Advanced Methods and Analysis for the Learning and Social Sciences

Advanced Methods and Analysis for the Learning and Social Sciences PSY505Spring term, 2012 March 26, 2012

Today’s Class • Sequential Pattern Mining

Related to • Association Rule Mining • MOTIF Extraction

Similarities • MOTIF Extraction can be seen as a type of sequential pattern mining • Though MOTIFs can also be non-sequential, like in the Shananbrook et al paper • Some SPM algorithms find simpler patterns than MOTIF, other algorithms find more complex patterns than MOTIF

Similarities • Some algorithms for Sequential Pattern Mining similar to Association Rule Mining

Association Rule Mining • Try to automatically find if-then rules within the data set

Sequential Pattern Mining • Try to automatically find temporal patterns within the data set

ARM Example • If person X buys diapers, • Person X buys beer • Purchases occur at the same time

SPM Example • If person X buys novel Foundation now, • Person X buys novel Second Foundation in a later transaction • Conclusion: recommend Second Foundation to people who have previously purchased Foundation

SPM Example • Many customers rent Star Wars, then the Empire Strikes Back, then Return of the Jedi • Doesn’t matter if they rent other stuff in-between

SPM Example • Many customers buy flowers, and then buy diapers AND diaper cream several months later

SPM Example • Many learners become confused, then game the system, then become frustrated, then complete gaming the system, then become re-engaged

Different Constraints than ARM • If-then elements do not need to occur in the same data point • Instead • If-then elements should have same user (or other organizing variable) • If elements can be within a certain time window of each other • Then element time should be within a certain window after if times

Sequential Pattern Mining • Find all subsequences in data with high support • Support calculated as number of sequences that contain subsequence, divided by total number of sequences

Sequential Pattern Mining • What are some subsequences with high support? (What is their support?) • Chuck: a, abc, ac, de, cef • Darlene: af, ab, acd, dabc, ef • Egoberto: aef, ab, aceh, d, ae • Francine: a, bc, acf, d, abeg

Questions? Comments?

Algorithms for SPM

GSP (Generalized Sequential Pattern) • Classic Algorithm • (Srikant & Agrawal, 1996)

Data pre-processing • Data transformed from individual actions to sequences by user • E.g. • Bob: {GAMING and BORED, OFF-TASK and BORED, ON-TASK and BORED, GAMING and BORED, GAMING and FRUSTRATED, ON-TASK and BORED}

Data pre-processing • In some cases, time also included • E.g. • Bob: {GAMING and BORED 5:05:20, OFF-TASK and BORED 5:05:40, ON-TASK and BORED 5:06:00, GAMING and BORED 5:06:20, GAMING and FRUSTRATED 5:06:40, ON-TASK and BORED 5:07:00}

Algorithm • Take the whole set of sequences of length 1 • May include “ANDed” combinations at same time • Find which sequences of length 1 have support over pre-chosen threshold • Compose potential sequences out of pairs of sequences of length 1 with acceptable support • Find which sequences of length 2 have support over pre-chosen threshold • Compose potential sequences out of triplets of sequences of length 1 and 2 with acceptable support • Continue until no new sequences found

Let’s execute GPS algorithm • With min support = 50%

Let’s execute GPS algorithm • With min support = 50% • Chuck: a, abc, ac, de, cef • Darlene: af, ab, acd, dabc, ef • Egoberto: aef, ab, aceh, d, ae • Francine: a, bc, acf, d, abeg

Other algorithms • Free-Span • Prefix-Span • Select sub-sets of data to search within • Faster, but same basic idea as in GPS

Uses in educational domains

Perera et al. (2009) • What were the three ways that Perera et al. (2009) used sequential pattern mining? • What did they learn, and how did they use the information?

Perera et al. (2009) • Overall uses of collaborative tools by groups • Sequences of collaborative tool use by different group members • Sequences of access of specific resources by different group members • In all cases, they found common patterns and then looked at how support differed for successful and unsuccessful groups

Perera et al. (2009):Important Findings • Overall uses of collaborative tools by groups • Successful groups used ticketing system more than the wiki; weaker groups used wiki more • Patterns were particularly strong for group leaders

Perera et al. (2009):Important Findings • Sequences of collaborative tool use by different group members • Successful groups characterized by leader opening ticket and other student working on ticket • Successful groups characterized by students other than leader opening ticket, and other students working on ticket

Perera et al. (2009):Important Findings • Sequences of access of specific resources by different group members • The best groups had interactions around the same resource by multiple students • The poor groups did no work on tickets before closing them

Zhang et al. (2005)Romero et al. (2008) • Analyze students’ paths through learning resources in order to find and suggest resources for students

Robinet et al. (2007) • Mine sequences of student actions in a system where students are allowed to skip steps • In order to infer intermediate/implicit steps during algebraic manipulation • In other words, if some students have A->B->C • Infer that A->C has B in the middle • Aids with choosing remedial feedback

What else? • What else could sequential pattern mining be used for in education?

Asgn. 8 • Solutions • Let’s look at solutions from • Sweet • Mike W.

Asgn. 9 • Questions? • Comments?

Next Class • Wednesday, March 28 • 3pm-5pm • AK232 • Learning Curves • Readings • Martin, B., Mitrovic, A., Koedinger, K.R., Mathan, S. (2011) Evaluating and improving adaptive educational systems with learning curves. User Modeling and User-Adapted Interaction, 21 (3), 249-283. • Assignments Due: None

The End

Advanced Methods and Analysis for the Learning and Social Sciences