252,839 research outputs found
MyLearningMentor: a mobile App to support learners participating in MOOCs
MOOCs have brought a revolution to education. However, their impact is mainly benefiting people with Higher Education degrees. The lack of support and personalized advice in MOOCs is causing that many of the learners that have not developed work habits and self-learning skills give them up at the first obstacle, and do not see MOOCs as an alternative for their education and training. My Learning Mentor (MLM) is a mobile application that addresses the lack of support and personalized advice for learners in MOOCs. This paper presents the architecture of MLM and practical examples of use. The architecture of MLM is designed to provide MOOC participants with a personalized planning that facilitates them following up the MOOCs they enroll. This planning is adapted to learners' profiles, preferences, priorities and previous performance (measured in time devoted to each task). The architecture of MLM is also designed to provide tips and hints aimed at helping learners develop work habits and study skills, and eventually become self-learners.This work has been funded by the Spanish Ministry of Economy and Competitiveness Project TIN2011-28308-C03-01, the Regional Government of Madrid project S2013/ICE-2715, and the postdoctoral fellowship Alliance 4 Universities. The authors would also like to thank Israel GutiĂ©rrez-Rojas for his contributions to the ideas behind MLM and Ricardo GarcĂa Pericuesta and Carlos de Frutos Plaza for their work implementing different parts of the architecture
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Beyond Risk Profiling: Achieving better investment outcomes for consumers and industry
In the wake of the Retail Distribution Review, there remain fundamental questions about how best to support consumers to make sound investment decisions, particularly those with modest amounts of money to invest, for whom a poor investment decision may have a disproportionate adverse impact. The advent of new pension freedoms from April 2015, which give people more choice and flexibility about how they use their retirement savings, adds further impetus to the issue. To help inform policy and practice on this important subject, in June 2015 we brought together consumer and industry experts to explore possible new approaches to improve risk profiling and investment decision-making
Explore, Exploit or Listen: Combining Human Feedback and Policy Model to Speed up Deep Reinforcement Learning in 3D Worlds
We describe a method to use discrete human feedback to enhance the
performance of deep learning agents in virtual three-dimensional environments
by extending deep-reinforcement learning to model the confidence and
consistency of human feedback. This enables deep reinforcement learning
algorithms to determine the most appropriate time to listen to the human
feedback, exploit the current policy model, or explore the agent's environment.
Managing the trade-off between these three strategies allows DRL agents to be
robust to inconsistent or intermittent human feedback. Through experimentation
using a synthetic oracle, we show that our technique improves the training
speed and overall performance of deep reinforcement learning in navigating
three-dimensional environments using Minecraft. We further show that our
technique is robust to highly innacurate human feedback and can also operate
when no human feedback is given
The Importance of Social and Government Learning in Ex Ante Policy Evaluation
We provide two methodological insights on \emph{ex ante} policy evaluation
for macro models of economic development. First, we show that the problems of
parameter instability and lack of behavioral constancy can be overcome by
considering learning dynamics. Hence, instead of defining social constructs as
fixed exogenous parameters, we represent them through stable functional
relationships such as social norms. Second, we demonstrate how agent computing
can be used for this purpose. By deploying a model of policy prioritization
with endogenous government behavior, we estimate the performance of different
policy regimes. We find that, while strictly adhering to policy recommendations
increases efficiency, the nature of such recipes has a bigger effect. In other
words, while it is true that lack of discipline is detrimental to prescription
outcomes (a common defense of failed recommendations), it is more important
that such prescriptions consider the systemic and adaptive nature of the
policymaking process (something neglected by traditional technocratic advice)
Human-Machine Collaborative Optimization via Apprenticeship Scheduling
Coordinating agents to complete a set of tasks with intercoupled temporal and
resource constraints is computationally challenging, yet human domain experts
can solve these difficult scheduling problems using paradigms learned through
years of apprenticeship. A process for manually codifying this domain knowledge
within a computational framework is necessary to scale beyond the
``single-expert, single-trainee" apprenticeship model. However, human domain
experts often have difficulty describing their decision-making processes,
causing the codification of this knowledge to become laborious. We propose a
new approach for capturing domain-expert heuristics through a pairwise ranking
formulation. Our approach is model-free and does not require enumerating or
iterating through a large state space. We empirically demonstrate that this
approach accurately learns multifaceted heuristics on a synthetic data set
incorporating job-shop scheduling and vehicle routing problems, as well as on
two real-world data sets consisting of demonstrations of experts solving a
weapon-to-target assignment problem and a hospital resource allocation problem.
We also demonstrate that policies learned from human scheduling demonstration
via apprenticeship learning can substantially improve the efficiency of a
branch-and-bound search for an optimal schedule. We employ this human-machine
collaborative optimization technique on a variant of the weapon-to-target
assignment problem. We demonstrate that this technique generates solutions
substantially superior to those produced by human domain experts at a rate up
to 9.5 times faster than an optimization approach and can be applied to
optimally solve problems twice as complex as those solved by a human
demonstrator.Comment: Portions of this paper were published in the Proceedings of the
International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and
in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper
consists of 50 pages with 11 figures and 4 table
Claims and confounds in economic experiments
We present a distinctiveness, relevance and plausibility (DRP) method for systematically evaluating potential experimental confounds. A claim is a statement being inferred on the basis of experimental data analysis. A potential confound is a statement providing a prima facie reason why the claim is not justified (other than internal weakness). In evaluating whether a potential confound is problematic, we can start by asking whether the potential confound is distinctive from the claim; we can then ask whether it is relevant for the claim; and we can conclude by asking whether it is plausible in the light of the evidence
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