Self-Directed Learning: A Cognitive and Computational Perspective

Information about action outcomes differentially affects learning from self-determined versus imposed choices

The valence of new information influences learning rates in humans: good news tends to receive more weight than bad news. We investigated this learning bias in four experiments, by systematically manipulating the source of required action (free versus forced choices), outcome contingencies (low versus high reward) and motor requirements (go versus no-go choices). Analysis of model-estimated learning rates showed that the confirmation bias in learning rates was specific to free choices, but was independent of outcome contingencies. The bias was also unaffected by the motor requirements, thus suggesting that it operates in the representational space of decisions, rather than motoric actions. Finally, model simulations revealed that learning rates estimated from the choice-confirmation model had the effect of maximizing performance across low- and high-reward environments. We therefore suggest that choice-confirmation bias may be adaptive for efficient learning of action-outcome contingencies, above and beyond fostering person-level dispositions such as self-esteem.

Multiscale Computation and Dynamic Attention in Biological and Artificial Intelligence

Biological and artificial intelligence (AI) are often defined by their capacity to achieve a hierarchy of short-term and long-term goals that require incorporating information over time and space at both local and global scales. More advanced forms of this capacity involve the adaptive modulation of integration across scales, which resolve computational inefficiency and explore-exploit dilemmas at the same time. Research in neuroscience and AI have both made progress towards understanding architectures that achieve this. Insight into biological computations come from phenomena such as decision inertia, habit formation, information search, risky choices and foraging. Across these domains, the brain is equipped with mechanisms (such as the dorsal anterior cingulate and dorsolateral prefrontal cortex) that can represent and modulate across scales, both with top-down control processes and by local to global consolidation as information progresses from sensory to prefrontal areas. Paralleling these biological architectures, progress in AI is marked by innovations in dynamic multiscale modulation, moving from recurrent and convolutional neural networks-with fixed scalings-to attention, transformers, dynamic convolutions, and consciousness priors-which modulate scale to input and increase scale breadth. The use and development of these multiscale innovations in robotic agents, game AI, and natural language processing (NLP) are pushing the boundaries of AI achievements. By juxtaposing biological and artificial intelligence, the present work underscores the critical importance of multiscale processing to general intelligence, as well as highlighting innovations and differences between the future of biological and artificial intelligence.

Active transitive inference: When learner control facilitates integrative encoding

A growing body of research indicates that active control of learning improves episodic memory for material experienced during study. It is less clear how active learning impacts the integration of those experiences into flexible, generalizable knowledge. This study uses a novel active transitive inference task to investigate how people learn a relational hierarchy through active selection of premise pairs. Active control improved memory for studied premises as well as transitive inferences involving items that were never experienced together during study. Active learners also exhibited a systematic search preference, generating sequences of overlapping premises that may facilitate relational integration. Critically, however, advantages from active control were not universal: Only participants with higher working memory capacity benefited from the opportunity to select premise pairs during learning. These findings suggest that active control enhances integrative encoding of studied material, but only among individuals with sufficient cognitive resources.

Self-regulated spacing in a massive open online course is related to better learning

In this study, we examined students' natural studying behaviors in massive, open, online course (MOOC) on introductory psychology. We found that, overall, distributing study across multiple sessions-increasing spacing-was related to increased performance on end-of-unit quizzes, even when comparing the same student across different time-points in the course. Moreover, we found important variation on who is more likely to engage in spaced study and benefit from it. Students with higher ability and students who were more likely to complete course activities were more likely to space their study. Spacing benefits, however, were largest for the lower-ability students and for those students who were less likely to complete activities. These results suggest that spaced study might work as a buffer, improving performance for low ability students and those who do not engage in active practices. This study highlights the positive impact of spacing in real-world learning situations, but more importantly, the role of self-regulated learning decisions in shaping the impact of spaced practice.

Estratégias de aprendizagem na educação a distância: revalidação de uma escala

Frente a uma nova realidade educacional, o ensino a distância atesta suas contribuições aos processos formativos de modo que compreender o padrão de uso das estratégias de aprendizagem online torna-se fundamental para o aperfeiçoamento do planejamento educacional pois favorece o reconhecimento dos processos individuais de aprendizagem bem como dos procedimentos instrucionais adequados. Este artigo objetiva contribuir com o campo educacional ao verificar evidências de validade da ferramenta “Estratégias de Aprendizagem”. Participaram do estudo 126 alunos inscritos em cursos a distância ofertados por instituições de ensino do interior paulista. Após coleta de dados foram efetuadas análises estatísticas de Componentes Principais e Fatoriais, e de consistência interna (alfa de Cronbach). Após validação estatística a escala apresentou quatro fatores: Monitoramento da compreensão, 14 itens (α = 0,87); Estratégias autorregulatórias, 7 itens (α = 0,80); Busca de ajuda interpessoal, 3 itens (α = 0,82); Estratégias cognitivo-comportamentais, 7 itens (α = 0,78). Os resultados indicam que a escala é estatisticamente válida e confiável. 

A common mechanism underlying choice's influence on preference and memory

Individual control over learning leads to better memory outcomes, yet it is still unclear which aspects of control matter. One's sense of agency could be a key component, but it can be challenging to dissociate it from its consequences on the environment. Here we used a paradigm in which participants in one condition had the opportunity to choose between cues (choice condition) and in another were instructed which cue to select (fixed condition). Because the cues had no effect on the memoranda, we could isolate the effect of choice on memory. Participants also rated the cues for preference before and after encoding, allowing us to test how the number of times a cue was chosen affected its preference. By pooling multiple behavioral studies, we were able to use an individual differences approach to examine the relationship between choice effects on preference and memory. Replicating previous work, we found that immediate and delayed (24-h) recognition memory was higher for items encountered in the choice condition. We also found that cues that were selected more often increased their preference in the choice condition, but actually decreased their preference in the fixed condition, suggesting that choice engaged value-related processes. Critically, we found a positive across-subjects relationship between choice memory enhancements and choice-induced preference change for delayed but not for immediate memory. These data suggest that a shared value-based mechanism enhances preference for choice cues and memory consolidation of the choice outcomes. Thus, the value of choice may play an important role in learning enhancements.

Young children consider the expected utility of others' learning to decide what to teach

Direct instruction facilitates learning without the costs of exploration, yet teachers must be selective because not everything can nor needs to be taught. How do we decide what to teach and what to leave for learners to discover? Here we investigate the cognitive underpinnings of the human ability to prioritize what to teach. We present a computational model that decides what to teach by maximizing the learner's expected utility of learning from instruction and from exploration, and we show that children (aged 5-7 years) make decisions that are consistent with the model's predictions (that is, minimizing the learner's costs and maximizing the rewards). Children flexibly considered either the learner's utility or their own, depending on the context, and even considered costs they had not personally experienced, to decide what to teach. These results suggest that utility-based reasoning may play an important role in curating cultural knowledge by supporting selective transmission of high-utility information.

Cost-benefit trade-offs in decision-making and learning

Value-based decision-making involves trading off the cost associated with an action against its expected reward. Research has shown that both physical and mental effort constitute such subjective costs, biasing choices away from effortful actions, and discounting the value of obtained rewards. Facing conflicts between competing action alternatives is considered aversive, as recruiting cognitive control to overcome conflict is effortful. Moreover, engaging control to proactively suppress irrelevant information that could conflict with task-relevant information would presumably also be cognitively costly. Yet, it remains unclear whether the cognitive control demands involved in preventing and resolving conflict also constitute costs in value-based decisions. The present study investigated this question by embedding irrelevant distractors (flanker arrows) within a reversal-learning task, with intermixed free and instructed trials. Results showed that participants learned to adapt their free choices to maximize rewards, but were nevertheless biased to follow the suggestions of irrelevant distractors. Thus, the perceived cost of investing cognitive control to suppress an external suggestion could sometimes trump internal value representations. By adapting computational models of reinforcement learning, we assessed the influence of conflict at both the decision and learning stages. Modelling the decision showed that free choices were more biased when participants were less sure about which action was more rewarding. This supports the hypothesis that the costs linked to conflict management were traded off against expected rewards. During the learning phase, we found that learning rates were reduced in instructed, relative to free, choices. Learning rates were further reduced by conflict between an instruction and subjective action values, whereas learning was not robustly influenced by conflict between one’s actions and external distractors. Our results show that the subjective cognitive control costs linked to conflict factor into value-based decision-making, and highlight that different types of conflict may have different effects on learning about action outcomes.

Value-based decision-making involves trading off the cost associated with an action–such as physical or mental effort–against its expected reward. Although facing conflicts between competing action alternatives is considered aversive and effortful, it remains unclear whether conflict also constitutes a cost in value-based decisions. We tested this hypothesis by combining a classic conflict (flanker) task with a reinforcement-learning task. Results showed that participants learned to maximise their earnings, but were nevertheless biased to follow irrelevant suggestions. Computational model-based analyses showed a greater choice bias with more uncertainty about the best action to make, supporting the hypothesis that the costs linked to conflict management were traded off against expected rewards. We additionally found that learning rates were reduced when following instructions, relative to when choosing freely what to do. Learning was further reduced by conflict between instructions and subjective action values. In short, we found that the subjective cognitive control costs linked to conflict factor into value-based decision-making, and that different types of conflict may have different effects on learning about action outcomes.

Stepwise versus globally optimal search in children and adults

How do children and adults search for information when stepwise-optimal strategies fail to identify the most efficient query? The value of questions is often measured in terms of stepwise information gain (expected reduction of entropy on the next time step) or other stepwise-optimal methods. However, such myopic models are not guaranteed to identify the most efficient sequence of questions, that is, the shortest path to the solution. In two experiments we contrast stepwise methods with globally optimal strategies and study how younger children (around age 8, N = 52), older children (around age 10, N = 99), and adults (N = 101) search in a 20-questions game where planning ahead is required to identify the most efficient first question. Children searched as efficiently as adults, but also as myopically. Both children and adults tended to rely on heuristic stepwise-optimal strategies, focusing primarily on questions' implications for the next time step, rather than planning ahead.

Clinical Case-Based Image Portfolios in Medical Histopathology

This descriptive article describes the use of clinical case-based portfolios in histopathology teaching laboratories in conjunction with virtual microscopy not only to integrate histology and pathology disciplines for first and second year medical students but also to stimulate student engagement, promote self-directed and group-based learning and enhance student-to-student interaction in a structured manner. Portfolios consisted of PowerPoint files encompassing four to five clinical case studies relevant to the topics covered that week. Portfolios integrated study materials provided in the module-specific lectures, clinical skill lectures, and online interactive content. Two sets of portfolios, Individual and Group, were used. Individual Portfolios were completed by each student and uploaded prior to the laboratory session. Group Portfolios were completed by students working together in small groups during the laboratory session with minimal faculty assistance. The functional utility and acceptance of Individual and Group Portfolios among first- and second-year medical students was evaluated using electronic surveys and examination performances. Both first- and second-year students agreed that the use of portfolios in conjunction with virtual microscopy promoted understanding and encouraged discussion of the topics covered during the week and that group members worked well together and contributed to the completion of the portfolios. Performances on the Histology and Cell Biology and Pathology sections on the United States Medical Licensing Examination^® (USMLE^® ) remained consistent and in line with national averages. Overall, use of portfolios promoted peer teaching and contributed towards successful transition to the new system-based integrated curriculum with continued strong performance on the USMLE.

Memory enhancements from active control of learning emerge across development

This paper investigates whether active control of study leads to enhanced learning in 5- to 11-year-old children. In Experiments 1 and 2, participants played a simple memory game with the instruction to try to remember and later recognize a set of 64 objects. In Experiment 3, the goal was to learn the French names for the same objects. For half of the materials presented, participants could decide the order and pacing of study (Active condition). For the other half, they passively observed the study decisions of a previous participant (Yoked condition). Recognition memory was more accurate for objects studied in the active as compared to the yoked condition. However, the active learning advantage was relatively small among 5-year-olds and increased with age, becoming comparable to adults' by age 8. Our results show that the ability to actively control study develops during early childhood and results in memory benefits that last over a week-long delay. We discuss possible interpretations for the observed developmental change, as well as the implications of these results for educational implementations.

Improving analytical reasoning and argument understanding: a quasi-experimental field study of argument visualization

The ability to analyze arguments is critical for higher-level reasoning, yet previous research suggests that standard university education provides only modest improvements in students' analytical-reasoning abilities. What pedagogical approaches are most effective for cultivating these skills? We investigated the effectiveness of a 12-week undergraduate seminar in which students practiced a software-based technique for visualizing the logical structures implicit in argumentative texts. Seminar students met weekly to analyze excerpts from contemporary analytic philosophy papers, completed argument visualization problem sets, and received individualized feedback on a weekly basis. We found that seminar students improved substantially more on LSAT Logical Reasoning test forms than did control students (d = 0.71, 95% CI: [0.37, 1.04], p < 0.001), suggesting that learning how to visualize arguments in the seminar led to large generalized improvements in students' analytical-reasoning skills. Moreover, blind scoring of final essays from seminar students and control students, drawn from a parallel lecture course, revealed large differences in favor of seminar students (d = 0.87, 95% CI: [0.26, 1.48], p = 0.005). Seminar students understood the arguments better, and their essays were more accurate and effectively structured. Taken together, these findings deepen our understanding of how visualizations support logical reasoning and provide a model for improving analytical-reasoning pedagogy.

Replicability of Machine Learning Models in the Social Sciences

Abstract. Machine learning tools are increasingly used in social sciences and policy fields due to their increase in predictive accuracy. However, little research has been done on how well the models of machine learning methods replicate across samples. We compare machine learning methods with regression on the replicability of variable selection, along with predictive accuracy, using an empirical dataset as well as simulated data with additive, interaction, and non-linear squared terms added as predictors. Methods analyzed include support vector machines (SVM), random forests (RF), multivariate adaptive regression splines (MARS), and the regularized regression variants, least absolute shrinkage and selection operator (LASSO), and elastic net. In simulations with additive and linear interactions, machine learning methods performed similarly to regression in replicating predictors; they also performed mostly equal or below regression on measures of predictive accuracy. In simulations with square terms, machine learning methods SVM, RF, and MARS improved predictive accuracy and replicated predictors better than regression. Thus, in simulated datasets, the gap between machine learning methods and regression on predictive measures foreshadowed the gap in variable selection. In replications on the empirical dataset, however, improved prediction by machine learning methods was not accompanied by a visible improvement in replicability in variable selection. This disparity is explained by the overall explanatory power of the models. When predictors have small effects and noise predominates, improved global measures of prediction in a sample by machine learning methods may not lead to the robust selection of predictors; thus, in the presence of weak predictors and noise, regression remains a useful tool for model building and replication.

Generalized Information Theory Meets Human Cognition: Introducing a Unified Framework to Model Uncertainty and Information Search

Searching for information is critical in many situations. In medicine, for instance, careful choice of a diagnostic test can help narrow down the range of plausible diseases that the patient might have. In a probabilistic framework, test selection is often modeled by assuming that people's goal is to reduce uncertainty about possible states of the world. In cognitive science, psychology, and medical decision making, Shannon entropy is the most prominent and most widely used model to formalize probabilistic uncertainty and the reduction thereof. However, a variety of alternative entropy metrics (Hartley, Quadratic, Tsallis, Rényi, and more) are popular in the social and the natural sciences, computer science, and philosophy of science. Particular entropy measures have been predominant in particular research areas, and it is often an open issue whether these divergences emerge from different theoretical and practical goals or are merely due to historical accident. Cutting across disciplinary boundaries, we show that several entropy and entropy reduction measures arise as special cases in a unified formalism, the Sharma-Mittal framework. Using mathematical results, computer simulations, and analyses of published behavioral data, we discuss four key questions: How do various entropy models relate to each other? What insights can be obtained by considering diverse entropy models within a unified framework? What is the psychological plausibility of different entropy models? What new questions and insights for research on human information acquisition follow? Our work provides several new pathways for theoretical and empirical research, reconciling apparently conflicting approaches and empirical findings within a comprehensive and unified information-theoretic formalism.

Organized simultaneous displays facilitate learning of complex natural science categories

Subjects learned to classify images of rocks into the categories igneous, metamorphic, and sedimentary. In accord with the real-world structure of these categories, the to-be-classified rocks in the experiments had a dispersed similarity structure. Our central hypothesis was that learning of these complex categories would be improved through observational study of organized, simultaneous displays of the multiple rock tokens. In support of this hypothesis, a technique that included the presentation of the simultaneous displays during phases of the learning process yielded improved acquisition (Experiment 1) and generalization (Experiment 2) compared to methods that relied solely on sequential forms of study and testing. The technique appears to provide a good starting point for application of cognitive-psychology principles of effective category learning to the science classroom.

A Knowledge-Based Arrangement of Prototypical Neural Representation Prior to Experience Contributes to Selectivity in Upcoming Knowledge Acquisition

Knowledge acquisition is a process in which one actively selects a piece of information from the environment and assimilates it with prior knowledge. However, little is known about the neural mechanism underlying selectivity in knowledge acquisition. Here we executed a 2-day human experiment to investigate the involvement of characteristic spontaneous activity resembling a so-called “preplay” in selectivity in sentence comprehension, an instance of knowledge acquisition. On day 1, we presented 10 sentences (prior sentences) that were difficult to understand on their own. On the following day, we first measured the resting-state functional magnetic resonance imaging (fMRI). Then, we administered a sentence comprehension task using 20 new sentences (posterior sentences). The posterior sentences were also difficult to understand on their own, but some could be associated with prior sentences to facilitate their understanding. Next, we measured the posterior sentence-induced fMRI to identify the neural representation. From the resting-state fMRI, we extracted the appearances of activity patterns similar to the neural representations for posterior sentences. Importantly, the resting-state fMRI was measured before giving the posterior sentences, and thus such appearances could be considered as preplay-like or prototypical neural representations. We compared the intensities of such appearances with the understanding of posterior sentences. This gave a positive correlation between these two variables, but only if posterior sentences were associated with prior sentences. Additional analysis showed the contribution of the entorhinal cortex, rather than the hippocampus, to the correlation. The present study suggests that prior knowledge-based arrangement of neural activity before an experience contributes to the active selection of information to be learned. Such arrangement prior to an experience resembles preplay activity observed in the rodent brain. In terms of knowledge acquisition, the present study leads to a new view of the brain (or more precisely of the brain’s knowledge) as an autopoietic system in which the brain (or knowledge) selects what it should learn by itself, arranges preplay-like activity as a position for the new information in advance, and actively reorganizes itself.

Motivational valence alters memory formation without altering exploration of a real-life spatial environment

Volitional exploration and learning are key to adaptive behavior, yet their characterization remains a complex problem for cognitive science. Exploration has been posited as a mechanism by which motivation promotes memory, but this relationship is not well-understood, in part because novel stimuli that motivate exploration also reliably elicit changes in neuromodulatory brain systems that directly alter memory formation, via effects on neural plasticity. To deconfound interrelationships between motivation, exploration, and memory formation we manipulated motivational state prior to entering a spatial context, measured exploratory responses to the context and novel stimuli within it, and then examined motivation and exploration as predictors of memory outcomes. To elicit spontaneous exploration, we used the physical space of an art exhibit with affectively rich content; we expected motivated exploration and memory to reflect multiple factors, including not only motivational valence, but also individual differences. Motivation was manipulated via an introductory statement framing exhibit themes in terms of Promotion- or Prevention-oriented goals. Participants explored the exhibit while being tracked by video. They returned 24 hours later for recall and spatial memory tests, followed by measures of motivation, personality, and relevant attitude variables. Promotion and Prevention condition participants did not differ in terms of group-level exploration time or memory metrics, suggesting similar motivation to explore under both framing contexts. However, exploratory behavior and memory outcomes were significantly more closely related under Promotion than Prevention, indicating that Prevention framing disrupted expected depth-of-encoding effects. Additionally, while trait measures predicted exploration similarly across framing conditions, traits interacted with motivational framing context and facial affect to predict memory outcomes. This novel characterization of motivated learning implies that dissociable behavioral and biological mechanisms, here varying as a function of valence, contribute to memory outcomes in complex, real-life environments.

Variation in behavioral engagement during an active learning activity leads to differential knowledge gains in college students

There are many pedagogical techniques used by educators in higher education; however, some techniques and activities have been shown to be more beneficial to student learning than others. Research has demonstrated that active learning and learning in which students cognitively engage with the material in a multitude of ways result in better understanding and retention. The aim of the present study was to determine which of three pedagogical techniques led to improvement in learning and retention in undergraduate college students. Subjects partook in one of three different types of pedagogical engagement: hands-on learning with a model, observing someone else manipulate the model, and traditional lecture-based presentation. Students were then asked to take an online quiz that tested their knowledge of the new material, both immediately after learning the material and 2 wk later. Students who engaged in direct manipulation of the model scored higher on the assessment immediately after learning the material compared with the other two groups. However, there were no differences among the three groups when assessed after a 2-wk retention interval. Thus active learning techniques that involve direct interaction with the material can lead to learning benefits; however, how these techniques benefit long-term retention of the information is equivocal.

Selection of Informative Examples in Chemogenomic Datasets

High-throughput and high-content screening campaigns have resulted in the creation of large chemogenomic matrices. These matrices form the training data which is used to build ligand-target interaction models for pharmacological and chemical biology research. While academic, government, and industrial efforts continuously add to the ligand-target data pairs available for modeling, major research efforts are devoted to improving machine learning techniques to cope with the sparseness, heterogeneity, and size of available datasets as well as inherent noise and bias. This "race of arms" has led to the creation of algorithms to generate highly complex models with high prediction performance at the cost of training efficiency as well as interpretability.In contrast, recent studies have challenged the necessity for "big data" in chemogenomic modeling and found that models built on larger numbers of examples do not necessarily result in better predictive abilities. Automated adaptive selection of the training data (ligand-target instances) used for model creation can result in considerably smaller training sets that retain prediction performance on par with training using hundreds of thousands of data points. In this chapter, we describe the protocols used for one such iterative chemogenomic selection technique, including model construction and update as well as possible techniques for evaluations of constructed models and analysis of the iterative model construction.

Long-term spatial clustering in free recall

We explored the influence of space on the organisation of items in long-term memory. In two experiments, we asked our participants to explore a virtual environment and memorise discrete items presented at specific locations. Memory for those items was later on tested in immediate (T1) and 24 hours delayed (T2) free recall tests, in which subjects were asked to recall as many items as possible in any order. In experiment 2, we further examined the contribution of active and passive navigation in recollection dynamics. Results across experiments revealed a significant tendency for participants to consecutively recall items that were encountered in proximate locations during learning. Moreover, the degree of spatial organisation and the total number of items recalled were positively correlated in the immediate and the delayed tests. Results from experiment 2 indicated that the spatial clustering of items was independent of navigation types. Our results highlight the long-term stability of spatial clustering effects and their correlation with recall performance, complementing previous results collected in immediate or briefly delayed tests.

Structural Learning in a Visuomotor Adaptation Task Is Explicitly Accessible

Structural learning is a phenomenon characterized by faster learning in a new situation that shares features of previously experienced situations. One prominent example within the sensorimotor domain is that human participants are faster to counter a novel rotation following experience with a set of variable visuomotor rotations. This form of learning is thought to occur implicitly through the updating of an internal forward model, which predicts the sensory consequences of motor commands. However, recent work has shown that much of rotation learning occurs through an explicitly accessible process, such as movement re-aiming. We sought to determine if structural learning in a visuomotor rotation task is purely implicit (e.g., driven by an internal model) or explicitly accessible (i.e., re-aiming). We found that participants exhibited structural learning: following training with a variable set of rotations, they more quickly learned a novel rotation. This benefit was entirely conferred by the explicit re-aiming of movements. Implicit learning offered little to no contribution. Next, we investigated the specificity of this learning benefit by exposing participants to a novel perturbation drawn from a statistical structure either congruent or incongruent with their prior experience. We found that participants who experienced congruent training and test phase structure (i.e., rotations to rotation) learned more quickly than participants exposed to incongruent training and test phase structure (i.e., gains to rotation) and a control group. These results suggest that structural learning in a visuomotor rotation task is specific to previously experienced statistical structure and expressed via explicit re-aiming of movements.

The Flipped Classroom - From Theory to Practice in Health Professional Education

The flipped classroom is growing in popularity in health professional education. As such, instructors are experiencing various growing pains in functionalizing this model, from justifying the approach to managing time inside and outside of class to assessing impact on learning. This review focuses on some key theories that support the flipped model and translates those key theories into practice across core aspects of the flipped classroom: pre-class preparation, in-class activities, after-class activities and assessment of student learning.

Strategies for Improving Learner Metacognition in Health Professional Education

Metacognition is an essential skill in critical thinking and self-regulated, lifelong learning. It is important for learners to have skills in metacognition because they are used to monitor and regulate reasoning, comprehension, and problem-solving, which are fundamental components/outcomes of pharmacy curricula. Instructors can help learners develop metacognitive skills within the classroom and experiential setting by carefully designing learning activities within courses and the curriculum. These skills are developed through intentional questioning, modeling techniques, and reflection. This article discusses key background literature on metacognition and identifies specific methods and strategies to develop learners' metacognitive skills in both the classroom and experiential settings.

Blended learning: how can we optimise undergraduate student engagement?

BACKGROUND

Blended learning is a combination of online and face-to-face learning and is increasingly of interest for use in undergraduate medical education. It has been used to teach clinical post-graduate students pharmacology but needs evaluation for its use in teaching pharmacology to undergraduate medical students, which represent a different group of students with different learning needs.

METHODS

An existing BSc-level module on neuropharmacology was redesigned using the Blended Learning Design Tool (BLEnDT), a tool which uses learning domains (psychomotor, cognitive and affective) to classify learning outcomes into those taught best by self-directed learning (online) or by collaborative learning (face-to-face). Two online courses were developed, one on Neurotransmitters and the other on Neurodegenerative Conditions. These were supported with face-to-face tutorials. Undergraduate students' engagement with blended learning was explored by the means of three focus groups, the data from which were analysed thematically.

RESULTS

Five major themes emerged from the data 1) Purpose and Acceptability 2) Structure, Focus and Consolidation 3) Preparation and workload 4) Engagement with e-learning component 5) Future Medical Education.

CONCLUSION

Blended learning was acceptable and of interest to undergraduate students learning this subject. They expressed a desire for more blended learning in their courses, but only if it was highly structured, of high quality and supported by tutorials. Students identified that the 'blend' was beneficial rather than purely online learning.

Multi-objective active machine learning rapidly improves structure-activity models and reveals new protein-protein interaction inhibitors

Active machine learning puts artificial intelligence in charge of a sequential, feedback-driven discovery process. We present the application of a multi-objective active learning scheme for identifying small molecules that inhibit the protein-protein interaction between the anti-cancer target CXC chemokine receptor 4 (CXCR4) and its endogenous ligand CXCL-12 (SDF-1). Experimental design by active learning was used to retrieve informative active compounds that continuously improved the adaptive structure-activity model. The balanced character of the compound selection function rapidly delivered new molecular structures with the desired inhibitory activity and at the same time allowed us to focus on informative compounds for model adjustment. The results of our study validate active learning for prospective ligand finding by adaptive, focused screening of large compound repositories and virtual compound libraries.

Health Professional Workforce Education in the Asia Pacific

OBJECTIVE

To design and implement an international and interprofessional Global Learning Partnership Model, which involves shared learning between academics and students from Universitas 21 network with other universities with United Nations Millennium Development Goal needs.

DESIGN

Two literature reviews were conducted to inform ethical aspects and curriculum design of the GLP model. Feedback from conference presentations and consultation with experts in education and public health has been incorporated to inform the current iteration of the GLP model.

INTERVENTION

The pilot group of 25 students from U21 universities and Kathmandu University, representing six health disciplines will meet in Nepal in April 2016 for a shared learning experience, including a one week university based workshop and three week community based experience.

OUTCOME MEASURES

A multi-phase, mixed method design was selected for the evaluation of the GLP model, utilising a combination of focus groups and questionnaires to evaluate the efficacy of the placement through student experience and learning outcomes in cultural competency, UN SDG knowledge, community engagement and health promotion skills.

RESULTS

The literature review demonstrated that cultural awareness and cultural knowledge were improved through participation in cultural immersion programs that incorporated preparatory workshops and clinical experiences. Data will be gathered in April 2006 and the results of the evaluation will be published in the future.

CONCLUSIONS

The GLP model proposes a project around the fundamental concept of engagement and sharing between students and academics across universities and cultural contexts to build capacity through education, while capitalising on strengths of existing global health placements. Further the inclusion of host-country students and academics in this learning exchange will promote the establishment of an international and interprofessional network for ongoing health promotion. Significance for public healthThe Global Learning Partnership model aims to contribute to the capacity building of a health workforce that is capable of working effectively in cross cultural and interprofessional health care teams. A shared public health focused global placement has the potential to catalyse collaborative relationships between educational institutions in the Asia Pacific region.

An In Vivo Study of Self-Regulated Study Sequencing in Introductory Psychology Courses

Study sequence can have a profound influence on learning. In this study we investigated how students decide to sequence their study in a naturalistic context and whether their choices result in improved learning. In the study reported here, 2061 undergraduate students enrolled in an Introductory Psychology course completed an online homework tutorial on measures of central tendency, a topic relevant to an exam that counted towards their grades. One group of students was enabled to choose their own study sequence during the tutorial (Self-Regulated group), while the other group of students studied the same materials in sequences chosen by other students (Yoked group). Students who chose their sequence of study showed a clear tendency to block their study by concept, and this tendency was positively associated with subsequent exam performance. In the Yoked group, study sequence had no effect on exam performance. These results suggest that despite findings that blocked study is maladaptive when assigned by an experimenter, it may actually be adaptive when chosen by the learner in a naturalistic context.

Deconstructing the effect of self-directed study on episodic memory

Self-directed learning is often associated with better long-term memory retention; however, the mechanisms that underlie this advantage remain poorly understood. This series of experiments was designed to "deconstruct" the notion of self-directed learning, in order to better identify the factors most responsible for these improvements to memory. In particular, we isolated the memory advantage that comes from controlling the content of study episodes from the advantage that comes from controlling the timing of those episodes. Across four experiments, self-directed learning significantly enhanced recognition memory, relative to passive observation. However, the advantage for self-directed learning was found to be present even under extremely minimal conditions of volitional control (simply pressing a button when a participant was ready to advance to the next item). Our results suggest that improvements to memory following self-directed encoding may be related to the ability to coordinate stimulus presentation with the learner's current preparatory or attentional state, and they highlight the need to consider the range of cognitive control processes involved in and influenced by self-directed study.

The Psychology and Neuroscience of Curiosity

Curiosity is a basic element of our cognition, but its biological function, mechanisms, and neural underpinning remain poorly understood. It is nonetheless a motivator for learning, influential in decision-making, and crucial for healthy development. One factor limiting our understanding of it is the lack of a widely agreed upon delineation of what is and is not curiosity. Another factor is the dearth of standardized laboratory tasks that manipulate curiosity in the lab. Despite these barriers, recent years have seen a major growth of interest in both the neuroscience and psychology of curiosity. In this Perspective, we advocate for the importance of the field, provide a selective overview of its current state, and describe tasks that are used to study curiosity and information-seeking. We propose that, rather than worry about defining curiosity, it is more helpful to consider the motivations for information-seeking behavior and to study it in its ethological context.

Opening the cuebox: the information children and young adults generate and rely on when making inferences from memory

We used a cue-generation and a cue-selection paradigm to investigate the cues children (9- to 12-year-olds) and young adults (17-year-olds) generate and select for a range of inferences from memory. We found that children generated more cues than young adults, who, when asked why they did not generate some particular cues, responded that they did not consider them relevant for the task at hand. On average, the cues generated by children were more perceptual but as informative as the cues generated by young adults. When asked to select the most informative of two cues, both children and young adults tended to choose a hidden (i.e., not perceptual) cue. Our results suggest a developmental change in the cuebox (i.e., the set of cues used to make inferences from memory): New cues are added to the cuebox as more cues are learned, and some old, perceptual cues, although informative, are replaced with hidden cues, which, by both children and young adults, are generally assumed to be more informative than perceptual cues.

Brief cognitive training interventions in young adulthood promote long-term resilience to drug-seeking behavior

Environmental stress and deprivation increase vulnerability to substance use disorders in humans and promote drug-seeking behavior in animal models. In contrast, experiences of mastery and stability may shape neural circuitry in ways that build resilience to future challenges. Cognitive training offers a potential intervention for reducing vulnerability in the face of environmental stress or deprivation. Here, we test the hypothesis that brief cognitive training can promote long-term resilience to one measure of drug-seeking behavior, cocaine conditioned place preference (CPP), in mice. In young adulthood, mice underwent cognitive training, received rewards while exploring a training arena (i.e. yoked control), or remained in their home cages. Beginning 4 weeks after cessation of training, we conditioned mice in a CPP paradigm and then tested them weekly for CPP maintenance or daily for CPP extinction. We found that a brief 9-day cognitive training protocol reduced maintenance of cocaine CPP when compared to standard housed and yoked conditions. This beneficial effect persisted long after cessation of the training, as mice remained in their home cages for 4 weeks between training and cocaine exposure. When mice were tested for CPP on a daily extinction schedule, we found that all trained and yoked groups that left their home cages to receive rewards in a training arena showed significant extinction of CPP, while mice kept in standard housing for the same period did not extinguish CPP. These data suggest that in early adulthood, deprivation may confer vulnerability to drug-seeking behavior and that brief interventions may promote long-term resilience.

Rich analysis and rational models: inferring individual behavior from infant looking data

Studies of infant looking times over the past 50 years have provided profound insights about cognitive development, but their dependent measures and analytic techniques are quite limited. In the context of infants' attention to discrete sequential events, we show how a Bayesian data analysis approach can be combined with a rational cognitive model to create a rich data analysis framework for infant looking times. We formalize (i) a statistical learning model, (ii) a parametric linking between the learning model's beliefs and infants' looking behavior, and (iii) a data analysis approach and model that infers parameters of the cognitive model and linking function for groups and individuals. Using this approach, we show that recent findings from Kidd, Piantadosi and Aslin (iv) of a U-shaped relationship between look-away probability and stimulus complexity even holds within infants and is not due to averaging subjects with different types of behavior. Our results indicate that individual infants prefer stimuli of intermediate complexity, reserving attention for events that are moderately predictable given their probabilistic expectations about the world.