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MODELING THE INFLUENCES OF PERSONALITY PREFERENCES ON THE SELECTION OF INSTRUCTIONAL STRATEGIES ININTELLIGENT TUTORING SYSTEMS
Title
MODELING THE INFLUENCES OF PERSONALITY PREFERENCES ON THE SELECTION OF INSTRUCTIONAL STRATEGIES ININTELLIGENT TUTORING SYSTEMS.
Creator
Sottilare, Robert, Proctor, Michael, University of Central Florida
Abstract / Description
This thesis hypothesizes that a method for selecting instructional strategies (specifically media) based in part on a relationship between learning style preference and personality preference provides more relevant and understandable feedback to students and thereby higher learning effectiveness. This research investigates whether personality preferences are valid predictors of learning style preferences. Since learning style preferences are a key consideration in instructional strategies and...
Show moreThis thesis hypothesizes that a method for selecting instructional strategies (specifically media) based in part on a relationship between learning style preference and personality preference provides more relevant and understandable feedback to students and thereby higher learning effectiveness. This research investigates whether personality preferences are valid predictors of learning style preferences. Since learning style preferences are a key consideration in instructional strategies and instructional strategies are a key consideration in learning effectiveness, this thesis contributes to a greater understanding of the relationship between personality preferences and effective learning in intelligent tutoring systems (ITS). This research attempts to contribute to the goal of a "truly adaptive ITS" by first examining relationships between personality preferences and learning style preferences; and then by modeling the influences of personality on learning strategies to optimize feedback for each student. This thesis explores the general question "what can personality preferences contribute to learning in intelligent tutoring systems?" So, why is it important to evaluate the relationship between personality preferences and learning strategies in ITS? "While one-on-one human tutoring is still superior to ITS in general, this approach is idiosyncratic and not feasible to deliver to [any large population] in any cost-effective manner." (Loftin, 2004). Given the need for ITS in large, distributed populations (i.e. the United States Army), it is important to explore methods of increasing ITS performance and adaptability. Findings of this research include that the null hypothesis that "there is no dependency between personality preference variables and learning style preference variables" was partly rejected. Highly significant correlations between the personality preferences, openness and extraversion, were established for both the active-reflective and sensing-intuitive learning style preferences. Discussion of other relationships is provided.
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Date Issued
2006
Identifier
CFE0001403, ucf:47074
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0001403
USING STUDENT MOOD AND TASK PERFORMANCE TO TRAIN CLASSIFIER ALGORITHMS TO SELECT EFFECTIVE COACHING STRATEGIES WITHIN INTELLIGENT TUTORING SYSTEMS (ITS)
Title
USING STUDENT MOOD AND TASK PERFORMANCE TO TRAIN CLASSIFIER ALGORITHMS TO SELECT EFFECTIVE COACHING STRATEGIES WITHIN INTELLIGENT TUTORING SYSTEMS (ITS).
Creator
Sottilare, Robert, Proctor, Michael, University of Central Florida
Abstract / Description
The ultimate goal of this research was to improve student performance by adjusting an Intelligent Tutoring System's (ITS) coaching strategy based on the student's mood. As a step toward this goal, this study evaluated the relationships between each student's mood variables (pleasure, arousal, dominance and mood intensity), the coaching strategy selected by the ITS and the student's performance. Outcomes included methods to increase the perception of the intelligent tutor to...
Show moreThe ultimate goal of this research was to improve student performance by adjusting an Intelligent Tutoring System's (ITS) coaching strategy based on the student's mood. As a step toward this goal, this study evaluated the relationships between each student's mood variables (pleasure, arousal, dominance and mood intensity), the coaching strategy selected by the ITS and the student's performance. Outcomes included methods to increase the perception of the intelligent tutor to allow it to adapt coaching strategies (methods of instruction) to the student's affective needs to mitigate barriers to performance (e.g. negative affect) during the one-to-one tutoring process. The study evaluated whether the affective state (specifically mood) of the student moderated the student's interaction with the tutor and influenced performance. This research examined the relationships, interactions and influences of student mood in the selection of ITS coaching strategies to determine which strategies were more effective in terms of student performance given the student's mood, state (recent sleep time, previous knowledge and training, and interest level) and actions (e.g. mouse movement rate). Two coaching strategies were used in this study: Student-Requested Feedback (SRF) and Tutor-Initiated Feedback (TIF). The SRF coaching strategy provided feedback in the form of hints, questions, direction and support only when the student requested help. The TIF coaching strategy provided feedback (hints, questions, direction or support) at key junctures in the learning process when the student either made progress or failed to make progress in a timely fashion. The relationships between the coaching strategies, mood, performance and other variables of interest were considered in light of five hypotheses. At alpha = .05 and beta at least as great as .80, significant effects were limited in predicting performance. Highlighted findings include no significant differences in the mean performance due to coaching strategies, and only small effect sizes in predicting performance making the regression models developed not of practical significance. However, several variables including performance, energy level and mouse movement rates were significant, unobtrusive predictors of mood. Regression algorithms were developed using Arbuckle's (2008) Analysis of MOment Structures (AMOS) tool to compare the predicted performance for each strategy and then to choose the optimal strategy. A set of production rules were also developed to train a machine learning classifier using Witten & Frank's (2005) Waikato Environment for Knowledge Analysis (WEKA) toolset. The classifier was tested to determine its ability to recognize critical relationships and adjust coaching strategies to improve performance. This study found that the ability of the intelligent tutor to recognize key affective relationships contributes to improved performance. Study assumptions include a normal distribution of student mood variables, student state variables and student action variables and the equal mean performance of the two coaching strategy groups (student-requested feedback and tutor-initiated feedback ). These assumptions were substantiated in the study. Potential applications of this research are broad since its approach is application independent and could be used within ill-defined or very complex domains where judgment might be influenced by affect (e.g. study of the law, decisions involving risk of injury or death, negotiations or investment decisions). Recommendations for future research include evaluation of the temporal, as well as numerical, relationships of student mood, performance, actions and state variables.
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Date Issued
2009
Identifier
CFE0002528, ucf:47644
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0002528
Modeling Learner Mood in Realtime through Biosensors for Intelligent Tutoring Improvements
Title
Modeling Learner Mood in Realtime through Biosensors for Intelligent Tutoring Improvements.
Creator
Brawner, Keith, Gonzalez, Avelino, Boloni, Ladislau, Georgiopoulos, Michael, Proctor, Michael, Beidel, Deborah, University of Central Florida
Abstract / Description
Computer-based instructors, just like their human counterparts, should monitor the emotional and cognitive states of their students in order to adapt instructional technique. Doing so requires a model of student state to be available at run time, but this has historically been difficult. Because people are different, generalized models have not been able to be validated. As a person's cognitive and affective state vary over time of day and seasonally, individualized models have had differing...
Show moreComputer-based instructors, just like their human counterparts, should monitor the emotional and cognitive states of their students in order to adapt instructional technique. Doing so requires a model of student state to be available at run time, but this has historically been difficult. Because people are different, generalized models have not been able to be validated. As a person's cognitive and affective state vary over time of day and seasonally, individualized models have had differing difficulties. The simultaneous creation and execution of an individualized model, in real time, represents the last option for modeling such cognitive and affective states. This dissertation presents and evaluates four differing techniques for the creation of cognitive and affective models that are created on-line and in real time for each individual user as alternatives to generalized models. Each of these techniques involves making predictions and modifications to the model in real time, addressing the real time datastream problems of infinite length, detection of new concepts, and responding to how concepts change over time. Additionally, with the knowledge that a user is physically present, this work investigates the contribution that the occasional direct user query can add to the overall quality of such models. The research described in this dissertation finds that the creation of a reasonable quality affective model is possible with an infinitesimal amount of time and without (")ground truth(") knowledge of the user, which is shown across three different emotional states. Creation of a cognitive model in the same fashion, however, was not possible via direct AI modeling, even with all of the (")ground truth(") information available, which is shown across four different cognitive states.
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Date Issued
2013
Identifier
CFE0004822, ucf:49734
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004822
Leveraging Help Requests in POMDP Intelligent Tutors
Title
Leveraging Help Requests in POMDP Intelligent Tutors.
Creator
Folsom-Kovarik, Jeremiah, Sukthankar, Gita, Schatz, Sarah, Gonzalez, Avelino, Shumaker, Randall, Schatz, Sarah, University of Central Florida
Abstract / Description
Intelligent tutoring systems (ITSs) are computer programs that model individual learners and adapt instruction to help each learner differently. One way ITSs differ from human tutors is that few ITSs give learners a way to ask questions. When learners can ask for help, their questions have the potential to improve learning directly and also act as a new source of model data to help the ITS personalize instruction. Inquiry modeling gives ITSs the ability to answer learner questions and refine...
Show moreIntelligent tutoring systems (ITSs) are computer programs that model individual learners and adapt instruction to help each learner differently. One way ITSs differ from human tutors is that few ITSs give learners a way to ask questions. When learners can ask for help, their questions have the potential to improve learning directly and also act as a new source of model data to help the ITS personalize instruction. Inquiry modeling gives ITSs the ability to answer learner questions and refine their learner models with an inexpensive new input channel.In order to support inquiry modeling, an advanced planning formalism is applied to ITS learner modeling. Partially observable Markov decision processes (POMDPs) differ from more widely used ITS architectures because they can plan complex action sequences in uncertain situations with machine learning. Tractability issues have previously precluded POMDP use in ITS models. This dissertation introduces two improvements, priority queues and observation chains, to make POMDPs scale well and encompass the large problem sizes that real-world ITSs must confront. A new ITS was created to support trainees practicing a military task in a virtual environment. The development of the Inquiry Modeling POMDP Adaptive Trainer (IMP) began with multiple formative studies on human and simulated learners that explored inquiry modeling and POMDPs in intelligent tutoring. The studies suggest the new POMDP representations will be effective in ITS domains having certain common characteristics.Finally, a summative study evaluated IMP's ability to train volunteers in specific practice scenarios. IMP users achieved post-training scores averaging up to 4.5 times higher than users who practiced without support and up to twice as high as trainees who used an ablated version of IMP with no inquiry modeling. IMP's implementation and evaluation helped explore questions about how inquiry modeling and POMDP ITSs work, while empirically demonstrating their efficacy.
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Date Issued
2012
Identifier
CFE0004506, ucf:49262
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004506
Learning Opportunities and Challenges of Sensor-enabled Intelligent Tutoring Systems on Mobile Platforms: Benchmarking the Reliability of Mobile Sensors to Track Human Physiological Signals and Behaviors to Enhance Tablet-Based Intelligent Tutoring System
Title
Learning Opportunities and Challenges of Sensor-enabled Intelligent Tutoring Systems on Mobile Platforms: Benchmarking the Reliability of Mobile Sensors to Track Human Physiological Signals and Behaviors to Enhance Tablet-Based Intelligent Tutoring Systems.
Creator
Vazquez, Luis, Proctor, Michael, Jentsch, Florian, Gonzalez, Avelino, Sottilare, Robert, University of Central Florida
Abstract / Description
Desktop-based intelligent tutoring systems have existed for many decades, but the advancement of mobile computing technologies has sparked interest in developing mobile intelligent tutoring systems (mITS). Personalized mITS are applicable to not only stand-alone and client-server systems but also cloud systems possibly leveraging big data. Device-based sensors enable even greater personalization through capture of physiological signals during periods of student study. However, personalizing...
Show moreDesktop-based intelligent tutoring systems have existed for many decades, but the advancement of mobile computing technologies has sparked interest in developing mobile intelligent tutoring systems (mITS). Personalized mITS are applicable to not only stand-alone and client-server systems but also cloud systems possibly leveraging big data. Device-based sensors enable even greater personalization through capture of physiological signals during periods of student study. However, personalizing mITS to individual students faces challenges. The Achilles heel of personalization is the feasibility and reliability of these sensors to accurately capture physiological signals and behavior measures.This research reviews feasibility and benchmarks reliability of basic mobile platform sensors in various student postures. The research software and methodology are generalizable to a range of platforms and sensors. Incorporating the tile-based puzzle game 2048 as a substitute for a knowledge domain also enables a broad spectrum of test populations. Baseline sensors include the on-board camera to detect eyes/faces and the Bluetooth Empatica E4 wristband to capture heart rate, electrodermal activity (EDA), and skin temperature. The test population involved 100 collegiate students randomly assigned to one of three different ergonomic positions in a classroom: sitting at a table, standing at a counter, or reclining on a sofa. Well received by the students, EDA proved to be more reliable than heart rate or face detection in the three different ergonomic positions. Additional insights are provided on advancing learning personalization through future sensor feasibility and reliability studies.
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Date Issued
2018
Identifier
CFE0007260, ucf:52177
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007260
Explicit Feedback Within Game-Based Training: Examining the Influence of Source Modality Effects on Interaction
Title
Explicit Feedback Within Game-Based Training: Examining the Influence of Source Modality Effects on Interaction.
Creator
Goldberg, Benjamin, Bowers, Clint, Cannon-Bowers, Janis, Kincaid, John, McDaniel, Thomas, Sottilare, Robert, University of Central Florida
Abstract / Description
This research aims to enhance Simulation-Based Training (SBT) applications to support training events in the absence of live instruction. The overarching purpose is to explore available tools for integrating intelligent tutoring communications in game-based learning platforms and to examine theory-based techniques for delivering explicit feedback in such environments. The primary tool influencing the design of this research was the Generalized Intelligent Framework for Tutoring (GIFT), a...
Show moreThis research aims to enhance Simulation-Based Training (SBT) applications to support training events in the absence of live instruction. The overarching purpose is to explore available tools for integrating intelligent tutoring communications in game-based learning platforms and to examine theory-based techniques for delivering explicit feedback in such environments. The primary tool influencing the design of this research was the Generalized Intelligent Framework for Tutoring (GIFT), a modular domain-independent architecture that provides the tools and methods to author, deliver, and evaluate intelligent tutoring technologies within any training platform. Influenced by research surrounding Social Cognitive Theory and Cognitive Load Theory, the resulting experiment tested varying approaches for utilizing an Embodied Pedagogical Agent (EPA) to function as a tutor during interaction in a game-based environment. Conditions were authored to assess the tradeoffs between embedding an EPA directly in a game, embedding an EPA in GIFT's browser-based Tutor-User Interface (TUI), or using audio prompts alone with no social grounding.The resulting data supports the application of using an EPA embedded in GIFT's TUI to provide explicit feedback during a game-based learning event. Analyses revealed conditions with an EPA situated in the TUI to be as effective as embedding the agent directly in the game environment. This inference is based on evidence showing reliable differences across conditions on the metrics of performance and self-reported mental demand and feedback usefulness items. This research provides source modality tradeoffs linked to tactics for relaying training relevant explicit information to a user based on real-time performance in a game.
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Date Issued
2013
Identifier
CFE0004850, ucf:49696
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004850