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Advanced Methods and Analysis for the Learning and Social Sciences PSY505 Spring 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 inbetween 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 reengaged 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) 1. Overall uses of collaborative tools by groups 2. Sequences of collaborative tool use by different group members 3. 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 1. 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 2. 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 3. 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 UserAdapted Interaction, 21 (3), 249-283. • Assignments Due: None The End