Transcript Research Methods for the Learning Sciences

Advanced Methods and Analysis for
the Learning and Social Sciences
PSY505
Spring term, 2012
March 16, 2012
Today’s Class
• Motif Extraction
Today…
• We’re going to discuss a method that I’ve
never used
• In fact, to the best of my knowledge it has
only been used once in EDM
• It is a key method in bioinformatics, and I
think it has a lot of potential for EDM
Today…
• You might ask: why are we discussing it?
• It is a key method in bioinformatics, and I
think it has a lot of potential for EDM
Since…
• It‘s not a well-established method in EDM
• We’ll focus on a single paper more than usual
• And brainstorm together for how the method
might be applicable more broadly in
educational problems
– As well as other relevant problems in the social
sciences
Motif
• Short, recurring pattern in a sequence of
categories occurring over time
Motif in Music
• Short, recurring pattern of notes in a musical
composition
Motif in Music
• What’s the motif?
• http://www.youtube.com/watch?v=rRgXUFnf
KIY
• How many times does the motif occur?
Motif in Music
• What’s the motif?
• http://www.youtube.com/watch?v=rRgXUFnf
KIY
• How many times does the motif occur?
– Depends on how you define it, right?
– And that’s part of the challenge…
Motif in Language
• Short, recurring pattern of characters in a
sequence of characters occurring over time
Motif in Genetics
• Short, recurring pattern of genes in a
sequence of genes occurring over time
• Typically written as letters
Goal of Motif Extraction
• Discern a common pattern of characters in a
large corpus of characters
• The characters may vary slightly from case to
case
Can you find the motif?
Can you find the motif?
UBSWWDFKLWPRHUC
INUSUNSGDAAICAV
JBDPXBDVEJVMBKK
XRZZWCDXOVZZJKQ
VBDWNLROFVUBFFW
OWIFTIENDOXJXIOB
AUAAOOXZAABZSBT
VOVCROMCJTOLXYU
MUAWSNTVZXSFHMI
LFQRKUTFRIENDOV
ONJORIFCGAUGIRN
HUVRYFREENDOBBGC
AQJBVXJCAJLEMAU
PJGCHBDQIWJJTMQ
LOMTPOQHJVYYMFJ
LWGJMVPKYOZNMSA
IQYQHKKBNBVDFPV
JJLHWPZAYZIGGEH
RUPMFOHPVSPPVPT
BAZXVFTPQFQJVBM
IGJZRMAAWJBESSS
HLPMOKUOXGRIENDO
IRPWYIRJISLFVFF
JXZFRIEMDOVZRBJY
How would you describe the motif?
UBSWWDFKLWPRHUC
INUSUNSGDAAICAV
JBDPXBDVEJVMBKK
XRZZWCDXOVZZJKQ
VBDWNLROFVUBFFW
OWIFTIENDOXJXIOB
AUAAOOXZAABZSBT
VOVCROMCJTOLXYU
MUAWSNTVZXSFHMI
LFQRKUTFRIENDOV
ONJORIFCGAUGIRN
HUVRYFREENDOBBGC
AQJBVXJCAJLEMAU
PJGCHBDQIWJJTMQ
LOMTPOQHJVYYMFJ
LWGJMVPKYOZNMSA
IQYQHKKBNBVDFPV
JJLHWPZAYZIGGEH
RUPMFOHPVSPPVPT
BAZXVFTPQFQJVBM
IGJZRMAAWJBESSS
HLPMOKUOXGRIENDO
IRPWYIRJISLFVFF
JXZFRIEMDOVZRBJY
Finding motifs
• Several algorithms
Finding motifs
• Variant on PROJECTION algorithm (Tompa &
Buhler, 2001) used in (Shanabrook et al.,
2010)
– Only example of motif extraction in educational
data mining so far
Big idea
• For each character string C that could be a
motif example (e.g. all character strings of
desired length)
– Create a set of projections, random variations of C
that vary in one or more ways
Big idea
• For each pair of strings C1 and C2, see how many
overlaps there are between their projection
matrices
• Take the pair with the most matches and combine
into a motif
– Creating multi-example motif if 3+ get added together
• Repeat until goal number of motifs is found, or
until new motif is below criterion goodness
Goodness
• Typically, likelihood is used
Motif in Education
• Short, recurring pattern of behaviors in a
sequence of behaviors occurring over time
• Written as letters in Shanabrook et al. (2010)
Detail for education
• How do you segment student behavior?
• Could use student’s interaction on an entire problem, and
compute letters across whole problem
– Might make more sense in tutors with shorter problems (e.g.
ASSISTments)
• Could use student’s interaction on an entire problem, and
define letters differently for context within whole problem
– Approach used by Shanabrook et al. (2010)
• Could use “sliding window” of N actions
Behaviors in Shanabrook et al.
• “hints (a, b, c) – Hints is a measure of the number of hints viewed
for this problem. Although each problem has a maximum number
of hints, the hint count does not have an upper bound because
students can repeat hints and the count will increase at each
repeated view. The three categories for hints are: (a) no hints,
meaning that thestudent did not use the hint facility for that
problem, (b) meaning the student used the hint facility, but was
not given the solution, and (c) last hint solved, meaning that the
student was given the solution to the problem by the last hint. As
described above, this metric combines two values logged by the
tutor: the count of hints seen, and an indicator that the final hint
giving the answer was seen. The data could have been simply
binned low, medium, high hints; however, this would have missed
the significance of zero hints and using hints to reveal the problem
solution.”
Behaviors in Shanabrook et al.
• “secFirst (d, e, f) – The seconds to first attempt is an
important measure as it is during this time that the student
is reading the problem and formulating their response. In
previous research [6], five seconds was determined to be a
threshold for this metric representing gaming: students
who make a first attempt in less than five seconds are
considered not working on-task. We divide secFirst into
three bins: (d) less than 5 sec, (e) 5 to 30 sec, (f) greater
than 30 sec. (d) represents students who are gaming the
system, (e) represents a moderate time to the first attempt,
(f) represents a long time to the first attempt. The cut at 30
seconds was chosen because it equalizes the distribution of
bins (e and f), representing a division between a moderate
and a long time to the first attempt.”
Behaviors in Shanabrook et al.
• “secOther (g, h, i, j, k) – This variable represents actions related to
answering the problem after the first attempt was made. While the first
attempt includes the problem reading and solution time, subsequent
solution attempts could be much quicker and the student could still be
making good effort. secOther is categorized in five bins: (g) skip, (h) solved
on first, (i) 0 to 1.2 sec, (j) 1.2 to 2.9 sec, (k) greater than 2.9 sec. First,
there are two categorical bins, skip and solve on first attempt. These are
each determined from an indicator in the log data for that problem.
Skipping a problem implies only that students never clicked on a correct
answer; they could have worked on the problem and then given up, or
immediately skipped to the next problem with only a quick look. Solved
on first attempt indicates correctly solving the problem. If neither of the
first two bins are indicated in the logs, then the secOther metric measures
the mean time for all attempts after the first. The divisions of 1.2 sec and
2.9 sec for the latter three bins were obtained using the mean and one
standard deviation above the mean for all tutor usage; (i) less than 1.2
seconds would indicate guessing, (j) would indicate normal attempts, and
(k) would indicate a long time between attempts.”
Behaviors in Shanabrook et al.
• “numIncorrect – (o, p, q) - Each problem has four
or five possible answer choices, that we divide
into three groups: (o) zero incorrect attempts,
indicates either solved on first attempt, skipped
problem, or last hint solves problem (defined by
the other metrics); (p) indicates choosing the
correct answer in the second or third attempt,
and (q) obtaining the answer by default in a four
answer problem or possibly guessing when there
is five answer problem.”
What other constructs
could be used?
• What other kinds of constructs could be used
for the atoms of motif analyses in educational
analyses?
– At this grain-size (e.g. specific actions)
What other constructs
could be used?
• What other kinds of constructs could be used
for the atoms of motif analyses in educational
analyses?
– At other grain-sizes?
Common Motifs
• {adgo, adip, adiq}
• {aeho, afho}
• {ceho}
• {adgo, aeho}
• {aeiq aeho aeho aekp aeho aeiq aeho aeip
aeho aeip}
Interpretations
(Shanabrook et al., 2010)
• {adgo, adip, adiq} – gaming the system
• {aeho, afho} – “This student is using the tutor
appropriately, but not being challenged.”
• {ceho} – problem is too difficult
• {adgo, aeho} – student is skipping problems
• {aeiq aeho aeho aekp aeho aeiq aeho aeip aeho
aeip} – working on-task
Do you agree with interpretations?
• {adgo, adip, adiq} – gaming the system
• {aeho, afho} – “This student is using the tutor
appropriately, but not being challenged.”
• {ceho} – problem is too difficult
• {adgo, aeho} – student is skipping problems
• {aeiq aeho aeho aekp aeho aeiq aeho aeip aeho
aeip} – working on-task
How can researchers form good
interpretations?
What other applications?
• What other applications could motif
extraction be used for in education?
Questions? Comments?
Asgn. 8
• Questions?
• Comments?
Next Class
• Monday, March 19
• 3pm-5pm
• AK232
• Association Rule Mining
• Readings
• Witten, I.H., Frank, E. (2005) Data Mining: Practical Machine
Learning Tools and Techniques. Section 4.5.
• Merceron, A., Yacef, K. (2008) Interestingness Measures for
Association Rules in Educational Data. Proceedings of the 1st
International Conference on Educational Data Mining, 57-66.
• Assignments Due: None
The End