=Paper=
{{Paper
|id=Vol-1388/PALE2015-paper2
|storemode=property
|title=The Student Advice Recommender Agent: SARA
|pdfUrl=https://ceur-ws.org/Vol-1388/PALE2015-paper2.pdf
|volume=Vol-1388
|dblpUrl=https://dblp.org/rec/conf/um/GreerFBTKWK15
}}
==The Student Advice Recommender Agent: SARA==
https://ceur-ws.org/Vol-1388/PALE2015-paper2.pdf
The Student Advice Recommender Agent: SARA
Jim Greer, Stephanie Frost, Ryan Banow, Craig Thompson, Sara Kuleza, Ken Wilson,
and Gina Koehn
University of Saskatchewan, Saskatoon, Canada
{firstname}.{lastname}@usask.ca
Abstract: SARA, the Student Advice Recommender Agent is a system some-
what like an early alert system, where predictive models of learners’ success
combined with incremental data on learners’ activity in a course can be used to
identify students in academic distress. With SARA, rather than give alerts to
academic advisors or professors, we provide personalized advice directly to
students. An advice string – “A note from SARA” is prepared for each student
every week in a semester-long course. The system attempts to direct students to
appropriate learning supports and resources according to their individual needs.
We have observed a significant year over year improvement in unadjusted stu-
dent grades after the SARA’s advice recommender was implemented in a 1200-
student freshman STEM course.
Keywords: early alert, personalized advice, persona, recommender agent
1 Introduction
Early alert systems for students at academic risk have been in use for several years. In
such systems, students who seem to be struggling in a course, as evidenced by lower
term grades, minimal engagement in learning management system (LMS) activity, or
low attendance may be issued warnings or alerts [1]. In most systems, instructors are
involved in directing the delivery of alert messages. In some systems, these alerts are
also issued to academic advisors (as in Starfish Early Alert or Ellucian Student Suc-
cess) so that follow up appointments with an advisor or learning specialist can be
booked if the advisor so wishes. For the most part, students who seem to be minimal-
ly engaged or who are falling behind in coursework, or who are failing intra-term
assessments are targeted for additional interventions.
We have taken a different approach to a somewhat similar problem. The problem
we are trying to address is how to best assist and support learners during a course
when the benefits of big data can be put to work. That is, if we know about the stu-
dents’ academic history, personal history (including demographics), and current activ-
ity (such as progress in a course and other related activity pertinent to academic suc-
cess), what could we do to help? Help would not be for only the struggling student,
but for the successful and exceptional students too. The approach we have taken is to
construct individualized, personalized advice for students in a large courses on the
basis of their academic, personal, and activity profiles (including current progress in
the course). We have developed and implemented the Student Advice Recommender
Agent (SARA), which generates and delivers an “advice string” to each student each
week throughout the term.
Predictive models of student success in the course are computed based on past aca-
demic performance and demographic student data. Advice string templates are con-
structed by instructional experts, focusing on available supports and resources, words
of encouragement, and content specific matters. These advice templates are personal-
ized (adjusted/adapted) based upon combinations of student demographic and student
activity data. The engineering of advice strings and conditional adaptation is aided by
focusing on personas of students who are predicted to fail, pass or excel (as mapped
out in [2]). The advice strings are then delivered as learning alerts to each and every
student in a course. The advice directs students toward help resources, help or advi-
sory personnel, supplementary course materials, or enrichment activities, as is appro-
priate.
2 Enhanced Demographics
Beginning in the fall of 2013, the University of Saskatchewan initiated a project to
gather enhanced demographic data about incoming freshmen. A 75-item census-style
survey is issued annually to students at the time of their first registration to the Uni-
versity. Students are invited to disclose personal information including: goals and
aspirations, anticipated and “disappointment threshold” grade point averages; living
arrangements; family history of university studies; sources of financial support; ex-
pected hours to be spent working for pay, volunteering, studying, engaging in extra-
curricular activity; disabilities; whether they are supporting dependents; expected
major; anticipated advanced or professional degrees sought; level of comfort and
connectedness with campus. In addition some short standardized instruments includ-
ing: a shortened version of Biggs’ Study Process Questionnaire [4], the Motivated
Strategies for Learning Questionnaire [5] and GRIT-S [3]. Response rates around
60% have been achieved in each of the first two years of this survey.
These data are merged into the University’s student data warehouse where infor-
mation is consolidated from student admissions and recruitment, student grades and
academic records, access to academic support services, and learning management
system activity.
This rich data repository offers an opportunity to develop comprehensive and rela-
tively accurate predictive models of student academic achievement. The rich data
repository also opens possible avenues for inappropriate and prejudicial decisions
about students. Data are carefully guarded, identities are encrypted, and personally
identifying variables are kept separate from other data. Strict ethical guidelines are
followed in making use of the data for student modeling and advising.
3 Temporally Improving Predictive Models
After various data mining attempts using decision trees, regression models, Bayesian
networks and naïve Bayes algorithms, predictive models of student academic success
in specific courses, overall GPA, retention, and degree completion were derived using
student data over the past 5 years. We took a closer look at a face-to-face introducto-
ry Biology class that reaches more than a thousand students (mostly freshmen) per
year. A sequence of predictive models for final course grades was developed, one
model for each week of the Biology course, using the data that would be known as of
that week. Models were built with half the students in the 2013 Biology cohort and
validated with the other half of the students. We found that log-linear regression
models based upon selected demographic data and high school grades could result in
good correlations with 2013 course grades (r=0.61). We also found that if LMS activ-
ity data and term grades were added in, the grade prediction improves even more as
the term progresses. After the course midterm examinations, regression models corre-
lated very highly (r=0.92) with final 2013 course grades.
When these models were applied to the 2014 students, we discovered that the mod-
els correlated very well with the 2014 students’ grades (r=0.62 at the beginning of
term and r=0.82 after the midterm exam). Because of the interventions associated
with the introduction of SARA in 2014, we expected correlations between model and
final grades might be reduced somewhat – the predictive model applied did not ac-
count for changes in 2014. This will be explained in the evaluation section below.
This predictive modeling methodology provides a temporally improving predictive
model of student academic achievement in a single course. The model gives a rela-
tively accurate estimate of student success. Factors in the model that offer the great-
est degree of predictive power include: actual assessment grades, High school GPA
and Biology grades, whether the student was intrinsically or extrinsically motivated,
whether the student was a deep versus surface learner, and whether or not the student
was the first in their family to attend university.
The methodology also identifies which demographic variables may be considered
as risk factors in student retention and success. Using risk factor variables we have
constructed a number of personas of canonical successful or less successful students
(cf. Brooks & Greer, 2014). Figure 1 shows three sample personas of students.
Student 1 Student 2 Student 3
- rural high school - mature student - exceptional admission GPA
- average admission GPA - has dependents at home - attended top high school
- first in family at univ - 20 hours per week job - recently settled immigrant
- living in univ. residence - returning - 5 yrs away - living at home with family
- high grit score - deep learner tendency -‐
surface
learning
tendency
- surface learner tendency predicted to receive a C -‐
aspirations
for
grad
school
predicted
to
receive
a
B
predicted to receive D grade grade in course
grade
in
the
course
Figure 1: Sample personas of learners
4 CREATION OF ADVICE STRINGS
The personas help our instructional designers, instructors, and academic support spe-
cialists construct advice templates that could be tailored for individuals in a particular
persona group. Engineering advice strings has turned out to be a fairly difficult ac-
tivity. Good quality advice for students is highly contextualized – dependent on the
time in the term, what is going on in the course, the content being presented, the sup-
ports and resources available outside the course, upcoming events and opportunities,
news and current events. Good advice also should reflect the student’s situation in-
terests and needs, the academic risks they may face, and their determination to survive
or excel. For some students useful advice is a message that somebody cares about
their success. For others such a message may be a threat and lead to discouragement.
Another challenge with generating advice strings is that predictions may be wrong.
Some students predicted to earn an A will not do so. Some students predicted to fail
may surprise everyone. We have paid a lot of attention to ensure that our advice to
students “does no harm”. Advice is framed as a set of positive suggestions, raising
opportunities, making reference to resources and supports while offering a supportive
and caring tone.
Figure 2 shows the rule for generation of advice in week 3 of our Biology course.
The advice strings are written by an subject area learning specialist and they are craft-
ed for stereotypical students who have certain attributes or whose persona has certain
features, specified as constraints. The advice string constraints are then interpreted
for the attributes of students in the class and a unique advice string is produced for
each student. The advice constraints in Figure 2 yield 24 different advice messages.
A student will receive the one that best fits their persona. Some weeks there might be
only half a dozen different advice messages and some weeks there may be hundreds
of different messages. The approximately 1200 students in this Biology course each
received a weekly message, tailored as much as possible for their individual context.
We see the messages from SARA as a type of mass personalization. Over the
course of a full semester, the collection of weekly advice messages for each of our
~1200 Biology students is nearly unique. That is, the cumulative advice for an indi-
vidual is likely to be distinct from the cumulative advice given to any other individual
in the course. The largest group of individuals who received identical cumulative
messages over the term was of size 10. These 10 students were students who were
average in every way and for whom we had no enriched demographic data (they did
not complete the entry census).
Our students use the BBLearn LMS for access to course materials, lecture record-
ings, presentation slides, and online quizzes. In order to be sure that students see the
weekly advice from SARA (knowing that students tend not to read email), we added
an iframe to the course home page where each student sees their weekly “A Note
From SARA” immediately as they connect to BBLearn. In addition, an LTI compo-
nent has been inserted into BBLearn, where all their advice strings for the term so far
are available for review, where the advice that SARA gives to others can be browsed
(identities hidden), and an opportunity is available to rate the usefulness of or com-
ment upon SARA’s most recent piece of advice.
Week: 3 Order: 0 Condition: Predicted GPA < 60%
Finding balance is important and is often one aspect of academic success that doesn't receive much focus. Regular
practice and review of course materials have been proven to help retention of information. This is why completing weekly
quizzes, preparing for your labs, and regularly reviewing your textbook and lecture notes will be beneficial to your learn-
ing in Biol120! Once you've addressed your academic responsibilities then you can take time for other responsibilities and
personal time. Check out this video for additional tips on how to find
balance this term.
Week: 3 Order: 0 Condition: Predicted GPA 60% - 80%
Finding balance is very important and often one aspect of academic success that doesn't receive much focus. Self-
awareness, knowing your goals, and self-reflection: these things will be helpful guides in your journey to finding balance.
Try to schedule your time so that you are accounting for time to study, time for your other responsibilities, and personal
time. Also keep in mind that this schedule will need to be flexible to account for preparation before important deadlines
and exams. For additional tips on how to find balance, check out this
video.
Week: 3 Order: 0 Condition: Predicted GPA > 80%
One aspect of academic success that doesn't receive much focus is how to find balance between the time spent on your
academics and the time dedicated to your other life responsibilities. Self-awareness, knowing your goals, and self-
reflection: these things will be helpful guides in your journey to finding balance. Scheduling time to study is important,
but so too is taking time for enjoying other things in life. Check out this
video for additional helpful tips on how to find balance this term.
Week: 3 Order: 1 Condition: Low # hours spent studying (according to survey response and
course load)
You indicated on the entry census that you intend to allocate a below average amount of study time on your
University courses. Making time to review and study the material is a very important aspect of your overall academic
success at University. Setting academic goals and constructing a plan to achieve those goals can be very helpful in guiding
how much time you need to prioritize to your studies. Check out these helpful guides to Goal Setting and
Time Management.
Week: 3 Order: 1 Condition: Medium # hours spent studying (according to survey response
and course load)
You indicated on the entry census that you intend to allocate an average amount of time to studying your University
courses. This is a great step towards helping you find balance while also achieving your academic goals! Understanding
more about your learning process may help you to use your study time more efficiently - check out this resource on Learning Styles for further infor-
mation.
Week: 3 Order: 1 Condition: High # hours spent studying (survey response and course load)
You indicated on the entry census that you would be spending more than an average amount of time studying
for your University courses. Did you know that often it's not how long you study, but how efficiently you study, that
makes the biggest difference? Using a variety of study methods and taking frequent breaks can help to increase your
retention of the material you are studying. To help you use the time you study most efficiently, check out this resource on
Learning Styles that might help you understand more about your learning process.
Week: 3 Order: 2 Condition: Working 13-20 hours per week or More and Predicted GPA < 80%
Working a lot of hours can place extra stress on you, especially as exam times draw near. Be sure you can find a bal-
ance between work and your academics. This may mean asking for some time off during heavier study periods. Keep in
mind that dropping a few shifts may be more cost and time effective than having to re-take a course!
Week: 3 Order: 3 Condition: Predicted GPA < 60
Volunteering can provide enriching experiences that can be beneficial to your academic experience and your future ca-
reer. However, making sure that you are achieving your academic goals needs to be your first priority. After you receive
your mid-term grades, if you're on track with success, you may want to consider getting involved with fun and interesting
volunteer experiences.
Week: 3 Order: 3 Condition: Predicted GPA 60% - 80%
One way to achieve balance is to get involved in interesting volunteer opportunities. Volunteering can provide enrich-
ing experiences that can be beneficial to your academic experience and your future career. There are lots of ways to get
involved on and off campus. To find volunteer positions, check out these
volunteering opportunities. If you're interested in gaining valuable experience in a Biology related field, find out
about possible volunteering positions by speaking to your Instructor or TA, or contacting the undergraduate's Biology Club.
Week: 3 Order: 3 Condition: Predicted GPA 60% - 80%
Volunteering can provide enriching experiences that can be beneficial to your academic experience and your future ca-
reer. Find volunteer opportunities that might be of interest to you by checking out these volunteering opportunities. In addition, if you're interested in gaining valua-
ble experience in a Biology related field, find out about possible volunteering positions by speaking to your Instructor or
TA, or contacting the undergraduate's Biology Club.
Figure 2. Different Advice Strings generated by SARA in week 3
5 Evaluation of SARA
As with many educational interventions initiated in a large course, controlled experi-
mentation is difficult to implement. In the freshman Biology course of 2014 many
factors remained the same as in prior offerings. The instructional team, the course
objectives and evaluation rubric, the laboratories, and the types of students remained
much the same. Key differences in 2014 included a new requirement of weekly
online quizzes, the introduction of SARA’s advice, and the addition of optional-
attendance peer-led study groups.
A remarkable difference in achievement was detected when comparing unadjusted
final grades in 2014 against the two previous years. The mean course grade increased
by 2.57 percentage points (t-test p<.00001). More remarkably, the number of D and F
grades in 2014 decreased by 25.3% over the previous year and the number of A
grades increased by 28.3% over the previous year. In the ~1200 student course, 100
fewer students scored D and F grades in 2014 than in previous years, indicating a
potential boost in student retention.
Students’ predicted grades for 2014 were also compared against their observed
grades. The predicted model at the beginning of term expected a class average 3.8
percentage points lower than the observed average. After the midterm, the improved
predictor expected a class average that turned out to be 1.8 percentage points lower
than what was observed. These significantly lower predictions may indicate that the
predictive model was lacking. Of course we hoped that the model would underpredict
grades if there was indeed a positive effect due to the new teaching interventions.
The fact that correlations between predicted and observed grades remained compa-
rable to the prior year indicates that there was a general lift to grades in 2014 (no sig-
nificant change in slope of the correlation line). There was clearly an across the board
increase in grades in 2014. Instructors believed this was due primarily to the intro-
duction of the weekly quizzes.
An end of term questionnaire was given to determine students’ reactions to chang-
es made in the class. Forty four percent of the students completed the survey but
nearly half of those students chose to remain anonymous so their responses could not
be linked with grades and demographics. Students who completed the survey and
gave their identities tended to have somewhat higher grades than those who did not
give identities but the patterns of responses among those who did and did not reveal
identities were not significantly different. Among the survey items students were
asked whether they read the weekly advice from SARA or ignored it and whether or
not they appreciated SARA’s advice. Only 1/3 of students said they paid attention to
SARA’s advice, while 40% of the students said they appreciated receiving advice
from SARA. Neither students who paid attention to SARA’s advice or students who
appreciated receiving advice from SARA showed differences in unadjusted final
grades or in differences between expected (pre-midterm) and actual grades as com-
pared to students who ignored SARA. Similarly there was no difference in grades
between those who chose to participate in the peer-led study groups and those who
did not.
Perhaps in an ideal world every student should embrace and act upon SARA’s ad-
vice. But it is important to realize that even if only a few students listen to and are
helped by SARA’s advice, and that the advice is helpful, a significant shift in
achievement can (and did) occur.
One important measure related to SARA’s advice did show promise. As stated
above, the post-midterm predictor, correlated with unadjusted final grades at 0.82.
We examined more closely the error in this predictor (the difference between actual
unadjusted final grade and the post-midterm prediction). Students who regularly read
the weekly advice from SARA scored significantly higher than the predictor (4.6
percentage points), while students who did not read SARA’s advice scored very near
the predicted grade. This could be taken to mean that student’s who regularly read
SARA’s advice had achievement levels higher than was expected.
Given our study and its limitations, we cannot directly attribute the overall
achievement improvements in this offering of the course to SARA alone. Improve-
ments could have been due to the weekly quizzes, which every student was required
to complete, or the study groups, or SARA or some combination. The Biology in-
structors were very happy with the outcomes in their course. They were convinced
that more learning occurred than in past years and that the combination of interven-
tions was a great success. Yet to more fully understand the impact of SARA, we are
contemplating dropping the SARA advice from the Biology course next year and
watching to see if the increase in level of achievement persists when only mandatory
quizzes and optional study groups are in play. In order to continue our research into
SARA we hope to expand SARA’s reach into other large freshman STEM courses in
the upcoming year (perhaps Physics, Chemistry or Engineering).
6 Conclusions
SARA provides a scalable advice personalization environment in large university
courses. In our first offering of a large course using SARA, student achievement im-
proved over previous years and students on average achieved significantly higher
grades than our predictive model (based on prior years’ features) would have ex-
pected. There is some evidence that the improvement could be caused by SARA’s
weekly advice, but further research is needed to confirm such a claim.
One of the persistent dangers that comes along with predictive modeling of learn-
ers is the possibility of prejudicial treatment that may bring negative consequences to
some learners. When instructors come to know, for example, that a particular student
has a very low probability of successfully completing a course, the instructor may
decide to minimize help and support for that learner, turning more attention toward
those with higher likelihood of success. Likewise an instructor may (consciously or
subconsciously) privilege the students who would most likely pursue advanced cours-
es or graduate programs. Of even greater concern, predictive models may be used to
shape enrolment, streaming, and admission policies. It is important for instructors
and especially admissions officers to understand that probabilities associated with
predictive estimates make it highly inappropriate to assume the predictive tendencies
of a group will apply to any particular individual in the group.
Finally, it is important to remember that the goal of improved learning is to make
gains in academic achievement. If using predictive models of achievement based on
past cohorts to inform decisions about future cohorts, one must be prepared to accept
that the predictive models may be a little less accurate than one might like. This para-
dox associated with modeling the state of learners as they consciously and steadily try
to move beyond their current learning state is not new. The temporal dimension of
predictive models that takes into account innovations or interventions in teaching and
academic support is vital to our growing understanding of the learning process for
distinct individual learners.
Acknowledgements
We would like to recognize the contributions, creativity, and willingness to flex of the
three faculty members who taught this Biology course in 2014: Susan Kaminskyj,
Ken Wilson and Jorge Chedrese.
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