Signal Processing - July 2016 - 68

Style and format

Table 3. Topics by week in the first edition
of Rice University's course, ELEC301x.
Weeks
0
1
2
3
4
5
6
7
8
9

Topics
Preclass activities (optional)
Introduction
Signals are vectors
Linear systems
Convolution
Discrete fourier transform
Discrete-time fourier transform
z transform
Analysis and design of filters
Exam

Our perusal of the cognitive science literature indicated
that a "talking head" video lecture did not lead to improved
learning outcomes in an online course, and so we produced
lecture videos consisting of the voice of the instructor as
he manipulated the slides and MATLAB windows on a tablet. See Figure 3 for sample screenshots from the course.
To personify the course, the instructor appeared in a lighthearted video introducing each week's concepts. To broaden
student experience and supplement the course, we produced
a range of Office Hours videos conducted by Rice University graduate student Raajen Patel. Video production support
was provided by Rice Online, a major MOOC initiative of
Rice University.

Homework and grading

Course organization
Outline
The class incorporates numerous learning elements to engage
students, stressing the balance between rigorous mathematical
theory and hands-on practical applications. The course flow of
the first edition is detailed in Table 3. The second edition was
split into two mini-courses, one covering time-domain tools
and one frequency-domain tools (with the split occurring at
week 5 in Table 3).
Both course editions include an optional one-week precourse refresher on the key prerequisites in mathematics
(complex arithmetic and linear algebra) and programming
(MATLAB). A collection of reference material was made
available in the Rice University-based open access education
platform OpenStax CNX [21]. See Table 4 for an overview of
the key course elements.

On the theory and analysis side, given the current limitations
of the edX platform, we assess students primarily using multiple-choice questions. In the second edition of the course,
each weekly homework contains one open-form response
question whose response is input via MathJax and peer graded by three other students. A model solution and grading
rubric are made available after each homework is due. And
as discussed next, each homework also includes numerical
problems in MATLAB that are assessed algorithmically via
the edX platform. The final student grade combines performance on the weekly homework, case studies (recall
Table 4), and the final exam. A score of 60% is required to
pass the course.

Course evolution
After research by the Rice Online team suggested that
MOOCs are more successful when they are shorter rather than

Table 4. Course elements in Rice ELEC301x in its first edition. The second edition split the course into two minicourses
(breaking at week 5) and integrated the case studies into the weekly homework.
Precourse math refresher
Preclass MATLAB tutorial
Introduction videos
Lecture videos
Quick questions
Supplemental resources
Office Hours videos
Homework
Discussion forum
MATLAB case studies
Final exam
Final case study

Students can self-review the required mathematical skills with practice exercises and tutorials before the start of the
formal course.
Introductory tutorials on the MATLAB programming language enable students to get up to speed.
Each week kicks off with a light-hearted overview of the week's material featuring Richard Baraniuk, Mr. Lan, and BIBO,
the bear.
Each week features several hours of lecture videos recorded by Baraniuk specifically for an online format. Lectures were
chunked into 5- to 20-minute segments.
Conceptual knowledge-check questions follow each segment of lecture video to test students' understanding and maintain
engagement.
Links to additional learning content, exciting related applications, and additional information add depth to the learning
experience.
Videos of a TA working out homework-type problems prepare students for the assignments and encourage appropriate
problem-solving techniques.
Each week, a rigorous problem set challenges students to apply what they have learned, graded via multiple choice and
peer review. For numerical problems, students program in the MATLAB language using an integrated development
environment built into the edX platform.
An active discussion forum enables students to ask and answer questions and receive feedback from course staff.
Biweekly programming case studies enable students to apply concepts learned to practical programming exercises
designed to show how signal processing is used.
A traditional final exam tests students' comprehensive course knowledge.
Serving as a final project, the final case study expands on the previous case studies as students gain programming
proficiency.

IEEE Signal Processing Magazine

July 2016

Table of Contents for the Digital Edition of Signal Processing - July 2016