Journey not the Destination Finding success in the journey of learning
Time 2021-11-10 15:02:58Web Name: Journey not the Destination Finding success in the journey of learning
WebSite: http://blog.mrwaddell.net
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keywords: description:I have a problem. Or rather, I had a problem. This problem has gone on for the last four years, and this year I am finally doing something about it. In my role as a Master Teacher, I have a role statement on file with my Dean at at the University. It says that I will spend 57.5% of my time on teaching activities, 17.5% of my time on service activities, and 25% of my time on other which is a lot of administrative and recruiting tasks.
But how much time do I actually spend on these activities? Am I even close? Is it 80% teaching, and 10% service and other? Who knows. I do my job, and I write well, so my reviews have been accepted.
But how much time do I actually spend during the work day on these areas? Not a clue. The clock just keeps spinning, and I do my work.
And so, my efforts to track my time. I started by search out some other academic sites where they explained how they tracked their time. From those searches, I learned about RescueTime.com. I use a PC and Android, so the Mac specific programs were out.
I used rescuetime.com (the app on my phone and the software on the computer) for two weeks before I started working on something new. Dont get me wrong, rescuetime.com is a solid piece of software. My problem is the way I work. As a Master Teacher, I am observing Pre-Service Teachers frequently. This requires using word, and a web browser for searching ideas, and calendars, as well as one note where I keep information. Rescuetime on the computer tracks these as three separate functions; each website, and one note. So, if I put in observation into the app on my phone, I just doubled my time tracked because rescuetime thinks that the time was distinct. This means I regularly worked 13 to 15 hour days in an 8 to 9 hour workday. This was not going to work.
But, I use IFTTT.com on my phone. And I use Google Sheets. Can I connect them?
Sure I can.
Using the Note feature in IFTTT, I have the note set up to drop a line into a Google Sheet. This note is whatever I type in, so I can type commuting from UNR to OBrien. IFTTT drops a time stamp and note into the sheet (it can do more, but this is all I need).
Then, when I am done, I click the button and type in Done. Then type in the next task when I start it.
The blue field is a drop down I created using the Data Validation feature. So yes, I have to go in and categorize each task, but I had to do that in rescuetime as well, because it wouldnt track the variety of tasks I had very well. (at all).
Next, I use the command, =split(A144, at ,false) to split the date and time in one cell into two cells and then the command =TEXT(D143,MMMM) to turn the date into the month. That matters for my tracking later.
Finally, the reason why I have to do a done at the end of the activity is to get elapsed time. I have a simple =E144-E143 to calculate the elapsed time.
Thats it. That is the entire process of tracking time. It takes me about 3 seconds at the beginning and 1 second at the end of a task to log the note and end the note. Then, once a day, I can go back, categorize the notes, and drag the rows down in yellow. This gives me an output like this:
Using a Pivot Table on a separate tab, it sums the time across the categories, giving me the time for each category, and the second column is the average time for the category. So, for example, I spent 1 hour, 24 minutes commuting today between schools (3 observations) but each commute was only, on average, 17 minutes long.
I also get this:
Where I can break it down month by month (hence the reason why the January was so important earlier in the spreadsheet design.) And, because I am a little bit mathy at times (stop laughing, I see you) I have this:
That giant spike for NVTC Admin work was the cause of all this work. Most of that was the time spent the first two weeks of January working in Digital Measures to build my annual review document. Yes, it took a LOT of time.
But, as the semester and year moves forward, it will diminish. I will add monthly bars on this, so I can see the trends of months, not just totals. Would you want to see the spreadsheet? I am not sure why, other than to torture yourself.
IFTTT.com and Google Sheets is an amazing combination. I use it in several different ways, but this is by far, one of the most time saving way. I spend about 5 min per day now on this, instead of 15 to 20 on rescuetime.com, and this is giving me the results I need to show my supervisor and my Dean how my time is being spent.
Hope this helps someone else!
Jennifer Fairbanks has been promoting the #MTBoS2020 hashtag, where you create a blog post on the 20th of each month this year. As my contribution for this first month, I thought I would continue my look at how hashtags are used on Twitter by the math community. My last post on hashtags looked at the quantity of tags (1300!) and this time I wanted to compare the top 10 clusters found in the downloads I made in December.
First off, the software I use to create the downloads and do the clustering is NodeXL. There is a free version, as well as the paid version. It goes through and clusters the participants based upon who they include in their conversations. If I connect with one person five times, and another person only 2 times, I will be clustered with the person I connected with five times. It is a frequency clustering (as I have it set up right now.)
This is not an apples and oranges comparison, but it is an apples and apples comparison. The question is, will the comparison be between all red apples, or is it green apples, red apples, etc.
With that said, here are a couple of tables with which to notice and wonder. Each table is 10 rows, corresponding to the largest to the 10th largest clusters, and ten columns, corresponding to the frequency count of the top 10 hashtags used in that cluster.
Sorry that you have to scroll on these tables. The amount of columns means it wont fit without scrolling. The first column here definitely tells us what the keyword was that generated the data. Same with the table below.
The first thing I noticed is that almost alwasy the second most frequent hashtag is the partner tag of the pair, #MTBoS and ITeachMath. And, if it is not the most frequent, it is in the list of top 10. Every time.
I can also see some great overlap between the other hashtags. Maths shows up frequently in both, and the top cluster is almost identical between them. There is a lot of similarity, as well as differences between these two data sets. Despite the huge amount of overlap, there are some important differences.
Doing this analysis makes me want to dig and do more more. I need to do some additional downloads so that I get different months of data. I also have downloaded using the search #mtbos NOT #iteachmath and #iteachmath NOT #mtbos to see what differences occur in the exclusive use.
There is a lot going on in the greater community that is the math teacher community.
Back in November, Judy Larsen posted this tweet about math hashtags.
Judys question got me thinking about all the math hashtags that really are used on Twitter. What are they? To answer this question, I downloaded the #MTBoS and #ITeachMath hashtags using NodeXL software, and went through all the hashtags mentioned in those data sets.
As it turns out, the math community uses a lot of very diverse and interesting hashtags. So, to date, as of December 2020, here is a list of hashtags used in by math teachers on Twitter. I will not claim this is an exhaustive list, but it is as thorough as I could make it, using both qualitative and quantitative methods.
The method I used to create this list is: I downloaded a data set using the #MTBoS hashtag, and copied all hashtags used in that data set into an excel document. I did the same for #ITeachMath. This download was done in the middle of December, and each download was comprised of tweets going backwards in time for approximately 2 months. I did delete all the Christmas hashtags, because they would not be used year round. Next, each list was deduplicated, so that the hashtags only occurred once in each list. I was not trying to see what were the most popular or frequently used hashtags, only answering the question of what are the hashtags. Next, both lists were combined, and deduplicated a second time. Finally, I started theming the list because with a list of 1343 hashtags, it was far too long to post in one long list. The theming took many passes, with each pass the tags were themed, alphabetized, themed, realphabetized, and sorted again. I went through this circular process repeatedly. The software does delete the # from all hashtags, so in the spreadsheet, you will not find any.
RECOMMENDATIONS! I did delete obvious typos with one exception, #MBToS. If you search for this hashtag, you will find that a surprising number of people use it. I recommend using #MTBoS OR #MBToS in your Tweetdeck to compensate for the typo. I have already mad that change. Also, please use CamelCase when writing hashtags. The software eliminated all cases completely, but in my writeup below, I will use CamelCase. It matters.
After the I came up with the following themes. Please note that some of the hashtags could go into more than one category. I did NOT duplicated it. When I encountered these types of hashtags, I kept them in the theme that I thought fit them best.
Chats (83 different tags): I noticed an interesting thing with chats. Several times there were two tags for a chat. For example, there is #ElemMath and also #ElemMathChat. I made the decision to keep them together, instead of separating them into different categories. I also made the decision to delete the typo generated hashtag #ElemmMathChat.
Grade / Course (53 different tags): This theme is where I placed all the hashtags which indicated as specific class. For example, #alg1 or #2ndGrade fell into this category. It is interesting to note that there is not a consistency found in the labeling. You will find #GradeOne as well as #FirstGrade, #2ndGrade and #SecondGrade.
Math Content (83 different tags): If a hashtag specifically mentioned math content, such as #quadratic or #subitizing, it went into this category.
Coding / STEM (43 different tags): This is a smaller category, but I thought worthy of pulling out because there were a variety of physics, and science tags.
Equity (52 different tags): I have a very broad use of the term Equity for this heading. For example, tags such as #aryabhata fell into this category because they were used to bring non-European mathematics into the discussion. Other tags such as #EveryOneCanDoMath or #HereForStudents are used more broadly to be learner centered.
Tools (82 different tags): Desmos? Geogebra? Mathematica? Yes these are all tools. As is the hashtag #ImproveMyAB, #NixTheTricks, and more. I had a broad view of what tool was when creating this theme.
I teach others (38 different tags): So many great hashtags here. From #ITeach1st to #ITeach7th, to #ITeachPhysics, to #ITeachSped and beyond. This category was a pleasant surprise to me.
General (488 different tags): This largest group I just dont know what to do with yet. It is large, and it is broad. Nothing here obviously fit into another category, and I havent looked at them enough (yet) to come up with smaller categories. If you have suggestions, please let me know.
Classroom Action (148 different tags): Ugh. This was a struggle category as well. If it was a pedagogical technique, it fit here nicely. Some things didnt fit nicely, and I went back and forth with them between General and this category.
Local / Conference / Orgs (260 different tags): The number of local schools who were celebrating the mathematical success of their learners was a joy to see. I also put NCTM, NCSM, and the CMC conference hashtags into this group.
Non-US (8 different tags): There were a very small number of these, so I will list them. That there were only these eight made me sad. It showed how little interaction from other countries the US / English math community really has. econometría globalcompetency logaritmica matematicas matematik matemàtiques notenglish mtbosfr
These categories total 1340, and the final three are #MTBoS, #MBToS, and #ITeachMath, for a total of 1343 hashtags used by the math community in October, November, and early December.
The full list can be downloaded below as an Excel .xlsx file. I wont torture your eyeballs with a table of 1343 hashtags posted here.
Hashtags-used-2019DownloadOne of the most enjoyable thing I have done over my career is work with teachers from other countries. The university is a host site for the Fulbright Teacher Excellence and Achievement program which brings teachers from other countries to the US, where they spend six weeks in classrooms and university classes.
As a teacher, I had the pleasure to host teachers from Jordan, South Africa, and Poland. They spend six weeks in my classroom, observing, and teaching. It was amazing. This semester, I had a different role as the university instructor for one math teacher, Jurgita from Lithuania. She was an amazing teacher, and I learned from her as much as she learned from me.
One of the things I am taking away from the time we spent together is how she, and everyone in her country, teaches the quadratic formula and quadratics. In her country teachers have a consistent method for teaching all materials. They are required to teach certain elements of mathematics the same way across all classrooms. The quadratic formula is one of them. She was shocked when she saw one of my preservice teachers write the entire quadratic formula down. In her country, the write it this way:
D=b^2 4ac
x= (-b +/- sqrt(D))/2a
I wrote these in text form, not graphic, pretty form, because you get the idea. Every teacher in her country teaches the quadratic formula in this exact way. Every learner FIRST finds D. They indicate whether the value of D is positive, negative, or zero. Then they substitute that value into the rest of the formula.
I always found that my learners struggled with the discriminant part of the formula. They rarely struggled with the rest, just the discriminant and the square root together. So, pull it out. Do it separately.
The quadratic formula does not need to be a one step process. It can be broken into two steps, make a decision on the discriminant, and then move forward or not depending upon the positive, zero, or negative value.
I like it. Now I want to try it with a class. If you try it, will you please share it out?
For my dissertation, a statistic I used to determine who was at the center of the network was the centrality calculation. This was not done by hand. I used software called NodeXL to do the downloading, calculating, and visualization of the network. The statistic ended up choosing to use is called the Betweenness Centrality, which measures the typical shortest path length between each and all of the vertices. A vertex (or node) is an account on Twitter. I wont call it a person, because it is possible that a vertex is a company, an organization, or even a bot.
In my data set, I had 1,319 vertices and 13,524 edges. Not a giant data set compared to some, but still, it was large enough to require some computing power and time to run the visualizations. After calculating the statistics for the data, the vertex with the largest betweenness centrality had a score of 215,883.10*. My advisor cried foul. He did not understand how the software could assign a score of 215,000 to a vertex in a data set with only 1,319 vertices and only 13,524 edges. Honestly, I didn’t either, at first. So I went back to the books and really dove into the calculation to figure out how betweenness centrality was calculated. One book I used was Hennig et al. (2012) who had this nice image and a wonderful table next to it which gave the raw score. I dove into the method of calculation to figure out how they came up with this score.
It wasn’t hard, but it was slightly complex. The formula Hennig et al. uses for the betweenness centrality is: (# of shortest paths through a vertex)/(# of shortest paths). The denominator in the given graph is 8 because they didn’t count the double arrows as two paths. The numerator is a little more complicated.
First, list every connected dyad (pair ofvertices) with the number of shortest path edges it takes to connect them.Then, pick a vertex you are interested in. For this purpose I am interested invertex 2, since it has the largest value.Finally, count how many edges go through the selected vertex, and divideby how many vertices are immediately connected to the selected vertex.
1-2-3: 2
1-2-3-4: 3
1-2-5: 2
4-2-5: 2
There are some tricky ones. For example, 2 to6 can occur two different ways, 2-3-6 and 2-5-6. Each is a value of 2, butthere are 2 equal ways, so the value of 2 is divided by 2, so the counted valueis one for each path. The same thing for 1 to 6, except the value is 1.5,counted twice.
In the end, this method counts 30 pathscrossed through vertex 2, divided by 8, resulting in the value of 3.75.
This is not how NodeXL counts. The same graphin NodeXL yields a value of 5 for vertex 2’s betweenness centrality. This confused me greatly. Clearly NodeXL isdoing something differently than Hennig et al. was doing.
What I figured out was NodeXL counts only thepaths through a vertex, and does not divide by the number of shortest paths.This is easily confirmed by taking the six vertex sample, and simplifying itdown to the simplest type of network, and then rebuilding it.
In this network, vertex 2 is at the heart ofthe star, and the other nodes are arranged around it. If vertex 1 is the rightmost arm of the star, and we count counter-clockwise, the pairs of dyadsconnected through vertex 2 are: 1-3; 1-4; 1-5; 1-6; 3-4; 3-5; 3-6; 4-5; 4-6;and 5-6.
Calculating the values on this arrangementyields the following values.
Vertex Degree Betweenness Centrality vertex 1 1 0 vertex 2 5 10 vertex 3 1 0 vertex 4 1 0 vertex 5 1 0 vertex 6 1 0NodeXL did not add any values for 2,1; 2-3; 2-4; 2-5 or 2-6. It only counts the paths THROUGH the vertex in question, not terminating at the vertex in question. This can be checked by removing paths one by one, and calculating the values. For example, if vertices 4 and 6 are connected and 3 moved inward to start matching Hennig et al.’s graph above, the following results.
This is what would be expected if the 4-6 dyadwas removed from the list above. Connecting additional dyads follows the samepattern. For example, connecting dyad 3-4 should result in multiple paths beingcounted between 3 and 6, so we should get a fractional count.
The fractional count is confirmed. 3-6 has twoequal shortest paths. One through 2 (3-2-6) and one through 4 (3-4-6). Sinceonly 1 of the pairs goes through vertex 2, only ½ is counted towards vertex 2’scentrality, the other ½ is counted towards vertex 4’s centrality.
That the calculation yields large values quiteeasily can also be checked. If a vertex 7 is added to the graph, in a similarway as vertex 1 is, the betweenness centrality should double.
It does. At this point, I amcomfortably saying that the formula for betweenness centrality used by NodeXLis the sum of all shortest paths between two vertices that goes through (butdoes not terminate at) a particular vertex.
This method of calculating centrality makes much more sense for large data sets found on social media, and Twitter. The method Hennig et al. used may work when the graphs are small or compact, but once hundreds of vertices are included in a data set, counting the shortest paths between all of them would be prohibitively intensive. In addition, while the value of the count changes, the order is similar. Hennig et al.’s calculation was directional, which increased the value of betweenness centrality for vertex 4.
Hennig, M., Brandes, U., Pfeffer, J., Mergel, I. (2012). Studying social networks: A guide to empirical research. Frankfurt am Main: Campus-Verlag.
*Who was this person with the amazingly huge betweenness centrality score? More on that to come! I have to keep some secrets right for the moment.
This is kind of odd to write, but I think I need to. I finished my dissertation, and defended in April. That was four to five months ago! I really have done nothing on this blog. I was stressed, and had my head down and working hard on the dissertation and defense.
Then, in August, I signed up with Shelli Temple and the Blaugust writing efforts and did nothing. It realize now that I was kind of depressed during the month of August. That has never happened to me before, so I didnt really know how to handle it. I wrote nothing. I really have written nothing since the defense. I think the come down from having that high demand for attention and focus to normal life (whatever that is) hit me kind of hard.
So, Blaugust was a bust for me. But, at the end of August, I was asked to teach a graduate level research course, which shook me up, gave me a new challenge, and made me realize where I was at. It helped me climb out of that place, into a new place. A better place.
So, I have some blogging plans. I recorded my dissertation defense, and will post the audio, the transcript, and the PowerPoint. I did a non-traditional defense, and I will write about that process. And, I have several other irons in the fire for writing prompts. I really feel better, am focused, and have the desire to write again!
This was a first for me, to have this level of self-focus. The Ph.D. process is brutal, and I took the completion kind of hard. I lost focus. I had no rudder, whereas for the last five years, I had a clear focus.
I think I have refound a focus, and it feels good.
Take care of yourselves, if you read this. Mental health is just as important as physical health.
Wow. So much to learn when dealing with software packages and analysis. It turns out that our computers do a TON of processing behind the scenes. When I send a tweet, Tweetdeck and / or Twitter shows the time of tweet as being a couple of minutes ago, and then the time counts from there.
However, what actually happens behind the scenes is Twitter stores the tweet at UTC 0, and the software platform we are using takes the time of the hardware we are using, checks the time, and shows the time in our software as adjusted. This means that when I download tweets, I am downloading every single tweet at UTC 0. But we were at UTC -5 (Eastern Time Zone) so I have to shift the entirety of the data set so that 12pm = 7am. That changes things dramatically!
Now, the peaks are at 8am and 12pm. That makes much more sense to the conference schedule. And there is a small hump from 6pm to 12am, which is the social hours afterwards.
I feel much better about this. It means I dont have to do and entire interpretation based on a 5pm spike. There are still a surprising number of people who need to go to to bed though. Tweeting at 1am through 5am is just odd.
TAGS:Destination Finding the Journey not of learning journey
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