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A data-driven model where individual learning behavior is a linear combination of certain stylized learning patterns scaled by learners' affinities is proposed. The absorption of stylized behavior through the affinities constitutes "building blocks" in the model. Non-negative matrix factorization is employed to extract common learning patterns and their affinities from online learning data ensuring meaningful non-negativity of the result. The empirical learning patterns resulting from the actual course interaction data of 111 undergraduate university students are connected to a learning style system. Bootstrap-based inference allows to check the significance of the pattern coefficients. Dividing the learners in two groups "failed" and "passed" and considering their mean affinities leads to a bootstrap-based test on whether the course structure is well balanced regarding the learning preferences. [ABSTRACT FROM AUTHOR]

The aim of the article is to study the prediction model of an individual learning style in e-learning systems to enhance the effectiveness of dentistry education. In developing the model, Neil Fleming's VARK concept of information perception was applied. The statistical data of the Russian Federal State Statistics Service on the students' distribution in the field of preparation of dentists in higher educational institutions for 2018–19 was used to assess the current state of development of dental education and the possibilities of introducing e-learning systems. In order to achieve the goal set, the anonymous survey was conducted among dental students of the I.M. Sechenov First Moscow State Medical University (MSMU). To compare the state of dental education with the developed countries, the study provides a survey performed by the American Dental Association in the United States. The investigation among the MSMU students involved 18 male and 24 female participants. The correlation analysis of the relationships between different learning styles revealed the closest relation between auditory and visual learning. Since the correlation was negative (r = −0.500), the visual style was considered less developed. Based on the data obtained, a model for predicting an individual learning style based on a specially designed questionnaire was proposed to improve the e-learning of dental students. The information resulted from the current research will contribute to the promotion of a profound study of academic disciplines in the field of dental education by developing an e-learning system. Thus, the use of students' cognitive skills could be improved. [ABSTRACT FROM AUTHOR]

In this paper, we propose a meta-learning model to hierarchically integrate individual learning and social learning schemes. This meta-learning model is incorporated into an agent-based model to show that Herbert Scarf's famous counterexample on Walrasian stability can become stable in some cases under a non-tâtonnement process when both learning schemes are involved, a result previously obtained by Herbert Gintis. However, we find that the stability of the competitive equilibrium depends on how individuals learn—whether they are innovators (individual learners) or imitators (social learners), and their switching frequency (mobility) between the two. We show that this endogenous behavior, apart from the initial population of innovators, is mainly determined by the agents' intensity of choice. This study grounds the Walrasian competitive equilibrium based on the view of a balanced resource allocation between exploitation and exploration. This balance, achieved through a meta-learning model, is shown to be underpinned by a behavioral/psychological characteristic. [ABSTRACT FROM AUTHOR]

Cognitive load theory has traditionally been associated with individual learning. Based on evolutionary educational psychology and our knowledge of human cognition, particularly the relations between working memory and long-term memory, the theory has been used to generate a variety of instructional effects. Though these instructional effects also influence the efficiency and effectiveness of collaborative learning, be it computer supported or face-to-face, they are often not considered either when designing collaborative learning situations/environments or researching collaborative learning. One reason for this omission is that cognitive load theory has only sporadically concerned itself with certain particulars of collaborative learning such as the concept of a collective working memory when collaborating along with issues associated with transactive activities and their concomitant costs which are inherent to collaboration. We illustrate how and why cognitive load theory, by adding these concepts, can throw light on collaborative learning and generate principles specific to the design and study of collaborative learning. [ABSTRACT FROM AUTHOR]

Many current authors point toward the heightening of networked individualism and how this affects community creation and engagement. This trend poses strong challenges to the potential beneficial effects of collective intelligence. Education is one of the realms that can strongly suffer from this globalized individualism. Learning is deeply enhanced by social interactions and losing this social dimension will have long-lasting effects in future generations. Networked learning is also a by-product of our societal context, but not per se individual. Our paper presents a case—the HANDSON massive open online course (MOOC)—in which a purposely designed learning environment fosters the emergence of a kind of collective intelligence which, by the learners own accord, brings about a heightened sense of community. The MOOC’s design managed to enable individual learning paces without killing the social dimension. Thus, we argue that when learning together intentionally and informally in networked online environments, small and temporary communities (pop-up communities we call them) will form. This nascent sense of community is a first step that will ultimately contribute to the common good. [ABSTRACT FROM AUTHOR]

Collaboration scripts have repeatedly been implemented in Computer-Supported Collaborative Learning (CSCL) to facilitate collaboration processes and individual learning. However, finding the right degree of structure is a subtle design task: scripts that are too rigid may impair self-regulation and hinder learning; scripts that are too flexible may fail to evoke high-level interactions. This study investigated whether making collaboration scripts adaptable would be a way to raise their effectiveness. Three experimental phases were realized: In a first phase (exposure phase), all students solved three problem cases by aid of a collaboration script in an asynchronous, text-based CSCL environment. In a second phase (treatment phase), another three cases were presented that were to be solved by aid of a different theory that was presented to the learners through a summary on a sheet of paper. During this phase, a three-groups between-subject design was realized: (a) an unscripted condition, in which students received no specific guidance how to structure their collaboration, (b) a non-adaptable script condition, in which students' collaboration was guided by the collaboration script they were trained in before, and (c) an adaptable script condition, in which students were allowed to modify parts of the trained script based on their self-perceived needs. In a third phase (subsequent transfer phase), students received a new case that they were to solve without guidance. N = 87 university students participated. Results showed that during the treatment phase, planning processes were most often performed in the unscripted condition. Yet, the adaptable script substantially increased students' engagement in metacognitive activities of planning compared to learning with a non-adaptable script, and increased monitoring and reflection activities when compared to learning without script. Mediation analyses showed that the adaptable script facilitated learners' use of self-regulation skills in the subsequent, unscripted transfer phase through the promotion of co-regulation processes of reflection in the treatment phase. The results reveal that adaptable scripting is a promising means of implementing flexible scripting and promoting self-regulation in CSCL. [ABSTRACT FROM AUTHOR]

The results of studies are presented for the elemental composition of peat from the Ilas ombrotrophic bog (White Sea drainage basin). The calculations of enrichment factors of trace elements over the section of the bog peat relatively to the average composition of the Earth's continental crust showed that the concentrations of most of chemical elements is determined by the contributions of lithogenic and biogenic sources, and the content of trace elements is equal to the background level. Enrichment growth since the beginning of intense development of European industry until the early 21th century was revealed only for Zn, Sb, Pb, and Cd. These elements were supplied to the bog resulting from long-range air transport and precipitation from the atmosphere. No pronounced heavy-metal contamination caused by the Arkhangelsk agglomeration was revealed for the peat in the Ilas bog. [ABSTRACT FROM AUTHOR]

Long before the origins of agriculture human ancestors had expanded across the globe into an immense variety of environments, from Australian deserts to Siberian tundra. Survival in these environments did not principally depend on genetic adaptations, but instead on evolved learning strategies that permitted the assembly of locally adaptive behavioral repertoires. To develop hypotheses about these learning strategies, we have modeled the evolution of learning strategies to assess what conditions and constraints favor which kinds of strategies. To build on prior work, we focus on clarifying how spatial variability, temporal variability, and the number of cultural traits influence the evolution of four types of strategies: (1) individual learning, (2) unbiased social learning, (3) payoff-biased social learning, and (4) conformist transmission. Using a combination of analytic and simulation methods, we show that spatial-but not temporal-variation strongly favors the emergence of conformist transmission. This effect intensifies when migration rates are relatively high and individual learning is costly. We also show that increasing the number of cultural traits above two favors the evolution of conformist transmission, which suggests that the assumption of only two traits in many models has been conservative. We close by discussing how (1) spatial variability represents only one way of introducing the low-level, nonadaptive phenotypic trait variation that so favors conformist transmission, the other obvious way being learning errors, and (2) our findings apply to the evolution of conformist transmission in social interactions. Throughout we emphasize how our models generate empirical predictions suitable for laboratory testing. [ABSTRACT FROM AUTHOR]

We examine the Cournot oligopoly model in the context of social and individual learning. In both models of learning, firms update their decisions about how much to produce via variants of the genetic algorithm updating procedure. Arifovic (1994) found that both models of social and individual learning converged to the Walrasian, competitive equilibrium. Vriend (2000) reports that the model of social learning converges to the Walrasian equilibrium outcome, while the model of individual learning converges to the Cournot–Nash equilibrium. We revisit the issue and conduct simulations varying elements of the updating algorithms, as well as of the underlying economic model. In the analysis of the outcomes of our simulations, we conclude that the convergence to the Cournot–Nash equilibrium is due to two things: the specific way in which production rules’ performance is evaluated coupled with a specific cost function specification. [ABSTRACT FROM AUTHOR]