177 research outputs found
Exploiting Cognitive Structure for Adaptive Learning
Adaptive learning, also known as adaptive teaching, relies on learning path
recommendation, which sequentially recommends personalized learning items
(e.g., lectures, exercises) to satisfy the unique needs of each learner.
Although it is well known that modeling the cognitive structure including
knowledge level of learners and knowledge structure (e.g., the prerequisite
relations) of learning items is important for learning path recommendation,
existing methods for adaptive learning often separately focus on either
knowledge levels of learners or knowledge structure of learning items. To fully
exploit the multifaceted cognitive structure for learning path recommendation,
we propose a Cognitive Structure Enhanced framework for Adaptive Learning,
named CSEAL. By viewing path recommendation as a Markov Decision Process and
applying an actor-critic algorithm, CSEAL can sequentially identify the right
learning items to different learners. Specifically, we first utilize a
recurrent neural network to trace the evolving knowledge levels of learners at
each learning step. Then, we design a navigation algorithm on the knowledge
structure to ensure the logicality of learning paths, which reduces the search
space in the decision process. Finally, the actor-critic algorithm is used to
determine what to learn next and whose parameters are dynamically updated along
the learning path. Extensive experiments on real-world data demonstrate the
effectiveness and robustness of CSEAL.Comment: Accepted by KDD 2019 Research Track. In Proceedings of the 25th ACM
SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'19
Good Research Practices for Measuring Drug Costs in Cost-Effectiveness Analyses: A Managed Care Perspective: The ISPOR Drug Cost Task Force Report—Part III
AbstractObjectivesThe objective of this report is to provide guidance and recommendations on how drug costs should be measured for cost-effectiveness analyses conducted from the perspective of a managed care organization (MCO).MethodsThe International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Task Force on Good Research Practices—Use of Drug Costs for Cost Effectiveness Analysis (DCTF) was appointed by the ISPOR Board of Directors. Members were experienced developers or users of CEA models. The DCTF met to develop core assumptions and an outline before preparing a draft report. They solicited comments on drafts from external reviewers and from the ISPOR membership at ISPOR meetings and via the ISPOR Web site.ResultsThe cost of a drug to an MCO equals the amount it pays to the dispenser for the drug's ingredient cost and dispensing fee minus the patient copay and any rebates paid by the drug's manufacturer. The amount that an MCO reimburses for each of these components can differ substantially across a number of factors that include type of drug (single vs. multisource), dispensing site (retail vs. mail order), and site of administration (self-administered vs. physician's office). Accurately estimating the value of cost components is difficult because they are determined by proprietary and confidential contracts.ConclusionEstimates of drug cost from the MCO perspective should include amounts paid for medication ingredients and dispensing fees, and net out copays, rebates, and other drug price reductions. Because of the evolving nature of drug pricing, ISPOR should publish a Web site where current DCTF costing recommendations are updated as new information becomes available
Dynamic Key-Value Memory Networks for Knowledge Tracing
Knowledge Tracing (KT) is a task of tracing evolving knowledge state of
students with respect to one or more concepts as they engage in a sequence of
learning activities. One important purpose of KT is to personalize the practice
sequence to help students learn knowledge concepts efficiently. However,
existing methods such as Bayesian Knowledge Tracing and Deep Knowledge Tracing
either model knowledge state for each predefined concept separately or fail to
pinpoint exactly which concepts a student is good at or unfamiliar with. To
solve these problems, this work introduces a new model called Dynamic Key-Value
Memory Networks (DKVMN) that can exploit the relationships between underlying
concepts and directly output a student's mastery level of each concept. Unlike
standard memory-augmented neural networks that facilitate a single memory
matrix or two static memory matrices, our model has one static matrix called
key, which stores the knowledge concepts and the other dynamic matrix called
value, which stores and updates the mastery levels of corresponding concepts.
Experiments show that our model consistently outperforms the state-of-the-art
model in a range of KT datasets. Moreover, the DKVMN model can automatically
discover underlying concepts of exercises typically performed by human
annotations and depict the changing knowledge state of a student.Comment: To appear in 26th International Conference on World Wide Web (WWW),
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Entropic Interactions in Suspensions of Semi-Flexible Rods: Short-Range Effects of Flexibility
We compute the entropic interactions between two colloidal spheres immersed
in a dilute suspension of semi-flexible rods. Our model treats the
semi-flexible rod as a bent rod at fixed angle, set by the rod contour and
persistence lengths. The entropic forces arising from this additional
rotational degree of freedom are captured quantitatively by the model, and
account for observations at short range in a recent experiment. Global fits to
the interaction potential data suggest the persistence length of fd-virus is
about two to three times smaller than the commonly used value of .Comment: 4 pages, 5 figures, submitted to PRE rapid communication
An Exploratory Analysis of the Latent Structure of Process Data via Action Sequence Autoencoder
Computer simulations have become a popular tool of assessing complex skills
such as problem-solving skills. Log files of computer-based items record the
entire human-computer interactive processes for each respondent. The response
processes are very diverse, noisy, and of nonstandard formats. Few generic
methods have been developed for exploiting the information contained in process
data. In this article, we propose a method to extract latent variables from
process data. The method utilizes a sequence-to-sequence autoencoder to
compress response processes into standard numerical vectors. It does not
require prior knowledge of the specific items and human-computers interaction
patterns. The proposed method is applied to both simulated and real process
data to demonstrate that the resulting latent variables extract useful
information from the response processes.Comment: 28 pages, 13 figure
Effective forces in colloidal mixtures: from depletion attraction to accumulation repulsion
Computer simulations and theory are used to systematically investigate how
the effective force between two big colloidal spheres in a sea of small spheres
depends on the basic (big-small and small-small) interactions. The latter are
modeled as hard-core pair potentials with a Yukawa tail which can be both
repulsive or attractive. For a repulsive small-small interaction, the effective
force follows the trends as predicted by a mapping onto an effective
non-additive hard-core mixture: both a depletion attraction and an accumulation
repulsion caused by small spheres adsorbing onto the big ones can be obtained
depending on the sign of the big-small interaction. For repulsive big-small
interactions, the effect of adding a small-small attraction also follows the
trends predicted by the mapping. But a more subtle ``repulsion through
attraction'' effect arises when both big-small and small-small attractions
occur: upon increasing the strength of the small-small interaction, the
effective potential becomes more repulsive. We have further tested several
theoretical methods against our computer simulations: The superposition
approximation works best for an added big-small repulsion, and breaks down for
a strong big-small attraction, while density functional theory is very accurate
for any big-small interaction when the small particles are pure hard-spheres.
The theoretical methods perform most poorly for small-small attractions.Comment: submitted to PRE; New version includes an important quantitative
correction to several of the simulations. The main conclusions remain
unchanged thoug
Effects of Insulin Detemir and NPH Insulin on Body Weight and Appetite-Regulating Brain Regions in Human Type 1 Diabetes: A Randomized Controlled Trial
Studies in rodents have demonstrated that insulin in the central nervous system induces satiety. In humans, these effects are less well established. Insulin detemir is a basal insulin analog that causes less weight gain than other basal insulin formulations, including the current standard intermediate-long acting Neutral Protamine Hagedorn (NPH) insulin. Due to its structural modifications, which render the molecule more lipophilic, it was proposed that insulin detemir enters the brain more readily than other insulins. The aim of this study was to investigate whether insulin detemir treatment differentially modifies brain activation in response to food stimuli as compared to NPH insulin. In addition, cerebral spinal fluid (CSF) insulin levels were measured after both treatments. Brain responses to viewing food and non-food pictures were measured using functional Magnetic Resonance Imaging in 32 type 1 diabetic patients, after each of two 12-week treatment periods with insulin detemir and NPH insulin, respectively, both combined with prandial insulin aspart. CSF insulin levels were determined in a subgroup. Insulin detemir decreased body weight by 0.8 kg and NPH insulin increased weight by 0.5 kg (p = 0.02 for difference), while both treatments resulted in similar glycemic control. After treatment with insulin detemir, as compared to NPH insulin, brain activation was significantly lower in bilateral insula in response to visual food stimuli, compared to NPH (p = 0.02 for right and p = 0.05 for left insula). Also, CSF insulin levels were higher compared to those with NPH insulin treatment (p = 0.003). Our findings support the hypothesis that in type 1 diabetic patients, the weight sparing effect of insulin detemir may be mediated by its enhanced action on the central nervous system, resulting in blunted activation in bilateral insula, an appetite-regulating brain region, in response to food stimuli.ClinicalTrials.gov NCT00626080
Neural Correlates of Appetite and Hunger-Related Evaluative Judgments
How much we desire a meal depends on both the constituent foods and how hungry we are, though not every meal becomes more desirable with increasing hunger. The brain therefore needs to be able to integrate hunger and meal properties to compute the correct incentive value of a meal. The present study investigated the functional role of the amygdala and the orbitofrontal cortex in mediating hunger and dish attractiveness. Furthermore, it explored neural responses to dish descriptions particularly susceptible to value-increase following fasting. We instructed participants to rate how much they wanted food menu items while they were either hungry or sated, and compared the rating differences in these states. Our results point to the representation of food value in the amygdala, and to an integration of attractiveness with hunger level in the orbitofrontal cortex. Dishes particularly desirable during hunger activated the thalamus and the insula. Our results specify the functions of evaluative structures in the context of food attractiveness, and point to a complex neural representation of dish qualities which contribute to state-dependent value
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