Making MOOCs Personal

While writing a paper for one of my courses, I became fascinated by the lengths to which people will go to make personal connections, particularly face-to-face connections. The fascination comes from the fact that my paper is about MOOCs - massive open ONLINE courses. As of today, there are 3444 Coursera communities within Meetup. Yes, you read that right - Meetup groups organized around online courses. All over the world, people are looking for others to join them to complete learning activities, explore assignments, or just study. In 2876 different cities around the world, people are trying to make those connections.

It's not just Meetup. People enrolled in MOOCs are taking the initiative to create study groups using Google+ and/or Google Hangouts. They are connecting in Second Life. And they are connecting face-to-face at universities and cybercafes.

I've taken 3 MOOCs so far, none of which I have completed. I wonder if I had a study group to meet with to bounce ideas off of and to hold me accountable if I would have had more success. Certainly, if I was leading a MOOC I'd want to try to set up opportunities for my students to connect in as many ways possible.

Learning to Learn

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We always talk about wanting our students to be lifelong learners. The Common Core asks students to integrate multiple sources of information as they construct their own knowledge. Even though I teach other adults how to access resources and use them to make meaning, I still find that it's easier said than done when it comes to my own learning! I guess this is where the last vestiges of my digital immigrant background hides - in the way I interact with online courses as a learner.

I would suggest that statistics may be one of the more difficult classes to take (and probably teach) online. The content is complicated - there are multiple ways of doing most analyses, with your optimum choice dependent on on the type and source(s) of data. There are steps that must be followed, in the correct order, in order to get the appropriate defensible results. In addition, the way in which you report the data - the choice of words, the phrasing, the level of detail on all the different statistical tests - must follow a fairly prescribed format. Our textbook has good detail about the formulas and how to manually calculate levels of significance or variance, but since SPSS does that work for me I typically only about 10% of the textbook to be helpful. Our instructor provides an overview video, but it's hard to predict the questions that will arise. The videos in the course sometimes provide sufficient guidance, but often show only the how of SPSS use, but not the why. 

I have found that I better understand the material when I do some additional research about the concepts. There are face to face Intro to Statistics teachers who have posted their lecture notes online, which really helps me to build my background knowledge. It turns out that YouTube is great not only for learning how to make turkey gravy, but for detailed explanations of statistics. I can find examples of how, as well as explanations of why. But this is where being a lifelong learner is harder than it seems - I have to asks Google the right questions in order to get answers that do me the most good. I have to start by synthesizing the info provided in the class, and then craft a query that fills in the gaps. THAT has been challenging for me!

Just in case anyone else is struggling with statistics, here are some of my favorite resources:

• MIT OpenCourseware Intro to Statistics and Probability - Lecture Notes. This site helped me backfill some knowledge gaps I had in basic statistical concepts.
• TheRMUoHP Biostatistics Resource Channel by tacappaert. Don't let the channel name throw you - there are some excellent descriptive videos for using SPSS, each between 5 and 10 minutes long.
• ProfKelley's Analysis of Variance playlist on YouTube. These 2 videos really helped me understand ANOVA.
• Brandon Foltz on YouTube. I don't know why, but this guy has recorded a huge number of 30 minute lectures that do a really good job explaining the concepts. The playlists aren't all that well organized, but if you search for Brandon Foltz and Statistics 101, you'll find some great resources for building background knowledge.
• SPSS for Newbies playlist by Statisticsmentor.com on YouTube. By far the most detailed SPSS videos I found during the course, these videos range from the basic (importing data from Excel) to the more advanced (bivariate linear regression model). At about 20 minutes, these videos are a bit long, but it's tough to find a more comprehensive explanation. 

Doing Common Core

I was fortunate enough to be invited to sit on my district Common Core Steering Committee. It's an interesting process to wrestle with the big ideas of what Common Core should look like, how we should get there, and what our benchmarks should be. One of the key messages has been that Common Core planning documents need to be flexible - as we learn more about what needs to be done and what implementation looks like in terms of assessment, the plan may need to be adjusted. Right now many planning documents are skeletons, with a lack of detail or specifics that actually allow decisions to be made or consistent implementation to happen.

For me, now working at a district rather than a county office, I am realizing how difficult it is to make decisions that "turn the ship" when a large district is involved. As a presenter and consultant, I suggested to districts that they convene a team, bring in stakeholders, identify priorities, etc., etc., etc. I acknowledged that it was going to be a multi-year process. But because I was outside the process looking in, I didn't have that gut-level FEELING of the push, pull, and frustrations that happen within the actual implementation. "Adapt to your reality" is a phrase I've used many times in terms of monitoring a plan, reflecting on what works, and making revisions. But reality is extremely hard to adapt to when money is scarce and time is even more so. Yes, I've said during presentation that time is the most difficult commodity to create, and offered a range of suggestions. But at the school site, between the constraints of the contract and the many demands we place on teachers, creating time is far more complicated than I ever internalized. Now, I need to provide CCSS implementation support for my staff in 40 minute chunks, delivered once a month. I need to provide support for my staff in single-page bulletins that are easily digestible and easily implemented. I need to bring parents up to speed in understandable 15-minute chunks, in English and in Spanish, embedded in ELAC, SSC and PTA meetings. The "how" of all that is incredibly difficult. And it requires a depth of knowledge and understanding that I am lucky to have, but many administrators do not. Sitting in presentations does not make for deep understanding.

More than ever, I think district leadership is key to building the scaffold for Common Core implementation, and site leadership is key to actual classroom implementation. The district needs to ensure equitable access to the resources (including time, money and materials) that is needed to implement. They must build the infrastructure, including a minimum amount of training, materials, and technology that all sites have in place. In terms of materials such as technology, hey must take the hard line that equitable is not equal, and that schools that already have the minimum may not get more paid from district funds. The district needs to coordinate professional development that includes cross-school collaboration and product development, so teachers leave the training with materials that are ready to use. At the site, administration needs to provide an ongoing focus on examining the implementation and refining practice. Administrators need to hold teachers accountable for moving forward with the standards, with individualized support plans and benchmarks if needed, providing ongoing opportunities for reflection, growth, and collaboration.

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MOOCs and Global Education

Below is the presentation by Dennis and I for EdTech 603. Feel free to comment within the VoiceThread!

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How vs. Why

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I am a big believer in "why". My parents could have told you that from the time I was very small. I never accepted an order to follow an algorithm, and because my father was a math professor, I never had to. He was always able to explain to me the "why" of mathematical formulas and algorithms, often using manipulatives, arrays, or diagrams to make it make sense.

Fast forward to today, taking a statistics class. My dad is no longer available to explain the "why" to me, and everything in the textbook, the lecture notes, on Wikipedia and on YouTube is about the how. This week I learned how to calculate an expected frequency from a cross-tabulation of data. But why is that the expected frequency? It seems logical to me that any calculation that relies only on the data collected is inherently skewed by that data, and could be fairly easily manipulated by adjusting the sampling.

I also learned this week that in order to construct a confidence interval of a proportion, I use z=1.96 or z=2.58, depending on whether I'm looking for a 95% or 99% confidence interval. Why? Where did these magic Z's come from? What do they represent? I understand the whole idea of confidence intervals, I am just frustrated that there is an element in my algorithm that I can't personally account for! Maybe I just need to see it like pi, and know that it just IS. But even pi I got to experiment with using string to prove to myself that it just is...

I can certainly get by running algorithms and using software to construct analyses and tests, and maybe I need to resign myself to that. Maybe it takes a degree in statistics to get to the "why". The sad thing is that if I understood the why, I would always be able to self-check the results of the algorithm to see if they make sense. Without the why, I have to just check my math, and assume that the results are logical.

MOOCs and Global Education

Approximately 80% of the world’s population is unable to access educational content published in English (Beaven, Comas-Quinn, de los Arcos, & Hauck, 2013). Massive open online courses (MOOCs) have the potential to provide educational opportunities across geographic, linguistic and cultural boundaries that would otherwise be difficult, if not impossible, to achieve. The core content of a MOOC is really no different than the core content of any online course; it is designed and taught in one language, typically by designers from one culture. It is the massiveness and openness that changes an online course into a system in which information is re-transmitted, shared, and adapted to participant needs. Solitary participants from remote areas have the opportunity to interact with other experts and learners in specialized areas through the MOOC environment. The result is a growing democratization of access to information and a diminishing language barrier.

One of the adaptations needed when participation in a MOOC grows and crosses international lines is the need for translation into other languages. Simon Thrun and Peter Norvig of Stanford taught one of the earliest MOOCs, Introduction to Artificial Intelligence (Rodriguez, 2012). It was offered free to anyone in the world, and attracted 160,000 enrollees. Of those, 20,000 from 190 different countries completed the course. With the help of more than 2,000 volunteers, the course was translated into 44 different languages (Murray, 2012). In an unplanned, grassroots movement, many participants connected on social media as well, in languages other than English, making the MOOC both more accessible and more relevant to their needs (Murray, 2012).

Coursera provides an ongoing example of an approach to overcoming geographinc, linguistic, and cultural boundaries. Coursera is a non-profit organization started out of Stanford University in 2012 that partners with universities around the world in order to provide free, high-quality MOOCs that are open to anyone in the world. That mission requires dealing with the issue of language. The mission of the Coursera project is “to connect the world to a great education. To do so, we have to overcome language barriers, which can be very real obstacles for our students who come from all over the world.” (Coursera, 2013). After one year of providing courses in English, Coursera formed the Global Translations Partners Program in order to begin the process of translating courses into Arabic, Chinese, Japanese, Kazakh, Portuguese, Russian, Turkish, and Ukrainian, with more languages to follow. Reaching 5.2 million users after only 18 months of existence, this project is worth noting because of its scale and because it is taking a lead role in modeling global access to the highest quality learning.

MOOCs and translation projects like the ones cited here have shown the potential to build language bridges across spans that would otherwise have seemed impassable. The nature of MOOCs leads to collaborative solving of the inherent language issues of the Internet.

References

Academic Vocabulary

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With new content comes new academic vocabulary. Some subjects seem to have academic vocabulary and academic language that is more complex than others. I’m finding that the academic language and vocabulary of statistics is hugely complicated, and its nuances and variations are frankly driving me crazy. My statistics textbook uses one set of academic vocabulary, the teacher uses another, and SPSS software uses another still. Combine that with esoteric definitions of statistical functions that seem contradictory, and the whole process feels like a rabbit hole that I can’t get out of.

Take, for example, the p value, which is computed using a t-test. The p value is also called the level of significance, and we want it to be as low as possible. A high level of significance means there is likely no significant difference between two tests. So essentially, a statistician wants a low level of significance in order to prove the significance of a comparison. To make things more interesting, when you conduct a t-test comparison in SPSS which is supposed to yield a p value, there is no p-value. There is a t value, which has nothing to do with the t in the t-test. There are 2 “sig” columns, but we aren’t currently worried about the one that goes with Levene's Test for Equality of Variances.

Even with a class glossary, 2 textbooks, and YouTube to help me make sense of things, I’m drowning in the new vocabulary. The base concepts themselves aren’t actually that complicated, but trying to read and write in the correct vocabulary is. There are concepts and comparisons I can draw, but I lose points in my descriptions because I use the wrong terms. On the plus side, I definitely have a much better gut-level understanding of what our English Language Learners are going through as they try to tackle complex vocabulary in the content areas.