AAC&U's Integrative Liberal Learning and the CAS Standards: Advising for a 21st Century Liberal Education

The Association of American Colleges and Universities presented and promoted integrative liberal learning as a collaborative goal that all institutions of higher education must strive to achieve. The similarities between the goals of integrative liberal learning and the Standards for Academic Advising by the Council for the Advancement of Standards in Higher Education are discussed with emphasis placed on the critical role that academic advising plays in support of an integrative liberal learning education, and in turn, future success for all students.

When conditioned responses "fire back": bidirectional cross-activation creates learning opportunities in synesthesia

In grapheme-color synesthesia, the letter "c" printed in black may be experienced as red, but typically the color red does not trigger the experience of the letter "c." Therefore, at the level of subjective experience, cross-activation is usually unidirectional. However, recent evidence from digit-color synesthesia suggests that at an implicit level bidirectional cross-activation can occur. Here we demonstrate that this finding is not restricted to this specific type of synesthesia. We introduce a new method that enables the investigation of bidirectionality in other types of synesthesia. We found that a group of grapheme-color synesthetes, but not a control group, showed a startle in response to a color-inducing grapheme after a startle response was conditioned to the specific corresponding color. These results implicate that when the startle response was associated with the real color an association between shock and the grapheme was also established. By this mechanism (i.e. implicit cross-activation) the conditioned response to the real color generalized to the synesthetic color. We suggest that parietal brain areas are responsible for this neural backfiring.

Learning flexible sensori-motor mappings in a complex network

Given the complex structure of the brain, how can synaptic plasticity explain the learning and forgetting of associations when these are continuously changing? We address this question by studying different reinforcement learning rules in a multilayer network in order to reproduce monkey behavior in a visuomotor association task. Our model can only reproduce the learning performance of the monkey if the synaptic modifications depend on the pre- and postsynaptic activity, and if the intrinsic level of stochasticity is low. This favored learning rule is based on reward modulated Hebbian synaptic plasticity and shows the interesting feature that the learning performance does not substantially degrade when adding layers to the network, even for a complex problem.

Performance of experienced dentists in Switzerland after an e-learning program on ICDAS occlusal caries detection

This study aimed to evaluate the effect of an e-learning program on the validity and reproducibility of the International Caries Detection and Assessment System (ICDAS) in detecting occlusal caries. For the study, 170 permanent molars were selected. Four dentists in Switzerland who had no previous contact with ICDAS examined the teeth before and after the e-learning program and scored the sites according to ICDAS. Teeth were histologically prepared and assessed for caries extension. The significance level was set at 0.05. Sensitivity before and after the e-learning program was 0.80 and 0.77 (D1), 0.72 and 0.63 (D2), and 0.74 and 0.67 (D3,4), respectively. Specificity was 0.64 and 0.69 (D1), 0.70 and 0.81 (D2), and 0.81 and 0.87 (D3,4). A McNemar test did not show any difference between the values of sensitivity, specificity, accuracy, and area under the ROC curve (AUC) before and after the e-learning program. The averages of wK values for interexaminer reproducibility were 0.61 (before) and 0.66 (after). Correlation with histology presented wK values of 0.62 (before) and 0.63 (after). A Wilcoxon test showed a statistically significant difference between before and after the e-learning program. In conclusion, even though ICDAS performed well in detecting occlusal caries, the e-learning program did not have any statistically significant effect on its performance by these experienced dentists.

Molecular mechanisms underlying a cellular analog of operant reward learning.

Operant conditioning is a ubiquitous but mechanistically poorly understood form of associative learning in which an animal learns the consequences of its behavior. Using a single-cell analog of operant conditioning in neuron B51 of Aplysia, we examined second-messenger pathways engaged by activity and reward and how they may provide a biochemical association underlying operant learning. Conditioning was blocked by Rp-cAMP, a peptide inhibitor of PKA, a PKC inhibitor, and by expressing a dominant-negative isoform of Ca2+-dependent PKC (apl-I). Thus, both PKA and PKC were necessary for operant conditioning. Injection of cAMP into B51 mimicked the effects of operant conditioning. Activation of PKC also mimicked conditioning but was dependent on both cAMP and PKA, suggesting that PKC acted at some point upstream of PKA activation. Our results demonstrate how these molecules can interact to mediate operant conditioning in an individual neuron important for the expression of the conditioned behavior.

Probabilistic contingency learning with limbic or prefrontal damage

AB A fundamental capacity of the human brain is to learn relations (contingencies) between environmental stimuli and the consequences of their occurrence. Some contingencies are probabilistic; that is, they predict an event in some situations but not in all. Animal studies suggest that damage to limbic structures or the prefrontal cortex may disturb probabilistic learning. The authors studied the learning of probabilistic contingencies in amnesic patients with limbic lesions, patients with prefrontal cortex damage, and healthy controls. Across 120 trials, participants learned contingent relations between spatial sequences and a button press. Amnesic patients had learning comparable to that of control subjects but failed to indicate what they had learned. Across the last 60 trials, amnesic patients and control subjects learned to avoid a noncontingent choice better than frontal patients. These results indicate that probabilistic learning does not depend on the brain structures supporting declarative memory.

The Role of BDNF, Leptin and Catecholamines in Reward Learning in Bulimia Nervosa

Background: A relationship between bulimia nervosa (BN) and reward-related behavior is supported by several lines of evidence. The dopaminergic dysfunctions in the processing of reward-related stimuli have been shown to be modulated by the neurotrophin brain derived neurotrophic factor (BDNF) and the hormone leptin. Methods: Using a randomized, double-blind, placebo-controlled, crossover design, a reward learning task was applied to study the behavior of 20 female subjects with remitted BN (rBN) and 27 female healthy controls under placebo and catecholamine depletion with alpha-methyl-para-tyrosine (AMPT). The plasma levels of BDNF and leptin were measured twice during the placebo and the AMPT condition, immediately before and 1 h after a standardized breakfast. Results: AMPT-induced differences in plasma BDNF levels were positively correlated with the AMPT-induced differences in reward learning in the whole sample (p = 0.05). Across conditions, plasma BDNF levels were higher in rBN subjects compared to controls (diagnosis effect; p = 0.001). Plasma BDNF and leptin levels were higher in the morning before compared to after a standardized breakfast across groups and conditions (time effect; p < 0.0001). The plasma leptin levels were higher under catecholamine depletion compared to placebo in the whole sample (treatment effect; p = 0.0004). Conclusions: This study reports on preliminary findings that suggest a catecholamine-dependent association of plasma BDNF and reward learning in subjects with rBN and controls. A role of leptin in reward learning is not supported by this study. However, leptin levels were sensitive to a depletion of catecholamine stores in both rBN and controls.

Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^

Learning kinds for students of technical high school : An analysis from the David Ausubel's theory

This article presents the findings of a field research, not experimental, observational, correlating, basic, of mixed data, micro sociologic, leading to a study of surveys.The object of study is to find learning kinds, and the unit of analysis were 529 high school students between 16 and 21 years old. Its purpose is to understand the impact of learning by rote, guided, self learned and meaningful learning and its achievement degree besides the learning outcomes of differentiated curriculum based on David Ausubel's thoughts, associated with different economic specialties (MINEDUC, 1998) where the population of the study is trained. To collect data, the test TADA - DO2 was used, this test has a reliability index of 0.911 according to Cronbach. From the hits it can be stated from the null hypothesis that there is a significant association (a = 0,05) between the learning kinds and the learning expected of differentiated training plan for both, male and female. It is complex to state that the training of the middle-level technicians leads to a successful employment.