How to Access the VPOL Online Learning Data

The Vice Provost Office for Online Learning facilitates the teaching of online classes. The instruction delivery platforms are instrumented to collect a variety of data around students' interaction with the study material. Examples are students manipulating video players as they view portions of a class, solution submissions to problem sets, uses of the online forum available for some classes, peer grading activities, and some demographic data. The type of data that is collected depends on the delivery platform used for a particular class. VPOL makes some of this data available for research on learning processes, and for explorations into improving instruction. All data that VPOL shares is available on machine datastage.stanford.edu. You have multiple methods of using this data: We provide self service answers to frequent questions, customizable to your context. Data viewing programs for MAC and Windows allow you to browse through our content. Going further, most analysis tools, like R, SPSS, and Excel can connect directly to our databases. Similarly, visualization tools are available that allow you to create static, or interactive charts of data directly from our sources. We explain these access methods here, or point to relevant instructions on the Web. Additionally, we run a Tableau Server to which you can publish visualizations you create with the Tableau Desktop visualization tool. Visitors can manipulate these visualizations to the degree you provide for. A comment area is available for each view to discuss the information. If desired, access to the views can be limited. Again, more in this document. We now run through the available access paths to the data, ordered in increasing power (and sometimes associated complexity).

Access Bureaucracy

Even though we do not keep highly sensitive information on this server, we do need to protect the data to some degree from malicious intrusion. The precise procedure is being worked out. But the result will be a user name, and either one, or two passwords for you. One password if you are only to access data that does not reveal the identity of the student that the data talks about. If you are permitted to access more sensitive information, then you will receive a second password. Appendix B explains the technical background of these two cases. In the following we assume that you have the necessary items in hand.

If We Anticipated your Question

In the simplest case your question has been asked and answered before, albeit for a different context. Such a context might be a particular class for which the question was originally asked, or a particular problem set. For cases like this our AnswerServer might help. This server differs from a typical FAQ list in that you are able to adjust the question to your context. We will be growing the coverage of that page as we go along. If you have an idea for a useful one-liner question, please send email to .

Analysis Tools

If you need to perform analysis over the data, Excel, R, and SPSS are among the tools that can access our data directly. After some setup, the data appears in your table. After that setup, you are in your home territory of the tool you are used to. This section explains how to accomplish this data import.

The All-Important Database Connector

For all these connections, you will need a 'database connector,' sometimes called an 'ODBC driver.' These drivers are set up just once in your machine---Mac or Windows. To grab one of them, head over to the MySQL connector download page, and download the version for your operating system (Windows or Mac). Note that even though your computer might be of the '64 bit' type (whatever that means), you are likely to need the 32-bit version of the database connector! This choice is definitely the right one for Macs; for Windows it is the most likely choice (see why). You start the downloaded program as you usually do on your computer. Here is information you will need for the mostly-open data: Server: datastage.stanford.edu
User: [your username]
Password: [your password]
Database: [see appendix A for the list of available databases]
Once you installed the connector, you will find it popping up in all the right places among the menu options that are referred to in the following sections.

Connecting Excel

See this reference for how to fill your spreadsheet with a particular dataset. The result will look like this:

For the phase in the above referenced instructions that looks like this:

... the information you enter will depend on whether you will be accessing more restricted information, or the mostly open data. Open data: Data source name:
Description: [a reminder for yourself]
Server: datastage.stanford.edu
Port: 3306
User: [your user name]
Password: [your password]
Database: [the name of the database of interest. See Appendix A].

Connecting SPSS

Here is how you connect an SPSS sheet to one of the VPOL data sources. Remember first to install your database connector. In SPSS:
File-->Open Database-->New Query
Assuming you have set your connector to point to database Edx, and that you named it MonoEdx (for whatever fanciful reason), you will then see this:

Choose whichever real name you gave your connector, and click next. You now get to choose the fields that you want to extract from the database:

SPSS has figured out which tables are available in this database, and you can start moving fields over to the right. Once you again clicked on next, you will have an opportunity to introduce conditions on which rows you would like to import:

I am still a bit puzzled by the interface of this screen. If you get stuck, we can try to figure it out. If all goes well, you will now see something like this:

Browsing and Querying the Data

A number of applications are available for browsing data stored in MySQL databases, the type in which VPOL data resides. Some applications are discussedon the Web. Froq for Mac, and RazorSQL for Mac, Linux, and Windows are freeware tools used in the VPOL group.

RazorSQL

The application RazorSQL, which works under Mac, Windows, and Linux, can create either a tunneled or a direct connection to MySQL. For details on the difference, please see Appendix B. You begin the connection process by choosing: File-->New Tab (no connection) In the subsequent screen, you click on Click to connect to a database:

...resulting in:

This screen allows you to select among different connections you have already defined. Not having defined any, you click Add Profile, and pick MySQL from the resulting list of database types that the application knows about.

Here is where you fill in the particulars. Leave all pre-filled fields alone. For Profile name you pick any name that reminds you later of what this connection is about. Login and Password are the information you received with your access material. For Host or IP Address you fill in datastage.stanford.edu. The Port is left blank. In the Database field you enter one of the databases of Appendix A. Click Connect, and you (hopefully) see a screen other than one informing you of failure. At this point, you can continue via the RazorSQL manual and help pages.

Appendix A: List of Available Databases

Course-to-Go Hosted Databases

c2g-db_winter_2013_forum
c2g-networking_fall2012_forum
c2g-solar_fall2012_forum

Coursera Hosted Databases

coursera-20130421-2305-12feb-gametheory_anonymized_forum
coursera-20130421-2305-12feb-gametheory_anonymized_general
coursera-20130421-2305-12feb-gametheory_hash_mapping
coursera-20130421-2305-12feb-gametheory_unanonymizable
coursera-20130421-2305-12jan-algo1_anonymized_forum
coursera-20130421-2305-12jan-algo1_anonymized_general
coursera-20130421-2305-12jan-algo1_hash_mapping
coursera-20130421-2305-12jan-crypto_anonymized_forum
coursera-20130421-2305-12jan-crypto_anonymized_general
coursera-20130421-2305-12jan-crypto_hash_mapping
coursera-20130421-2305-12jan-pgm_anonymized_forum
coursera-20130421-2305-12jan-pgm_anonymized_general
coursera-20130421-2305-12jan-pgm_hash_mapping
coursera-20130421-2305-12jan-pgm_unanonymizable
coursera-20130422-0245-12jan-ml_anonymized_forum
coursera-20130422-0245-12jan-ml_anonymized_general
coursera-20130422-0245-12jan-ml_hash_mapping
coursera-20130422-0245-12jan-ml_unanonymizable
coursera-20130422-0337-12feb-cs101_anonymized_forum
coursera-20130422-0337-12feb-cs101_anonymized_general
coursera-20130422-0337-12feb-cs101_hash_mapping
coursera-20130422-0459-12mar-automata_anonymized_forum
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coursera-20130422-0459-12mar-automata_hash_mapping
coursera-20130422-0459-12mar-automata_unanonymizable
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coursera-20130422-0522-12mar-intrologic_anonymized_general
coursera-20130422-0522-12mar-intrologic_hash_mapping
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coursera-20130422-0530-12apr-compilers_hash_mapping
coursera-20130422-0530-12apr-compilers_unanonymizable
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coursera-20130422-0632-12jan-hci_anonymized_general
coursera-20130422-0632-12jan-hci_hash_mapping
coursera-20130422-0632-12jan-hci_unanonymizable
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coursera-20130422-0720-12-002-crypto_hash_mapping
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coursera-20130422-0809-12-002-algo_hash_mapping
coursera-20130422-0919-12-002-ml_anonymized_forum
coursera-20130422-0919-12-002-ml_anonymized_general
coursera-20130422-0919-12-002-ml_hash_mapping
coursera-20130422-0919-12-002-ml_unanonymizable
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coursera-20130422-0936-002-crypto_hash_mapping
coursera-20130422-0936-002-crypto_unanonymizable
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coursera-20130422-1039-005-crypto_hash_mapping
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coursera-20130422-2135-12-selfservice-cs101_hash_mapping
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coursera-20130430-2127-12-001-worldmusic_hash_mapping
coursera-20130430-2127-12-001-worldmusic_unanonymizable
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coursera-20130430-2128-12-001-design_anonymized_general
coursera-20130430-2128-12-001-design_hash_mapping
coursera-20130430-2128-12-001-design_unanonymizable
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coursera-20130430-2129-12-001-gamification_hash_mapping
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coursera-20130430-2129-12-001-genomescience_anonymized_general
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coursera-20130701-1010-childnutrition-001_hash_mapping
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coursera-20130701-1010-crypto-006_anonymized_forum
coursera-20130701-1010-crypto-006_anonymized_general
coursera-20130701-1010-crypto-006_hash_mapping
coursera-20130701-1010-crypto-006_unanonymizable
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coursera-20130701-1010-democraticdev-001_hash_mapping
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coursera-20130701-1010-einstein-001_hash_mapping
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coursera-20130701-1010-hci-003_unanonymizable
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coursera-20130701-1103-12-001-maththink_anonymized_general
coursera-20130701-1103-12-001-maththink_hash_mapping
coursera-20130701-1103-12-001-maththink_unanonymizable
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coursera-20130701-1114-pgm-003_hash_mapping
coursera-20130701-1114-pgm-003_unanonymizable
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coursera-20130701-1234-startup-001_hash_mapping
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coursera-20130701-1426-12-002-compilers_hash_mapping
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coursera-20130701-1434-12-002-intrologic_anonymized_general
coursera-20130701-1434-12-002-intrologic_hash_mapping
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coursera-20130701-1518-12-002-hci_anonymized_general
coursera-20130701-1518-12-002-hci_hash_mapping
coursera-20130701-1518-12-002-hci_unanonymizable
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coursera-20130701-1546-12-003-crypto_anonymized_general
coursera-20130701-1546-12-003-crypto_hash_mapping
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coursera-demog-algo
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coursera-demog-antimicrobial-002
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coursera-demog-automata
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coursera-demog-childnutrition-001
coursera-demog-clinicaltrials-001
coursera-demog-compilers
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coursera-demog-compilers-selfservice
coursera-demog-computervision-001
coursera-demog-crypto
coursera-demog-crypto2-2013-001
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coursera-demog-crypto-005
coursera-demog-crypto-006
coursera-demog-crypto-007
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coursera-demog-crypto-2012-003
coursera-demog-cs101
coursera-demog-cs101-002
coursera-demog-cs101-selfservice
coursera-demog-db
coursera-demog-democraticdev-001
coursera-demog-democraticdev-002
coursera-demog-dgplay-001
coursera-demog-emscardiology-001
coursera-demog-environphys-001
coursera-demog-environphys-002
coursera-demog-gametheory
coursera-demog-gametheory-003
coursera-demog-gametheory-2012-002
coursera-demog-ggp-001
coursera-demog-hci
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coursera-demog-intrologic
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coursera-demog-maththink-002
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coursera-demog-ml
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coursera-demog-nlp
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coursera-demog-organalysis-002
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coursera-demog-palliativemed-001
coursera-demog-pgm
coursera-demog-pgm-003
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coursera-demog-sciwrite-2012-001
coursera-demog-security
coursera-demog-startup-001
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coursera-demog-vidmooc-001
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coursera_2012_hci_anonymized_forum
coursera_2012_hci_anonymized_general
coursera_2012_hci_hash_mapping
coursera_2012_hci_pii
coursera_2012_hci_unanonymizable
coursera_2012_logic_anonymized_forum
coursera_2012_logic_anonymized_general
coursera_2012_logic_hash_mapping
coursera_2012_logic_pii
coursera_2012_maththink_anonymized_forum
coursera_2012_maththink_anonymized_general
coursera_2012_maththink_hash_mapping
coursera_2012_maththink_pii
coursera_2012_maththink_unanonymizable
coursera_2012_ml_anonymized_forum
coursera_2012_ml_anonymized_general
coursera_2012_ml_hash_mapping
coursera_2012_ml_pii
coursera_2012_ml_unanonymizable
coursera_2012_pgm_anonymized_forum
coursera_2012_pgm_anonymized_general
coursera_2012_pgm_hash_mapping
coursera_2012_pgm_pii
coursera_2012_pgm_unanonymizable
coursera_2012_sciwri_anonymized_forum
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coursera_2012_sciwri_hash_mapping
coursera_2012_sciwri_pii
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coursera_eventing_001-sustainableag
coursera_eventing_002-crypto
coursera_eventing_005-crypto
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coursera_eventing_12-001-maththink
coursera_eventing_12-001-organalysis
coursera_eventing_12-001-sciwrite
coursera_eventing_12-002-algo
coursera_eventing_12-002-compilers
coursera_eventing_12-002-crypto
coursera_eventing_12-002-gametheory
coursera_eventing_12-002-hci
coursera_eventing_12-002-intrologic
coursera_eventing_12-002-ml
coursera_eventing_12-002-pgm
coursera_eventing_12-003-algo
coursera_eventing_12-003-crypto
coursera_eventing_Machine Learning (ml-003)_clickstream_export
coursera_eventing_childnutrition-001
coursera_eventing_compilers-003
coursera_eventing_crypto-006
coursera_eventing_democraticdev-001
coursera_eventing_einstein-001
coursera_eventing_intrologic-003
coursera_eventing_maththink-002
coursera_eventing_pgm-003
coursera_eventing_startup-001

OpenEdx Hosted

Edx
ExPrivate

NovoEd Hosted Databases

novoed-crs_23_ACrashCourseonCreativity
novoed-crs_24_TechnologyEntrepreneurshipPart1
novoed-crs_25_Finance
novoed-crs_29_OrganizationalBehavior
novoed-crs_30_HippocratesChallenge
novoed-crs_46_DesignThinkingActionLab
novoed-crs_ACrashCourseonCreativity
novoed-crs_AdvancedEntrepreneurship
novoed-crs_BusinessManagementforEEandCSStudents
novoed-crs_DesigningaNewLearningEnvironment
novoed-crs_EntrepreneurshipinEnvironmentalEngineering
novoed-crs_Finance
novoed-crs_GraphPartitioningandExpanders
novoed-crs_MobileHealthWithoutBorders
novoed-crs_OrganizationalBehavior
novoed-crs_StartupBoards
novoed-crs_SustainableProductDevelopment
novoed-crs_Technology_Entrepreneurship
novoed-crs_email_23_ACrashCourseonCreativity
novoed-crs_email_24_TechnologyEntrepreneurshipPart1
novoed-crs_email_25_Finance
novoed-crs_email_29_OrganizationalBehavior
novoed-crs_email_30_HippocratesChallenge
novoed-crs_email_46_DesignThinkingActionLab
novoed-crs_email_ACrashCourseonCreativity
novoed-crs_email_AdvancedEntrepreneurship
novoed-crs_email_BusinessManagementforEEandCSStudents
novoed-crs_email_DesigningaNewLearningEnvironment
novoed-crs_email_EntrepreneurshipinEnvironmentalEngineering
novoed-crs_email_Finance
novoed-crs_email_GraphPartitioningandExpanders
novoed-crs_email_MobileHealthWithoutBorders
novoed-crs_email_OrganizationalBehavior
novoed-crs_email_StartupBoards
novoed-crs_email_SustainableProductDevelopment
novoed-crs_email_Technology_Entrepreneurship
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Appendix B: How to Set up an SSH Tunnel

An ssh tunnel is a connection between two computers that encrypts the information it carries. Sometimes a server machine is running a service that client applications on another machine can connect to. For example, Excel connecting directly to a MySQL database is such an example. Similarly, Froq and RazorSQL, the graphical querying tools mentioned earlier, might connect directly from your computer to the MySQL database that runs on the datastage.stanford.edu machine. This style of direct connection has your password go over the Internet unencrypted when you log into the database. This vulnerability to wire tapping is a disadvantage of direct connections. On the other hand, setting up a direct connection is relatively easy, and client side applications, the programs you use on your computer to connect to the database, know how to construct a direct connection. To protect data more securely, a tunnel may first be constructed between your computer and the database server computer. Your commands to the database then first travel encrypted to the server computer through the tunnel. Once they arrive, the commands are handled as if they had been issued on the server computer itself in the first place.

To set up a tunnel between your computer and datastage.stanford.edu, you type the command at the bottom of the illustration into a terminal window before you ask your data access application to try and get to the data. The command says: set up a secure connection (ssh) to machine datastage.stanford.edu; to applications on my own computer the connection is to look like a database to which I write via my out-port 5556. The connection should function like a tunnel (-L). On the database on the other end, the commands I send should look like they were issued on its machine (localhost from the database's point of view), and were issued directly into the in-port where the database normally listens to (3306), as it does in the direct connection case. The -f -N just make it so that you can keep using the terminal window you used to type in the command. Important point: in order to get into that in-port 22 in the figure, you will need a password; the same password you would need to log into that machine. In order to get through the in-port 3306 into the database within the server, you will need a second password, the one that authenticates you to the database. You would need that one in the direct connection as well, unless the database maintainer has not set a password for you.