5,523 research outputs found
Personalized Course Sequence Recommendations
Given the variability in student learning it is becoming increasingly
important to tailor courses as well as course sequences to student needs. This
paper presents a systematic methodology for offering personalized course
sequence recommendations to students. First, a forward-search
backward-induction algorithm is developed that can optimally select course
sequences to decrease the time required for a student to graduate. The
algorithm accounts for prerequisite requirements (typically present in higher
level education) and course availability. Second, using the tools of
multi-armed bandits, an algorithm is developed that can optimally recommend a
course sequence that both reduces the time to graduate while also increasing
the overall GPA of the student. The algorithm dynamically learns how students
with different contextual backgrounds perform for given course sequences and
then recommends an optimal course sequence for new students. Using real-world
student data from the UCLA Mechanical and Aerospace Engineering department, we
illustrate how the proposed algorithms outperform other methods that do not
include student contextual information when making course sequence
recommendations
Quantum entanglement in plasmonic waveguides with near-zero mode indices
We investigate the quantum entanglement between two quantum dots in a
plasmonic waveguide with near-zero mode index, considering the dependence of
concurrence on interdot distance, quantum dot-waveguide frequency detuning and
coupling strength ratio. High concurrence is achieved for a wide range of
interdot distance due to the near-zero mode index, which largely relaxes the
strict requirement of interdot distance in conventional dielectric waveguides
or metal nanowires. The proposed quantum dot-waveguide system with near-zero
phase variation along the waveguide near the mode cutoff frequency shows very
promising potential in quantum optics and quantum information processing
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