I haven't posted anything not serious lately. Playoff time seems like a good time to restart. I've made my predictions below. I didn't pick any upsets (5 over 4 doesn't count), so I'll probably be wrong in at least two series. Keep in mind that I don't predict series to end in 7 games, b/c that's like predicting who is going to get a lucky bounce.
Eastern Conference
(1) Washington vs (8) Montreal
There shouldn't be any question about Washington's goaltending. There should be questions about its defense. Conversely, Montreal had a rough year, but got healthy at the right time. It'll buy them one more game. Washington in 5.
(2) New Jersey vs (7) Philadelphia
New Jersey has the games best goaltender of all time in Martin Brodeur, but he has finally looked human this year. They have underrated offense capabilities, but their defense hasn't been up to their glorious past. Philly has some offensive talent, a great defensive corps, and NO goaltending. These teams hate each other, and Philly will be out to play the body. This is going to be one of the best first-round series. NJ in 6.
(3) Buffalo vs (6) Boston
This series features the top 2 goaltenders in the East. It also features two inept offenses, and two fantastic 6'8'' defenders. I'm picking Buffalo for 2 reasons. 1) It's a homer pick. 2) Buffalo has some young guns, who I'm banking to be impervious to the pressure. Buffalo in 6.
(4) Pittsburgh vs (5) Ottawa
If memory serves me correct, this is a repeat of last year's first round. Pittsburgh has lost some luster compared to last year. If Malkin is up to par, and Fleury regains his stride, watch out. Ottawa is a weird team that could give Pittsburgh a run for their money if they click. I don't think they will. But this could be a great series. Pittsburgh in 6.
Western Conference
(1) San Jose vs (8) Colorado
If this matchup occured in December, it would've featured the top two teams in the West. But its April. Unless Craig Anderson has 3 shutouts, Colorado is done. San Jose in 4. (SWEEP!!!)
(2) Chicago vs (7) Nashville
LIke Washington, there are questions about Chicago's goaltending, and like Washington, I don't know why. They have a great defense and offense. Nashville had a rough beginning of the season but became hot at the right time. I like Chicago in this one. Chicago in 5.
(3) Vancouver vs (6) Los Angeles
This was supposed to be Vancouver's year to tear up the league, but Luongo struggled a bit. The Sedin twins more than enough made up for it. LA has a young team, but a lot rides on Jonathan Quick's shoulders. LA can wait until next year. Vancouver in 5.
(4) Phoenix vs (5) Detroit
This will be the best first round series. Detroit struggled early, but was baller since Christmas. Phoenix almost won their division, and surprised everybody. This series could go 7 games, but I can't pick that. I'm gonna pick Detroit in 6, but I'm not confident about this series. I do look forward to watching this one.
Stanley Cup Prediction: I can't shy away from picking Washington. No other team in the East seems to have played as consistently well as the Caps.
Any team in the West above the 5th seed has a chance at getting to the Cup. Chicago reminds me of Washington, and they have a Buffalo boy (Patrick Kane).
Washington meets Chicago in the Finals. You can flip a coin to see who wins.
Wednesday, April 14, 2010
Friday, April 9, 2010
Publicly Accessible AND Peer-Reviewed Academic Journals?!?!?!
Over the past few years, it has become more popular for content on the web to be fee based. Napster was shut down, and iTunes rose to glory soon after. Soon, parts of the NY Times will become pay-per-view.
Access to scholarly journals has always seemed to live in this world of "pay-for-play". Recently, a guest speaker mentioned a seemingly innovative collection of publications controlled by The Berkeley Electronic Press. The articles published in these journals are all peer-reviewed, and available for access by anyone with a computer!!
The publishing guidelines vary between journals, and although I can't comment on all the publications, the International Journal of Biostatistics has some unique policies regarding submission. Like all journals, it charges the authors a price for publication of a paper. However, the author has the option to waive the cost of publishing in exchange for a reviewing others' paper submissions and can actually earn 'publication' credits for reviewing papers.
Furthermore, the paper prides itself on being expedient in its review. There is a 21 day limit on the reviewer to review the paper and provide a response to the author or it will actually cost the reviewer money!!! Along with this focus on expediency, the journal also does not have any requirements on text formatting, which means that the authors are able use the style they deem most efficient at getting their message across.
Lastly, the authors retain the right to post their work on their personal websites as a not only an additional resource for the web-going public, but allows an easy way for a author to showcase their work.
Personally, I think this concept is really cool. It allows for the conveyance of important information in a rapid manner that allows the author some authority as to the dissemination of their work. I have no idea on how the quality of this journal compares to other statistical journals, but if one person was able to use the publicly accessible information to advance the field, I think that it's a victory for open information on the web. Furthermore, the introduction of open-web/public contribution concepts with traditional academic publishing in a journal-like setting could in time threaten the established practices in which the journals operate.
Access to scholarly journals has always seemed to live in this world of "pay-for-play". Recently, a guest speaker mentioned a seemingly innovative collection of publications controlled by The Berkeley Electronic Press. The articles published in these journals are all peer-reviewed, and available for access by anyone with a computer!!
The publishing guidelines vary between journals, and although I can't comment on all the publications, the International Journal of Biostatistics has some unique policies regarding submission. Like all journals, it charges the authors a price for publication of a paper. However, the author has the option to waive the cost of publishing in exchange for a reviewing others' paper submissions and can actually earn 'publication' credits for reviewing papers.
Furthermore, the paper prides itself on being expedient in its review. There is a 21 day limit on the reviewer to review the paper and provide a response to the author or it will actually cost the reviewer money!!! Along with this focus on expediency, the journal also does not have any requirements on text formatting, which means that the authors are able use the style they deem most efficient at getting their message across.
Lastly, the authors retain the right to post their work on their personal websites as a not only an additional resource for the web-going public, but allows an easy way for a author to showcase their work.
Personally, I think this concept is really cool. It allows for the conveyance of important information in a rapid manner that allows the author some authority as to the dissemination of their work. I have no idea on how the quality of this journal compares to other statistical journals, but if one person was able to use the publicly accessible information to advance the field, I think that it's a victory for open information on the web. Furthermore, the introduction of open-web/public contribution concepts with traditional academic publishing in a journal-like setting could in time threaten the established practices in which the journals operate.
Monday, April 5, 2010
Learning with Google and Wikipedia
A recent discussion I had on how Wikipedia and Google effect learning focused mostly on how the veracity of content of these sources. While the discussion was very good, I was disappointed that one point, that may have been alluded to once or twice, wasn't discussed more.
Using Wikipedia and Google allow you to see a plethora of information in mere seconds. As a former physics nerd, there was no reason for me to remember the gravitational acceleration (9.8m/s^2) or Schrodinger's equation (since forgotten) since it was available in seconds if I was sitting at a computer (which I often was).
Now, I do have a particularly poor memory for facts. I have forgotten friends' names before, even the one I knew since Pre-K. I do believe that I am more suited to learning how solve problems, either statistically or otherwise. Memorizing facts, equations, and so forth seems like a misuse of time since they can be easily researched on the Internet. It makes more sense to me to focus my time on methods development and the use of the plethora of knowledge that people already wrote down...
...which actually leads me to the crux of my discussion. With a profusion of knowledge out on the web, accessible in seconds, how will academia react to the time-honored tradition of cramming knowledge into every nook and cranny of students' brains? I have yet to see how instructing students how to gain the necessary skills to find and process knowledge as an end-user is a bad thing. Of course, considerable time must be spent to ensure the veracity of the information gathered, and I am certainly not proposing that we either discourage or downplay students' memorizing key pieces of information. However, the sheer memorization of facts is only a tool to process the information, and if we can focus on teaching students to process rather than memorize, we will be building up a much more rational student body.
A caveat to this my opinions is that my experience is severely restricted to analytical fields (physics and statistics). I am very curious to hear how other fields use the internet as a resource in their work. Please feel free to comment!
Using Wikipedia and Google allow you to see a plethora of information in mere seconds. As a former physics nerd, there was no reason for me to remember the gravitational acceleration (9.8m/s^2) or Schrodinger's equation (since forgotten) since it was available in seconds if I was sitting at a computer (which I often was).
Now, I do have a particularly poor memory for facts. I have forgotten friends' names before, even the one I knew since Pre-K. I do believe that I am more suited to learning how solve problems, either statistically or otherwise. Memorizing facts, equations, and so forth seems like a misuse of time since they can be easily researched on the Internet. It makes more sense to me to focus my time on methods development and the use of the plethora of knowledge that people already wrote down...
...which actually leads me to the crux of my discussion. With a profusion of knowledge out on the web, accessible in seconds, how will academia react to the time-honored tradition of cramming knowledge into every nook and cranny of students' brains? I have yet to see how instructing students how to gain the necessary skills to find and process knowledge as an end-user is a bad thing. Of course, considerable time must be spent to ensure the veracity of the information gathered, and I am certainly not proposing that we either discourage or downplay students' memorizing key pieces of information. However, the sheer memorization of facts is only a tool to process the information, and if we can focus on teaching students to process rather than memorize, we will be building up a much more rational student body.
A caveat to this my opinions is that my experience is severely restricted to analytical fields (physics and statistics). I am very curious to hear how other fields use the internet as a resource in their work. Please feel free to comment!
Monday, March 22, 2010
What makes a good teacher
A lot of discussion has been directed to how to maximize your teaching effort. An article in the New York Times, which can be found here, talks of an interesting solutions to increasing the effectiveness of teachers. While the main focus of the article is not about the collegiate level, a couple of general points can be made pertaining to collegiate education.
Just as recently hired college faculty are paired up with more tenured colleague to get their research started, a similar technique can be made with regards to teaching. Since most incoming faculty members have little or no teaching experience, I think having a more experienced lecturer to bounce ideas off and ask for advice would be an extremely beneficial system for maximizing the efficiency of the lectures.
This mentor figure would be most valuable in classroom management, which the article describes as incredibly important to the learning process. Since most incoming faculty do not have the teaching experience to manage a classroom efficiently, advice from the senior faculty member would be invaluable in the classroom.
So why isn't this mentor relationship done more with an eye on teaching? In my view, teaching is viewed as a birthday cake at a party. It needs to be there, but the lack of a great cake isn't going to make or break the party. Considering faculty members need to bring in 85% of their effort in non-teaching related duties, spending the extra effort on teaching is often viewed as unnecessary.
Just as recently hired college faculty are paired up with more tenured colleague to get their research started, a similar technique can be made with regards to teaching. Since most incoming faculty members have little or no teaching experience, I think having a more experienced lecturer to bounce ideas off and ask for advice would be an extremely beneficial system for maximizing the efficiency of the lectures.
This mentor figure would be most valuable in classroom management, which the article describes as incredibly important to the learning process. Since most incoming faculty do not have the teaching experience to manage a classroom efficiently, advice from the senior faculty member would be invaluable in the classroom.
So why isn't this mentor relationship done more with an eye on teaching? In my view, teaching is viewed as a birthday cake at a party. It needs to be there, but the lack of a great cake isn't going to make or break the party. Considering faculty members need to bring in 85% of their effort in non-teaching related duties, spending the extra effort on teaching is often viewed as unnecessary.
Wednesday, March 3, 2010
Learning Journal Assessment
Apparently it is time for me to assess my use of this blog as tool to help me see how helpful this blog is at summarizing, developing, and advancing my thoughts on any subject, but specifically as related to the material covered in class. I have been given a rubric to assess myself, and discuss the rubric and my assigned grades, as well as offer myself up suggestions as to how increase the scores in later assessments.
The rubric has five categories: Engagement with Reading and Topics, Reflection on Learning, Community Engagement, Quality of Writing, and Overall Use of Learning Journal.
There are four grading categories: Insufficient, Transitional, Proficient, and Advanced.
Engagement with Reading and Topics: Grade: Transitional- Journal entries indicate engagement with most of the readings and topics. Links to additional readings are occasionally discussed.
But, to get the most out of the journal, I need to make a concerted effort to get the most out of the journal. That means no more posting at 11pm on Wed. nights and reading on Thursday during lunch. That means reading others' posts to see different points of view in order to help come to my own conclusions. In essence, I need consider this journal an investment into the development of myself into becoming a quality biostatistician, researcher, and educator, and treat it as such. Hopefully I can do it.
The rubric has five categories: Engagement with Reading and Topics, Reflection on Learning, Community Engagement, Quality of Writing, and Overall Use of Learning Journal.
There are four grading categories: Insufficient, Transitional, Proficient, and Advanced.
Engagement with Reading and Topics: Grade: Transitional- Journal entries indicate engagement with most of the readings and topics. Links to additional readings are occasionally discussed.
- I was going to give myself a score of Insufficient, but I decided to start out on a high note. While I feel I am engaged with the topics discussed, the evidence is not really apparent in this blog. To increase this score to a solid Proficient (I'm a B-student, shooting for A's is too much work), I really need to make a concerted effort to put some of those thoughts whirling around my head down on paper.
- While I do believe my personal learning experiences are elaborated on, or at least elaborated on in my head (see above comments), the connections to specific topics aren't always transparent. This definitely has it's roots in foresight. I need to plan what I write better.
- Simply, I need to start reading them to comment on them. Enough said.
- Judging by the title of this blog, a little casualness is to be expected. However, it probably detracts from the development of ideas. Development of the ideas is linked to the problem discussed in the "Reflection on Learning" section. Again, I need to make a concerted effort to think through what I write down.
- That about sums it up now, doesn't it...
But, to get the most out of the journal, I need to make a concerted effort to get the most out of the journal. That means no more posting at 11pm on Wed. nights and reading on Thursday during lunch. That means reading others' posts to see different points of view in order to help come to my own conclusions. In essence, I need consider this journal an investment into the development of myself into becoming a quality biostatistician, researcher, and educator, and treat it as such. Hopefully I can do it.
Wednesday, February 17, 2010
Dramatic Learning Experience
Well, I'm a little behind in my posting, so the next couple of posts are going to be out of order with respect to what we covered (or will cover) in class, so please bear with me.
The most dramatic learning experience for me was while I was at Pitt working on my masters. It was in the first semester of the second year of the two year program, and I was all ready to get out of there with my masters and call myself done with school FOREVER. After two semesters of taking classes that I only marginally understood what was going on, I was forced to take a Statistical Consulting class as part of my degree requirement.
This class is set up as follows: For the first two weeks, you talk about consulting and meeting clients in broad terms, and then the instructors says "You have a meeting on this date, and at this time". The client then lays out his statistical problem and you attempt to solve it using whatever knowledge you may have accumulated over your brief life in statistics (which was about a year for me). The class, which had a size of 6, then presented their project to the class and we were all able to comment or ask questions of the consultant to not only further his knowledge but help them with their project.
My first client had an interesting problem, but no big lights went on in my mind and I struggled to get through the project (it was a reliability problem, and I don't care about that topic that much). However, I received a second client a short time later and the project that I was given KICKED ASS.
To analyze the data correctly, I had to do a test of equivalence on a longitudinal, random coefficient model. For all the non-statisticians, it's a difficult model to conceptualize let alone analyze, and admittedly I did not analyze it correctly (I did a test of difference, not equivalence, but spun it well ;-) ).
Now, I'd be lying if I said this particular topic was "the most dramatic learning experience", but as part of this class it was. The whole class was that experience because it showed me a bunch of things. First of all, it showed me how much I actually learned in the first year of grad school. But it also showed me that I actually like statistics, and love being able to put real-life problems into mathematical solutions and use "hard-science" constraints to come to and objective decision**.
This realization didn't come into play just from the aforementioned project, but from all of the topics presented in class. It was clear to me that I was not only grasping the projects assigned to me, but the projects' of the other students as well. This allowed my confidence to grow to a point that I was able and willing to submit my analysis plan to my professor, who just happened to be the most intimidating man on earth (I'm not even being sarcastic, I'm still scared of him). And it was this project that showed me that I may actually be good at this statistics thing, and I may actually like it.
So what did I learn from this class. Two things: The first, as I mentioned before, is that I actually like statistics, especially the applied part, and I want to continue in the field. That's important because that'll be my job.
The second is much more important. I learned that for me to truly enjoy something, and to really excel in it, I need to immerse myself in that topic. I found out that my personality is such that I can't window shop for things that I like. I need to go in, take everything for a test drive, and discard the things that I don't like. And once I do find something I like, I need to work to be able to incorporate as an important part of my life.
**The distinction between "squishy-science" and "hard-science" was made by a colleague in her blog. I have a whole posting on how it applies to my field in my head, but have yet been able to sit down and work through some of the philosophical nuances that I need to complete some arguments. Catch her blog here.
The most dramatic learning experience for me was while I was at Pitt working on my masters. It was in the first semester of the second year of the two year program, and I was all ready to get out of there with my masters and call myself done with school FOREVER. After two semesters of taking classes that I only marginally understood what was going on, I was forced to take a Statistical Consulting class as part of my degree requirement.
This class is set up as follows: For the first two weeks, you talk about consulting and meeting clients in broad terms, and then the instructors says "You have a meeting on this date, and at this time". The client then lays out his statistical problem and you attempt to solve it using whatever knowledge you may have accumulated over your brief life in statistics (which was about a year for me). The class, which had a size of 6, then presented their project to the class and we were all able to comment or ask questions of the consultant to not only further his knowledge but help them with their project.
My first client had an interesting problem, but no big lights went on in my mind and I struggled to get through the project (it was a reliability problem, and I don't care about that topic that much). However, I received a second client a short time later and the project that I was given KICKED ASS.
To analyze the data correctly, I had to do a test of equivalence on a longitudinal, random coefficient model. For all the non-statisticians, it's a difficult model to conceptualize let alone analyze, and admittedly I did not analyze it correctly (I did a test of difference, not equivalence, but spun it well ;-) ).
Now, I'd be lying if I said this particular topic was "the most dramatic learning experience", but as part of this class it was. The whole class was that experience because it showed me a bunch of things. First of all, it showed me how much I actually learned in the first year of grad school. But it also showed me that I actually like statistics, and love being able to put real-life problems into mathematical solutions and use "hard-science" constraints to come to and objective decision**.
This realization didn't come into play just from the aforementioned project, but from all of the topics presented in class. It was clear to me that I was not only grasping the projects assigned to me, but the projects' of the other students as well. This allowed my confidence to grow to a point that I was able and willing to submit my analysis plan to my professor, who just happened to be the most intimidating man on earth (I'm not even being sarcastic, I'm still scared of him). And it was this project that showed me that I may actually be good at this statistics thing, and I may actually like it.
So what did I learn from this class. Two things: The first, as I mentioned before, is that I actually like statistics, especially the applied part, and I want to continue in the field. That's important because that'll be my job.
The second is much more important. I learned that for me to truly enjoy something, and to really excel in it, I need to immerse myself in that topic. I found out that my personality is such that I can't window shop for things that I like. I need to go in, take everything for a test drive, and discard the things that I don't like. And once I do find something I like, I need to work to be able to incorporate as an important part of my life.
**The distinction between "squishy-science" and "hard-science" was made by a colleague in her blog. I have a whole posting on how it applies to my field in my head, but have yet been able to sit down and work through some of the philosophical nuances that I need to complete some arguments. Catch her blog here.
Thursday, February 11, 2010
Why the School Superintendents Screwed Up (big time)
This past Wednesday, Richmond received about 2 inches of snow during morning rush hour. Over 150 accidents were reported during this time. While all of those accidents were unfortunate, those actions were not the most distressing part of the snow event (2 inches is not a storm).
The most distressing action was performed by the school superintendents and the VCU brain trust. After seeing how much travel was snarled by the snow squall, some superintendents as well as VCU canceled school. This occurred between 8:00 and 9:00 in the morning. This is of course after
a) Students were either in class or on the buses.
b) Faculty and staff were either in school or on their way.
c) Parents of the students were going to work or on their way.
So after all of these people were going about their normal workday, all of a sudden their day was thrown on end. They had to pick up their kids from school, worry about getting home, or flat out turn around and go home. Making this decision at the time was foolish, and it could be argued bordering on reckless.
Obviously, the decision to cancel school was made to protect the students and parents in the treacherous driving conditions that were encountered that morning. BUT EVERYBODY WAS ALREADY IN DANGER!!!! I'm not making fault at the decision to cancel school, but at the time, the decision was not in the best interest of anybody and led to more confusion.
All the superintendents needed to do was to make 1 phone call that would have kept those they were charged to protect safe. Call the weatherman. Here's how the conversation would go:
Superintendent: Good morning Mr. Weatherman. You are up early at 6:00am.
Weatherman: Yes, I love weather.
S: I was wondering what the weather for the morning commute was going to be. Any suggestions?
W: Well, according to my Super-Dee-Duper Radar, it's going to horrible. Considering Richmond does not salt the roads, the commute is going to be very slippery.
S: That's not good. Do you have any idea how long the snow will last?
W: Just during the rush hour. It looks like we're in for just a quick shot of snow, but it should be nice once it clears.
S: Well, I'm going to act conservatively, and cancel school. Thanks!!!
W: No problem, I'm just your handy-dandy weatherman.
[Conversation ends at 6:01, schools are officially canceled by 6:10, nobody is endangered]
So, let's act rational. If school isn't canceled by 6:30, school is still on. Any other choice endangers the well-being of everyone involved.
The most distressing action was performed by the school superintendents and the VCU brain trust. After seeing how much travel was snarled by the snow squall, some superintendents as well as VCU canceled school. This occurred between 8:00 and 9:00 in the morning. This is of course after
a) Students were either in class or on the buses.
b) Faculty and staff were either in school or on their way.
c) Parents of the students were going to work or on their way.
So after all of these people were going about their normal workday, all of a sudden their day was thrown on end. They had to pick up their kids from school, worry about getting home, or flat out turn around and go home. Making this decision at the time was foolish, and it could be argued bordering on reckless.
Obviously, the decision to cancel school was made to protect the students and parents in the treacherous driving conditions that were encountered that morning. BUT EVERYBODY WAS ALREADY IN DANGER!!!! I'm not making fault at the decision to cancel school, but at the time, the decision was not in the best interest of anybody and led to more confusion.
All the superintendents needed to do was to make 1 phone call that would have kept those they were charged to protect safe. Call the weatherman. Here's how the conversation would go:
Superintendent: Good morning Mr. Weatherman. You are up early at 6:00am.
Weatherman: Yes, I love weather.
S: I was wondering what the weather for the morning commute was going to be. Any suggestions?
W: Well, according to my Super-Dee-Duper Radar, it's going to horrible. Considering Richmond does not salt the roads, the commute is going to be very slippery.
S: That's not good. Do you have any idea how long the snow will last?
W: Just during the rush hour. It looks like we're in for just a quick shot of snow, but it should be nice once it clears.
S: Well, I'm going to act conservatively, and cancel school. Thanks!!!
W: No problem, I'm just your handy-dandy weatherman.
[Conversation ends at 6:01, schools are officially canceled by 6:10, nobody is endangered]
So, let's act rational. If school isn't canceled by 6:30, school is still on. Any other choice endangers the well-being of everyone involved.
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