43,528 research outputs found
Learning with multiple representations: An example of a revision lesson in mechanics
We describe an example of learning with multiple representations in an
A-level revision lesson on mechanics. The context of the problem involved the
motion of a ball thrown vertically upwards in air and studying how the
associated physical quantities changed during its flight. Different groups of
students were assigned to look at the ball's motion using various
representations: motion diagrams, vector diagrams, free-body diagrams, verbal
description, equations and graphs, drawn against time as well as against
displacement. Overall, feedback from students about the lesson was positive. We
further discuss the benefits of using computer simulation to support and extend
student learning.Comment: 10 pages, 5 figures, 2 tables http://iopscience.iop.org/0031-912
A Personalized System for Conversational Recommendations
Searching for and making decisions about information is becoming increasingly
difficult as the amount of information and number of choices increases.
Recommendation systems help users find items of interest of a particular type,
such as movies or restaurants, but are still somewhat awkward to use. Our
solution is to take advantage of the complementary strengths of personalized
recommendation systems and dialogue systems, creating personalized aides. We
present a system -- the Adaptive Place Advisor -- that treats item selection as
an interactive, conversational process, with the program inquiring about item
attributes and the user responding. Individual, long-term user preferences are
unobtrusively obtained in the course of normal recommendation dialogues and
used to direct future conversations with the same user. We present a novel user
model that influences both item search and the questions asked during a
conversation. We demonstrate the effectiveness of our system in significantly
reducing the time and number of interactions required to find a satisfactory
item, as compared to a control group of users interacting with a non-adaptive
version of the system
Five Strategies to Support all Teachers: Suggestions to Get Off the Slippery Slope of Cookbook Science Teaching
Many teachers shudder at the thought of implementing an inquiry curriculum. Perhaps they envision a rowdy classroom with little learning. Maybe they wonder, How will this connect to all the standards? Fortunately, these legitimate concerns can be addressed, and all students can engage in thoughtfully constructed inquiry science experiences. In this article, we outline five strategies that we have used with elementary school teachers as they moved from a cookbook approach in science to an approach that is inquiry-based. Having presented these five strategies in a linear format, we know that on the surface this may seem close to the slippery slope of cookbook science teaching, but we also know that thoughtful practitioners working in classrooms across the country will see these strategies as interactive, overlapping, and nonsequential
Statistical inference with anchored Bayesian mixture of regressions models: A case study analysis of allometric data
We present a case study in which we use a mixture of regressions model to
improve on an ill-fitting simple linear regression model relating log brain
mass to log body mass for 100 placental mammalian species. The slope of this
regression model is of particular scientific interest because it corresponds to
a constant that governs a hypothesized allometric power law relating brain mass
to body mass. A specific line of investigation is to determine whether the
regression parameters vary across subgroups of related species.
We model these data using an anchored Bayesian mixture of regressions model,
which modifies the standard Bayesian Gaussian mixture by pre-assigning small
subsets of observations to given mixture components with probability one. These
observations (called anchor points) break the relabeling invariance typical of
exchangeable model specifications (the so-called label-switching problem). A
careful choice of which observations to pre-classify to which mixture
components is key to the specification of a well-fitting anchor model.
In the article we compare three strategies for the selection of anchor
points. The first assumes that the underlying mixture of regressions model
holds and assigns anchor points to different components to maximize the
information about their labeling. The second makes no assumption about the
relationship between x and y and instead identifies anchor points using a
bivariate Gaussian mixture model. The third strategy begins with the assumption
that there is only one mixture regression component and identifies anchor
points that are representative of a clustering structure based on case-deletion
importance sampling weights. We compare the performance of the three strategies
on the allometric data set and use auxiliary taxonomic information about the
species to evaluate the model-based classifications estimated from these
models
The effect of functional roles on group efficiency
The usefulness of ‘roles’ as a pedagogical approach to support small group performance can be often read, however, their effect is rarely empirically assessed. Roles promote cohesion and responsibility and decrease so-called ‘process losses’ caused by coordination demands. In addition, roles can increase awareness of intra-group interaction. In this article, the effect of functional roles on group performance, efficiency and collaboration during computer-supported collaborative learning (CSCL) was investigated with questionnaires and quantitative content analysis of e-mail communication. A comparison of thirty-three questionnaire observations, distributed over ten groups in two research conditions: role (n = 5, N = 14) and non-role (n = 5, N = 19), revealed no main effect for performance (grade). A latent variable was interpreted as ‘perceived group efficiency’ (PGE). Multilevel modelling (MLM) yielded a positive marginal effect of PGE. Groups in the role condition appear to be more aware of their efficiency, compared to groups in the ‘non-role’ condition, regardless whether the group performs well or poor. Content analysis reveals that students in the role condition contribute more ‘task content’ focussed statements. This is, however, not as hypothesised due to the premise that roles decrease coordination and thus increase content focused statements; in fact, roles appear to stimulate coordination and simultaneously the amount of ‘task content’ focussed statements increases
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