IEEE Journal of Selected Topics in Signal Processing

Volume 11 Issue 5 • Aug. 2017

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  • Frontcover

    Publication Year: 2017, Page(s): C1
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  • IEEE Journal of Selected Topics in Signal Processing publication information

    Publication Year: 2017, Page(s): C2
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  • Table of Contents

    Publication Year: 2017, Page(s): 711
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  • Blank Page

    Publication Year: 2017, Page(s): B712
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  • Introduction to the Issue on Signal Processing and Machine Learning

    Publication Year: 2017, Page(s):713 - 715
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  • Behavior-Based Grade Prediction for MOOCs Via Time Series Neural Networks

    Publication Year: 2017, Page(s):716 - 728
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2081 KB) | HTML iconHTML

    We present a novel method for predicting the evolution of a student's grade in massive open online courses (MOOCs). Performance prediction is particularly challenging in MOOC settings due to per-student assessment response sparsity and the need for personalized models. Our method overcomes these challenges by incorporating another, richer form of data collected from each student-lecture video-watc... View full abstract»

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  • Context-Aware Recommendation-Based Learning Analytics Using Tensor and Coupled Matrix Factorization

    Publication Year: 2017, Page(s):729 - 741
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1071 KB) | HTML iconHTML

    Student retention and timely graduation are enduring challenges in higher education. With the rapidly expanding collection and availability of learning data and related analytics, student performance can be accurately monitored, and possibly predicted ahead of time, thus, enabling early warning and degree planning “expert systems” to provide disciplined decision support to counselors, advisors, an... View full abstract»

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  • A Machine Learning Approach for Tracking and Predicting Student Performance in Degree Programs

    Publication Year: 2017, Page(s):742 - 753
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1881 KB) | HTML iconHTML

    Accurately predicting students' future performance based on their ongoing academic records is crucial for effectively carrying out necessary pedagogical interventions to ensure students' on-time and satisfactory graduation. Although there is a rich literature on predicting student performance when solving problems or studying for courses using data-driven approaches, predicting student performance... View full abstract»

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  • BLAh: Boolean Logic Analysis for Graded Student Response Data

    Publication Year: 2017, Page(s):754 - 764
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (295 KB) | HTML iconHTML

    Machine learning (ML) models and algorithms can enable a personalized learning experience for students in an inexpensive and scalable manner. At the heart of ML-driven personalized learning is the automated analysis of student responses to assessment items. Existing statistical models for this task enable the estimation of student knowledge and question difficulty solely from graded response data ... View full abstract»

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  • Blank Page

    Publication Year: 2017, Page(s): B765
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    Publication Year: 2017, Page(s): B766
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  • IEEE Journal of Selected Topics in Signal Processing information for authors

    Publication Year: 2017, Page(s):767 - 768
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  • Become a published author in 4 to 6 weeks

    Publication Year: 2017, Page(s): 769
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  • IEEE Signal Processing Society Information

    Publication Year: 2017, Page(s): C3
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    Publication Year: 2017, Page(s): C4
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Aims & Scope

The Journal of Selected Topics in Signal Processing (J-STSP) solicits special issues on topics that cover the entire scope of the IEEE Signal Processing Society including the theory and application of filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals by digital or analog devices or techniques.

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Meet Our Editors

Editor-in-Chief

Shrikanth (Shri) S. Narayanan
Viterbi School of Engineering 
University of Southern California
Los Angeles, CA 90089 USA
shri@sipi.usc.edu