left behind by an educational system that some people believe is in crisis. Improving educational outcomes will require efforts on many fronts, but a central premise of this monograph is that one part of a solution involves helping students to better regulate their learning through the use of effective learning techniques. Fortunately, cognitive and educational psychologists have been developing and evaluating easy-to-use learning techniques that could help students achieve their learning goals. In this monograph, we discuss 10 learning techniques in detail and offer recommendations about their relative utility. We selected techniques that were expected to be relatively easy to use and hence could be adopted by many students. Also, some techniques (e.g., highlighting and rereading) were selected because students report relying heavily on them, which makes it especially important to examine how well they work. The techniques include elaborative interrogation, self-explanation, summarization, highlighting (or underlining), the keyword mnemonic, imagery use for text learning, rereading, practice testing, distributed practice, and interleaved practice.

To offer recommendations about the relative utility of these techniques, we evaluated whether their benefits generalize across four categories of variables: learning conditions, student characteristics, materials, and criterion tasks. Learning conditions include aspects of the learning environment in which the technique is implemented, such as whether a student studies alone or with a group. Student characteristics include variables such as age, ability, and level of prior knowledge. Materials vary from simple concepts to mathematical problems to complicated science texts. Criterion tasks include different outcome measures that are relevant to student achievement, such as those tapping memory, problem solving, and comprehension.

We attempted to provide thorough reviews for each technique, so this monograph is rather lengthy. However, we also wrote the monograph in a modular fashion, so it is easy to use. In particular, each review is divided into the following sections: General description of the technique and why it should work How general are the effects of this technique?  2a. Learning conditions  2b. Student characteristics  2c. Materials  2d. Criterion tasks Effects in representative educational contexts Issues for implementation Overall assessment

The review for each technique can be read independently of the others, and particular variables of interest can be easily compared across techniques.

To foreshadow our final recommendations, the techniques vary widely with respect to their generalizability and promise for improving student learning. Practice testing and distributed practice received high utility assessments because they benefit learners of different ages and abilities and have been shown to boost students’ performance across many criterion tasks and even in educational contexts. Elaborative interrogation, self-explanation, and interleaved practice received moderate utility assessments. The benefits of these techniques do generalize across some variables, yet despite their promise, they fell short of a high utility assessment because the evidence for their efficacy is limited. For instance, elaborative interrogation and self-explanation have not been adequately evaluated in educational contexts, and the benefits of interleaving have just begun to be systematically explored, so the ultimate effectiveness of these techniques is currently unknown. Nevertheless, the techniques that received moderate-utility ratings show enough promise for us to recommend their use in appropriate situations, which we describe in detail within the review of each technique.

Five techniques received a low utility assessment: summarization, highlighting, the keyword mnemonic, imagery use for text learning, and rereading. These techniques were rated as low utility for numerous reasons. Summarization and imagery use for text learning have been shown to help some students on some criterion tasks, yet the conditions under which these techniques produce benefits are limited, and much research is still needed to fully explore their overall effectiveness. The keyword mnemonic is difficult to implement in some contexts, and it appears to benefit students for a limited number of materials and for short retention intervals. Most students report rereading and highlighting, yet these techniques do not consistently boost students’ performance, so other techniques should be used in their place (e.g., practice testing instead of rereading).

Our hope is that this monograph will foster improvements in student learning, not only by showcasing which learning techniques are likely to have the most generalizable effects but also by encouraging researchers to continue investigating the most promising techniques. Accordingly, in our closing remarks, we discuss some issues for how these techniques could be implemented by teachers and students, and we highlight directions for future research.

Failure to engage spatial working memory contributes to age-related declines in visuomotor learning

It is well documented that both cognitive and motor learning abilities decline with normative aging. Given that cognitive processes such as working memory are engaged during the early stages of motor learning [Anguera, J., Reuter-Lorenz, P., Willingham, D., & Seidler, R. Contributions of spatial working memory to visuomotor learning. Journal of Cognitive Neuroscience, 22(9), 1917-1930, 2010], age-related declines in motor learning may be due in part to reductions in cognitive ability. The present study examined whether age-related declines in spatial working memory (SWM) contribute to deficits in visuomotor adaptation. Young and older adult participants performed a visuomotor adaptation task that involved adapting manual aiming movements to a 30° rotation of the visual feedback display as well as an SWM task in an fMRI scanner. Young adults showed a steeper learning curve than older adults during the early adaptation period. The rate of early adaptation was correlated with SWM performance for the young, but not older, adults. Both groups showed similar brain activation patterns for the SWM task, including engagement of the right dorsolateral prefrontal cortex and bilateral inferior parietal lobules. However, when the SWM activation was used as a limiting mask, younger adults showed neural activation that overlapped with the early adaptation period, whereas older adults did not. A partial correlation controlling for age revealed that the rate of early adaptation correlated with the amount of activation at the right dorsolateral prefrontal cortex. These findings suggest that a failure to effectively engage SWM processes during learning contributes to age-related deficits in visuomotor adaptation.

Contributions of Spatial Working Memory to Visuomotor Learning

Previous studies of motor learning have described the importance of cognitive processes during the early stages of learning; however, the precise nature of these processes and their neural correlates remains unclear. The present study investigated whether spatial working memory (SWM) contributes to visuomotor adaptation depending on the stage of learning. We tested the hypothesis that SWM would contribute early in the adaptation process by measuring (i) the correlation between SWM tasks and the rate of adaptation, and (ii) the overlap between the neural substrates of a SWM mental rotation task and visuomotor adaptation. Participants completed a battery of neuropsychological tests, a visuomotor adaptation task, and an SWM task involving mental rotation, with the latter two tasks performed in a 3.0-T MRI scanner. Performance on a neuropsychological test of SWM (two-dimensional mental rotation) correlated with the rate of early, but not late, visuomotor adaptation. During the early, but not late, adaptation period, participants showed overlapping brain activation with the SWM mental rotation task, in right dorsolateral prefrontal cortex and the bilateral inferior parietal lobules. These findings suggest that the early, but not late, phase of visuomotor adaptation engages SWM processes.

An egocentric frame of reference in implicit motor sequence learning

We investigated which frame of reference is evoked during implicit motor sequence learning. Participants completed a typical serial reaction time task. In the first experiment, we isolated egocentric and allocentric frames of reference and found that learning was solely in an egocentric reference frame. In a second experiment, we isolated hand-centered space from other egocentric frames of reference. We found that for a one-handed sequencing task, the sequence was coded in an egocentric reference frame but not a hand-centered reference frame. Our results are restricted to implicit learning of novel sequences in the early stages of learning. These findings are consistent with claims that the neural mechanisms involved in motor skill learning operate in egocentric coordinates.

Time of day accounts for overnight improvement in sequence learning

The theory that certain skills improve with a night of sleep has received considerable interest in recent years. However, because sleep typically occurs at the same time of day in humans, it is difficult to separate the effects of sleep from those of time of day. By using a version of the Serial Response Time Task, we assessed the role of sleep in implicit sequence learning while controlling for possible time-of-day effects. We replicated the apparent benefit of sleep on human participants. However, our data show that sleep does not affect implicit sequence learning; rather, time of day affects the ability of participants to express what they have learned.

The representation of explicit motor sequence knowledge

Much research has investigated the representation of implicitly learned motor sequences: Do subjects learn sequences of stimuli, responses, response locations, or some combination? Most of the work on this subject indicates that when sequences are learned implicitly, it is in terms of response locations. The present work investigated the representation of explicitly learned motor sequences. In four experiments, we found consistent evidence that explicitly learned sequences are represented in terms of stimulus locations. This conclusion held true for both self-report measures (subjects said that they learned stimuli) and performance measures, but when stimuli changed, performance degraded. We interpret these data in a multiple-memory-systems framework.

Strategic Modulation of Cognitive Control

The neural substrate of cognitive control is thought to comprise an evaluative component located in the anterior cingulate cortex (ACC) and an executive component in the prefrontal cortex (PFC). The control mechanism itself is mainly local, triggered by response conflict (monitored by the ACC) and involving the allocation of executive resources (recruited by the PFC) in a trial-to-trial fashion. However, another way to achieve control would be to use a strategic mechanism based on long-term prediction of upcoming events and on a chronic response strategy that ignores local features of the task. In the current study, we showed that such a strategic control mechanism was based on a functional dissociation or complementary relationship between the ACC and the PFC. When information in the environment was available to make predictions about upcoming stimuli, local task features (e.g., response conflict) were no longer used as a control signal. We suggest that having separate control mechanisms based on local or global task features allows humans to be persistent in pursuing their goals, yet flexible enough to adapt to changes in the environment.

Neurophysiological mechanisms involved in transfer of procedural knowledge

Learning to perform a motor task with one hand results in performance improvements in the other hand, a process called intermanual transfer. To gain information on its neural mechanisms, we studied this phenomenon using the serial reaction-time task (SRTT). Sixteen, right-handed volunteers trained a 12-item sequence of key presses repeated without the subjects' knowledge. Blocks with no repeating sequence, called random blocks, were interspersed with sequence-training blocks. Response times improved in random and training blocks in both hands. The former result reflects nonspecific improvement in performance, and the latter represents a sequence-specific improvement. To evaluate changes in the primary motor cortex (M1), we tested resting motor thresholds (RMT), recruitments curves to transcranial magnetic stimulation (RC), short intracortical inhibition (SICI), and interhemispheric inhibition (IHI) from the dominant left (learning) to the nondominant right (transfer) hemisphere, before and after SRTT training. Training resulted in (1) increased RC and decreased SICI but no changes in RMT in the learning hemisphere, (2) decreased SICI and no changes in RC or RMT in the transfer hemisphere, and (3) decreased IHI. The amount in IHI after training correlated with nonspecific performance improvements in the transfer hand but not with sequence-specific performance improvements. Our results indicate that modulation of interhemispheric inhibition between the M1 areas may, as a result of the learning that has occurred in one hemisphere after practice with one hand, contribute to faster, more skilled performance of the opposite hand.

Non-declarative sequence learning does not show savings in relearning

Researchers have utilized the savings in relearning paradigm in a variety of settings since Ebbinghaus developed the tool over a century ago. In spite of its widespread use, we do not yet understand what type(s) of memory are measurable by savings. Specifically, can savings measure both declarative and non-declarative memories? The lack of conscious recollection of the encoded material in some studies indicates that non-declarative memories may show savings effects, but as all studies to date have used declarative tasks, we cannot be certain. Here, we administer a non-declarative task and then measure savings in relearning the material declaratively. Our results show that while material outside of awareness may show savings effects, non-declarative sequence memory does not. These data highlight the important distinction between memory without awareness and non-declarative memory.

Frames of reference during implicit and explicit learning

There is a significant overlap between the processes and neural substrates of spatial cognition and those subserving memory and learning. However, for procedural learning, which often is spatial in nature, we do not know how different forms of spatial knowledge, such as egocentric and allocentric frames of reference, are utilized nor whether these frames are differentially engaged during implicit and explicit processes. To address this issue, we trained human subjects on a movement sequence presented on a bi-dimensional (2D) geometric frame. We then systematically manipulated the geometric frame (allocentric) or the sequence of movements (egocentric) or both, and retested the subjects on their ability to transfer the sequence knowledge they had acquired in training and also determined whether the subjects had learned the sequence implicitly or explicitly. None of the subjects (implicit or explicit) showed evidence of transfer when both frames of reference were changed which suggests that spatial information is essential. Both implicit and explicit subjects transferred when the egocentric frame was maintained indicating that this representation is common to both processes. Finally, explicit subjects were also able to benefit from the allocentric frame in transfer, which suggests that explicit procedural knowledge may have two tiers comprising egocentric and allocentric representations.

Evidence for separate representations for action and location in implicit motor sequencing

We examined sequential learning of actions in an experiment in which four different actions (push, twist, pinch, switch) were placed at four horizontal locations. At transfer, participants responded to a sequence that required performing the same sequence of actions at different locations and to a different sequence of actions at the same sequence of locations. Participants with explicit knowledge demonstrated only learning the sequence of response locations. However, participants with implicit knowledge learned the sequence of actions just as well as the sequence of locations, and performance on individual sequences was just as good as performance when both sequences were presented. These results demonstrated that two types of sequences, one of actions and another of response locations, can be learned simultaneously, suggesting that parallel representations are involved in implicit motor skill acquisition.

Probability detection mechanisms and motor learning

The automatic detection of patterns or regularities in the environment is central to certain forms of motor learning, which are largely procedural and implicit. The rules underlying the detection and use of probabilistic information in the perceptual-motor domain are largely unknown. We conducted two experiments involving a motor learning task with direct and crossed mapping of motor responses in which probabilities were present at the stimulus set level, the response set level, and at the level of stimulus-response (S-R) mapping. We manipulated only one level at a time, while controlling for the other two. The results show that probabilities were detected only when present at the S-R mapping and motor levels, but not at the perceptual one (experiment 1), unless the perceptual features have a dimensional overlap with the S-R mapping rule (experiment 2). The effects of probability detection were mostly facilitatory at the S-R mapping, both facilitatory and inhibitory at the perceptual level, and predominantly inhibitory at the response-set level. The facilitatory effects were based on learning the absolute frequencies first and transitional probabilities later (for the S-R mapping rule) or both types of information at the same time (for perceptual level), whereas the inhibitory effects were based on learning first the transitional probabilities. Our data suggest that both absolute frequencies and transitional probabilities are used in motor learning, but in different temporal orders, according to the probabilistic properties of the environment. The results support the idea that separate neural circuits may be involved in detecting absolute frequencies as compared to transitional probabilities.

Neural substrates of response-based sequence learning using fMRI

Representation of sequential structure can occur with respect to the order of perceptual events or the order in which actions are linked. Neural correlates of sequence retrieval associated with the order of motor responses were identified in a variant of the serial reaction time task in which training occurred with a spatially incompatible mapping between stimuli and finger responses. After transfer to a spatially compatible version of the task, performance enhancements indicative of learning were only present in subjects required to make finger movements in the same order used during training. In contrast, a second group of subjects performed the compatible task using an identical sequence of stimuli (and different order of finger movements) as in training. They demonstrated no performance benefit, indicating that learning was response based. Analysis was restricted to subjects demonstrating low recall of the sequence structure to rule out effects of explicit awareness. The interaction of group (motor vs. perceptual transfer) with sequence retrieval (sequencing vs. rest) revealed significantly greater activation in the bilateral supplementary motor area, cingulate motor area, ventral premotor cortex, left caudate, and inferior parietal lobule for subjects in the motor group (illustrating successful sequence retrieval at the response level). Retrieval of sequential responses occurs within mesial motor areas and related motor planning areas.

What neuroimaging and brain localization can do, cannot do and should not do for social psychology

Interest in bridging social psychology and neuroscience has seen a significant upsurge. Much of this interest has centered on brain localization--the attempt to relate psychological events to locations of brain events. Although many articles have sought to localize brain activity that supports social behavior, scant attention has been paid to the specific methods to be used in integrating brain localization data into psychological theory. The authors describe 4 strategies psychologists can use to integrate brain localization data and psychological theory, and they consider whether social psychology presents special considerations in the use of these strategies. They conclude that brain localization offers a useful tool for some but not all problems in social psychology, and they discuss the types of problems for which it may and may not prove useful.