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PubMed2006

The influence of tutoring competencies on problems, group functioning and student achievement in problem-based learning.

CONTEXT: Prominent factors in problem-based learning (PBL) are the problems to be solved, tutorial group functioning and tutors' competencies. These factors mutually affect one another and largely determine whether a powerful learning environment will be created. It is a tutor's task to stimulate active, self-directed, contextual and collaborative learning and display interpersonal behaviour that is conducive to students' learning. We investigated the effects of tutors' competencies on students' learning and on other variables, such as group functioning and student achievement. OBJECTIVES: We investigated whether tutors who stimulate active, self-directed, contextual and collaborative learning make better use of problems and meaningful contexts in PBL and also enhance group functioning. We also investigated whether the quality of problems has a positive impact on group functioning and whether group functioning advanced student achievements. METHODS: Questionnaires were used to collect data from students at the end of 11 modules in Years 1 and 2 of a PBL undergraduate medical curriculum. We used structural equation modelling to test the fit of a theoretical model representing the factors of interest and their relationships. RESULTS: Stimulation of active and constructive learning, self-directed learning and collaborative learning by tutors enhanced the quality of the problems and group functioning. The quality of the problems promoted group functioning, which was found to have a positive effect on student achievement. CONCLUSIONS: Tutors' competencies had a positive effect on the learning of students. This suggests that it would be worthwhile including these competencies in staff development.

Belgium↗
PubMed2006

Disentangling clinical learning experiences: an exploratory study on the dynamic tensions in internship.

Clinical practice is an essential component of medical training, but not all internships yield the appropriate and expected learning results. We report on an exploratory study of the learning process during internship in undergraduate medical education. We hypothesised that learning experiences in clinical practice are determined by characteristics of the interns, characteristics of the training setting and the meaningful interactions between them. As the study focused on the perceptions and interpretations of both interns and their supervisors of interns' experiences in practical training, qualitative research methods were used for data collection and analysis. This consisted of student shadowing, complemented by informal and semistructured interviews with both interns and supervisors. Analysis revealed 5 components that constitute learning experiences in clinical internship. These components represent dynamics in the clinical environment that constantly require students to (re-)define and (re-)position themselves: the agenda of the internship (working versus learning); the attitude of the supervisor (evaluator versus coach); the culture of the training setting (work-orientated versus training-orientated); the intern's learning attitude (passive versus proactive), and the nature of the learning process (informal versus formal). The model of components and tensions offers a conceptual framework to analyse and understand students' learning during internship. It not only contributes to a grounded theoretical conceptualisation of clinical learning, but may also be used in efforts to improve the quality of learning during internship, as well as the level of support and supervision.

Attitude of Health Personnel↗
PubMed2004

Psychosocial functioning of young children with learning problems.

BACKGROUND: In this study, psychosocial functioning of different groups of young children with learning problems was investigated using a diverse set of psychosocial variables (including behaviour problems, academic motivation, social preference, and self-concept). METHODS: For this purpose, children with low academic achievement, with a specific learning disability based on an IQ-achievement discrepancy, and with a specific learning disability based on an achievement discrepancy, were selected out of 276 children of the first grade of regular primary schools. By means of multivariate analyses, their psychosocial functioning was compared to the functioning of children without learning problems. RESULTS: The total set of psychosocial variables was able to discriminate between children with and without learning problems, with medium effect size. Attention problems as reported by the teachers turned out to be the most important single psychosocial predictor for group discrimination. However, results varied according to the type of learning problem and the type of psychosocial problem. Children with a specific reading/spelling disability and children with low general academic achievement differed most from their peers without learning problems with regard to their psychosocial functioning. Poor cognitive self-concept was related primarily to low academic achievement, poor learning motivation might be specific for math problems, and a low social preference score seemed most characteristic of children with a specific learning disability. CONCLUSIONS: Studying several psychosocial variables simultaneously in different groups of children with learning problems leads to a further refinement of the current knowledge.

Child↗
PubMed1996

Transfer and retention of implicit and explicit learning.

Two parallel tasks involving rule learning were used to assess implicit and explicit learning. The complex task (occurring in the complex rule-line condition) involves predominantly implicit learning, and the simple task (occurring in the simple rule-number condition) requires primarily explicit learning. Implicit learning clearly showed negative transfer from previous implicit learning experience, whereas explicit learning showed strong positive transfer. Subjects' explicit knowledge declined and implicit knowledge remained at the same level when retested one week later. The results are discussed in terms of principles guiding the learning and transfer of explicit and implicit knowledge. This is consistent with the assertion that implicit and explicit learning involve two functionally different learning systems.

Analysis of Variance↗
PubMed1996

Predictive value of preschool surveillance in detecting learning difficulties.

OBJECTIVES: The Hall report specified the early detection of mild to moderate learning difficulties as one aim of child health surveillance (CHS). This study examines the efficacy of preschool CHS in the early recognition of children with these disorders. DESIGN: A retrospective case-control study. SUBJECTS: All children (n = 408) with mild to moderate learning difficulties born between 1 July 1983 and 30 June 1984 and resident in North and West Belfast. CONTROLS: 2406 birth records and 150 full child health records controlled for age and geographical area. RESULTS: The prevalence of mild to moderate learning difficulties in North and West Belfast was 16%. Only 6% of children with learning difficulties were identified by the CHS in the preschool period, although the detection rate for children eventually requiring placement in schools for moderate learning difficulties was better. Coverage of the CHS ranged from 90% at the 2 year examination to 98% at the 4 year examination. Perinatal variables associated with learning difficulties after multiple logistic regression analysis were lower social class (odds ratio (OR) 3.9), prematurity < 35 weeks (OR 3.0), male sex (OR 1.6), and birth to an unmarried mother (OR 0.6). Independent preschool variables identified by the CHS were speech delay (OR 3.3), poor parenting skills (OR 4.0), behaviour problems (OR 2.8), enuresis (OR 2.4), poor visual acuity (OR 1.8), and otitis media with effusion (OR 1.4). A statistical model for the early detection of learning difficulties using these risk factors is unable to predict accurately the children who will develop mild to moderate learning difficulties. CONCLUSIONS: The CHS as it existed from 1983 to 1989 in North and West Belfast was poorly sensitive to the detection of mild to moderate learning difficulties despite excellent coverage. An accurate predictive model for learning difficulties could not be developed from the risk factors documented by the CHS.

Case-Control Studies↗
PubMed2002

Direct comparison of neural systems mediating conscious and unconscious skill learning.

Procedural learning, such as perceptual-motor sequence learning, has been suggested to be an obligatory consequence of practiced performance and to reflect adaptive plasticity in the neural systems mediating performance. Prior neuroimaging studies, however, have found that sequence learning accompanied with awareness (declarative learning) of the sequence activates entirely different brain regions than learning without awareness of the sequence (procedural learning). Functional neuroimaging was used to assess whether declarative sequence learning prevents procedural learning in the brain. Awareness of the sequence was controlled by changing the color of the stimuli to match or differ from the color used for random sequences. This allowed direct comparison of brain activation associated with procedural and declarative memory for an identical sequence. Activation occurred in a common neural network whether initial learning had occurred with or without awareness of the sequence, and whether subjects were aware or not aware of the sequence during performance. There was widespread additional activation associated with awareness of the sequence. This supports the view that some types of unconscious procedural learning occurs in the brain whether or not it is accompanied by conscious declarative knowledge.

Adult↗
PubMed1998

Dissociable processes for learning the surface structure and abstract structure of sensorimotor sequences.

A sensorimotor sequence may contain information structure at several different levels. In this study, we investigated the hypothesis that two dissociable processes are required for the learning of surface structure and abstract structure, respectively, of sensorimotor sequences. Surface structure is the simple serial order of the sequence elements, whereas abstract structure is defined by relationships between repeating sequence elements. Thus, sequences ABCBAC and DEFEDF have different surface structures but share a common abstract structure, 123213, and are therefore isomorphic. Our simulations of sequence learning performance in serial reaction time (SRT) tasks demonstrated that (1) an existing model of the primate fronto-striatal system is capable of learning surface structure but fails to learn abstract structure, which requires an additional capability, (2) surface and abstract structure can be learned independently by these independent processes, and (3) only abstract structure transfers to isomorphic sequences. We tested these predictions in human subjects. For a sequence with predictable surface and abstract structure, subjects in either explicit or implicit conditions learn the surface structure, but only explicit subjects learn and transfer the abstract structure. For sequences with only abstract structure, learning and transfer of this structure occurs only in the explicit group. These results are parallel to those from the simulations and support our dissociable process hypothesis. Based on the synthesis of the current simulation and empirical results with our previous neuropsychological findings, we propose a neuro-physiological basis for these dissociable processes: Surface structure can be learned by processes that operate under implicit conditions and rely on the fronto-striatal system, whereas learning abstract structure requires a more explicit activation of dissociable processes that rely on a distributed network that includes the left anterior cortex.

Computer Simulation↗
PubMed2005

Attention-gated reinforcement learning of internal representations for classification.

Animal learning is associated with changes in the efficacy of connections between neurons. The rules that govern this plasticity can be tested in neural networks. Rules that train neural networks to map stimuli onto outputs are given by supervised learning and reinforcement learning theories. Supervised learning is efficient but biologically implausible. In contrast, reinforcement learning is biologically plausible but comparatively inefficient. It lacks a mechanism that can identify units at early processing levels that play a decisive role in the stimulus-response mapping. Here we show that this so-called credit assignment problem can be solved by a new role for attention in learning. There are two factors in our new learning scheme that determine synaptic plasticity: (1) a reinforcement signal that is homogeneous across the network and depends on the amount of reward obtained after a trial, and (2) an attentional feedback signal from the output layer that limits plasticity to those units at earlier processing levels that are crucial for the stimulus-response mapping. The new scheme is called attention-gated reinforcement learning (AGREL). We show that it is as efficient as supervised learning in classification tasks. AGREL is biologically realistic and integrates the role of feedback connections, attention effects, synaptic plasticity, and reinforcement learning signals into a coherent framework.

Animals↗
PubMed1999

A unified analysis of value-function-based reinforcement- learning algorithms.

Reinforcement learning is the problem of generating optimal behavior in a sequential decision-making environment given the opportunity of interacting with it. Many algorithms for solving reinforcement-learning problems work by computing improved estimates of the optimal value function. We extend prior analyses of reinforcement-learning algorithms and present a powerful new theorem that can provide a unified analysis of such value-function-based reinforcement-learning algorithms. The usefulness of the theorem lies in how it allows the convergence of a complex asynchronous reinforcement-learning algorithm to be proved by verifying that a simpler synchronous algorithm converges. We illustrate the application of the theorem by analyzing the convergence of Q-learning, model-based reinforcement learning, Q-learning with multistate updates, Q-learning for Markov games, and risk-sensitive reinforcement learning.

Algorithms↗
PubMed2004

Perceptual learning in contrast discrimination and the (minimal) role of context.

Unlike most visual tasks, contrast discrimination has been reported to be unchanged by practice (Dorais & Sagi, 1997; Adini, Sagi, & Tsodyks, 2002), unless practice is undertaken in the presence of flankers (context-enabled learning, Adini et al., 2002). Here we show that under experimental conditions nearly identical to those in the no-flanker practice experiment of Adini et al. (2002), practice significantly improved contrast discrimination. Moreover, in a separate experiment, we found that practice without flankers can improve contrast discrimination to a level only reached with flankers in Adini et al. (2002), but further practice with flankers produces no further improvement of contrast discrimination. These results call into question whether the "context-enabled learning" proposed by Adini et al. (2002) is different from regular contrast learning without flankers. In separate experiments, we found that contrast learning is tuned to spatial frequency, orientation, retinal location, and, unexpectedly, contrast. We also replicated Sagi, Adini, Tsodyks, and Wilkonsky's (2003) more recent finding that no regular contrast learning occurs if reference contrasts are randomly interleaved (contrast roving), and further demonstrated that flankers have no effect on contrast learning under contrast roving, another piece of evidence equating "context-enabled learning" to regular contrast learning. The contrast specificity of learning and the lack of learning under contrast roving provide new evidence in favor of a multiple contrast-selective channels model of contrast discrimination, and against saturating transducer models and multiplicative noise models.

Contrast Sensitivity↗
PubMed2003

The promise of new ideas and new technology for improving teaching and learning.

There have been enormous advances in our understanding of human learning in the past three decades. There have also been important advances in our understanding of the nature of knowledge and new knowledge creation. These advances, when combined with the explosive development of the Internet and other technologies, permit advances in educational practices at least as important as the invention of the printing press in 1460. We have built on the cognitive learning theory of David Ausubel and various sources of new ideas on epistemology. Our research program has focused on understanding meaningful learning and on developing better methods to achieve such learning and to assess progress in meaningful learning. The concept map tool developed in our program has proved to be highly effective both in promoting meaningful learning and in assessing learning outcomes. Concept mapping strategies are also proving powerful for eliciting, capturing, and archiving knowledge of experts and organizations. New technology for creating concept maps developed at the University of West Florida permits easier and better concept map construction, thus facilitating learning, knowledge capture, and local or distance creation and sharing of structured knowledge, especially when utilized with the Internet. A huge gap exists between what we now know to improve learning and use of knowledge and the practices currently in place in most schools and corporations. There are promising projects in progress that may help to achieve accelerated advances. These include projects in schools at all educational levels, including projects in Colombia, Costa Rica, Italy, Spain, and the United States, and collaborative projects with corporate organizations and distance learning projects. Results to date have been encouraging and suggest that we may be moving from the lag phase of educational innovation to a phase of exponential growth.

Comprehension↗
PubMed2006

ERP indicators of learning in adults.

This study investigated learning-related changes in the brain activity of young adults. A group of 29 undergraduate students (18-24 years) participated in a learning study that included a pretest, a training session, and a posttest. Each trial involved presentation of a complex visual stimulus and its spoken "name." Auditory event-related potentials (ERPs) were recorded in response to matching and mismatching names. In the pretest, the participants guessed whether the names were matching the figures. During training they learned the names of a set of simple elements making up the complex figures and were required to master a simple rule for combining the visual and auditory stimuli. The posttest included presentation of the combinations learned during training as well as novel pairings of the same elements. Following training the number of correct responses for learned items doubled and the amplitudes of the auditory ERPs to learned and rule transfer stimuli were more positive than brain waves to the not learned or novel items over most of the analysis window. The ERPs further differentiated between a familiarity response (late positive shift) and learning-specific changes (N2-P3 range). Overall, the findings suggest that ERPs can be a useful tool for learning assessment and offer new insights in the study of individual differences associated with the learning process.

Acoustic Stimulation↗
PubMed2003

Sequence learning by action and observation: evidence for separate mechanisms.

In the Serial Reaction Time (SRT) task, participants respond to a set of stimuli the order of which is apparently random, but which consists of repeating sub-sequences. Participants can become sensitive to this regularity, as measured by an indirect test of reaction time, but can remain apparently unaware of the sequence, as measured by direct tests of prediction or recognition. Some researchers have claimed that this learning may take place by observation alone. We suggest that observational learning may be due to explicit acquired knowledge of the sequence, and is not mediated by the same processes which give rise to learning by action. In Expt 1, we show that it is very difficult to acquire explicit sequence knowledge under dual task conditions, even when participants are told that a regular sequence exists. In Expt 2, we use the same conditions to compare actors, who respond to the sequence during learning, and observers, who merely watch the stimuli. Furthermore, we manipulate the salience of the sequence, in order to encourage learning. There is no evidence of observational learning in these conditions, despite the usual effects of learning being demonstrated by actors. In Expt 3, we show that observational learning does occur, but only when observers have no secondary task and even then only reliably for a sequence which has been made salient by chunking subcomponents. We conclude that sequence learning by observation is mediated by explicit processes, and is eliminated under conditions which support learning by action, but make it difficult to acquire explicit knowledge.

Awareness↗
PubMed2004

The role of the frontal pursuit area in learning in smooth pursuit eye movements.

The frontal pursuit area (FPA) in the cerebral cortex is part of the circuit for smooth pursuit eye movements. The present paper asks whether the FPA is upstream, downstream, or at the site of learning in pursuit eye movements. Learning was induced by having monkeys repeatedly pursue targets that moved at one speed for 150 msec before changing speed. Single-cell recording showed no consistent correlate of pursuit learning in the responses of FPA neurons. Some neurons showed changes in firing in the same direction as the learning, others showed changes in the opposite direction, and many showed no changes at all. In contrast, the eye movements evoked by electrical stimulation of the FPA showed clear correlates of learning. Learning effects were observed when microstimulation was delivered during the initiation of pursuit and during fixation of a stationary target. In addition, learning caused changes in the degree to which stimulation of the FPA enhanced the eye velocity evoked by brief perturbations of a stationary target. The magnitude of the change in the stimulation-evoked eye movement in each tracking condition was proportional to the size of the eye movement evoked under that condition before learning. We conclude that learning occurs downstream from the FPA, possibly within the cerebellum, and that learning may be related to mechanisms that also control the gain of visual-motor responses on a rapid time scale.

Animals↗
PubMed2005

Frontal networks for learning and executing arbitrary stimulus-response associations.

Flexible rule learning, a behavior with obvious adaptive value, is known to depend on an intact prefrontal cortex (PFC). One simple, yet powerful, form of such learning consists of forming arbitrary stimulus-response (S-R) associations. A variety of evidence from monkey and human studies suggests that the PFC plays an important role in both forming new S-R associations and in using learned rules to select the contextually appropriate response to a particular stimulus cue. Although monkey lesion studies more strongly implicate the ventrolateral PFC (vlPFC) in S-R learning, clinical data and neurophysiology studies have implicated both the vlPFC and the dorsolateral region (dlPFC) in associative rule learning. Previous human imaging studies of S-R learning tasks, however, have not demonstrated involvement of the dlPFC. This may be because of the design of previous imaging studies, which used few stimuli and used explicitly stated one-to-one S-R mapping rules that were usually practiced before scanning. Humans learn these rules very quickly, limiting the ability of imaging techniques to capture activity related to rule acquisition. To address these issues, we performed functional magnetic resonance imaging while subjects learned by trial and error to associate sets of abstract visual stimuli with arbitrary manual responses. Successful learning of this task required discernment of a categorical type of S-R rule in a block design expected to yield sustained rule representation. Our results show that distinct components of the dorsolateral, ventrolateral, and anterior PFC, lateral premotor cortex, supplementary motor area, and the striatum are involved in learning versus executing categorical S-R rules.

Adult↗
PubMed2004

Mental models and meaningful learning.

If you understand something, you can use the information you have acquired to solve problems to which that knowledge is relevant. Meaningful learning is learning with understanding. Achieving meaningful learning begins with the building of correct, appropriate mental models, or representations, of the knowledge being acquired. The next step is learning to use the available mental models to solve problems. In many of the biomedical sciences, this means being able to either calculate something, predict the responses of the system, or explain the responses of the system. Since only the learner can do the learning, the only possible role for the teacher is to help the learner to learn. This means creating an active learning environment in which the learner can acquire the needed information, continually test the mental models being built, and correct or refine those models as needed. In an active learning environment, students are given ample opportunities to learn to solve problems. If the goal of the course is the achievement of meaningful learning, it is essential that the students then be assessed to determined whether they have reached that goal.

Animals↗
PubMed2005

Eye movements are functional during face learning.

In a free viewing learning condition, participants were allowed to move their eyes naturally as they learned a set of new faces. In a restricted viewing learning condition, participants remained fixated in a single central location as they learned the new faces. Recognition of the learned faces was then tested following the two learning conditions. Eye movements were recorded during the free viewing learning condition, as well as during recognition. The recognition results showed a clear deficit following the restricted viewing condition, compared with the free viewing condition, demonstrating that eye movements play a functional role during human face learning. Furthermore, the features selected for fixation during recognition were similar following free viewing and restricted viewing learning, suggesting that the eye movements generated during recognition are not simply a recapitulation of those produced during learning.

Eye Movements↗
PubMed1998

Talker-specific learning in speech perception.

The effects of perceptual learning of talker identity on the recognition of spoken words and sentences were investigated in three experiments. In each experiment, listeners were trained to learn a set of 10 talkers' voices and were then given an intelligibility test to assess the influence of learning the voices on the processing of the linguistic content of speech. In the first experiment, listeners learned voices from isolated words and were then tested with novel isolated words mixed in noise. The results showed that listeners who were given words produced by familiar talkers at test showed better identification performance than did listeners who were given words produced by unfamiliar talkers. In the second experiment, listeners learned novel voices from sentence-length utterances and were then presented with isolated words. The results showed that learning a talker's voice from sentences did not generalize well to identification of novel isolated words. In the third experiment, listeners learned voices from sentence-length utterances and were then given sentence-length utterances produced by familiar and unfamiliar talkers at test. We found that perceptual learning of novel voices from sentence-length utterances improved speech intelligibility for words in sentences. Generalization and transfer from voice learning to linguistic processing was found to be sensitive to the talker-specific information available during learning and test. These findings demonstrate that increased sensitivity to talker-specific information affects the perception of the linguistic properties of speech in isolated words and sentences.

Humans↗