Master’s Degree in Nutritional Sciences

Master’s Degree in Nutritional Sciences
from The University of Texas at Austin

1-year track tuition & fees:$25,300
2-year track tuition & fees:$25,600

Important dates & deadlines
Fall 2020
Rolling admissions: July 15, 2020
Program Start Date: August 30, 2020

Prerequisites for Admission Consideration:
1.Completed Bachelor’s degree from accredited institution
2.Completion Specific Coursework (or equivalent) including:
◦ Organic Chemistry
◦ Biochemistry*
◦ Human Physiology
◦ Introductory Nutrition

* HarvardX’s Principles of Biochemistry
Length: 15 Weeks
Price: Verified Certificate for $199

Course coursework includes:
1. Advanced Nutritional Sciences I: Macronutrient Metabolism
2. Advanced Nutritional Sciences II: Micronutrient Metabolism
3. Molecular Nutrition
4. Experimental Design and Statistics
5. Advanced Experimental Design and Statistics

Students have the choice to complete 1 of 2 concentrations:
• Health Promotion & Disease Prevention:
1. Theories of Nutrition Behavior
2. Nutrition Through the Lifecycle
3. Energy Balance and Obesity
4. Disease Prevention
5. Current Issues in Nutritional Sciences

• Biochemical & Functional Nutrition:
1. Nutrition Immunology
2. Nutrition as Medicine
3. Nutrition and Cancer
4. Nutrigenomics
5. Current Issues in Nutritional Sciences



Use of MOOC Discussion Forums

Exploring the Use of MOOC Discussion Forums
London International Conference on Education (LICE-2014). London, United Kingdom. 10th – 12th November, 2014
D.F.O.Onah*, J.E.Sinclair and R.Boyatt
The University of Warwick, United Kingdom

topics becoming fragmented over many threads and a lack of search facilities.

Some MOOCs have made forum participation a required part of the course. However, some learners may not be comfortable with this and it may also lead to a large number of pointless posts.

Despite the widespread use of forums there is still a lack of understanding of effect pedagogy.

The Mindset Meter

The Mindset Meter

Teresa Cooper
deep and deliberate practice that is defined as: “Working on technique, seeking critical feedback, and focusing ruthlessly on shoring up weaknesses.”

Jobs that don’t yet exist

Insights and Trends that Make MOOCs Matter
Tony Wan
Aug 4, 2014

Can’t Even Start
Something that often gets lost in the “learn to program” craze is the complicated task of setting up a proper coding environment. For many students, getting properly set up to do coding exercises proves to be much more difficult than learning to code itself.

Citing reports from the U.S. Department of Labor, McKinsey and Company, and the World Economic Forum, Shannon Hughes (Senior Director of Marketing at Udemy ) offered statistics suggesting a mismatch between what kids are learning and what skills future jobs will require:

  • 65% of grade-school kids will have jobs that don’t exist today;
  • 72% of education institutions say recent graduates are ready for work; only 42% of employers agree.


Online peer assessment

Online peer assessment: effects of cognitive and affective feedback
Instructional Science. March 2012, 40(2): 257-275
Jingyan Lu, Nancy Law

This study reports the effects of online peer assessment, in the form of peer grading and peer feedback, on students’ learning.

One hundred and eighty one high school students engaged in peer assessment via an online system—iLap.
The number of grade-giving and grade-receiving experiences was examined and the peer feedback was coded according to different cognitive and affective dimensions.
The effects, on both assessors and assessees, were analyzed using multiple regression.

The results indicate that the provision by student assessors of feedback that identified problems and gave suggestions was a significant predictor of the performance of the assessors themselves, and that positive affective feedback was related to the performance of assessees.
However, peer grading behaviors were not a significant predictor of project performance.
This study explains the benefits of online peer assessment in general and highlights the importance of specific types of feedback.
Moreover, it expands our understanding of how peer assessment affects the different parties involved.

This article is cited by:
A Beginner’s Guide to Irrational Behavior
Duke University

Quantitative studies of student self-assessment in higher education: a critical analysis of findings
Higher Education, 1989, Volume 18, Issue 5, pp 529-549
David Boud, Nancy Falchikov

Student self-assessment occurs when learners make judgements about aspects of their own performance. This paper focuses on one aspect of quantitative self-assessments: the comparison of student-generated marks with those generated by teachers. Studies including such comparisons in the context of higher education courses are reviewed and the following questions are addressed: (i) do students tend to over- or under-rate themselves vis-á-vis teachers?, (ii) do students of different abilities have the same tendencies?, (iii) do students in different kinds or levels of course tend to under- or over-rate themselves?, (iv) do students improve their ability to rate themselves over time or with practice?, (v) are the same tendencies evident when self-marks are used for formal assessment purposes?, and (vi) are there gender differences in self-rating? The paper also discusses methodological issues in studies of this type and makes recommendations concerning the analysis and presentation of information.

This article is cited by:
An Introduction to Interactive Programming in Python
Rice University. 2014

A MOOC Mystery: Where Do Online Students Go?

A MOOC Mystery: Where Do Online Students Go?
The New Yorker. February 28, 2014

An average of only 4 % of registered users finished their MOOCs in a recent University of Pennsylvania study:
Penn GSE Study Shows MOOCs Have Relatively Few Active Users, With Only a Few Persisting to Course End
December 5, 2013

a celebrated partnership between San Jose State and Udacity, the company co-founded by Sebastian Thrun, a Stanford professor turned MOOC magnate , also failed, when students in the online pilot courses consistently fared worse than their counterparts in the equivalent courses on campus:

MOOC student completion rate: 4%

The Online Education Revolution Drifts Off Course
December 31, 2013

One year ago, many were pointing to the growth of massive open online courses, or MOOCs, as the most important trend in higher education. Many saw the rapid expansion of MOOCs as a higher education revolution that would help address two long-vexing problems: access for underserved students and cost.

In theory, students saddled by rising debt and unable to tap into the best schools would be able to take free classes from rock star professors at elite schools via Udacity, edX, Coursera and other MOOC platforms.

But if 2012 was the “Year of the MOOC,” as The New York Times famously called it, 2013 might be dubbed the year that online education fell back to earth. Faculty at several institutions rebelled against the rapid expansion of online learning

by all accounts, the San Jose State University experiment was a bust. Completion rates and grades were worse than for those who took traditional campus-style classes. And the students who did best weren’t the underserved students San Jose most wanted to reach.

A recent University of Pennsylvania study confirmed a massive problem: MOOCs have painfully few active users. About half who registered for a class ever viewed a lecture, and completion rates averaged just 4 percent across all courses.

Sebastian Thrun, Udacity’s co-founder and a prime mover in MOOCs, recently told Fast Company magazine, “We were on the front pages of newspapers and magazines, and at the same time, I was realizing, we don’t educate people as others wished, or as I wished. We have a lousy product.”

“Online education that leaves almost everybody behind except for highly motivated students, to me, can’t be a viable path to education.

What was missing, many students complained, was a human connection beyond the streamed lecture.

Thrun says those critics simply don’t get the nature of tech innovation: You closely evaluate failures, think forward, adjust — and use the word “iterate.” A lot.
“It’s certainly an iteration,” Thrun says. “And the truth is, look, this is Silicon Valley. We try things out, we look at the data, and we learn from it.”