1. Feature Engineering Studio Special Session January 26, 2015

2. Assignment One Problem Proposal – Due next Monday Be ready to talk for 3 minutes on: – A data set Give where it came from and how big it is You need to already have this data set, or be able to acquire it in the next two weeks – A prediction (or other statistical) model you will build in this data set – What variable will you predict? – What kind of variables will you use to predict it? – Why is this worth doing?

3. Example (Pardos et al., 2014) Data set – ASSISTments system, formative assessment and learning software for math used by 60k students a year (Razzaq et al., 2007) – 810,000 data points from 229 students studied – Student actions in the software have been overlaid with synchronized field codes of student affect (boredom, frustration, etc.) 3075 field codes Each field code connects to 20 seconds of log file actions

4. Example (Pardos et al., 2014) We will predict whether a student is bored at a specific time – So that we can replicate the human judgments without needing a field observer We will predict this from what was going on in the log files at the time the field observation was made – We know every student action’s correctness, timing, relevant skill, and probability they knew the skill

5. Example (Pardos et al., 2014) This is worth doing because boredom is known to predict student learning (Craig et al., 2004; Rodrigo et al., 2009; Pekrun et al., 2010) And building a detector will help us study boredom more thoroughly As well as enabling us to intervene on boredom in real time

6. Important Considerations Is the problem genuinely important? (usable or publishable) Is there a good measure of ground truth? (the variable you want to predict) Do we have rich enough data to distill meaningful features? Is there enough data to be able to take advantage of data mining?

7. What concerns you? Data set What variable will you predict? What kind of variables will you use to predict it?

8. Data Set Who here has a data set, but has concerns about it? Who here doesn’t have a data set?

9. Data Set Who here has a data set, but has concerns about it? Who here doesn’t have a data set?

10. Data Set Who here has a data set, but has concerns about it? Who here doesn’t have a data set?

11. Online Learning ASSISTments (Neil Heffernan) Genetics Tutor (Albert Corbett) Impulse (Elizabeth Rowe) Inq-ITS (Janice Gobert) Physics Playground/Newton’s Playground (Val Shute) Refraction (Taylor Martin) Mathemantics (Herb Ginsburg) Vialogues (Gary Natriello, Hui Soo Chae) Project LISTEN (Jack Mostow) TC3-Sim (Robert Sottilare)

12. Online Learning Big Data and Education (me) Data, Analytics, and Learning (me) SQL-Tutor (Tanja Mitrovic) Project ARIES (Art Graesser) ALEKS (Xiangen Hu) Ecolab (Genaro Rebolledo-Mendez) Fractions Tutor (Vincent Aleven) Help Tutor (Ido Roll) InventionLab (Ido Roll) BlueJ (Matt Jadud) Aplusix (Jean-Francois Nicaud) Second Life (Bruce Homer)

13. Online Learning International use of Scatterplot Tutor (me) Zombie Division (Jake Habgood) Virtual Performance Assessments (Jody Clarke-Midura) EcoMUVE (Shari Metcalfe) Reasoning Mind (George Khachatryan) Chemistry Virtual Laboratory (David Yaron) Tuunu data (Fewof Mopfsan)

14. Potential Data Sources Grade data (Alex Bowers) Course-taking and dropout data (Cristobal Romero) BROMP data (me) Center for the Science of Learning Data (Krishna Srinivasan)

15. Procedure Pick a data set If I have it on hand, we talk right away If not, I broker a conversation

16. What variable will you predict? Something already directly labeled – Student was bored at 2:10:13 pm Something indirectly labeled – Student had 15% overall learning gain Something you can label with text replays – Student gamed the system while using learning system

17. Let’s discuss specific data sets you guys are interested in

18. What kind of variables will you use as predictors? You don’t need to have specific ideas at this stage The main question is, do you have the right kind of data to be able to do this at all?

19. Questions? Concerns?