Our group meets on Thursdays at 11:00 a.m., FL 246.
 Jan 22
 No meeting this week
 Jan 29
 No meeting this week
 Feb 05
 Dovan Rai and Yue Gong
 "Motivational effect in learning: an empirical analysis"
In the field of educational systems, there has been considerable work
in detecting the affective state of students as higher motivation is
believed to give better learning and enhancing motivation has been a
major pursuit. Intelligent Tutoring Systems (ITS) researchers are
also becoming increasingly aware of making the learning process more
motivating. In this study, we are interested in using empirical
analyses to estimate the impact of motivation on learning.
We used a model of motivation that divides it into four
componentsinterest, effort, confidence and satisfactionand
compared their relative impact on learning. Based on a questionnaire
survey, we grouped students into high and low categories of motivation
and its components. We estimated knowledge tracing parameters for each
subgroup using a Dynamic Bayesian network. We analyzed the learning
rates for each group and found that motivation and all its components
have a varying but positive effect on learning. We also calculated the
number of practice opportunities required to master a skill for each
subgroup, and found that the low motivation group required 40 practice
opportunities while the high motivation group only needed 28
opportunities.
We are trying to create a paradigm to measure and compare learning in
different motivational subgroups. This can give us insight about
interrelations among motivation, motivational components and learning,
in order to help us build more efficient or better motivating tutors.
 Feb 12
 Zach Pardos
 "A Method for Detecting Learning Effects of Items In a Randomized Problem Set"
Researchers that make tutoring systems would like to know which pieces
of educational content are most effective at promoting learning
among their students. Randomized controlled experiments are often
used to determine which content produces more learning in an
ITS. While these experiments are powerful they are often very
costly to setup and run. The majority of data collected in many ITS
systems consist of answers to a finite set of questions of a given
skill often presented in a random sequence. We propose a Bayesian
method to detect which questions produce the most learning in this
random sequence of data.
We confine our analysis to random sequences with two, three and
four questions. Two simulation studies were run to investigate the
validity of the method and boundaries on what learning probability
differences could be reliably detected with various numbers of
users. Finally, real tutor data from random sequence problem sets
was analyzed. Results of the simulation data analysis showed that
the method reported reliability (p < 0.05) in 56 of 112 simulation
experiments with less than 5% error. In the analysis of real
student data, the method returned statistically reliable choices of
best question in four out of seven problem sets.
 Feb 19
 Academic Advising Appointment Day: No meeting
 Feb 26
 Elijah ForbesSummers
 "Determining the effect of gaming behavior on learning using a
Bayesian network model"
Yue Gong and I recently began a followup study to her earlier
work with Dovan Rai. Previously Dovan and Yue studied the effect
of motivation on learning utilizing a Bayesian net model that was
based on the model presented in Joseph Beck's work "Does help
help? Introducing the Bayesian Evaluation and Assessment
methodology". In our study we will be using a similar model to
investigate whether students gaming of the tutoring system affects
their learning. As this project is still being planned and
implemented this will be a short talk (~20 minutes) in order to
receive feedback and suggestions from other AIRG members. I will
outline the study, discuss briefly the aspects of Assistments
that are important to our study, and describe the Bayesian model
and the methods for estimating gaming behavior that we use.
 Mar 05
 Mike Sao Pedro
 Postponed
 Mar 12
 Spring Recess
 Mar 19
 No meeting
 Mar 26
 Mingyu Feng
 "Using learning decomposition to analyze instructional effectiveness
in the ASSISTment system"
A basic question of instruction is how effective it is in
promoting student learning. This paper presents a study
determining the relative efficacy of different instructional
content by applying an educational data mining technique, learning
decomposition. We use logistic regression to determine how much
learning caused by different methods of presenting same skill,
relative to each other. We analyze more than 60,000 performance
data across 181 items from more than 2,000 students. Our results
show that items are not all as effective on promoting student
learning. We also did preliminary study on validating our results
by comparing them with rankings from human experts. Our study
demonstrates an easier and quicker approach of evaluating the
quality of ITS contents than experimental studies.
 Apr 02
 Leena Razzaq
 "Adapting tutoring strategies to students"
Tutoring systems often rely on interactive tutored problem
solving to help students learn math, which requires students to work
through problems stepbystep while the system provides help and
feedback. This approach has been shown to be effective in improving
student performance in numerous studies. However, tutored problem
solving may not be the most effective approach for all students. In
previous studies, we found that tutored problem solving was more
effective than less interactive approaches, such as simply presenting
a worked out solution, for students who were not proficient in math.
More proficient students benefited more from seeing solutions rather
than going through all of the steps. Can we use the results of these
studies to adapt the tutoring strategy to the students' math
proficiency? In this presentation, I will talk about an experiment to
examine whether adapting the tutoring to the student's proficiency is
better than randomly assigning students to a condition.
 Apr 9
 Keith Pray
 Sleep Data Exploration  Results Update
Sleep disorder studies offer a rich data set which includes: time sequence
data in the form of electroencephalograms, electromyograms,
electrooculograms, electrocardiograms, blood oxygen levels, body position,
and blood pressure; data collected in questionnaires on day time
sleepiness and depression which can vary in accuracy and interpretation
from patient to patient; more reliable data on patient demographics such
as smoking and drinking habits, and medical history; data summarized by
technicians from the time sequence data and classifications of sleep
disorder severity. In the interest of providing ways in which to use the
time sequence data directly in the construction of models for predicting
sleep disorder severity a set of exploratory experiments are being
conducted to characterize the predictive power of the nontime sequence
data. In particular the questionnaire and demographic data are examined in
relationship to the Beck Depression Index.
 Apr 16
 Amro Khasawneh
 Human Sleep Data Analysis Using Hidden Markov Models
This talk describes an ongoing exploratory analysis of a large human sleep
database. We focus on prediction of patient sleep disorder severity based
on polysomnographic time sequence signals, in particular EEG. Hidden Markov
Models are used to describe the underlying process that generates the EEG
time series, aiming to capture temporal patterns specific to each severity
level on a mild, moderate and severe scale. Ongoing experimental results
are presented.
 Apr 23
 Project Presentation Day
 Apr 30
 Yu Guo
 title & abstract tbd
 May 1
 Last day of Semester
