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T.: Modeling Individualization in a Bayesian Networks Implementation of Knowledge Tracing
- In P. De Bra, A. Kobsa, and D. Chin (Eds.): UMAP 2010, LNCS 6075
, 2010
"... Abstract. The field of intelligent tutoring systems has been using the well known knowledge tracing model, popularized by Corbett and Anderson (1995), to track student knowledge for over a decade. Surprisingly, models currently in use do not allow for individual learning rates nor individualized est ..."
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Cited by 56 (16 self)
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Abstract. The field of intelligent tutoring systems has been using the well known knowledge tracing model, popularized by Corbett and Anderson (1995), to track student knowledge for over a decade. Surprisingly, models currently in use do not allow for individual learning rates nor individualized estimates of student initial knowledge. Corbett and Anderson, in their original articles, were interested in trying to add individualization to their model which they accomplished but with mixed results. Since their original work, the field has not made significant progress towards individualization of knowledge tracing models in fitting data. In this work, we introduce an elegant way of formulating the individualization problem entirely within a Bayesian networks framework that fits individualized as well as skill specific parameters simultaneously, in a single step. With this new individualization technique we are able to show a reliable improvement in prediction of real world data by individualizing the initial knowledge parameter. We explore three difference strategies for setting the initial individualized knowledge parameters and report that the best strategy is one in which information from multiple skills is used to inform each student’s prior. Using this strategy we achieved lower prediction error in 33 of the 42 problem sets evaluated. The implication of this work is the ability to enhance existing intelligent tutoring systems to more accurately estimate when a student has reached mastery of a skill. Adaptation of instruction based on individualized knowledge and learning speed is discussed as well as open research questions facing those that wish to exploit student and skill information in their user models.
Applying Data Mining Techniques to e-Learning Problems: a Survey and State of the Art: Evolution of Technology and Pedagogy
"... Abstract. This chapter aims to provide an up-to-date snapshot of the current state of research and applications of Data Mining methods in e-learning. The cross-fertilization of both areas is still in its infancy, and even academic references are scarce on the ground, although some leading education- ..."
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Cited by 33 (2 self)
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Abstract. This chapter aims to provide an up-to-date snapshot of the current state of research and applications of Data Mining methods in e-learning. The cross-fertilization of both areas is still in its infancy, and even academic references are scarce on the ground, although some leading education-related publications are already beginning to pay attention to this new field. In order to offer a reasonable organization of the available bibliographic information according to different criteria, firstly, and from the Data Mining practitioner point of view, references are organized according to the type of modelling techniques used, which include: Neural Networks, Genetic Algorithms, Clustering and Visualization Methods, Fuzzy Logic, Intelligent agents, and Inductive Reasoning, amongst others. From the same point of view, the information is organized according to the type of Data Mining problem dealt with: clustering, classification, prediction, etc. Finally, from the standpoint of the e-learning practitioner, we provide a taxonomy of e-learning problems to which Data Mining techniques have been applied, including, for instance: Students ’ classification based on their learning performance; detection of irregular learning behaviours; e-learning system navigation and interaction optimization; clustering according to similar e-learning system usage; and systems ’ adaptability to students ’ requirements and capacities. 1
Does help help? Introducing the Bayesian Evaluation and Assessment methodology
"... Abstract. Most ITS have a means of providing assistance to the student, either on student request or when the tutor determines it would be effective. Presumably, such assistance is included by the ITS designers since they feel it benefits the students. However, whether—and how—help helps students ha ..."
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Cited by 29 (7 self)
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Abstract. Most ITS have a means of providing assistance to the student, either on student request or when the tutor determines it would be effective. Presumably, such assistance is included by the ITS designers since they feel it benefits the students. However, whether—and how—help helps students has not been a well studied problem in the ITS community. In this paper we present three approaches for evaluating the efficacy of the Reading Tutor’s help: creating experimental trials from data, learning decomposition, and Bayesian Evaluation and Assessment, an approach that uses dynamic Bayesian networks. We have found that experimental trials and learning decomposition both find a negative benefit for help--that is, help hurts! However, the Bayesian Evaluation and Assessment framework finds that help both promotes student long-term learning and provides additional scaffolding on the current problem. We discuss why these approaches give divergent results, and suggest that the Bayesian Evaluation and Assessment framework is the strongest of the three. In addition to introducing Bayesian Evaluation and Assessment, a method for simultaneously assessing students and evaluating tutorial interventions, this paper describes how help can both scaffold the current problem attempt as well as teach the student knowledge that will transfer to later problems. Key words: educational data mining, dynamic Bayesian networks, assessment,
Difficulties in inferring student knowledge from observations (and why you should care
, 2007
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Factorization models for forecasting student performance
- In Proceedings of the 4th International Conference on Educational Data Mining (EDM
, 2011
"... Predicting student performance (PSP) is one of the educational data mining task, where we would like to know how much knowledge the students have gained and whether they can perform the tasks (or exercises) correctly. Since the student’s knowledge improves and cumulates over time, the sequential (te ..."
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Cited by 17 (2 self)
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Predicting student performance (PSP) is one of the educational data mining task, where we would like to know how much knowledge the students have gained and whether they can perform the tasks (or exercises) correctly. Since the student’s knowledge improves and cumulates over time, the sequential (temporal) effect is an important information for PSP. Previous works have shown that PSP can be casted as rating prediction task in recommender systems, and therefore, factorization techniques can be applied for this task. To take into account the sequential effect, this work proposes a novel approach which uses tensor factorization for forecasting student performance. With this approach, we can personalize the prediction for each student given the task, thus, it can also be used for recommending the tasks to the students. Experimental results on two large data sets show that incorporating forecasting techniques into the factorization process is a promising approach.
Faster Teaching by POMDP Planning
"... Abstract. Both human and automated tutors must infer what a student knows and plan future actions to maximize learning. Though substantial research has been done on tracking and modeling student learning, there has been significantly less attention on planning teaching actions and how the assumed st ..."
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Cited by 16 (2 self)
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Abstract. Both human and automated tutors must infer what a student knows and plan future actions to maximize learning. Though substantial research has been done on tracking and modeling student learning, there has been significantly less attention on planning teaching actions and how the assumed student model impacts the resulting plans. We frame the problem of optimally selecting teaching actions using a decision-theoretic approach and show how to formulate teaching as a partially-observable Markov decision process (POMDP) planning problem. We consider three models of student learning and present approximate methods for finding optimal teaching actions given the large state and action spaces that arise in teaching. An experimental evaluation of the resulting policies on a simple concept-learning task shows that framing teacher action planning as a POMDP can accelerate learning relative to baseline performance. 1
The Sum is Greater than the Parts: Ensembling Models of Student Knowledge in Educational Software
- THE JOURNAL OF MACHINE LEARNING RESEARCH W & CP
, 2012
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Using Logistic Regression to Trace Multiple Subskills in a Dynamic Bayes Net
, 2011
"... A challenge in estimating students ’ changing knowledge from sequential observations of their performance arises when each observed step involves multiple subskills. To overcome this mismatch in grain size between modelled skills and observed actions, we use logistic regression over each step’s subs ..."
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Cited by 10 (7 self)
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A challenge in estimating students ’ changing knowledge from sequential observations of their performance arises when each observed step involves multiple subskills. To overcome this mismatch in grain size between modelled skills and observed actions, we use logistic regression over each step’s subskills in a dynamic Bayes net (LR-DBN) to model transition probabilities for the overall knowledge required by the step. Unlike previous methods, LR-DBN can trace knowledge of the individual subskills without assuming they are independent. We evaluate how well it fits children’s oral reading fluency data logged by Project LISTEN’s Reading Tutor, compared to other methods. Key terms: dynamic Bayes net, logistic regression, knowledge tracing, multiple subskills, oral reading fluency
Ensembling predictions of student knowledge within intelligent tutoring systems.
- In User Modeling, Adaption and Personalization,
, 2011
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Does Help Help? A Bayes Net Approach to Modeling Tutor Interventions
- in proceedings of Workshop on Educational Data Mining at AAAI 2006, 41-6. Menlo
, 2006
"... This paper describes an effort to measure the effectiveness of tutor help in an intelligent tutoring system. Conventional pre- and post- test experimental methods can determine whether help is effective but are expensive to conduct. Furthermore, a pre and post- test methodology ignores a source of i ..."
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Cited by 7 (3 self)
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This paper describes an effort to measure the effectiveness of tutor help in an intelligent tutoring system. Conventional pre- and post- test experimental methods can determine whether help is effective but are expensive to conduct. Furthermore, a pre and post- test methodology ignores a source of information: students request help about words they do not know. Therefore, we propose a dynamic Bayes net (which we call the help model) that models tutor help and student knowledge in one coherent framework. The help model distinguishes two different effects of help: scaffolding immediate performance vs. teaching persistent knowledge that improves long term performance. We train the help model to fit the student performance data gathered from usage of Reading Tutor. The parameters of the trained model suggest that students benefit from both the scaffolding and teaching effects of help. Thus, our framework is able to distinguish two types of influence that help has on the student, and can determine whether help helps learning without an explicit controlled study.
