Tutoring Students with Adaptive Strategies

Adaptive learning is a crucial part in intelligent tutoring systems. It provides students with appropriate tutoring interventions, based on students’ characteristics, status, and other related features, in order to optimize their learning outcomes. It is required to determine students’ knowledge level or learning progress, based on which it then uses proper techniques to choose the optimal interventions. In this dissertation work, I focus on these aspects related to the process in adaptive learning: student modeling, k-armed bandits, and contextual bandits. Student modeling. The main objective of student modeling is to develop cognitive models of students, including modeling content skills and knowledge about learning. In this work, we investigate the effect of prerequisite skill in predicting students’ knowledge in post skills, and we make use of the prerequisite performance in different student models. As a result, this makes them superior to traditional models. K-armed bandits. We apply k-armed bandit algorithms to personalize interventions for students, to optimize their learning outcomes. Due to the lack of diverse interventions and small difference of intervention effectiveness in educational experiments, we also propose a simple selection strategy, and compare it with several k-armed bandit algorithms. Contextual bandits. In contextual bandit problem, additional side information, also called context, can be used to determine which action to select. First, we construct a feature evaluation mechanism, which determines which feature to be combined with bandits. Second, we propose a new decision tree algorithm, which is capable of detecting aptitude treatment effect for students. Third, with combined bandits with the decision tree, we apply the contextual bandits to make personalization in two different types of data, simulated data and real experimental data

Characterizing Productive Perseverance Using Sensor-Free Detectors of Student Knowledge, Behavior, and Affect

Failure is a necessary step in the process of learning. For this reason, there has been a myriad of research dedicated to the study of student perseverance in the presence of failure, leading to several commonly-cited theories and frameworks to characterize productive and unproductive representations of the construct of persistence. While researchers are in agreement that it is important for students to persist when struggling to learn new material, there can be both positive and negative aspects of persistence. What is it, then, that separates productive from unproductive persistence? The purpose of this work is to address this question through the development, extension, and study of data-driven models of student affect, behavior, and knowledge. The increased adoption of computer-based learning platforms in real classrooms has led to unique opportunities to study student learning at both fine levels of granularity and longitudinally at scale. Prior work has leveraged machine learning methods, existing learning theory, and previous education research to explore various aspects of student learning. These include the development of sensor-free detectors that utilize only the student interaction data collected through such learning platforms. Building off of the considerable amount of prior research, this work employs state-of-the-art machine learning methods in conjunction with the large scale granular data collected by computer-based learning platforms in alignment with three goals. First, this work focuses on the development of student models that study learning through the use of advancements in student modeling and deep learning methodologies. Second, this dissertation explores the development of tools that incorporate such models to support teachers in taking action in real classrooms to promote productive approaches to learning. Finally, this work aims to complete the loop in utilizing these detector models to better understand the underlying constructs that are being measured through their application and their connection to productive perseverance and commonly-observed learning outcomes

Student Modeling From Different Aspects

With the wide usage of online tutoring systems, researchers become interested in mining data from logged files of these systems, so as to get better understanding of students. Varieties of aspects of students’ learning have become focus of studies, such as modeling students’ mastery status and affects. On the other hand, Randomized Controlled Trial (RCT), which is an unbiased method for getting insights of education, finds its way in Intelligent Tutoring System. Firstly, people are curious about what kind of settings would work better. Secondly, such a tutoring system, with lots of students and teachers using it, provides an opportunity for building a RCT infrastructure underlying the system. With the increasing interest in Data mining and RCTs, the thesis focuses on these two aspects. In the first part, we focus on analyzing and mining data from ASSISTments, an online tutoring system run by a team in Worcester Polytechnic Institute. Through the data, we try to answer several questions from different aspects of students learning. The first question we try to answer is what matters more to student modeling, skill information or student information. The second question is whether it is necessary to model students’ learning at different opportunity count. The third question is about the benefits of using partial credit, rather than binary credit as measurement of students’ learning in RCTs. The fourth question focuses on the amount that students spent Wheel Spinning in the tutoring system. The fifth questions studies the tradeoff between the mastery threshold and the time spent in the tutoring system. By answering the five questions, we both propose machine learning methodology that can be applied in educational data mining, and present findings from analyzing and mining the data. In the second part, we focused on RCTs within ASSISTments. Firstly, we looked at a pilot study of reassessment and relearning, which suggested a better system setting to improve students’ robust learning. Secondly, we proposed the idea to build an infrastructure of learning within ASSISTments, which provides the opportunities to improve the whole educational environment

Analysis and control of chaos for lateral dynamics of electric vehicles

In this paper, the nonlinear dynamic model of the lateral system for electric vehicles (EVs) is proposed. Different from the traditional steering system, a driver’s reaction model is introduced and meanwhile the disturbance caused by irregularities of road surface is also considered in this paper. Based on the integrated nonlinear dynamic equations, it shows that the stability of lateral system of EVs is closely related to the heading speed of the vehicle. The lateral system has a Hopf bifurcation when the vehicle heading speed equals a critical value, and then enters into chaos domain along with the increment of the vehicle heading speed. The unstable behaviors may make EVs spin and even turn over, which are quite harmful to the safety of EVs. As for this issue, a control method is proposed and implemented to protect the vehicle from spinning and thus improve the safety of EVs. The computer simulation is utilized in this paper to analyze nonlinear dynamics, as well as to validate the existence of chaotic motions and the feasibility of the control scheme. From the simulation results, it shows that the chaotic motions existing in the EV lateral dynamics can be suppressed by the proposed control method, and thus the corresponding cornering performance and safety are improved.published_or_final_versio

Student Modeling in Intelligent Tutoring Systems

After decades of development, Intelligent Tutoring Systems (ITSs) have become a common learning environment for learners of various domains and academic levels. ITSs are computer systems designed to provide instruction and immediate feedback, which is customized to individual students, but without requiring the intervention of human instructors. All ITSs share the same goal: to provide tutorial services that support learning. Since learning is a very complex process, it is not surprising that a range of technologies and methodologies from different fields is employed. Student modeling is a pivotal technique used in ITSs. The model observes student behaviors in the tutor and creates a quantitative representation of student properties of interest necessary to customize instruction, to respond effectively, to engage students¡¯ interest and to promote learning. In this dissertation work, I focus on the following aspects of student modeling. Part I: Student Knowledge: Parameter Interpretation. Student modeling is widely used to obtain scientific insights about how people learn. Student models typically produce semantically meaningful parameter estimates, such as how quickly students learn a skill on average. Therefore, parameter estimates being interpretable and plausible is fundamental. My work includes automatically generating data-suggested Dirichlet priors for the Bayesian Knowledge Tracing model, in order to obtain more plausible parameter estimates. I also proposed, implemented, and evaluated an approach to generate multiple Dirichlet priors to improve parameter plausibility, accommodating the assumption that there are subsets of skills which students learn similarly. Part II: Student Performance: Student Performance Prediction. Accurately predicting student performance is one of the most desired features common evaluations for student modeling. for an ITS. The task, however, is very challenging, particularly in predicting a student¡¯s response on an individual problem in the tutor. I analyzed the components of two common student models to determine which aspects provide predictive power in classifying student performance. I found that modeling the student¡¯s overall knowledge led to improved predictive accuracy. I also presented an approach, which, rather than assuming students are drawn from a single distribution, modeled multiple distributions of student performances to improve the model¡¯s accuracy. Part III: Wheel-spinning: Student Future Failure in Mastery Learning. One drawback of the mastery learning framework is its possibility to leave a student stuck attempting to learn a skill he is unable to master. We refer to this phenomenon of students being given practice with no improvement as wheel-spinning. I analyzed student wheel-spinning across different tutoring systems and estimated the scope of the problem. To investigate the negative consequences of see what wheel-spinning could have done to students, I investigated the relationships between wheel-spinning and two other constructs of interest about students: efficiency of learning and ¡°gaming the system¡±. In addition, I designed a generic model of wheel-spinning, which uses features easily obtained by most ITSs. The model can be well generalized to unknown students with high accuracy classifying mastery and wheel-spinning problems. When used as a detector, the model can detect wheel-spinning in its early stage with satisfying satisfactory precision and recall

A LiDAR-Inertial SLAM Tightly-Coupled with Dropout-Tolerant GNSS Fusion for Autonomous Mine Service Vehicles

Multi-modal sensor integration has become a crucial prerequisite for the real-world navigation systems. Recent studies have reported successful deployment of such system in many fields. However, it is still challenging for navigation tasks in mine scenes due to satellite signal dropouts, degraded perception, and observation degeneracy. To solve this problem, we propose a LiDAR-inertial odometry method in this paper, utilizing both Kalman filter and graph optimization. The front-end consists of multiple parallel running LiDAR-inertial odometries, where the laser points, IMU, and wheel odometer information are tightly fused in an error-state Kalman filter. Instead of the commonly used feature points, we employ surface elements for registration. The back-end construct a pose graph and jointly optimize the pose estimation results from inertial, LiDAR odometry, and global navigation satellite system (GNSS). Since the vehicle has a long operation time inside the tunnel, the largely accumulated drift may be not fully by the GNSS measurements. We hereby leverage a loop closure based re-initialization process to achieve full alignment. In addition, the system robustness is improved through handling data loss, stream consistency, and estimation error. The experimental results show that our system has a good tolerance to the long-period degeneracy with the cooperation different LiDARs and surfel registration, achieving meter-level accuracy even for tens of minutes running during GNSS dropouts

Pioneer spacecraft operation at low and high spin rates

The feasibility of executing major changes upward or downward from the nominal spin rate for which the Pioneer F&G spacecraft was designed was investigated along with the extent of system and subsystem modifications required to implement these mode changes in future spacecraft evolving from the baseline Pioneer F and G. Results of a previous study are re-examined and updated for an extended range of spin rate variations for missions that include outer planet orbiters, outer planet flyby and outer planet probe delivery. However, in the interest of design simplicity and cost economy, major modifications of the baseline Pioneer system and subsystem concept were avoided

The role of embodied scaffolding in revealing “enactive potentialities” in intergenerational science exploration

Although adults are known to play an important role in young children's development, little work has focused on the enactive features of scaffolding in informal learning settings, and the embodied dynamics of intergenerational interaction. To address this gap, this paper undertakes a microinteractional analysis to examine intergenerational collaborative interaction in a science museum setting. The paper presents a fine-grained moment-by-moment analysis of video-recorded interaction of children and their adult carers around science-themed objects. Taking an enactive cognition perspective, the analysis enables access to subtle shifts in interactants’ perception, action, gesture, and movement to examine how young children engage with exhibits, and the role adult action plays in supporting young children's engagement with exhibits and developing ideas about science. Our findings demonstrate that intergenerational “embodied scaffolding” is instrumental in making “enactive potentialities” in the environment more accessible for children, thus deepening and enriching children's engagement with science. Adult action is central to revealing scientific dimensions of objects’ interaction and relationships in ways that expose novel types of perception and action opportunities in shaping science experiences and meaning making. This has implications for science education practices since it foregrounds not only “doing” science, through active hands-on activities, but also speaks to the interconnectedness between senses and the role of the body in thinking. Drawing on the findings, this paper also offers design implications for informal science learning environments

Solidification at the high and low rate extreme

The microstructure selection at both high and low growth rates is studied. For the high rate extreme, melt spinning of a Fe-Si-B alloy is employed. The microstructural variations with changing wheel speed and factors affecting these variations are examined through various characterization techniques. Particular attention was given for the influence of melt pool behavior on the competition between nucleation of crystalline solidification products and glass formation. It is found that there exists a window of wheel speeds which give rise to a stable melt-pool and production of amorphous ribbons. The surface-controlled melt-pool oscillation is found as the dominant factor governing the onset of unsteady thermal conditions accompanied by varying amounts of crystalline nucleation observed near the lower wheel speed limit. For the upper wheel speed limit, a criterion based on mass-balance and momentum transfer is developed for predicting the window of wheel speeds for obtaining uniform and fully amorphous ribbons. For the low rate extreme, solidification and morphological selection of the faceted silicon phase is investigated in a near eutectic Al-Si system by utilizing a Bridgman type directional solidification unit. Particularly, the role of certain defect mechanisms namely, twinning, in the selection of microstructure and growth crystallography is investigated. At the imposed growth rates of 0.5 and 1 micron/s and temperature gradient of 7.5 K/mm, a unique silicon morphology consisting of 8-pointed stars is observed to grow with \u3c001\u3e texture within continuous domains across the sample. The growth crystallography of this unique silicon structure is characterized and it is found that substantial amount of 210 type twinning exists within the central core of this star-shaped morphology. It is found that the twinning phenomenon at the core is an essential feature for branching, morphological selection and adjustment of spacing between the star-like silicon features. These mechanisms and the associated growth characteristics are examined in detail

A Model For Implementing An Optimized Casino Degree Curriculum Within The Two-Year College

A research project was undertaken to develop a casino degree curriculum model for two-year colleges. The study began with an overview of the gambling industry and the advent of gaming curricula at various institutions of higher learning; The casino model was developed using Bloom\u27s Taxonomy for course structure and Kalani\u27s Model for curricular framework. Additional studies were conducted on curriculum development methods, and various other curriculum designs and models; The research design included data from two questionnaires and one personal interview instrument. Further data was provided by gaming employment statistics, gaming revenue statistics, and proprietary gaming school programs. This data was used to develop a proposed Casino Curriculum Model for Clark County Community College in Las Vegas. The model utilized a four-step approach to curriculum design encompassing (1) Demand Factor, (2) Selection Factor, (3) Skills and Knowledge Factor, (4) Curriculum Factor. The Demand Factor determined various gaming occupations available. The Selection Factor determined highest employment opportunities. The Skills and Knowledge Factor determined core and specialized learnings. The Curriculum Factor determined basic curricular elements. Also shown were model variations for specialized programs and a comparison between proposed and existing CCCC curriculum models; Recommendations included the development of various gaming certificates and degRees Further studies on the potential of gaming programs in other institutions and locales, and a greater vocational/technical emphasis for CCCC