Communication in collaborative discovery learning

Background. Constructivist approaches to learning focus on learning environments in which students have the opportunity to construct knowledge themselves, and negotiate this knowledge with others. Discovery learning and collaborative learning are examples of learning contexts that cater for knowledge construction processes. We introduce a computer‐based learning environment in which the two forms of learning are implemented simultaneously. We focus on the interaction between discovery learning and collaborative learning. Aim. We aim to investigate which communicative activities are frequently used in the discovery learning process and which communicative and discovery activities co‐occur. Sample. The study involved 21 pairs of 10th‐grade students enrolled in pre‐university education, ranging from 15 to 17 years of age. Method. Participants worked in dyads on separate screens in a shared discovery learning environment. They communicated using a chat box. In order to find a possible relationship between communicative activities and discovery learning processes, correlational analysis and principal component analysis were performed. Result. Significant relationships were found between communicative and discovery activities, as well as five factors combining the communicative process and the discovery learning processes. Communicative activities are performed most frequently during the activities in generating hypotheses, experimental design, and conclusion construction. Argumentation occurs less than expected, and is associated with the construction of conclusions, rather than generating hypotheses. Conclusion. Communicative activities co‐occur with discovery activities most of the time, as we expected. Further research should concentrate on means to augment communicative and discovery activities that are related to positive learning outcomes.     

The impact of training sequence and between-category similarity on unsupervised induction

Studies of supervised categorization often show better learning when examples are presented in random alternation rather than massed by category, but such interleaving impairs learning in unsupervised tasks. The exemplar comparison hypothesis explains this result by assuming that people in unsupervised tasks discover generalizations about categories by comparing individual examples, and that interleaving increases the difficulty of such within-category comparisons. The category invention hypothesis explains the interleaving effect by assuming that people are more likely to merge or aggregate potentially separable categories when they are interleaved, and this initial failure to recognize separate categories then acts as an effective barrier to further learning. The present experiments show that the interleaving effect depends on the similarity or alignability of the presented categories. This result provides evidence in favour of the category invention hypothesis, which expects that highly dissimilar (nonalignable) categories will resist aggregation and hence will not be affected by interleaving. The nonmonotonic pattern of learning, and the interaction between sequence and similarity, observed in the alignable conditions of Experiment 3 were also consistent with category invention, but not with exemplar comparison. Implications are discussed for real-world learning, especially the relationship between exposure and learning and between supervised and unsupervised learning.     

Providing worked examples for learning multiple principles

Worked examples support learning. However, if they introduce easy-to-confuse concepts or principles, specific ways of providing worked examples may influence their effectiveness. Multiple worked examples can be introduced blocked (i.e., several for the same principle) or interleaved (i.e., switching between principles), and can be sequentially or simultaneously presented. Crossing these two factors provides four ways of presenting worked examples: blocked/sequential, interleaved/sequential, blocked/simultaneous, and interleaved/simultaneous. In an experiment with university students (N = 174), we investigated how these two factors influence the acquisition of procedural and conceptual knowledge about different, but closely related (thus, easy-to-confuse) stochastic principles. Additionally, we assessed the ability of students to discriminate between principles with verification tasks. Simultaneous presentation benefitted procedural knowledge whereas, interleaved presentation benefitted conceptual knowledge. No significant differences were found for verification tasks. The results suggest that it is worthwhile to adapt the presentation of the worked examples to the learning goals.     

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.     

Greatness and misery in the teaching of the psychology of learning

Overshadowed by more popular disciplines, the study of learning seems to have lost its prominent place in the undergraduate psychology curriculum. In the first part of this essay, we argue that one reason for this state of affairs is the current content of psychology of learning courses, namely, its disproportionate emphasis on facts, procedures, and everyday examples at the expense of functional and conceptual investigations. In the second part of the essay, we outline an alternative approach to the teaching of learning, one that emphasizes basic contents such as the conceptualization of learning as a biological adaptation or the study of temporal regulation, critical methodological issues such as the logic of experimental designs or the difficulties of measuring behavior, and broad epistemological problems such as the role of hypothetical constructs, the advantages of quantitative reasoning, or the origins of knowledge and its integration. By using learning as a means towards more fundamental ends, the splendor of the discipline and its prominent place in the undergraduate curriculum may be restored.     

The role of overt language production in the Hebb repetition effect

The Hebb repetition effect (Hebb, 1961) occurs when recall performance improves for a list that is repeated during a serial-recall task. This effect is considered a good experimental analogue to language learning. Our objective was to evaluate the role of overt language production in language learning by manipulating recall direction during a Hebb repetition paradigm. In each trial, seven nonsense syllables were presented auditorily. Participants had to orally recall the items either in the presentation order or in reverse order. One sequence was repeated every third trial. In Experiment 1, we compared learning from a group that had recalled the items in their presentation order to learning from a group that had recalled the items in the reverse order. The two groups yielded similar learning rates. In Experiment 2, recall direction was varied between trials. The learning rate was not affected when recall direction varied between trials, suggesting a limited role of overt language production in language learning.     

The Origin of Error

The present two experiments were designed to investigate the effects of two competing retrieval strategies—category and position cueing.

One hundred college students participated in Experiment I in which Presentation Rate, List Type, and Stimulus Position were manipulated within an FR paradigm. Sixty students participated in Experiment II in which Formal Response Similarity and Stimulus Position were manipulated within a PA paradigm.

Findings were the following: (a) that greater clustering and FR is obtained at slower presentation rates; (b) that greater than chance clustering occurs with a category list structure; and (c) that HS response learning is more difficult than LS response learning. No support was found for position cueing.

Results were discussed within FR cueing, von Restorff, and intentional versus incidental learning paradigms as possible explanations for the nonsignificant position cueing results in the present experiments. A competing strategy hypothesis of category and position cues was preferred with category recall and clustering as the dominant mode of retrieval.     

Explaining the modality and contiguity effects: New insights from investigating students' viewing behaviour

The study examined viewing behaviour and learning outcome during multimedia learning in order to explore split‐attention processes in modality and spatial contiguity effects. Fourty students viewed a computer instruction depicting the process of lightning. Exploratory text was spoken, written near or written far from accompanying animations. Students who received spoken text outperformed students who received written text in recalling the major steps (retention) and in identifying correct solutions to problems (transfer), replicating a modality effect. Differences between near and far written text presentation in retention (d = 0.51) and transfer (d = 0.68) tests failed statistical significance. Two major characteristics concerning the learners' viewing behaviour were identified: (a) in written text presentation, learning was largely text directed and (b) learning success was related to the time learners' spent looking at animations, indicating that the processing of animations is a crucial factor in explanations of modality and spatial contiguity effects. Copyright © 2009 John Wiley & Sons, Ltd.     

Effects of order of presentation on conditional reasoning

ABSTRACT

The main goal of this research is to study whether or not the order of presentation of the premises in a logical argument form, such as a conditional reasoning task, could affect the processing time of premises and conclusion and the conclusions that participants accept as valid in an evaluation task. One experiment is reported in which participants are asked to evaluate computer-presented conditionals. Half of the problems were presented in traditional order (“if p then q, p, therefore q”) and half in inverse order (“p, if p then q, therefore q”). The experiment showed that there was an order effect in processing the premises and conclusion: participants took longer to read the premises in traditional order than in inverse order, but they took longer to read the conclusion in inverse order than in traditional order. The finding is discussed with respect to the main theories of conditional reasoning.     

Exemplar models as a mechanism for performing Bayesian inference

Probabilistic models have recently received much attention as accounts of human cognition. However, most research in which probabilistic models have been used has been focused on formulating the abstract problems behind cognitive tasks and their optimal solutions, rather than on mechanisms that could implement these solutions. Exemplar models are a successful class of psychological process models in which an inventory of stored examples is used to solve problems such as identification, categorization, and function learning. We show that exemplar models can be used to perform a sophisticated form of Monte Carlo approximation known as importance sampling and thus provide a way to perform approximate Bayesian inference. Simulations of Bayesian inference in speech perception, generalization along a single dimension, making predictions about everyday events, concept learning, and reconstruction from memory show that exemplar models can often account for human performance with only a few exemplars, for both simple and relatively complex prior distributions. These results suggest that exemplar models provide a possible mechanism for implementing at least some forms of Bayesian inference.     

A Bayesian Theory of Sequential Causal Learning and Abstract Transfer

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause‐effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre‐training (or even post‐training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue‐outcome co‐occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge.     

Syntactic features and synonymy relations: a unified treatment of some proofs of the compactness and interpolation theorems

This paper introduces the notion of syntactic feature to provide a unified treatment of earlier model theoretic proofs of both the compactness and interpolation theorems for a variety of two valued logics including sentential logic, first order logic, and a family of modal sentential logic includingM,B,S ₄ andS ₅. The compactness papers focused on providing a proof of the consequence formulation which exhibited the appropriate finite subset. A unified presentation of these proofs is given by isolating their essential feature and presenting it as an abstract principle about syntactic features. The interpolation papers focused on exhibiting the interpolant. A unified presentation of these proofs is given by isolating their essential feature and presenting it as a second abstract principle about syntactic features. This second principle reduces the problem of exhibiting the interpolant to that of establishing the existence of a family of syntactic features satisfying certain conditions. The existence of such features is established for a variety of logics (including those mentioned above) by purely combinatorial arguments.Presented byMelvin Fitting     

Do examples of failure effectively prepare students for learning from subsequent instruction?

Studies on the productive failure (PF) approach have demonstrated that attempting to solve a problem prepares students more effectively for later instruction compared to observing failed problem-solving attempts prior to instruction. However, the examples of failure used in these studies did not display the problem-solving-and-failing process, which may have limited the preparatory effects. In this quasi-experiment, we investigated whether observing someone else engaging in problem solving can prepare students for instruction, and whether examples that show the problem-solving-and -failing process are more effective than those that only show the outcome of this process. We also explored whether the perceived model–observer similarity had an impact on the effectiveness of observing examples of failure. The results showed that observing examples effectively prepares students for learning from instruction. However, observing the model's problem-solving-and-failing process did not prepare students more effectively than merely looking at the outcome. Studying examples were more effective if model–observer similarity was high.     

Learning from the existence of models: On psychic machines,tortoises, and computer simulations

Using four examples of models and computer simulations from the history of psychology, I discuss some of the methodological aspects involved in their construction and use, and I illustrate how the existence of a model can demonstrate the viability of a hypothesis that had previously been deemed impossible on a priori grounds. This shows a new way in which scientists can learn from models that extends the analysis of Morgan (1999), who has identified the construction and manipulation of models as those phases in which learning from models takes place.     

Analyzing the Rate at Which Languages Lose the Influence of a Common Ancestor

Analyzing the rate at which languages change can clarify whether similarities across languages are solely the result of cognitive biases or might be partially due to descent from a common ancestor. To demonstrate this approach, we use a simple model of language evolution to mathematically determine how long it should take for the distribution over languages to lose the influence of a common ancestor and converge to a form that is determined by constraints on language learning. We show that modeling language learning as Bayesian inference of n binary parameters or the ordering of n constraints results in convergence in a number of generations that is on the order of n log n. We relax some of the simplifying assumptions of this model to explore how different assumptions about language evolution affect predictions about the time to convergence; in general, convergence time increases as the model becomes more realistic. This allows us to characterize the assumptions about language learning (given the models that we consider) that are sufficient for convergence to have taken place on a timescale that is consistent with the origin of human languages. These results clearly identify the consequences of a set of simple models of language evolution and show how analysis of convergence rates provides a tool that can be used to explore questions about the relationship between accounts of language learning and the origins of similarities across languages.     

Becoming a high‐fidelity – super – imitator: what are the contributions of social and individual learning?

In contrast to other primates, human children's imitation performance goes from low to high fidelity soon after infancy. Are such changes associated with the development of other forms of learning? We addressed this question by testing 215 children (26–59 months) on two social conditions (imitation, emulation) – involving a demonstration – and two asocial conditions (trial‐and‐error, recall) – involving individual learning – using two touchscreen tasks. The tasks required responding to either three different pictures in a specific picture order (Cognitive: Airplane→Ball→Cow) or three identical pictures in a specific spatial order (Motor‐Spatial: Up→Down→Right). There were age‐related improvements across all conditions and imitation, emulation and recall performance were significantly better than trial‐and‐error learning. Generalized linear models demonstrated that motor‐spatial imitation fidelity was associated with age and motor‐spatial emulation performance, but cognitive imitation fidelity was only associated with age. While this study provides evidence for multiple imitation mechanisms, the development of one of those mechanisms – motor‐spatial imitation – may be bootstrapped by the development of another social learning skill – motor‐spatial emulation. Together, these findings provide important clues about the development of imitation, which is arguably a distinctive feature of the human species.     

Review of Cultural Psychology,by Steven J. Heine

Summary

An A-B, B-C, A-C mediation paradigm was used to investigate observational learning of attitudes in a laboratory situation. The A-B stage involved the learning of dissyllables as responses to instances of three concepts. In the B-C stage, Ss observed a model apparently receiving different levels of shock in association with the dissyllables. In the A-C stage, Ss pulled a lever after the presentation of each of the previously learned concept instances and an equal number of new instances of the same concepts. During a second presentation, Ss gave evaluative ratings of the concept instances. The latency, speed, and amplitude of the lever pull response were not affected by the experimental manipulations. With respect to ratings, instances of concepts associated with shock to the model were significantly more disliked than instances of concepts not paired with shock to the model.     

Interference of Sentence and Picture Contexts on Learning Prepositions

The effects of extra stimulus cues, such as pictures and sentences, on learning of preposition words were examined in two experiments. A one-way repeated-measures design was applied in the study. The results show that both sentences and pictures interfered with and blocked the learning of these context-dependent words in the simultaneous presentations. The superiority of feedback presentations over simultaneous presentations indicate that feedback techniques neutralize the blocking effect. More interestingly, however, the prepositions were learned more quickly in the absence of any context cues and this suggested that it is better to teach basic preposition words either by the presentation of them alone or if extra stimulus cues are to be used they should be presented as feedback.     

Logics for Epistemic Programs

We construct logical languages which allow one to represent a variety of possible types of changes affecting the information states of agents in a multi-agent setting. We formalize these changes by defining a notion of epistemic program. The languages are two-sorted sets that contain not only sentences but also actions or programs. This is as in dynamic logic, and indeed our languages are not significantly more complicated than dynamic logics. But the semantics is more complicated. In general, the semantics of an epistemic program is what we call aprogram model. This is a Kripke model of ‘actions’,representing the agents' uncertainty about the current action in a similar way that Kripke models of ‘states’ are commonly used in epistemic logic to represent the agents' uncertainty about the current state of the system. Program models induce changes affecting agents' information, which we represent as changes of the state model, called epistemic updates. Formally, an update consists of two operations: the first is called the update map, and it takes every state model to another state model, called the updated model; the second gives, for each input state model, a transition relation between the states of that model and the states of the updated model. Each variety of epistemic actions, such as public announcements or completely private announcements to groups, gives what we call an action signature, and then each family of action signatures gives a logical language. The construction of these languages is the main topic of this paper. We also mention the systems that capture the valid sentences of our logics. But we defer to a separate paper the completeness proof. The basic operation used in the semantics is called the update product. A version of this was introduced in Baltag et al. (1998), and the presentation here improves on the earlier one. The update product is used to obtain from any program model the corresponding epistemic update, thus allowing us to compute changes of information or belief. This point is of interest independently of our logical languages. We illustrate the update product and our logical languages with many examples throughout the paper.     

LOGIQUE ET PSYCHOLOGIE DE L'APPRENTISSAGE DES CONCEPTS

In the field of concept learning, there are very few studies in which logícal conditions are used as independent variables. A logical analysis of concept learning phenomena is presented: it is an application of some elementary theoremas of logics and set theory. To learn a concept means to identify relevant attributes of the phenomena observed; the theoretical analysis of the situation shows that concept learning can be seen as a problem, the solution of which depends on a precise algorithm. Numerous experiments have been carried out on the basis of such an analysis, some of which are described here. Results provide evidence that time and quality of performance depend on the length and logical structure of the set of successive stimuli presented to subjccts. So logical features of the situation influence concept learning; but, there are various other factors which control subject's performance : for instance, number of redundant examples, and subject's memory and perceptual-motor activity. Current interpretations of concept learning, i.e., stochastic models of learning and logical and mathematical interpretation are discussed and the need of an approach which would combine both to fit the complexity of concept learning phenomena is emphasized.