2,474 research outputs found

    Student Modeling in Intelligent Tutoring Systems

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    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

    The ProfessionAl Go annotation datasEt (PAGE)

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    The game of Go has been highly under-researched due to the lack of game records and analysis tools. In recent years, the increasing number of professional competitions and the advent of AlphaZero-based algorithms provide an excellent opportunity for analyzing human Go games on a large scale. In this paper, we present the ProfessionAl Go annotation datasEt (PAGE), containing 98,525 games played by 2,007 professional players and spans over 70 years. The dataset includes rich AI analysis results for each move. Moreover, PAGE provides detailed metadata for every player and game after manual cleaning and labeling. Beyond the preliminary analysis of the dataset, we provide sample tasks that benefit from our dataset to demonstrate the potential application of PAGE in multiple research directions. To the best of our knowledge, PAGE is the first dataset with extensive annotation in the game of Go. This work is an extended version of [1] where we perform a more detailed description, analysis, and application.Comment: Journal version of arXiv:2205.00254, under revie

    Learning Path Construction in e-Learning – What to Learn and How to Learn?

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    Whether in traditional or e learning, it is important to consider: what to learn, how to learn, and how well students have learned. Since there are various types of students with different learning preferences, learning styles, and learning abilities, it is not easy to provide the best learning approach for a specific student. Designing learning contents for different students is very time consuming and tedious for teachers. No matter how the learning process is carried out, both teachers and students must be satisfied with students’ learning performance. Therefore, it is important to provide helpful teaching and learning guidance for teachers and students. In order to achieve this, we proposed a fined-grained outcome-based learning path model, which allows teachers to explicitly formulate learning activities as the learning units of a learning path. This allows teachers to formulate the assessment criteria related to the subject-specific knowledge and skills as well as generic skills, so that the pedagogy could be defined and properly incorporated. Apart from defining the pedagogical approaches, we also need to provide tailored learning contents of the courses, so that different types of students can better learn the knowledge according to their own learning abilities, knowledge backgrounds, etc. On the other hand, those learning contents should be well-structured, so that students can understand them. To achieve this, we have proposed a learning path generation method based on Association Link Network to automatically identify the relationships among different Web resources. This method makes use of the Web resources that can be freely obtained from the Web to form well-structured learning resources with proper sequences for delivery. Although the learning path defines what to learn and how to learn, we still needed to monitor student learning progress in order to determine proper learning contents and learning activities in an e-Learning system. To address the problem, we proposed the use of student progress indicators based on Fuzzy Cognitive Map to analyze both performance and non-performance attributes and their causal relationships. The aim is to help teachers improve their teaching approaches and help students reflect their strengths and weaknesses in learning. . This research focuses on the intelligent tutoring e-Learning system, which provides an intelligent approach to design and delivery learning activities in a learning path. Many experiments and comparative studies on both teachers and students have been carried out in order to evaluate the research of this PhD thesis. The results show that our research can effectively help teachers generate high quality learning paths, help students improve their learning performance, and offer both teachers and students a better understanding on student learning progress

    Adaptive plasticity in the healthy reading network investigated through combined neurostimulation and neuroimaging

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    The reading network in the human brain comprises several regions, including the left inferior frontal cortex (IFC), ventral occipito-temporal cortex (vOTC) and dorsal temporo-parietal cortex (TPC). The left TPC is crucial for phonological decoding, i.e., for learning and retaining sound-letter mappings. Here, we tested the causal contribution of this area for reading with repetitive transcranial magnetic stimulation (rTMS) and explored the response of the reading network using functional magnetic resonance imaging (fMRI). 28 healthy adult readers overtly read simple and complex words and pseudowords during fMRI after effective or sham TMS over the left TPC. Behaviorally, effective stimulation slowed pseudoword reading. A multivariate pattern analysis showed a shift in activity patterns in the left IFC for pseudoword reading after effective relative to sham TMS. Furthermore, active TMS led to increased effective connectivity from the left vOTC to the left TPC, specifically for pseudoword processing. The observed changes in task-related activity and connectivity suggest compensatory reorganization in the reading network following TMS-induced disruption of the left TPC. Our findings provide first evidence for a causal role of the left TPC for overt pseudoword reading and emphasize the relevance of functional interactions in the healthy reading network for successful pseudoword processing

    Understanding the Keystroke Log:The Effect of Writing Task on Keystroke Features

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    Keystroke logging is used to automatically record writers' unfolding typing process and to get insight into moments when they struggle composing text. However, it is not clear which and how features from the keystroke log map to higher-level cognitive processes, such as planning and revision. This study aims to investigate the sensitivity of requently used keystroke features across tasks with different cognitive demands. Two keystroke datasets were analyzed: one consisting of a copy task and an email writing task, and one with a larger difference in cognitive demand: a copy task and an academic summary task. The differences across tasks were modeled using Bayesian linear mixed effects models. Posterior distributions were used to compare the strength and direction of the task effects across features and datasets. The results showed that the average of all interkeystroke intervals were found to be stable across tasks. Features related to the time between words and (sub)sentences only differed between the copy and the academic task. Lastly, keystroke features related to the number of words, revisions, and total time, differed across tasks in both datasets. To conclude, our results indicate that the latter features are related to cognitive load or task complexity. In addition, our research shows that keystroke features are sensitive to small differences in the writing tasks at hand
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