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It’s here folks. The most advanced aggregation and visualisation of tweets for the JISC Innovating e-Learning 2012 online conference taking place next week. Over two years ago I started developing a Google Spreadsheet to archive tweets and since not only have I been evolving the code I’ve been creating tools which use the spreadsheet as a data source. It’s pleasing to see these tools being used for a wide range of projects from citizen journalism,  to a long list of academics, students and community groups, and even TV broadcasters.

I’ve been a little remise in posting some of the latest developments and I’ll have to cover those soon. For now here’s your #jiscel12 Twitter basecamp.  

Overview of features

 #jiscel12 Twitter basecamp

Whilst I probably just looks like another spreadsheet you should explore:

A. The ability to easily filter archive by person

The ability to easily filter archive by person
[Still need to document]

B. The TAGSExplorer conversation overview

TAGSExplorer conversation overview
[TAGSExplorer: Interactively visualising Twitter conversations archived from a Google Spreadsheet]

C. The entire searchable/filterable archive

entire searchable/filterable archive
[Still need to document]

D. The question and answer filter

question and answer filter
[Any Questions? Filtering a Twitter hashtag community for questions and responses]

Dashboard

image[Contains a number of summaries – I find ‘most RTs in last 24hrs’ one of the most useful (how this works also need documenting]

Currently these are automatically updating every hour, but I’ll probably crank up the frequency next week. Your thought on these always gratefully received ;) 

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This week saw me submit my application to the Shuttleworth Foundation  to investigate and implement cMOOC architecture solutions. It seems rather fitting that for this week’s CFHE12 exploration that an element of this is included. It’s in part inspired by a chance visit from Professor David Nicol to our office on Friday. It didn't take long for the conversation to get around to assessment and David’s latest work in the cognitive processes of feedback, particularly student generated, which sounds like it directly aligns to cMOOCs. It was David’s earlier work, which I was involved in around assessment and feedback principles that got me thinking about closing the feedback loop. In particular, the cMOOC model promotes participants working in their own space, the danger is with this distributed network participants can potentially become isolated nodes, producing content but not receiving any feedback from the rest of the network.

Currently within gRSSHopper course participants are directed to new blog posts from registered feeds via the Daily Newsletter. Below is a typical entry:

CFHE12 Week 3 Analysis: Exploring the Twitter network through tweets
Martin Hawksey, JISC CETIS MASHe
Taking an ego-centric approach to Twitter contributions to CFHE12 looking at how activity data can be extracted and used [Link] Sun, 28 Oct 2012 15:35:17 +0000 [Comment]

One of the big advantages of blogging is that most platforms provide an easy way for readers to feedback their own views via comments. In my opinion this is slightly complicated when using gRSSHopper as it provides it’s own commenting facility, the danger being discussions can get broken (I imagine what gRSSHopper is trying to do is cover the situation when you can’t comment at source).

Even so commenting activity, either from source posts or within gRSSHopper itself, isn't included in the daily gRSSHopper email. This means it’s difficult for participants to know where the active nodes are. The posts receiving lots of comments, which could be useful for vicarious learning or through making their own contributions. Likewise it might be useful to know where the inactive nodes are so that moderators might want to either respond or direct others to comment.

[One of the dangers here is information overload, which is why I think it’s going to start being important to personalise daily summaries, either by profiling or some other recommendation type system. One for another day.]

To get feel for blog post comment activity I thought I’d have a look at what data is available, possible trends and provide some notes on how this data could be systematically collected and used.

Overview of cfhe12 blog post comment activity

Before I go into the results it’s worth saying how the data was collected. I need to write this up as a full tutorial, but for now I’ll just give an outline and highlight some of the limitations.

Data source

An OPML bundle of feeds extracted in week 2 was added to an installation of FeedWordPress. This has been collecting posts from 71 feeds filtering for posts that contain ‘cfhe12’ by using the Ada FeedWordPress Keyword Filters plugin. In total 120 posts have been collected between 5th October and 3rd November 2012 (this compares to the 143 links included in Daily Newsletters). Data from FeedWordPress was extracted from the MySQL database using same query used in the ds106 data extraction as a .csv file.

This was imported to Open (née Google) Refine. As part of the data FeedWordPress collects a comment RSS feed per post (a dedicated comment feed for comments only made on a particular post – a number of blogging platforms have a general comment feed which outputs comments for all posts). 31 records from FeedWordPress included ‘NULL’ values (this appears to happen if FeedWordPress cannot detect a comment feed, or the original feed comes from a Feedburner feed with links converted to feedproxy). Using Refine the comments feed was fetched and then comment authors and post dates were extracted. In total 161 comments were extracted and downloaded into MS Excel for analysis

Result

Below is a graph of cfhe12 posts and comments (the Excel file is also available on Skydrive). Not surprisingly there’s a tail off in blog posts.

CFHE12 Blog Posts and Comments

Initially looking at this on a per post basis (shown below left) showed that three of the posts were been commented on for over 15 days. On closer inspection it was apparent this was due to pingbacks (comments automatically left on posts as a result of it being commented in another post). Filtering out pingbacks produced the graph shown on the bottom right.

CFHE12 blog comments timeline  CFHE12 blog comments timeline (without pingbacks)

Removing pingbacks, on average 3.5 days after a post was published comments would have stopped but in this data there is a wide range from 0.2 days to 17 days. It was also interesting to note that some of the posts have high velocity, Why #CFHE12 is not a MOOC! receiving 8 comments in 1.3 days and Unfit for purpose – adapting to an open, digital and mobile world (#oped12) (#CFHE12) gaining 7 comments in 17 days (in part because the post author took 11 days to respond to a comment).

Looking at who the comment authors are is also interesting. Whilst initially it appears 70 authors have made comments it’s apparent that some of these are the same author using different credentials making them ‘analytically cloaked’ (H/T @gsiemens).

analytically cloaked

Technical considerations when capturing comments

There are technical consideration when monitoring blog post comments and my little exploration around #cfhe12 data has highlighted a couple:

  • multiple personas - analytically cloaked
  • pingbacks in comments – there are a couple of patterns you could use to extract these but not sure if there is a 100% reliable technique
  • comment feed availability – FeedWordPress appears to happily detect WordPress and Blogger comment feeds if not passed through a Feedburner feedproxy. Other blogging platforms look problematic. Also not all platforms provide a facility to comment
  • 3rd party commenting tools – commenting tools like Disqus provide options to maintain a comment RSS feed but it may be down to the owner to implement and it’s unclear if FeedWordPress registers the url
  • maximum number of comments – most feeds limit to the last 10 items. Reliable collection would require aggregating data on a regular basis.

This last point also opens the question about whether it would be better to regularly collect all comments from a target blog and do some post processing to match comments to the posts your tracking rather than hit a lot of individual comment feed urls. This last point is key of you want to reliably track and reuse comment data both during and after a cMOOC course. You might want to refine this and extract comments for specific tags using the endpoints outlined by Jim Groom, but my experience from the OERRI programme is that getting the consistent use of tags by others is very difficult.

Discovering TEL-Map Mediabase

Twitter hasn’t completely abolished 3rd party clients just yet. The text is the red circle is generated from the details a users/company submits when they create an application that uses the Twitter API. As part of the registration the user has to provide a url for the application. In this example ‘TEL-Map Mediabase’ redirects to Learning Frontiers, which is powered by Tel-Map. I should probably know more about TEL-Map because one of the partners is CETIS (before my time). 

But what about ‘Mediabase’. Well a search of ‘TEL-Map Mediabase’ returns the page ‘MediaBase - Collecting, Analysing and Visualizing Large Collections of Social Software Artifacts’ which contains the presentation by Ralf Klamma.

So you basically have a system that can crawl and analysis predefined source feeds, analyse the collected data and either manual or automatically tag links, which can be pushed to a portal or tweeted from the system (and much more). Anyone else thinking cMOOC infrastructure?

[If the system is as good as I think it is I’m expecting another tweet from TEL-Map Mediabase]

Here's some posts which have caught my attention this month:

Automatically generated from my Diigo Starred Items.

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For week 3 of cfhe12 analysis I thought I’d turn back to the Twitter data. I’m currently trying to prepare a Shuttleworth Fellowship application which has got me thinking more about the general premise of cMOOCs that “knowledge is distributed across a network of connections, and therefore that learning consists of the ability to construct and traverse those networks”  (from week 1 of cck11).

The aspect, which features in my Shuttleworth application, is providing mechanisms that aggregate data from distributed sub-networks which then can be processed to produce actionable insights to tutors or participants. The process I plan to adopted is to look at the data using heavyweight tools, like NodeXL, or just applying a bit of curiosity (this person has stopped tweeting, why? etc), and then converting some of these patterns into very lightweight applications or views to remove the complexity and highlight key parts of the data.

Some examples for you:

Summary of CFHE12 participant activity

Tweets

Tweets from CFHE12 are being collected in this Google Spreadsheet. As part of this template there are a number of summary views, one of these being a breakdown of individual participant activity. As part of this sparklines are used to display someone's twitter activity. Looking at gsiemens you can see there is steady activity posting 45 tweets tagged #cfhe12. Towards the bottom of the graph is ViplavBaxi, who after initial high activity is no longer contributing to the hashtag. So what has happened to ViplavBaxi? There are a number of possible answers but let me highlight a couple which also highlights the limitation of the technique:

  • they have lost interest in the course ot time commitments prevent them from contributing (high drop outs aren’t unexpected in MOOCs)
  • no longer using #cfhe12 hashtag – the archive is only of #cfhe12 so if the have joined a sub community communicating without the hashtag it’s not recorded
  • found a different communication channel – this technique is only looking at Twitter activity, the person may have moved to another network channel like the discussion forum

Another interesting activity summary is for dieGoerelebt. They are one of the top 5 contributors in terms number of tweets, but recently their activity has trailed off. You can also see the ‘@s’ column, which is the number of times they’ve been mentioned in tweets is one of the lowest. Is the decline in activity a result of the lack of engagement?

The next question that springs to my mind is what did these people say. Within the spreadsheet it’s easy to filter what they said. To let you see too I’ve got this simple web interface primed with filtered tweets (I modified an existing tool I’ve developed to do this – unfortunately I’ve never documented it, but as I use it more and more I must get around to it):

Summary of CFHE12 participant activity with RT percentageFrom visual inspection dieGoerelebt had a high proportion of retweets. This is confirmed when I added a percentage of tweets that are retweets.

Something I noted in the filtered view for a persons tweets was that a lot of the context is lost (I can see they are @replying to someone, but I don’t know what they said.

To help with this I started looking at modifying the twitter questions filter I built to enable a view of the conversation.

This is a start, but as I noted when I published the question filter clicking through messages like the one showed below reveal there is more of the conversation that is missing.

 Part of the conversation

Bigger picture

Summary

So again I start exploring some ideas that branch off into many more avenues to follow. One thought is that the micro analysis of tweets might not my beneficial or practical, and given the issues with extracting a full conversation from Twitter a macro view might be better. Providing a summary of overall activity and the mode in which Twitter is being by people may be of the most use to tutors and participants to identify people they might want to connect with. As always your thoughts are greatly appreciated.

In this post I’ve taken an ego-centric approach contributions. In the next couple of days I’ll share an ego-centric approach to community connections.

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On Monday I’ll be helping Tony (Hirst) deliver a workshop at Internet Librarian International … W2 – Appropriating IT: innovative uses of emerging technologies. Tony has already posted a draft outline of the day. For the event I wanted to prepare some notes on using Google Spreadsheets/Sheets (I very confused about what they are called now. There was a very quiet announcement here of a change).

I’ve come up with some practical exercises for participants to try. There will be a lot of contextualisation going on as part of the workshop, the main idea is to seed some ideas of what’s possible. Here’s the Google Docs version of the guide. The focus is on what you can do with the importHTML, importFeed, importXML, importRange and importData spreadsheet formula with a few others, including QUERY, thrown into the mix. Using QUERY isn’t straight forward but it opens up lots of possibilities in how you reshape data (extra examples of these are included in the Google Docs version).

Note: All text and images with the exception of text in grey boxes is licensed Creative Commons Attribution 3.0 Unported License. CC-BY Hirst & Hawksey (if this isn’t clear check the source or get in touch)

importHtml

Syntax: ImportHtml(URL, query, index)

URL is the URL of the HTML page.

Query is either “list” or “table” indicates what type of structure to pull in from the webpage. If it’s “list,” the function looks for the contents of <UL>, <OL>, or <DL> tags; if it’s “table,” it just looks for <TABLE> tags.

Index is the 1-based index of the table or the list on the source web page. The indices are maintained separately so there might be both a list #1 and a table #1.

Example: =ImportHtml(“http://en.wikipedia.org/wiki/Demographics_of_India“; “table”;4). This function returns demographic information for the population of India.

Note: The limit on the number of ImportHtml functions per spreadsheet is 50.

Exercise 1: Importing a html table and graphing the result

  1. Create a new Google Spreadsheet
  2. In cell A1 enter the formula to import a table from Wikipedia=importHTML("http://en.wikipedia.org/wiki/2012_Summer_Olympics_medal_table","table",3)
  3. Select Insert > Chart and then select the data ranges for country name and total medals
    Selecting chart data
  4. While still in the Chart Editor select the Charts tab, then Maps > Geo charts - regions
  5. Still in the Chart Editor select Customise and change the No value colour to blank.
  6. Finally click Insert

You should now have a chart that looks like this:

World map with missing data

Notice that the chart has No values (black) for most of the countries.  To fix this we need to remove the country codes in brackets. One way to do this is trim the text from the left until the first bracket “(“. This can be done using a combination of the LEFT, FIND and ARRAYFORMULA (ARRAYFORMULA allows you to apply formulas to multiple cells).

  1. In cell H2 enter the formula =ARRAYFORMULA(LEFT(B2:B86,FIND("(",B2:B86)-2)) this should create a column of country names without brackets)
  2. Click on your Chart and select Advanced edit.
  3. Make sure you are on the Start tab in the Chart Editor and edit the data range forSheet1!B2:B86 to Sheet1!H2:H86 then click Update

Your chart should now look like this (my solution http://goo.gl/8qUI9):

World map without missing data

importFeed

Syntax: =ImportFeed(URL; [feedQuery | itemQuery]; [headers]; [numItems]). The arguments to the function are as follows:

URL is the url of the RSS or ATOM feed.

feedQuery/itemQuery is one of the following query strings: "feed", "feed title", "feed author", "feed description", "feed url", "items", "items author", "items title", "items summary", "items url", or "items created". The feed queries return properties of the feed as a whole: the feed's title, the feed's author, etc.Note: To get the data included in the feed, you need to do an "items" request.

  1. the "feed" query returns a single row with all of the feed information.
  2. the "feed <type>" query returns a single cell with the requested feed information.
  3. the "items" query returns a full table, with all of the item information about each item in the feed.
  4. the "items <type>" query returns a single column with the requested information about each item.
  5. if a query is given that begins with "feed", the numItems parameter isn't necessary and is replaced by the option headers param.
  6. if a query is given that begins with "items", the numItems parameter is expected as the 3rd param, and headers as the 4th.
  7. headers - "true" if column headers is desired. This will add an extra row to the top of the output labeling each column of the output.

Example: =ImportFeed("http://news.google.com/?output=atom")

Note: The limit on the number of ImportHtml functions per spreadsheet is 50.

Exercise 2: Importing a RSS feed and getting social share counts

  1. Open http://goo.gl/aai2p
  2. In cell B5 enter the RSS feed url for a blog (or you can use http://feeds.feedburner.com/MASHe) and hit enter

You should end up with something like:

RSS social counts

An aside: Spreadsheet Addiction by Patrick Burns (http://goo.gl/P6pQP) - highlights the dangers of using spreadsheets for analytics. Particular issues include the ambiguity of a cell being a value or a formula. For example, if I sort cells on the value in the Twitter count column all the data is lost because cells are sorted as values but actually contain formula which get broken.

How it works

In cell A11 is the formula =IF(ISBLANK(B5),,IMPORTFEED(B5,"items",FALSE)) If the feed url is not blank this fetches the RSS feed defined in B5. Results are returned in cells A11:E30. You may have noticed that column E is hidden this is because it contains the feed item description.

The social share counts are returned by a custom function written in Google Apps Script (https://script.google.com). Google Apps Script is similar to Excel Macros, written using a JavaScript syntax. If you open Tools > Script editor in your spreadsheet you can see some of the custom script powering the spreadsheet. This includes the getSharedCount formula used in cells F11:F30 which passes the post url to the SharedCount.com API and returns social share counts. The code used is:

function getSharedCount(sourceLink){
//var url = "https://mashe.hawksey.info/2012/02/oer-visualisation-project-fin-day-40-5/"
 var url = extractLink(sourceLink);
 var cache = CacheService.getPublicCache(); // using Cache service to prevent too many urlfetch
 var cached = cache.get("C"+url);
 if (cached != null) { // if value in cache return it
   //var test = cached.split(",")
   return cached.split(",");
 }
 try {
   var options =
   {
     "method" : "get",
     "contentType" : "application/json"
   };
   var response = UrlFetchApp.fetch("http://api.sharedcount.com/?url="+encodeURI(url), options);
   var data = Utilities.jsonParse(response.getContentText());
   var output = [];
   for (i in data){
     if (i == "Facebook"){
       output.push(data[i].total_count)
     } else {
       output.push(data[i]);
     }
   }
   cache.put("C"+url, output, 86400); // cache set for 1 day
   return output;
 } catch(e){
   Logger.log(e);
 }
}

For more examples of Google App Script see http://scoop.it/t/gas.

importXML

Syntax: =ImportXML(URL, query)

URL is the URL of the XML or HTML file.

Query is the XPath query to run on the data given at the URL. Each result from the XPath query is placed in its own row of the spreadsheet. For more information about XPath, please visithttp://zvon.org/xxl/XPathTutorial/Output/.

Example: =importXml("http://www.toysrus.com"; "//a/@href"). This returns all of the href attributes (the link URLs) in all the <a> tags on http://www.toysrus.com homepage.

Note: The limit on the number of ImportXML functions per spreadsheet is 50.

Exercise 3: Turn a page of RSS Feeds into an OPML file

  1. Create a new spreadsheet
  2. In cell A1 enter the text ‘Title’ and in cell B2 ‘Url’
  3. Now in cell A2 enter the formula=ImportXML("http://edfuture.mooc.ca/feeds.htm","//b/a")
  4. Cell B2=ImportXML("http://edfuture.mooc.ca/feeds.htm","//a[.='XML']/@href")
  5. File > Publish to the web and click ‘Start publishing’, copy the link in the bottom box then ‘Close’
  6. Visit http://opml-generator.appspot.com/ and paste your spreadsheet link in the box and copy the generated link into your broswer address bar
How it works

Using XPath we can identify parts of a XML (including HTML) page we want to extract. The screenshow below shows how parts of the page are identified. [I always struggle with XPath so use browser extensions to help (Scraper and XPath Helper)]. The results are pulled into the spreadsheet as live data so if the source page is updated the data in the spreadsheet will also be updated.

XPath parts

ImportRange

Syntax: =ImportRange(spreadsheet-key, range)

Spreadsheet-key is a STRING which is the key value from the spreadsheet URL.

Range is a STRING representing the range of cells you want to import, optionally including the sheet name (defaults to first sheet). You can also use a range name if you prefer. Given that the two arguments are STRINGs, you need to enclose them in quotes or refer to cells which have string values in them.

Example: =importrange("abcd123abcd123", "sheet1!A1:C10")

"abcd123abcd123" is the value in the "key=" attribute on the URL of the target spreadsheet and "sheet1!A1:C10" is the range which is desired to be imported.

Note: In order to use ImportRange, you need to have been added as a viewer or collaborator to the spreadsheet from which ImportRange is pulling the data. Otherwise, you'll get this error: "#REF! error: The requested spreadsheet key, sheet title, or cell range was not found."

[Here’s http://goo.gl/b8FXC is a copy of the completed spreadsheet used in exercises 4, 5 and 6]

Exercise 4: Importing data from Guardian Datastore

  1. Visit http://goo.gl/j6RBU and click Get the Data, then DATA: download the full spreadsheet
  2. Keep this window open and create a new spreadsheet.
  3. In cell A1 enter=ImportRange("0AonYZs4MzlZbdFdrRGthS3dLVzlBdWVrV2lIbzZKY0E","Times Higher Education rankings 2012!A1:D104") - notice how the key and range are entered

Lets now reshape the data so we can generate some graphs. Lets first calculate the change in rank between 2011 and 2012

  1. In cell E2 enter the formula =B2-A2
  2. Fill this formula for the rest of the rows (there are a couple of ways of doing this including copying cell E2, highlighting the other empty cells in Column E and pasting, or whilst E2 is active grab and drag the bottom right corner of the cell
  3. Now we want to get a list of the countries included in column D. To do this in cell G2 enter the formula =UNIQUE(D2:D102)
  4. Now we want to sum the rank difference per country by entering the following formula in cell H2:=SUMIF(D$2:D$102,G2,E$2:E$102)
  5. Copy this value down for the remaining rows
  6. Select the data range G2:H16 and Insert > Chart > Bar chart

University world ranking delta

Graph of change in top 100 world university ranking.

Is the process for producing this chart valid? Is it displaying a meaningful representation of the data? Is this chart a lie?

Important: Notice that the calculation for France has an error:

Broken value

This is because on row 93 the source data doesn’t have a number value. Because we’ve used ImportRange to get the data we can’t edit it as are changes get overwritten by the importRange formula in cell A1. In our scenario we can remove the calculated value in E93 or use a formula to handle the error. Other ways around this are to flatten the imported data by copying all of it and paste as values (other solutions exist which we cover later) 

ImportData

Syntax: =ImportData(URL)

URL is the URL of the CSV or TSV file. This imports a comma- or tab-separated file.

Note: The limit on the number of ImportData functions per spreadsheet is 50.

Exercise 5: Importing CSV data from Google Maps

  1. In the spreadsheet you created for exercise 4 Insert > New Sheet
  2. In cell A1 of the new sheet enter the formula =FILTER(Sheet1!A:E,Sheet1!D:D="United Kingdom") to filter the data on sheet1 where column D is the United Kingdom
  3. Now in column F1 enter the formula =ImportData("http://maps.google.com/maps/geo?output=csv&q="&C2) and press return
  4. Copy this cell down for the remaining rows

You should now have a table that looks a little like this:

ImportData from Google Maps

Import... and QUERY

Syntax: =QUERY(data, query, headers)

Data - An array of data. For example: A1:B6, data!B2:H6, ImportRange(spreadsheet_key, [sheet!]range),FILTER(sourceArray, arrayCondition_1, …)

Query - A query string for applying data operations. The query operates on column IDs directly from the input range and uses a subset of the SQL language. For example, "select E," "select A , B," "sum(B),C group by C," "select D where D < 'Nick' ." In certain instances, for example when using FILTER as a data source, column identifiers are Col1, Col2 etc. For more information on creating queries read see Google Visualization API Query Language

Headers (optional) - A number specifying the number of header rows in the input range. If omitted, or set to -1, the number of header rows is guessed from the input range. This parameter enables transformation of multi-header rows range input to be transformed to a single row header input which the QUERY supports.

Exercise 6: Import and QUERY

  1. In the spreadsheet you created for exercise 4 Insert > New Sheet
  2. Form your original sheet (Sheet1) copy the importRange formula in cell A1
  3. In your new sheet (Sheet3) paste the formula you just copied inside a QUERY formula shown below

    =QUERY(ImportRange("0AonYZs4MzlZbdFdrRGthS3dLVzlBdWVrV2lIbzZKY0E","Times Higher Education rankings 2012!A1:D104"), "SELECT Col1, Col2, Col3, Col4, Col2-Col1 LABEL Col2-Col1 'Difference'")

You should now have a table that looks like this:

QUERY for difference

How it works

The QUERY function imports the data and using the Google query language selects columns 1 to 4 and also adds a fifth by taking the difference between Col2 and Col1, this new column is labeled as Difference. Notice that on row 93 the French entry no longer has an error, but is blank.

We could continue exercise 4 and get a summary chart using UNIQUE and SUMIF. An alternative would be to use the QUERY formula again to do this for us by:

  1. In Sheet 3 enter the following formula in cell G1 =QUERY(A:E,"SELECT D, SUM(E) WHERE D <> '' GROUP BY D ORDER BY D")

This time we are setting the data source as all the data in columns A to E in Sheet3. Next we are creating a query that selects column D and a sum of column E where D is no blank and grouped by the value in column D (the country names).

A reminder that here’s a copy of the completed spreadsheet used in exercises 4, 5 and 6 http://goo.gl/b8FXC

Summary/Keypoints

Hopefully you’ve seen that Google Sheets (Spreadsheets) can be a useful tool for importing data from other sources and reshaping it to fit your needs. The last set of exercises are more advanced so don’t worry if you don’t fully understand what is happening, they are there as an indication of what is possible and hopefully inspire you to find out more.

Three points worth remembering when using import formula:

  • The data is a live link - if the data source changes or disappears  your imported data will also change or disappear (this can be both an advantage and disadvantage).
  • The ambiguity of a cell - because a spreadsheet cell can a value and a formula sorting values generated by formulas can lead to unforeseen problems.
  • There are other ways of getting data into Google Sheets - this guide has only looked at ‘Import...’ formula for getting pulling data in. There are other ways of populating sheets using Google Forms, Google Apps Script and 3rd party services like IFTTT (for an example of this last one see ‘IFTTT: IF I do THAT on {insert social network/rss feed/other} THEN add row to Google Spreadsheet - http://goo.gl/nB0vD)

A reminder that the Google Docs version of the guide is here and contains more examples. If you get stuck leave a comment.

5 Comments

Repositories are living archives. In terms of the support it must provide for stored files, it must take into account two important functions of the files it holds:

  1. Access: The files are held so that users can access them. This means that they must be stored in formats that can be used by today's intended audience
  2. Preservation: The files are held so that users in 5, 10, 50, or more years can still access them. This means that they must be stored in formats that can be used by future audiences, or in formats that can easily be migrated

These two considerations are not always complementary. A file format that is good for access today may not be a format that is easy to migrate, but a format that is easy to migrate may not be easy to read.

The text above is taken from the JISC infoNet Digital Repositories infoKit. An added complication when considering the deposit of OER is if you are not using a ‘No Derivatives’ licence how can you support remix/editing.  Here’s a scenario taken from the WikiEducator:

A teacher wants to make a collage. She imports several PNG photos into Photoshop and creates the collage. She saves the file as a PSD and exports a copy as a PNG to post on the web. While others can edit the PNG, it would be a lot easier to edit the PSD file. However, in order to use PSD files, the person has to have a copy of Photoshop.

Already it’s starting to get more tricky. PSD is a proprietary file format developed and owned by Abobe and used in Photoshop. You can actually open and edit PSD files in open source tools like GIMP (I’m not sure how legally Gimp can do this – waiting for a response from OSSWatch Update: I've had a response. Upshot 'it can be awkward on all levels'. I'll point to a related blog post when it's published. Post by Scott Wilson at OSS Watch on using proprietary file formats in open source projects). Similarly you can use open source alternatives to Microsoft Office like LibreOffice to open and edit DOC/XLS/PPT etc but in this case Microsoft's proprietary file formats under their Open Specification Promise, which if you read this page on Wikipedia itself has a number of issues and limitations.

The next issue is, as highlighted by Chris Rusbridge in his Open letter to Microsoft on specs for obsolete file formats, the OSP doesn’t cover older file formats. So if you were an earlier adopter publishing OER in editable formats there is a danger that the format you used won’t be suitable down the line.

I’m mindful of the Digital Repository infoKit’s last point of guidance

Be practical: Being overly-strict about file formats may mean collecting no files leading to an empty repository! A sensible approach must be used that weighs up the cost and benefits of different file formats and the effort required to convert between them.

Should OER file formats be tomorrow’s problem?

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In CFHE12 Week 2 Analysis: Data! Show me your data and I’ll show you mine I highlighted some of the issues with collecting RSS feeds for participant blogs. The main issues are:

  • participant knowing their blog’s RSS feed
  • providing a feed filtered for a specific tag
  • data entry (missing http://, whitespace etc)
  • automatic registration of a feed with another system

This last one is very dependant on the system you are using for aggregating participant contributions. gRSShopper (developed by Stephen Downes) is an integrated solution whilst, as far as I’m aware, the FeedWordPress plugin used in ds106 and others requires some manual data entry, but bulk import is possible.

Before outlining my vision of a cMOOC registration system there is a basic decision about what you want to aggregate feeds on. Given the issue with getting Tag Feeds for a variety of Blogging Platforms I’m swaying towards asking participants to use a course identifier in each post title rather than as a tag/category/label. This make feed detection easier and whilst not familiar with the backend of gRSShopper think it would be a trivial bit of extra code and I’m already aware of extra plugins for FeedWordPress to filter posts. I will however provide outlines for both:

Registration flow with course posts by tag/category/label

Part of this is a modification of the existing registration process used in ds106.

  1. Optional: Ask user to generate a post in their blog with course tag (you could provide some set text advertising course)
  2. Enter details:
    1. name, social media accounts etc
    2. blog homepage
    3. blogging platform
  3. From blog url/platform display guessed (auto-detected) feed (if you’re using optional step this can be validated with auto-detection). If not a recognised blogging platform or tag/category/label feed not available instruct participant to include course tag in all post titles.
  4. Submit details

Registration flow with course posts by title

  1. Optional: Ask user to generate a post in their blog with course tag (you could provide some set text advertising course)
  2. Enter details:
    1. name, social media accounts etc
    2. blog homepage
  3. From blog url display guessed (auto-detected) feed (if you’re using optional step this can be validated with auto-detection).
  4. Submit details

Another aspect not mentioned here is letting the user edit their feed.

That’s my suggestion anyway. Your thoughts very welcome! BTW Yishay Mor at the OU has started thinking about the wider functionality of a cMOOC aggregation system.

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Focusing on some of the data behind Current/Future State of Higher Education (CFHE12) has been very stimulating and has got me thinking about the generation and flow of data on many levels. Having recently produced some data visualisations of ds106 for OpenEd12 it was reassuring that one of the first questions was “is the #ds106 data openly licensed?”. Reassuring because it is good to see that people are thinking beyond open content and open learning experiences and thinking about open data as well. So what data is available around CFHE12? In this post I look at data feeds available from CFHE12, see what we can get and suggest some alternative ways of getting the data and pulling it in to other services for analysis. Finally I highlight the issues with collecting participant feeds filtered by tag/category/label.

edfuture homepage sidebar

Working down some of the menu options on the CFHE12 home page lets see what we’ve got and how easy it is to digest.

Newsletter Archives

This page contains a link to each ‘Daily Newsletter’ sent out by the gRSShopper (Stephen Downes’ RSS/content collection, remix and distribution system). I’m not familiar with how/if the Daily data can be exported by an officially API, but with tools like Google Spreadsheet, Yahoo Pipes and others it's possible to extract a link to each edition of the Daily using some basic screen scraping techniques like using XPath. So in this sheet in cell A2 I can get a list of archive pages using =ImportXML("http://edfuture.mooc.ca/cgi-bin/archive.cgi?page=newsletter.htm","//a/@href"). Using the ImportXML using the resulting list of pages it’s possible to get a comma separated list of all the posts in each Daily (column B).

The formula in column B includes a QUERY statement and is perhaps worthy of a blog post in it's own right. Here it is: =IF(ISBLANK(A2),"",JOIN(",",QUERY(ImportXML(A2,"//a[.='Link']/@href"),"Select * WHERE not(Col1 contains('twitter.com'))"))). In brief the pseudocode is: if the cell is blank return nothing otherwise comma join the array of results from importXML for links which use he text ‘Link’ where it doesn’t contain a link to twitter.com. Note: there is a limitation of 50 importXML formula per spreadsheet so I’ll either have to flatten the data or switch to Yahoo Pipes

The resulting data is of limited use but it’s useful to see how many posts have been published via gRSShopper and by who:

CFHE12 posts per day CFHE12 posts per domain

It’s at this point tat we start to get an sign that the data isn’t entirely clean. For example, in cell A153 I’ve queried the Daily Newsletter posts from this blog and I get four results shown below:

You can see there is a double post and actually, at time of writing I've only made two posts tagged with cfhe12. Moving on, but coming back to this point later, lets now look at the feed options.

Feeds

The course has feed options for: Announcements RSS; Blog Posts RSS; and OPML List of Feeds. I didn’t have much luck with any of these. The RSS feeds have data but aren’t valid RSS and the OPML (a file format which can be used for bundling lots of blog feeds together) only had 16 items and was also not valid (I should really have a peak at the source for gRSShopper and make suggestions for fixes, but time and lack of Perl knowledge has prevented that so far). I did attempt some custom Apps Script to capture the Blog Posts RSS to this sheet, but I’m not convinced it’s working properly, in part because the source feed is not valid. There are other feeds not listed on the home page I might dig into like the Diigo CFHE12 Group which I’m collecting data from in a Google Spreadsheet using this IFTTT recipe.

Generating an OPML and Blog Post RSS from ‘View List of Blogs’ data

OPML

All is not lost. gRSShopper also generates a page of blogs it’s aggregating. With a bit more XPath magic (=ImportXML("http://edfuture.mooc.ca/feeds.htm","//a[.='XML']/@href")) I can scrape the XML links for each registered blog into this sheet. Using the Spreadsheet -> OPML Generator I get this OPML file of CFHE12 blogs (because the spreadsheet and OPML generator sit in the cloud this link will automatically update as blogs are added or removed from the Participant Feeds page). For more details on this recipe see Generating an OPML RSS bundle from a page of links using Google Spreadsheets.

Blog posts RSS

Earlier I highlighted a possible issue with posts being included in the Daily Newsletter. This is because it can be very tricky to get an RSS feed for a particular tag/category/label from someone's blog. You only need to look at Jim Groom’s post on Tag Feeds for a variety of Blogging Platforms to get an idea of the variation between platforms. It’s worth underlying the issue here, each blogging platform has a different way of getting a filtered RSS feed for a specific tag/category/label. Also, in certain cases it’s not possible to get a filtered RSS feed. When a student registers a feed for an online course it can be difficult for them to identify their own blogs RSS feed, let alone a filtered feed.

CFHE12 PipeworkAs an experiment reusing the Participant Feeds page as a data source I’ve come up with this Yahoo Pipe which fetches all the feeds and tries to filter the results. It’s slightly crude in the way it’s filtering posts by looking for a course tag: as a category/tag (if exposed in the source feed), or in the post title or in the post content. Using this pipe it’s currently returning 48 items (although I it says 36 when outputting in a different file format) compared to the 77 from the Daily Newsletter Archives. The nice thing about pipes is I can get the data in different formats (e.g. RSS, JSON, CSV) so more mashing up is possible.

Before you go off thinking Yahoo Pipes is the answer for all your open course RSS aggregation there are some big questions over reliability and how this solution would scale. It’s also interesting to note all the error messages because of bad source feeds:

This Pipe ran successfully but encountered some problems:

warning Error fetching http://edfuture.mooc.ca/feed://bigedanalytics.blogspot.com/feeds/posts/default?alt=rss. Response: Not Found (404)

warning Error fetching http://florianmeyer.blogspot.com. Response: OK (200). Error: Invalid XML document. Root cause: org.xml.sax.SAXParseException; lineNumber: 1483; columnNumber: 5; The element type "meta" must be terminated by the matching end-tag " ".

warning Error fetching http://edfuture.mooc.ca/cain.blogspot.com. Response: Not Found (404)

warning Error fetching http://larrylugo.blogspot.com/feeds/comments/default?alt=rss/-/CFHE12. Response: Bad Request (400)

warning Error fetching http://edfuture.mooc.ca/taiarnold.wordpress.com/feed. Response: Not Found (404)

warning Error fetching http://futuristiceducation.wordpress.com/wp-admin/index.php?page=my-blogs. Response: OK (200). Error: Invalid XML document. Root cause: org.xml.sax.SAXParseException; lineNumber: 5; columnNumber: 37; The entity "rsaquo" was referenced, but not declared.

warning Error fetching http://edtech.vccs.edu/feed/: Results may be truncated because the run exceeded the allowed max response timeout of 30000ms.

Rather than trying to work with messy data one strategy would be to start with a better data source. I have a couple of thoughts on this I’ve shared in Sketch of a cMOOC registration system.

Summary

So what have I shown? Data is messy. Data use often leads to data validation. Making any data available means someone else might be able to do something useful with it. In the context cMOOCs getting a filtered feed of content isn’t easy.

Something I haven’t touched upon is how the data is licensed. There are lots of issues with embedding license information in data files. For example, I’m sure technically the OPML file I generated should be licensed CC-BY-NC-SA CFHE12 because this is the license of the source data? I’m going to skip over this point but welcome your comments (you might also want to check the Open Data Licensing Animation from the OERIPR Support Project).

[I’ve also quietly ignored getting data from the course discussion forums and Twitter archive (the later is here)]

PS Looking forward to next weeks CFHE12 topic Big data and Analytics ;)

The JISC OER Rapid Innovation programme is coming to a close and as the 15 projects do their final tiding up it’s time to start thinking about the programme as a whole, emerging trends, lesson learned and help projects disseminate their outputs. One of the discussions we’ve started with Amber Thomas, the programme manager, is how best to go about this. Part of our support role at CETIS has been to record some of the technical and standards decisions taken by the projects. Phil Barker and I ended up having technical conversations with 13 of the 15 projects which are recorded in the CETIS PROD Database in the Rapid Innovation strand. One idea was see if there were any technology or standards themes we could use to illustrate what has been done in these areas. Here are a couple of ways to look at this data.

To start with PROD has some experimental tools to visualise the data. By selecting the Rapid Innovation strand as selecting ‘Stuff’ we get this tag cloud. We can see JSON, HTML5 and RSS are all very prominent. Unfortunately some of the context is lost as we don’t know without digging deeper which projects used JSON etc. 

PROD Wordcloud

To get more context I thought it would be useful to put the data in a network graph (click to enlarge).

NodeXL Graph

NodeXL Graph - top selectedWe can now see which projects (blue dots) used which tech/standards (brown dots) and again JSON, HTML5 and RSS are prominent. Selecting these (image right) we can see it covers most of the projects (no. 10), so these might be some technology themes we could talk about. But what about the remain projects?

As a happy accident I put the list of technologies/standards into Voyant Tools (similar to Wordle but way more powerful – I need to write a post about it) and got the graph below:

Voyant Tools Wordcloud

Because the wordcloud is generated from words rather than phrases the frequency is different: : api (16), rss (6), youtube (6), html5 (5), json (5). So maybe there is also a story about APIs and YouTube.