Profile

Dale anon456 profile image

Dale anon456

Student

  • 3 reviews
  • 3 completed
Content 
Instructor 
Provider 
I (electronics engineer with > 20 years experience) am nearly finished (80%) with the course and believe that I can give a fair and balanced review of Statistical Mechanics: Algorithms and Computations (Feb2014). Pros: excellent video production consists of lecture and tutorial, each ~20-30 minutes 5-8 very short Python programs provided per video interesting material you learn to Markov walk, which is straightforward. Homeworks are extensions of lecture and build upon themselves HWs are unlimited submissions Python2 lang used (not 3); 1st section of HWs sometimes devoted to new usage instructor provided solutions grading is 50% HW and 50% final exam (Pro? Con?) instructor knows his stuff Cons: no slide materials; no free textbook; just 2 videos per week no in-video quizzes for comprehension you better take copious notes of the lectures, since slides were totally absent since HWs are extensions of videos, you suffer hugely if you don’t grok things no feedback whatsoever on early homework submissions (not designed for it) HWs are not reviews: you must grok the material (do you always?) HW peer assessments are dreadful: expect 10-20% lower grade, really! peer assessment feedback missing-in-action: lazy and/or unqualified 5-10% of HW questions are vague and open to misinterpretation course is severely misrepresented expect to spend 10-30 hours per week without sufficient background college level mathematical maturity is required one formal quantum mechanics course required deceivingly short programs fit into textbook page, not your brain programs contain absolutely no comments to assist the student! Python source code indentation quite strict; you will suffer w/o experience this course is of the type that requires an ASAP post- lecture Q & A class the discussion forums can be helpful but usually too late to help do you already understand partition functions, trotter decomposition, probability density functions, path integrals, list comprehensions? If so, good! instructor not receptive to constructive criticism in discussion forum Summary: I am being generous for giving it 3 stars. I had hopes of learning much. That was not the case. The recommended workload of 4-6 hours is a joke for the 99%. Best to have had prior exposure to Statistical Mechanics, and certainly QM. HW solutions provided really are trivial modifications of provided programs. Couldn’t do it for the QM stuff however. I was left-behind after 4 weeks, and so were many others (per discussion forum). Many HW sections not even attempted by students. I would recommend augmenting all HWs with a multiple choice review section that can instantly provide feedback before proceeding to the balance of the HW. I wonder if the course difficulty introduced a bias in the peer assessments? Anyway, I hope some of the above CONs get remedied for future sessions, if there are any. Finally, this course is my first “difficult” one. During it, I wondered where I stood in relation to the rest of the students. In university, a curve of scores was usually provided so that everyone could really determine how well they were doing, at least by midterm. I have yet to see such a mechanism at Coursera. It would be very simple, near effortless, to provide these statistics on a homework basis for all to view. Since it is very simple to implement for graded classes, don’t you wonder why it isn’t already available? One conclusion is, if it were provided, you would see more students un-enroll sooner from the more difficult classes, which the instructor(s) and MOOC administrators clearly don’t want to happen. This is a legitimacy issue for MOOCs. UPDATE 6/19/2014. Finally, almost 2 months after the course finished, certificates were provided.  I won't mention what I got :).  Here is a direct quote from the instructor regarding the "achievement": [We are thankful to all of you who followed the course, even without attempting to pass it. We are also very proud of all of you who "passed" the course. You did a great job, and you learned a lot. It took real dedication to reach this level, which corresponds to final year undergraduate, beginning graduate level at ENS or another tough University.] (I underlined for emphasis) One more quote: [ Finally, the > 89% level (15 students out there)........ ] It was rumored that 29000 signed up for the course. I've wondered at times, does the "tough" reputation of a school correspond to the inability to teach the material? Apparently, the course will be offered again February 2015.  I hope my review properly prepares those who choose to sign up.  Good luck!
Content 
Instructor 
Provider 
Excellent 8 week college introductory course to probability, from a mathematics perspective. First edition of the course finished April 2015. The perspective is critical as the course is somewhat more difficult than a course on introductory statistics, requiring the ability to manipulate equations, with just a smidgen of calculus. The instructor is very passionate about the subject and put together an absolutely amazing set of numerous videos, one of the best I have yet seen for MOOCs. The videos included interesting historical references. The videos are somewhat verbose but easily addressed by watching at accelerated speeds. Certificate provided, 65% passing and 90% distinction. Course statistics were provided that showed the average weekly score on the homeworks was ~70%. ~42000 enrolled, but by the end of the course only about 600 students were still submitting homeworks. Discussion forum very active and well supported at the beginning but tailed off as the class participation declined. The instructor was very active throughout. No quizzes or exams or in-video questions or peer assessments, just seven weekly homeworks each consisting of 7-8 multiple choice questions with 2 attempts, solutions provided 24 hours after the 24 hour 50% penalty deadline. I am a well experienced engineer and managed to barely achieve a perfect score, however, there were ~10 problems that I solved by means outside of the course, primarily simulation. Materials released on a weekly basis. In my opinion, the homeworks were short of problems as compared to other MOOCs and the lecture materials usually did not provide more than one example of each concept. A few of the homework problems were ambiguously stated, so the 2nd attempt came in handy. In my opinion, the shortage of worked out examples was the only significant shortcoming of the course. I highly recommend the course and would take it again.
Content 
Instructor 
Provider 
I (engineer with > 20 years experience) have completed the course and believe that I can give a fair and balanced review of Introduction to Thermodynamics: Transferring Energy from Here to There (Feb2014). Pros: consists of 5-6 lectures by the instructor, each ~8-20 minutes PDF slides provided; links to free textbooks workload only 3-6 hours per week in-video quizzes (don’t count towards grade) interesting material; you learn a lot, including usage of steam tables easy to not difficult for most; gentle intro homeworks (HWs) limited to 2 submissions HWs are combinations of multiple choice questions and quantitative problems grading is 100% HW; no exam (Pro? Con?) instructor knows her stuff signature track offered discussion forums supported well through TAs not peer graded (not necessary) Cons: slides rather empty, the instructor draws diagrams and equations in the videos thus you should take notes of the lecture videos instructor did not participate in discussion forum you can ace the course by relying on deductive reasoning after 1st HW submission HWs usually had 2-3 errors, but the TAs were there in time to correct Summary: This introduction to thermodynamics is very introductory. I think that serious high school students will do well in this course. You can easily get by without calculus. I was expecting a bit more from the University of Michigan. Deduct one star for that, despite the FAQ being quite clear. Discussion of entropy is pointedly missing, but gas/liquid qualities, pressures, temperatures, joules, kilograms, enthalpies and specific heats abound. Conservation of energy and mass is central. Power plant analysis is interesting. However, the “bar” (pun intended) should be raised somewhat. As to the rather empty slides, I believe it was intentional, to get you to take notes. It worked for me. A timed final exam should be included. If you already have had one course in thermo, skip it. I did not, so I found it very useful and look forward to the next level of instruction, which I’m sure won’t be as easy. A successful Coursera course.