The Experts below are selected from a list of 23703 Experts worldwide ranked by ideXlab platform
Roger Azevedo - One of the best experts on this subject based on the ideXlab platform.
-
integrating metacognitive judgments and eye movements using sequential pattern Mining to understand processes underlying multimedia learning
Computers in Human Behavior, 2019Co-Authors: Nicholas V Mudrick, Roger Azevedo, Michelle TaubAbstract:Abstract Metacomprehension is key to successful learning of complex topics when using multimedia materials. The goal of this study was to determine if eye-movement dyads could be: (1) identified by Sequence Mining techniques, and (2) aligned with self-reported metacognitive judgments during learning with multimedia materials that contain conceptual discrepancies designed to interfere with participants' metacomprehension. Thirty-two undergraduate students' metacognitive judgments were examined with RM-MANOVAs, and sequential pattern Mining and differential Sequence Mining were conducted on their eye movements as they learned with complex multimedia materials. Additionally, we distinguished between event- (i.e., if participants looked at specific areas of the content) and duration-based (i.e., if participants looked at areas of interest [AOIs] for a medium or long amount of time) eye-movement dyads to assess if qualitative and quantitative differences existed in their eye-movement behaviors. For content with text and graph discrepancies, results indicated participants' metacognitive judgments were lower and less accurate, and more fixation dyads were found between the text and graph. Furthermore, specific dyads of different length (i.e., long fixations on the graph to medium fixations on the text) fixations may align with lowered and inaccurate metacognitive judgments for content with text and graph discrepancies. This study begins to address how to identify behavioral indices of metacomprehension processes during multimedia learning.
-
How Does Prior Knowledge Influence Eye Fixations and Sequences of Cognitive and Metacognitive SRL Processes during Learning with an Intelligent Tutoring System?
International Journal of Artificial Intelligence in Education, 2019Co-Authors: Michelle Taub, Roger AzevedoAbstract:The goal of this study was to use eye-tracking and log-file data to investigate the impact of prior knowledge on college students’ (N = 194, with a subset of n = 30 for eye tracking and Sequence Mining analyses) fixations on (i.e., looking at) self-regulated learning-related areas of interest (i.e., specific locations on the interface) and on the Sequences of engaging in cognitive and metacognitive self-regulated learning processes during learning with MetaTutor, an Intelligent Tutoring System that teaches students about the human circulatory system. Results revealed that there were no significant differences in fixations on single areas of interest by the prior knowledge group students were assigned to; however there were significant differences in fixations on pairs of areas of interest, as evidenced by eye-tracking data. Furthermore, there were significant differences in sequential patterns of engaging in cognitive and metacognitive self-regulated learning processes by students’ prior knowledge group, as evidenced from log-file data. Specifically, students with high prior knowledge engaged in processes containing cognitive strategies and metacognitive strategies whereas students with low prior knowledge did not. These results have implications for designing adaptive intelligent tutoring systems that provide individualized scaffolding and feedback based on individual differences, such as levels of prior knowledge.
-
using Sequence Mining to analyze metacognitive monitoring and scientific inquiry based on levels of efficiency and emotions during game based learning
Educational Data Mining, 2018Co-Authors: Michelle Taub, Roger AzevedoAbstract:Self-regulated learning conducted through metacognitive monitoring and scientific inquiry can be influenced by many factors, such as emotions and motivation, and are necessary skills needed to engage in efficient hypothesis testing during game-based learning. Although many studies have investigated metacognitive monitoring and scientific inquiry skills during game-based learning, few studies have investigated how the Sequence of behaviors involved during hypothesis testing with game-based learning differ based on both efficiency level and emotions during gameplay. For this study, we analyzed 59 undergraduate students’ (59% female) metacognitive monitoring and hypothesis testing behavior during learning and gameplay with CRYSTAL ISLAND, a game-based learning environment that teaches students about microbiology. Specifically, we used sequential pattern Mining and differential Sequence Mining to determine if there were Sequences of hypothesis testing behaviors and to determine if the frequencies of occurrence of these Sequences differed between high or low levels of efficiency at finishing the game and high or low levels of facial expressions of emotions during gameplay. Results revealed that students with low levels of efficiency and high levels of facial expressions of emotions had the most Sequences of testing behaviors overall, specifically engaging in more Sequences that were indicative of less strategic hypothesis testing behavior than the other students, where students who were more efficient with both levels of emotions demonstrated strategic testing behavior. These results have implications for the strengths of using educational data Mining techniques for deterMining the processes underlying patterns of engaging in self-regulated learning conducted through hypothesis testing as they unfold over time; for training students on how to engage in the self-regulation, scientific inquiry, and emotion regulation processes that can result in efficient gameplay; and for developing adaptive game-based learning environments that foster effective and efficient self-regulation and scientific inquiry during learning.
-
using Sequence Mining to reveal the efficiency in scientific reasoning during stem learning with a game based learning environment
Learning and Instruction, 2017Co-Authors: Michelle Taub, Roger Azevedo, Garrett C Millar, Amanda E Bradbury, James C LesterAbstract:Abstract The goal of this study was to assess how metacognitive monitoring and scientific reasoning impacted the efficiency of game completion during learning with Crystal Island, a game-based learning environment that fosters self-regulated learning and scientific reasoning by having participants solve the mystery of what illness impacted inhabitants of the island. We conducted sequential pattern Mining and differential Sequence Mining on 64 undergraduate participants’ hypothesis testing behavior. Patterns were coded based on the relevancy of what items were being tested for, and the items themselves. Results revealed that participants who were more efficient at solving the mystery tested significantly fewer partially-relevant and irrelevant items than less efficient participants. Additionally, more efficient participants had fewer Sequences of testing items overall, and significantly lower instance support values of the PartiallyRelevant -- Relevant to Relevant -- Relevant and PartiallyRelevant -- PartiallyRelevant to Relevant--Partially Relevant Sequences compared to less efficient participants. These findings have implications for designing adaptive GBLEs that scaffold participants based on in-game behaviors.
-
identifying students characteristic learning behaviors in an intelligent tutoring system fostering self regulated learning
Educational Data Mining, 2012Co-Authors: Francois Bouchet, Gautam Biswas, John S Kinnebrew, Roger AzevedoAbstract:Identification of student learning behaviors, especially those that characterize or distinguish students, can yield important insights for the design of adaptation and feedback mechanisms in Intelligent Tutoring Systems (ITS). In this paper, we analyze trace data to identify distinguishing patterns of behavior in a study of 51 college students learning about a complex science topic with an agent-based ITS that fosters self-regulated learning (SRL). Preliminary analysis with an Expectation-Maximization clustering algorithm revealed the existence of three distinct groups of students, distinguished by their test and quiz scores (low for the first group, medium for the second group, and high for the third group), their learning gains (low, medium, high), the frequency of their note-taking (rare, frequent, rare) and note-checking (rare, rare, frequent), the proportion of sub-goals attempted (low, low, high), and the time spent reading (high, high, low). In this paper, we extend this analysis to identify characteristic learning behaviors and strategies that distinguish these three groups of students. We employ a differential Sequence Mining technique to identify differentially frequent activity patterns between the student groups and interpret these patterns in terms of relevant learning behaviors. The results of this analysis reveal that high-performing students tend to be better at quickly identifying the relevance of a page to their subgoal, are more methodical in their exploration of the pedagogical content, rely on system prompts to take notes and summarize, and are more strategic in their preparation for the post-test (e.g., using the end of their session to briefly review pages). These results provide a first step in identifying the group to which a student belongs during the learning session, thus making possible a real-time adaptation of the system.
Tias Guns - One of the best experts on this subject based on the ideXlab platform.
-
constraint based Sequence Mining using constraint programming
Integration of AI and OR Techniques in Constraint Programming, 2015Co-Authors: Benjamin Negrevergne, Tias GunsAbstract:The goal of constraint-based Sequence Mining is to find Sequences of symbols that are included in a large number of input Sequences and that satisfy some constraints specified by the user. Many constraints have been proposed in the literature, but a general framework is still missing. We investigate the use of constraint programming as general framework for this task.
-
constraint based Sequence Mining using constraint programming
arXiv: Artificial Intelligence, 2015Co-Authors: Benjamin Negrevergne, Tias GunsAbstract:The goal of constraint-based Sequence Mining is to find Sequences of symbols that are included in a large number of input Sequences and that satisfy some constraints specified by the user. Many constraints have been proposed in the literature, but a general framework is still missing. We investigate the use of constraint programming as general framework for this task. We first identify four categories of constraints that are applicable to Sequence Mining. We then propose two constraint programming formulations. The first formulation introduces a new global constraint called exists-embedding. This formulation is the most efficient but does not support one type of constraint. To support such constraints, we develop a second formulation that is more general but incurs more overhead. Both formulations can use the projected database technique used in specialised algorithms. Experiments demonstrate the flexibility towards constraint-based settings and compare the approach to existing methods.
Georgiana Ifrim - One of the best experts on this subject based on the ideXlab platform.
-
interpretable time series classification using linear models and multi resolution multi domain symbolic representations
arXiv: Learning, 2020Co-Authors: Thach Le Nguyen, Severin Gsponer, Iulia Ilie, Martin Oreilly, Georgiana IfrimAbstract:The time series classification literature has expanded rapidly over the last decade, with many new classification approaches published each year. Prior research has mostly focused on improving the accuracy and efficiency of classifiers, with interpretability being somewhat neglected. This aspect of classifiers has become critical for many application domains and the introduction of the EU GDPR legislation in 2018 is likely to further emphasize the importance of interpretable learning algorithms. Currently, state-of-the-art classification accuracy is achieved with very complex models based on large ensembles (COTE) or deep neural networks (FCN). These approaches are not efficient with regard to either time or space, are difficult to interpret and cannot be applied to variable-length time series, requiring pre-processing of the original series to a set fixed-length. In this paper we propose new time series classification algorithms to address these gaps. Our approach is based on symbolic representations of time series, efficient Sequence Mining algorithms and linear classification models. Our linear models are as accurate as deep learning models but are more efficient regarding running time and memory, can work with variable-length time series and can be interpreted by highlighting the discriminative symbolic features on the original time series. We show that our multi-resolution multi-domain linear classifier (mtSS-SEQL+LR) achieves a similar accuracy to the state-of-the-art COTE ensemble, and to recent deep learning methods (FCN, ResNet), but uses a fraction of the time and memory required by either COTE or deep models. To further analyse the interpretability of our classifier, we present a case study on a human motion dataset collected by the authors. We release all the results, source code and data to encourage reproducibility.
-
Interpretable time series classification using linear models and multi-resolution multi-domain symbolic representations
Data Mining and Knowledge Discovery, 2019Co-Authors: Thach Nguyen, Severin Gsponer, Iulia Ilie, Martin O’reilly, Georgiana IfrimAbstract:The time series classification literature has expanded rapidly over the last decade, with many new classification approaches published each year. Prior research has mostly focused on improving the accuracy and efficiency of classifiers, with interpretability being somewhat neglected. This aspect of classifiers has become critical for many application domains and the introduction of the EU GDPR legislation in 2018 is likely to further emphasize the importance of interpretable learning algorithms. Currently, state-of-the-art classification accuracy is achieved with very complex models based on large ensembles (COTE) or deep neural networks (FCN). These approaches are not efficient with regard to either time or space, are difficult to interpret and cannot be applied to variable-length time series, requiring pre-processing of the original series to a set fixed-length. In this paper we propose new time series classification algorithms to address these gaps. Our approach is based on symbolic representations of time series, efficient Sequence Mining algorithms and linear classification models. Our linear models are as accurate as deep learning models but are more efficient regarding running time and memory, can work with variable-length time series and can be interpreted by highlighting the discriminative symbolic features on the original time series. We advance the state-of-the-art in time series classification by proposing new algorithms built using the following three key ideas: (1) Multiple resolutions of symbolic representations: we combine symbolic representations obtained using different parameters, rather than one fixed representation (e.g., multiple SAX representations); (2) Multiple domain representations: we combine symbolic representations in time (e.g., SAX) and frequency (e.g., SFA) domains, to be more robust across problem types; (3) Efficient navigation in a huge symbolic-words space: we extend a symbolic Sequence classifier (SEQL) to work with multiple symbolic representations and use its greedy feature selection strategy to effectively filter the best features for each representation. We show that our multi-resolution multi-domain linear classifier (mtSS-SEQL+LR) achieves a similar accuracy to the state-of-the-art COTE ensemble, and to recent deep learning methods (FCN, ResNet), but uses a fraction of the time and memory required by either COTE or deep models. To further analyse the interpretability of our classifier, we present a case study on a human motion dataset collected by the authors. We discuss the accuracy, efficiency and interpretability of our proposed algorithms and release all the results, source code and data to encourage reproducibility.
Michelle Taub - One of the best experts on this subject based on the ideXlab platform.
-
integrating metacognitive judgments and eye movements using sequential pattern Mining to understand processes underlying multimedia learning
Computers in Human Behavior, 2019Co-Authors: Nicholas V Mudrick, Roger Azevedo, Michelle TaubAbstract:Abstract Metacomprehension is key to successful learning of complex topics when using multimedia materials. The goal of this study was to determine if eye-movement dyads could be: (1) identified by Sequence Mining techniques, and (2) aligned with self-reported metacognitive judgments during learning with multimedia materials that contain conceptual discrepancies designed to interfere with participants' metacomprehension. Thirty-two undergraduate students' metacognitive judgments were examined with RM-MANOVAs, and sequential pattern Mining and differential Sequence Mining were conducted on their eye movements as they learned with complex multimedia materials. Additionally, we distinguished between event- (i.e., if participants looked at specific areas of the content) and duration-based (i.e., if participants looked at areas of interest [AOIs] for a medium or long amount of time) eye-movement dyads to assess if qualitative and quantitative differences existed in their eye-movement behaviors. For content with text and graph discrepancies, results indicated participants' metacognitive judgments were lower and less accurate, and more fixation dyads were found between the text and graph. Furthermore, specific dyads of different length (i.e., long fixations on the graph to medium fixations on the text) fixations may align with lowered and inaccurate metacognitive judgments for content with text and graph discrepancies. This study begins to address how to identify behavioral indices of metacomprehension processes during multimedia learning.
-
How Does Prior Knowledge Influence Eye Fixations and Sequences of Cognitive and Metacognitive SRL Processes during Learning with an Intelligent Tutoring System?
International Journal of Artificial Intelligence in Education, 2019Co-Authors: Michelle Taub, Roger AzevedoAbstract:The goal of this study was to use eye-tracking and log-file data to investigate the impact of prior knowledge on college students’ (N = 194, with a subset of n = 30 for eye tracking and Sequence Mining analyses) fixations on (i.e., looking at) self-regulated learning-related areas of interest (i.e., specific locations on the interface) and on the Sequences of engaging in cognitive and metacognitive self-regulated learning processes during learning with MetaTutor, an Intelligent Tutoring System that teaches students about the human circulatory system. Results revealed that there were no significant differences in fixations on single areas of interest by the prior knowledge group students were assigned to; however there were significant differences in fixations on pairs of areas of interest, as evidenced by eye-tracking data. Furthermore, there were significant differences in sequential patterns of engaging in cognitive and metacognitive self-regulated learning processes by students’ prior knowledge group, as evidenced from log-file data. Specifically, students with high prior knowledge engaged in processes containing cognitive strategies and metacognitive strategies whereas students with low prior knowledge did not. These results have implications for designing adaptive intelligent tutoring systems that provide individualized scaffolding and feedback based on individual differences, such as levels of prior knowledge.
-
using Sequence Mining to analyze metacognitive monitoring and scientific inquiry based on levels of efficiency and emotions during game based learning
Educational Data Mining, 2018Co-Authors: Michelle Taub, Roger AzevedoAbstract:Self-regulated learning conducted through metacognitive monitoring and scientific inquiry can be influenced by many factors, such as emotions and motivation, and are necessary skills needed to engage in efficient hypothesis testing during game-based learning. Although many studies have investigated metacognitive monitoring and scientific inquiry skills during game-based learning, few studies have investigated how the Sequence of behaviors involved during hypothesis testing with game-based learning differ based on both efficiency level and emotions during gameplay. For this study, we analyzed 59 undergraduate students’ (59% female) metacognitive monitoring and hypothesis testing behavior during learning and gameplay with CRYSTAL ISLAND, a game-based learning environment that teaches students about microbiology. Specifically, we used sequential pattern Mining and differential Sequence Mining to determine if there were Sequences of hypothesis testing behaviors and to determine if the frequencies of occurrence of these Sequences differed between high or low levels of efficiency at finishing the game and high or low levels of facial expressions of emotions during gameplay. Results revealed that students with low levels of efficiency and high levels of facial expressions of emotions had the most Sequences of testing behaviors overall, specifically engaging in more Sequences that were indicative of less strategic hypothesis testing behavior than the other students, where students who were more efficient with both levels of emotions demonstrated strategic testing behavior. These results have implications for the strengths of using educational data Mining techniques for deterMining the processes underlying patterns of engaging in self-regulated learning conducted through hypothesis testing as they unfold over time; for training students on how to engage in the self-regulation, scientific inquiry, and emotion regulation processes that can result in efficient gameplay; and for developing adaptive game-based learning environments that foster effective and efficient self-regulation and scientific inquiry during learning.
-
using Sequence Mining to reveal the efficiency in scientific reasoning during stem learning with a game based learning environment
Learning and Instruction, 2017Co-Authors: Michelle Taub, Roger Azevedo, Garrett C Millar, Amanda E Bradbury, James C LesterAbstract:Abstract The goal of this study was to assess how metacognitive monitoring and scientific reasoning impacted the efficiency of game completion during learning with Crystal Island, a game-based learning environment that fosters self-regulated learning and scientific reasoning by having participants solve the mystery of what illness impacted inhabitants of the island. We conducted sequential pattern Mining and differential Sequence Mining on 64 undergraduate participants’ hypothesis testing behavior. Patterns were coded based on the relevancy of what items were being tested for, and the items themselves. Results revealed that participants who were more efficient at solving the mystery tested significantly fewer partially-relevant and irrelevant items than less efficient participants. Additionally, more efficient participants had fewer Sequences of testing items overall, and significantly lower instance support values of the PartiallyRelevant -- Relevant to Relevant -- Relevant and PartiallyRelevant -- PartiallyRelevant to Relevant--Partially Relevant Sequences compared to less efficient participants. These findings have implications for designing adaptive GBLEs that scaffold participants based on in-game behaviors.
Benjamin Negrevergne - One of the best experts on this subject based on the ideXlab platform.
-
constraint based Sequence Mining using constraint programming
Integration of AI and OR Techniques in Constraint Programming, 2015Co-Authors: Benjamin Negrevergne, Tias GunsAbstract:The goal of constraint-based Sequence Mining is to find Sequences of symbols that are included in a large number of input Sequences and that satisfy some constraints specified by the user. Many constraints have been proposed in the literature, but a general framework is still missing. We investigate the use of constraint programming as general framework for this task.
-
constraint based Sequence Mining using constraint programming
arXiv: Artificial Intelligence, 2015Co-Authors: Benjamin Negrevergne, Tias GunsAbstract:The goal of constraint-based Sequence Mining is to find Sequences of symbols that are included in a large number of input Sequences and that satisfy some constraints specified by the user. Many constraints have been proposed in the literature, but a general framework is still missing. We investigate the use of constraint programming as general framework for this task. We first identify four categories of constraints that are applicable to Sequence Mining. We then propose two constraint programming formulations. The first formulation introduces a new global constraint called exists-embedding. This formulation is the most efficient but does not support one type of constraint. To support such constraints, we develop a second formulation that is more general but incurs more overhead. Both formulations can use the projected database technique used in specialised algorithms. Experiments demonstrate the flexibility towards constraint-based settings and compare the approach to existing methods.
