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  1. ; ; ; ; ; ( , In Proceedings of the 14th International Conference on Educational Data Mining (EDM) 2021)
    Accepted Manuscript Full Text Available
  2. ; ; ; ; ; ( , International Educational Data Mining Society.)
    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; ( , International Educational Data Mining Society.)
    Accepted Manuscript Full Text Available
  4. ; ; ; ; ( , In Proceedings of the 13th International Conference on Educational Data Mining (EDM) 2020)
    Abstract: Modeling student learning processes is highly complex since it is influenced by many factors such as motivation and learning habits. The high volume of features and tools provided by computer-based learning environments confounds the task of tracking student knowledge even further. Deep Learning models such as Long-Short Term Memory (LSTMs) and classic Markovian models such as Bayesian Knowledge Tracing (BKT) have been successfully applied for student modeling. However, much of this prior work is designed to handle sequences of events with discrete timesteps, rather than considering the continuous aspect of time. Given that time elapsed between successive elements inmore »a student’s trajectory can vary from seconds to days, we applied a Timeaware LSTM (T-LSTM) to model the dynamics of student knowledge state in continuous time. We investigate the effectiveness of T-LSTM on two domains with very different characteristics. One involves an open-ended programming environment where students can self-pace their progress and T-LSTM is compared against LSTM, Recent Temporal Pattern Mining, and the classic Logistic Regression (LR) on the early prediction of student success; the other involves a classic tutor-driven intelligent tutoring system where the tutor scaffolds the student learning step by step and T-LSTM is compared with LSTM, LR, and BKT on the early prediction of student learning gains. Our results show that TLSTM significantly outperforms the other methods on the self-paced, open-ended programming environment; while on the tutor-driven ITS, it ties with LSTM and outperforms both LR and BKT. In other words, while time-irregularity exists in both datasets, T-LSTM works significantly better than other student models when the pace is driven by students. On the other hand, when such irregularity results from the tutor, T-LSTM was not superior to other models but its performance was not hurt either.« less
    Accepted Manuscript Full Text Available
  5. ; ; ( , Applied Microscopy)
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  6. ; ; ( , Microscopy and Microanalysis)
    Abstract Quasi-periodic excitation of the tunneling junction in a scanning tunneling microscope, by a mode-locked ultrafast laser, superimposes a regular sequence of 15 fs pulses on the DC tunneling current. In the frequency domain, this is a frequency comb with harmonics at integer multiples of the laser pulse repetition frequency. With a gold sample the 200th harmonic at 14.85 GHz has a signal-to-noise ratio of 25 dB, and the power at each harmonic varies inversely with the square of the frequency. Now we report the first measurements with a semiconductor where the laser photon energy must be less than themore »bandgap energy of the semiconductor; the microwave frequency comb must be measured within 200 μ m of the tunneling junction; and the microwave power is 25 dB below that with a metal sample and falls off more rapidly at the higher harmonics. Our results suggest that the measured attenuation of the microwave harmonics is sensitive to the semiconductor spreading resistance within 1 nm of the tunneling junction. This approach may enable sub-nanometer carrier profiling of semiconductors without requiring the diamond nanoprobes in scanning spreading resistance microscopy.« less
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  7. ; ; ; ; ; ; ; ( , Neurochemistry International)
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