Feature Based User Modeling

@Article{WebbPazzaniBillsus01,
Title = {Machine learning for user modeling},
Author = {Webb, G. I. and Pazzani, M. J. and Billsus, D.},
Journal = {User Modeling and User-Adapted Interaction},
Year = {2001},
Pages = {19-20},
Volume = {11},
Abstract = {At first blush, user modeling appears to be a prime candidate for straight forward application of standard machine learning techniques. Observations of the user's behavior can provide training examples that a machine learning system can use to form a model designed to predict future actions. However, user modeling poses a number of challenges for machine learning that have hindered its application in user modeling, including: the need for large data sets; the need for labelled data; concept drift; and computational complexity. This paper examines each of these issues and reviews approaches to resolving them.},
Address = {Netherlands},
Audit-trail = {Link to pdf via UMUAI site. Also available at http://www.kluweronline.com/issn/0924-1868},
Doi = {10.1023/A:1011117102175},
Keywords = {Feature Based Modeling and User Modeling},
Publisher = {Springer},
Related = {feature-based-modeling}
}

ABSTRACT At first blush, user modeling appears to be a prime candidate for straight forward application of standard machine learning techniques. Observations of the user's behavior can provide training examples that a machine learning system can use to form a model designed to predict future actions. However, user modeling poses a number of challenges for machine learning that have hindered its application in user modeling, including: the need for large data sets; the need for labelled data; concept drift; and computational complexity. This paper examines each of these issues and reviews approaches to resolving them.

@InProceedings{ChiuWebb99a,
author = {Chiu, B. C. and Webb, G. I.},
booktitle = {Proceedings of the Seventh International Conference on Computers in Education (ICCE '99)},
title = {Dual-Model: An Architecture for Utilizing Temporal Information in Student Modeling},
year = {1999},
address = {Amsterdam},
editor = {Cumming, G. and Okamoto, T. and Gomez, L.},
pages = {111-118},
publisher = {IOS Press},
volume = {1},
abstract = {A modeling system may be required to predict an agent's future actions even when confronted by inadequate or contradictory relevant evidence from observations of past actions. This can result in low prediction accuracy, or otherwise, low prediction rates, leaving a set of cases for which no predictions are made. This raises two issues. First, when maximizing prediction rate is preferable, what mechanisms can be employed such that a system can make more predictions without severely degrading prediction accuracy? Second, for contexts in which accuracy is of primary importance, how can we further improve prediction accuracy? A recently proposed Dual-model approach, which takes models' temporal characteristics into account, suggests a solution to the first problem, but leaves room for further improvement. This paper presents two classes of Dual-model variant. Each aims to achieve one of the above objectives. With the performance of the original system as a baseline, which does not utilize the temporal information, empirical evaluations in the domain of elementary subtraction show that one class of variant outperforms the baseline in prediction rate while the other does so in prediction accuracy, without significantly affecting other overall measures of the original performance. Keywords: Agent modeling, Student modeling, Temporal model, Decision tree.},
audit-trail = {*},
keywords = {Feature Based Modeling and User Modeling},
location = {Chiba, Japan.(Also appeared in the Proceedings of ACAI Workshop W03: Machine Learning in User Modeling, pp 46-53)},
related = {feature-based-modeling},
}

ABSTRACT A modeling system may be required to predict an agent's future actions even when confronted by inadequate or contradictory relevant evidence from observations of past actions. This can result in low prediction accuracy, or otherwise, low prediction rates, leaving a set of cases for which no predictions are made. This raises two issues. First, when maximizing prediction rate is preferable, what mechanisms can be employed such that a system can make more predictions without severely degrading prediction accuracy? Second, for contexts in which accuracy is of primary importance, how can we further improve prediction accuracy? A recently proposed Dual-model approach, which takes models' temporal characteristics into account, suggests a solution to the first problem, but leaves room for further improvement. This paper presents two classes of Dual-model variant. Each aims to achieve one of the above objectives. With the performance of the original system as a baseline, which does not utilize the temporal information, empirical evaluations in the domain of elementary subtraction show that one class of variant outperforms the baseline in prediction rate while the other does so in prediction accuracy, without significantly affecting other overall measures of the original performance. Keywords: Agent modeling, Student modeling, Temporal model, Decision tree.

@InProceedings{WebbKuzmycz98,
author = {Webb, G. I. and Kuzmycz, M.},
booktitle = {Lecture Notes in Computer Science Vol. 1452: Proceedings of the Fourth International Conference on Intelligent Tutoring Systems (ITS '98)},
title = {Evaluation Of Data Aging: A Technique For Discounting Old Data During Student Modeling},
year = {1998},
address = {Berlin},
editor = {Goettl, B.P. and Halff, H. M. and Redfield, C. and Shute, V.},
pages = {384-393},
publisher = {Springer-Verlag},
abstract = {Student modeling systems must operate in an environment in which a student's mastery of a subject matter is likely to change as a lesson progresses. A student model is formed from evaluation of evidence about the student's mastery of the domain. However, given that such mastery will change, older evidence is likely to be less valuable than recent evidence. Data aging addresses this issue by discounting the value of older evidence. This paper provides experimental evaluation of the effects of data aging. While it is demonstrated that data aging can result in statistically significant increases in both the number and accuracy of predictions that a modeling system makes, it is also demonstrated that the reverse can be true. Further, the effects experienced are of only small magnitude. It is argued that these results demonstrate some potential for data aging as a general strategy, but do not warrant employing data aging in its current form.},
audit-trail = {PDF posted},
keywords = {Feature Based Modeling and User Modeling},
location = {San Antonio, Texas},
related = {feature-based-modeling},
}

ABSTRACT Student modeling systems must operate in an environment in which a student's mastery of a subject matter is likely to change as a lesson progresses. A student model is formed from evaluation of evidence about the student's mastery of the domain. However, given that such mastery will change, older evidence is likely to be less valuable than recent evidence. Data aging addresses this issue by discounting the value of older evidence. This paper provides experimental evaluation of the effects of data aging. While it is demonstrated that data aging can result in statistically significant increases in both the number and accuracy of predictions that a modeling system makes, it is also demonstrated that the reverse can be true. Further, the effects experienced are of only small magnitude. It is argued that these results demonstrate some potential for data aging as a general strategy, but do not warrant employing data aging in its current form.

@Article{ChiuWebb98,
Title = {Using Decision Trees For Agent Modelling: Improving Prediction Performance},
Author = {Chiu, B. C. and Webb, G. I.},
Journal = {User Modeling and User-Adapted Interaction},
Year = {1998},
Number = {1-2},
Pages = {131-152},
Volume = {8},
Abstract = {A modeling system may be required to predict an agent's future actions under constraints of inadequate or contradictory relevant historical evidence. This can result in low prediction accuracy, or otherwise, low prediction rates, leaving a set of cases for which no predictions are made. A previous study that explored techniques for improving prediction rates in the context of modeling students' subtraction skills using Feature Based Modeling showed a tradeoff between prediction rate and predication accuracy. This paper presents research that aims to improve prediction rates without affecting prediction accuracy. The FBM-C4.5 agent modeling system was used in this research. However, the techniques explored are applicable to any Feature Based Modeling system, and the most effective technique developed is applicable to most agent modeling systems. The default FBM-C4.5 system models agents' competencies with a set of decision trees, trained on all historical data. Each tree predicts one particular aspect of the agent's action. Predictions from multiple trees are compared for consensus. FBM-C4.5 makes no prediction when predictions from different trees contradict one another. This strategy trades off reduced prediction rates for increased accuracy. To make predictions in the absence of consensus, three techniques have been evaluated. They include using voting, using a tree quality measure and using a leaf quality measure. An alternative technique that merges multiple decision trees into a single tree provides an advantage of producing models that are more comprehensible. However, all of these techniques demonstrated the previous encountered trade-off between rate of prediction and accuracy of prediction, albeit less pronounced. It was hypothesized that models built on more current observations would outperform models built on earlier observations. Experimental results support this hypothesis. A Dual-model system, which takes this temporal factor into account, has been evaluated. This fifth approach achieved a significant improvement in prediction rate without significantly affecting prediction accuracy.},
Address = {Netherlands},
Audit-trail = {Link via {ACM} Portal},
Keywords = {Feature Based Modeling and User Modeling},
Publisher = {Springer},
Related = {feature-based-modeling}
}

ABSTRACT A modeling system may be required to predict an agent's future actions under constraints of inadequate or contradictory relevant historical evidence. This can result in low prediction accuracy, or otherwise, low prediction rates, leaving a set of cases for which no predictions are made. A previous study that explored techniques for improving prediction rates in the context of modeling students' subtraction skills using Feature Based Modeling showed a tradeoff between prediction rate and predication accuracy. This paper presents research that aims to improve prediction rates without affecting prediction accuracy. The FBM-C4.5 agent modeling system was used in this research. However, the techniques explored are applicable to any Feature Based Modeling system, and the most effective technique developed is applicable to most agent modeling systems. The default FBM-C4.5 system models agents' competencies with a set of decision trees, trained on all historical data. Each tree predicts one particular aspect of the agent's action. Predictions from multiple trees are compared for consensus. FBM-C4.5 makes no prediction when predictions from different trees contradict one another. This strategy trades off reduced prediction rates for increased accuracy. To make predictions in the absence of consensus, three techniques have been evaluated. They include using voting, using a tree quality measure and using a leaf quality measure. An alternative technique that merges multiple decision trees into a single tree provides an advantage of producing models that are more comprehensible. However, all of these techniques demonstrated the previous encountered trade-off between rate of prediction and accuracy of prediction, albeit less pronounced. It was hypothesized that models built on more current observations would outperform models built on earlier observations. Experimental results support this hypothesis. A Dual-model system, which takes this temporal factor into account, has been evaluated. This fifth approach achieved a significant improvement in prediction rate without significantly affecting prediction accuracy.

@InProceedings{ChiuWebbZheng97,
Title = {Using Decision Trees for Agent Modelling: A Study on Resolving Conflicting Predictions},
Author = {Chiu, B. C. and Webb, G. I. and Zheng, Z.},
Booktitle = {Lecture Notes in Computer Science Vol. 1342: Proceedings of the Tenth Australian Joint Conference on Artificial Intelligence (AI'97)},
Year = {1997},
Address = {Berlin},
Editor = {Sattar, A.},
Pages = {349-358},
Publisher = {Springer-Verlag},
Abstract = {Input-Output Agent Modelling (IOAM) is an approach to modelling an agent in terms of relationships between the inputs and outputs of the cognitive system. This approach, together with a leading inductive learning algorithm, C4.5, has been adopted to build a subtraction skill modeller, C4.5-IOAM. It models agents' competencies with a set of decision trees. C4.5-IOAM makes no prediction when predictions from different decision trees are contradictory. This paper proposes three techniques for resolving such situations. Two techniques involve selecting the more reliable prediction from a set of competing predictions using a free quality measure and a leaf quality measure. The other technique merges multiple decision trees into a single tree. This has the additional advantage of producing more comprehensible models. Experimental results, in the domain of modelling elementary subtraction skills, showed that the tree quality and the leaf quality of a decision path provided valuable references for resolving contradicting predictions and a single tree model representation performed nearly equally well to the multi-tree model representation.},
Audit-trail = {Reconstructed paper posted 11/10/05},
Keywords = {Feature Based Modeling and User Modeling},
Location = {Perth, Australia},
Related = {feature-based-modeling}
}

ABSTRACT Input-Output Agent Modelling (IOAM) is an approach to modelling an agent in terms of relationships between the inputs and outputs of the cognitive system. This approach, together with a leading inductive learning algorithm, C4.5, has been adopted to build a subtraction skill modeller, C4.5-IOAM. It models agents' competencies with a set of decision trees. C4.5-IOAM makes no prediction when predictions from different decision trees are contradictory. This paper proposes three techniques for resolving such situations. Two techniques involve selecting the more reliable prediction from a set of competing predictions using a free quality measure and a leaf quality measure. The other technique merges multiple decision trees into a single tree. This has the additional advantage of producing more comprehensible models. Experimental results, in the domain of modelling elementary subtraction skills, showed that the tree quality and the leaf quality of a decision path provided valuable references for resolving contradicting predictions and a single tree model representation performed nearly equally well to the multi-tree model representation.

@InProceedings{ChiuWebbKuzmycz97,
author = {Chiu, B. C. and Webb, G. I. and Kuzmycz, M.},
booktitle = {Proceedings of the Sixth International Conference on User Modeling (UM'97)},
title = {A Comparison of First-Order and Zeroth-Order Induction for Input-Output Agent Modelling},
year = {1997},
address = {New York/Vienna},
editor = {Jameson, A. and Paris, C. and Tasso, C.},
pages = {347-358},
publisher = {Springer},
abstract = {Most student modelling systems seek to develop a model of the internal operation of the cognitive system. In contrast, Input-Output Agent Modelling (IOAM) models an agent in terms of relationships between the inputs and outputs of the cognitive system. Previous IOAM systems have demonstrated high predictive accuracy in the domain of elementary subtraction. These systems use zeroth-order induction. Many of the predicates used, however, represent relations. This suggests that first-order induction might perform well in this domain. This paper reports a study in which zeroth-order and first-order induction engines were used to build models of student subtraction skills. Comparative evaluation shows that zeroth-order induction performs better than first-order in detecting regularities indicating misconceptions while first-order induction leads zeroth-order in detecting regularities indicating correct concepts and inducing a more comprehensible student model. This suggests there exists a trade-off between these factors and that there is still scope for improvement.},
audit-trail = {*},
doi = {10.1007/978-3-7091-2670-7_35},
keywords = {Feature Based Modeling and User Modeling},
location = {Chia Laguna, Sardinia},
related = {feature-based-modeling},
}

ABSTRACT Most student modelling systems seek to develop a model of the internal operation of the cognitive system. In contrast, Input-Output Agent Modelling (IOAM) models an agent in terms of relationships between the inputs and outputs of the cognitive system. Previous IOAM systems have demonstrated high predictive accuracy in the domain of elementary subtraction. These systems use zeroth-order induction. Many of the predicates used, however, represent relations. This suggests that first-order induction might perform well in this domain. This paper reports a study in which zeroth-order and first-order induction engines were used to build models of student subtraction skills. Comparative evaluation shows that zeroth-order induction performs better than first-order in detecting regularities indicating misconceptions while first-order induction leads zeroth-order in detecting regularities indicating correct concepts and inducing a more comprehensible student model. This suggests there exists a trade-off between these factors and that there is still scope for improvement.

@InProceedings{ChiuWebb97,
author = {Chiu, B. C. and Webb, G. I.},
booktitle = {Proceedings (on-line) of The First Machine Learning for User Modeling Workshop (UM'97)},
title = {Using C4.5 as an Induction Engine for Agent Modeling: An Experiment of Optimisation},
year = {1997},
doi = {10.1023/A:1008296930163},
keywords = {Feature Based Modeling and User Modeling},
location = {Chia Laguna, Sardinia},
related = {feature-based-modeling},
}

ABSTRACT 

@Article{WebbChiuKuzmycz97,
Title = {Comparative Evaluation of Alternative Induction Engines for Feature Based Modelling},
Author = {Webb, G. I. and Chiu, B. C. and Kuzmycz, M.},
Journal = {International Journal of Artificial Intelligence in Education},
Year = {1997},
Pages = {97-115},
Volume = {8},
Abstract = {Feature Based Modelling has demonstrated the ability to produce agent models with high accuracy in predicting an agent's future actions. There are a number of respects in which this modelling technique is novel. However, there has been no previous analysis of which aspects of the approach are responsible for its performance. One distinctive feature of the approach is a purpose built induction module. This paper presents a study in which the original custom built Feature Based Modelling induction module was replaced by the C4.5 machine learning system. Comparative evaluation shows that the use of C4.5 increases the number of predictions made without significantly altering the accuracy of those predictions. This suggests that it is the general input-output agent modelling methodology used with both systems that has primary responsibility for the high predictive accuracy previously reported for Feature Based Modelling, rather than its initial idiosyncratic induction technique.},
Address = {NAmsterdam},
Doi = {10.1.1.36.3545},
Keywords = {Feature Based Modeling and User Modeling},
Publisher = {IOS Press},
Related = {feature-based-modeling}
}

ABSTRACT Feature Based Modelling has demonstrated the ability to produce agent models with high accuracy in predicting an agent's future actions. There are a number of respects in which this modelling technique is novel. However, there has been no previous analysis of which aspects of the approach are responsible for its performance. One distinctive feature of the approach is a purpose built induction module. This paper presents a study in which the original custom built Feature Based Modelling induction module was replaced by the C4.5 machine learning system. Comparative evaluation shows that the use of C4.5 increases the number of predictions made without significantly altering the accuracy of those predictions. This suggests that it is the general input-output agent modelling methodology used with both systems that has primary responsibility for the high predictive accuracy previously reported for Feature Based Modelling, rather than its initial idiosyncratic induction technique.

@Article{WebbKuzmycz96,
author = {Webb, G. I. and Kuzmycz, M.},
journal = {User Modeling and User-Adapted Interaction},
title = {Feature Based Modelling: A Methodology for Producing Coherent, Consistent, Dynamically Changing Models of Agents Competencies},
year = {1996},
number = {2},
pages = {117-150},
volume = {5},
abstract = {Feature Based Modelling uses attribute value machine learning techniques to model an agent's competency. This is achieved by creating a model describing the relationships between the features of the agent's actions and of the contexts in which those actions are performed. This paper describes techniques that have been developed for creating these models and for extracting key information therefrom. An overview is provided of previous studies that have evaluated the application of Feature Based Modelling in a number of educational contexts including piano keyboard playing, the unification of Prolog terms and elementary subtraction. These studies have demonstrated that the approach is applicable to a wide spectrum of domains. Classroom use has demonstrated the low computational overheads of the technique. A new study of the application of the approach to modelling elementary subtraction skills is presented. The approach demonstrates accuracy in excess of 90% when predicting student solutions. It also demonstrates the ability to identify and model student's buggy arithmetic procedures.},
address = {Netherlands},
keywords = {Feature Based Modeling},
publisher = {Springer},
related = {feature-based-modeling},
}

ABSTRACT Feature Based Modelling uses attribute value machine learning techniques to model an agent's competency. This is achieved by creating a model describing the relationships between the features of the agent's actions and of the contexts in which those actions are performed. This paper describes techniques that have been developed for creating these models and for extracting key information therefrom. An overview is provided of previous studies that have evaluated the application of Feature Based Modelling in a number of educational contexts including piano keyboard playing, the unification of Prolog terms and elementary subtraction. These studies have demonstrated that the approach is applicable to a wide spectrum of domains. Classroom use has demonstrated the low computational overheads of the technique. A new study of the application of the approach to modelling elementary subtraction skills is presented. The approach demonstrates accuracy in excess of 90% when predicting student solutions. It also demonstrates the ability to identify and model student's buggy arithmetic procedures.

@InProceedings{Webb93c,
Title = {Feature Based Modelling: A Methodology for Producing Coherent, Consistent, Dynamically Changing Models of Agents Competency},
Author = {Webb, G. I.},
Booktitle = {Proceedings of the 1993 World Conference on Artificial Intelligence in Education (AI-ED'93)},
Year = {1993},
Address = {Charlottesville, VA},
Editor = {Brna, P. and Ohlsson, S. and Pain, H.},
Pages = {497-504},
Publisher = {AACE},
Abstract = {Feature Based Modelling uses attribute value machine learning techniques to model an agent's competency. This is achieved by creating a model describing the relationships between the features of the agent's actions and of the contexts in which those actions are performed. This paper describes techniques that have been developed for creating these models and for extracting key information therefrom. An overview is provided of previous studies that have evaluated the application of Feature Based Modelling in a number of educational contexts including piano keyboard playing, the unification of Prolog terms and elementary subtraction. These studies have demonstrated that the approach is applicable to a wide spectrum of domains. Classroom use has demonstrated the low computational overheads of the technique. A new study of the application of the approach to modelling elementary subtraction skills is presented. The approach demonstrates accuracy in excess of 90\% when predicting student solutions. It also demonstrates the ability to identify and model students' buggy arithmetic procedures.},
Keywords = {Feature Based Modeling and Case Based Learning},
Location = {Edinburgh, Scotland. Also published in User Modeling and User-Adapted Interaction. 5: 117-150, 1996},
Related = {feature-based-modeling}
}

ABSTRACT Feature Based Modelling uses attribute value machine learning techniques to model an agent's competency. This is achieved by creating a model describing the relationships between the features of the agent's actions and of the contexts in which those actions are performed. This paper describes techniques that have been developed for creating these models and for extracting key information therefrom. An overview is provided of previous studies that have evaluated the application of Feature Based Modelling in a number of educational contexts including piano keyboard playing, the unification of Prolog terms and elementary subtraction. These studies have demonstrated that the approach is applicable to a wide spectrum of domains. Classroom use has demonstrated the low computational overheads of the technique. A new study of the application of the approach to modelling elementary subtraction skills is presented. The approach demonstrates accuracy in excess of 90\% when predicting student solutions. It also demonstrates the ability to identify and model students' buggy arithmetic procedures.

@InProceedings{KuzmyczWebb92,
Title = {Evaluation of Feature Based Modelling in Subtraction},
Author = {Kuzmycz, M. and Webb, G. I.},
Booktitle = {Lecture Notes in Computer Science Vol. 608: Proceedings of the Second International Conference on Intelligent Tutoring Systems (ITS'92)},
Year = {1992},
Address = {Berlin},
Editor = {Frasson, C. and Gauthier, G. and McCalla, G. I.},
Pages = {269-276},
Publisher = {Springer-Verlag},
Abstract = {One aim of intelligent tutoring systems is to tailor lessons to each individual student's needs. To do this a tutoring system requires a model of the student's knowledge. Cognitive modelling aims to produce a detailed explanation of the student's progress. Feature Based Modelling forms a cognitive model of the student by creating aspects of problem descriptions and of students' responses. This paper will discuss Feature Based Modelling and show the results of an evaluation carried out in the domain of elemental subtraction.},
Audit-trail = {*},
Keywords = {Feature Based Modeling and Case Based Learning},
Location = {Montrial, Canada},
Related = {feature-based-modeling}
}

ABSTRACT One aim of intelligent tutoring systems is to tailor lessons to each individual student's needs. To do this a tutoring system requires a model of the student's knowledge. Cognitive modelling aims to produce a detailed explanation of the student's progress. Feature Based Modelling forms a cognitive model of the student by creating aspects of problem descriptions and of students' responses. This paper will discuss Feature Based Modelling and show the results of an evaluation carried out in the domain of elemental subtraction.

@InProceedings{Webb91b,
Title = {Inside the Unification Tutor: The Architecture of an Intelligent Educational System},
Author = {Webb, G. I.},
Booktitle = {Simulation \& Academic Gaming in Tertiary Education, The Proceedings of the Eighth Annual Conference of ASCILITE (ASCILITE '91)},
Year = {1991},
Address = {Launceston},
Editor = {Godfrey, R.},
Pages = {677-684},
Publisher = {University of Tasmania},
Abstract = {The Unification Tutor provides practice and tuition on the unification of terms from the Prolog programming language. It integrates multiple knowledge sources encompassing both performance and declarative knowledge. A key feature of the tutor is the use of a detailed student model. It has been used since 1989 in Computer Science courses at Deakin and La Trobe Universities. Previous papers have examined the student modelling component of this system. This paper investigates the internal operation of the Unification Tutor, the sub-systems it incorporates and their interaction.},
Audit-trail = {Reconstructed paper posted Oct 05},
Keywords = {Feature Based Modeling and Computer Based Learning},
Related = {feature-based-modeling}
}

ABSTRACT The Unification Tutor provides practice and tuition on the unification of terms from the Prolog programming language. It integrates multiple knowledge sources encompassing both performance and declarative knowledge. A key feature of the tutor is the use of a detailed student model. It has been used since 1989 in Computer Science courses at Deakin and La Trobe Universities. Previous papers have examined the student modelling component of this system. This paper investigates the internal operation of the Unification Tutor, the sub-systems it incorporates and their interaction.

@InProceedings{KuzmyczWebb91,
Title = {Modelling Elementary Subtraction: Intelligent Warfare Against Bugs},
Author = {Kuzmycz, M. and Webb, G. I.},
Booktitle = {Simulation \& Academic Gaming in Tertiary Education, The Proceedings of the Eighth Annual Conference of ASCILITE (ASCILITE '91)},
Year = {1991},
Address = {Launceston},
Editor = {Godfrey, R.},
Pages = {367-376},
Publisher = {University of Tasmania},
Abstract = {This paper discusses an intelligent system .that uses Input/ Output Cognitive Modelling (IOCM) techniques to form a model of the student. The paper describes FBM, an IOCM system that uses features to represent the inputs and outputs of the tasks being presented to the student and forms a relationship which describes in essence the knowledge the student has in the domain. Also presented is ASPMoRe, an intelligent tool that takes the model of the student and adapts the lesson to both refine the model and give the student practice in weak areas of his knowledge. Results have shown that the system can be an effective tool for educational purposes.},
Audit-trail = {*},
Keywords = {Feature Based Modeling and Computer Based Learning},
Location = {Launceston, TAS, Australia},
Related = {feature-based-modeling}
}

ABSTRACT This paper discusses an intelligent system .that uses Input/ Output Cognitive Modelling (IOCM) techniques to form a model of the student. The paper describes FBM, an IOCM system that uses features to represent the inputs and outputs of the tasks being presented to the student and forms a relationship which describes in essence the knowledge the student has in the domain. Also presented is ASPMoRe, an intelligent tool that takes the model of the student and adapts the lesson to both refine the model and give the student practice in weak areas of his knowledge. Results have shown that the system can be an effective tool for educational purposes.

@InProceedings{Webb91e,
author = {Webb, G. I.},
booktitle = {Proceedings of the {IJCAI} Workshop {W.4}: Agent Modelling for Intelligent Interaction},
title = {An Attribute-Value Machine Learning Approach To Student Modelling},
year = {1991},
editor = {Kay, J. and Quilici, A.},
pages = {128-136},
abstract = {This paper describes an application of machine learning to student modelling. Unlike previous machine learning approaches to student modelling, the new approach is based on attribute-value machine learning. In contrast to many previous approaches it is not necessary for the lesson author to identify all forms of error that may be detected or to identify the possible approaches to problem solving in the domain that may be adopted. Rather, the lesson author need only identify the relevant attributes both of the tasks to be performed by the student and of the student's actions. The values of these attributes are automatically processed by the student modeler to produce the student model.},
audit-trail = {Paper scanned and converted to word. PDF now up},
keywords = {Feature Based Modeling and Computer Based Learning},
location = {Sydney, Australia},
related = {feature-based-modeling},
}

ABSTRACT This paper describes an application of machine learning to student modelling. Unlike previous machine learning approaches to student modelling, the new approach is based on attribute-value machine learning. In contrast to many previous approaches it is not necessary for the lesson author to identify all forms of error that may be detected or to identify the possible approaches to problem solving in the domain that may be adopted. Rather, the lesson author need only identify the relevant attributes both of the tasks to be performed by the student and of the student's actions. The values of these attributes are automatically processed by the student modeler to produce the student model.

@InProceedings{WebbCummingRichardsYum90,
author = {Webb, G. I. and Cumming, G. and Richards, T. and Yum, K-K.},
booktitle = {Proceedings of the IFIP TC3 International Conference on Advanced Research on Computers in Education (ARCE'90)},
title = {Educational Evaluation of Feature Based Modelling in a Problem Solving Domain},
year = {1990},
address = {Amsterdam},
editor = {Lewis, R. and Otsuki, S.},
pages = {101-108},
publisher = {Elsevier},
abstract = {Feature-Based Modelling is a machine learning based cognitive modelling methodology. An intelligent educational system has been implemented, for the purpose of evaluating the methodology, which helps students learn about the unification of terms from the Prolog programming language. The system has been used by Third Year Computer Science students at La Trobe University during September 1989. Students were randomly allocated to an Experimental condition, in which FBM modelling was used to select tasks, and give extra comments, or to a Control condition in which similar tasks and comments were given, but without FBM tailoring to the individual. Ratings of task appropriateness, and comment usefulness, were collected on-line as the students worked with the tutor; overall ratings were obtained by questionnaire at the end; and semester exam results were examined. Despite the fact that only a minority of students showed sufficient misunderstanding for FBM to have potential value, of the ten comparisons chat relate most directly to the aims of the Tutor, while in no case reaching significance, seven were in favour of the Tutor, and only two against. These preliminary results are very encouraging for the FBM principles of the Tutor.},
audit-trail = {Pre-pub pdf posted 26/5/05},
keywords = {Feature Based Modeling and Computer Based Learning},
location = {Tokyo, Japan},
related = {feature-based-modeling},
}

ABSTRACT Feature-Based Modelling is a machine learning based cognitive modelling methodology. An intelligent educational system has been implemented, for the purpose of evaluating the methodology, which helps students learn about the unification of terms from the Prolog programming language. The system has been used by Third Year Computer Science students at La Trobe University during September 1989. Students were randomly allocated to an Experimental condition, in which FBM modelling was used to select tasks, and give extra comments, or to a Control condition in which similar tasks and comments were given, but without FBM tailoring to the individual. Ratings of task appropriateness, and comment usefulness, were collected on-line as the students worked with the tutor; overall ratings were obtained by questionnaire at the end; and semester exam results were examined. Despite the fact that only a minority of students showed sufficient misunderstanding for FBM to have potential value, of the ten comparisons chat relate most directly to the aims of the Tutor, while in no case reaching significance, seven were in favour of the Tutor, and only two against. These preliminary results are very encouraging for the FBM principles of the Tutor.

@InProceedings{Webb89a,
Title = {A Machine Learning Approach to Student Modelling},
Author = {Webb, G. I.},
Booktitle = {Proceedings of the Third Australian Joint Conference on Artificial Intelligence (AI 89)},
Year = {1989},
Pages = {195-205},
Abstract = {This paper describes an application of established machine learning principles to student modelling. Unlike previous machine learning based approaches to student modelling, the new approach is based on attribute-value machine learning. In contrast to many previous approaches it is not necessary for the lesson author to identify all forms of error that may be detected. Rather, the lesson author need only identify the relevant attributes both of the tasks to be performed by the student and of the student's actions. The values of these attributes are automatically processed by the student modeler to produce the student model.},
Keywords = {Feature Based Modeling and Computer Based Learning},
Location = {Melbourne, Australia},
Related = {feature-based-modeling}
}

ABSTRACT This paper describes an application of established machine learning principles to student modelling. Unlike previous machine learning based approaches to student modelling, the new approach is based on attribute-value machine learning. In contrast to many previous approaches it is not necessary for the lesson author to identify all forms of error that may be detected. Rather, the lesson author need only identify the relevant attributes both of the tasks to be performed by the student and of the student's actions. The values of these attributes are automatically processed by the student modeler to produce the student model.

@InProceedings{WebbCummingRichardsYum89,
Title = {The Unification Tutor: An Intelligent Educational System in the Classroom},
Author = {Webb, G. I. and Cumming, G. and Richards, T. and Yum, K-K.},
Booktitle = {Proceedings of the Seventh Annual Conference of the Australian Society for Computers in Learning in Tertiary Education (ASCILITE '89)},
Year = {1989},
Address = {Gold Coast},
Editor = {Bishop, G. and Baker, J.},
Pages = {408-420},
Publisher = {Bond University},
Abstract = {The Unification Tutor is experimental Intelligent Tutoring System for the domain of the unification of Prolog terms. It demonstrates the interactive use of Feature-Based Modelling - an approach to cognitive modelling that has been presented at previous ASCILITE Conferences (Webb, 1988b.) The Unification Tutor has been used by Third Year Computer Science students at La Trobe University during September 1989. This paper describes the Unification Tutor and evaluates its performance at La Trobe.},
Audit-trail = {Reconstructed paper posted Nov 05},
Keywords = {Feature Based Modeling and Computer Based Learning and Computer Science Education},
Location = {Gold Coast, QLD, Australia},
Related = {feature-based-modeling}
}

ABSTRACT The Unification Tutor is experimental Intelligent Tutoring System for the domain of the unification of Prolog terms. It demonstrates the interactive use of Feature-Based Modelling - an approach to cognitive modelling that has been presented at previous ASCILITE Conferences (Webb, 1988b.) The Unification Tutor has been used by Third Year Computer Science students at La Trobe University during September 1989. This paper describes the Unification Tutor and evaluates its performance at La Trobe.

@InProceedings{Webb88a,
Title = {Cognitive Diagnosis Using Student Attributions},
Author = {Webb, G. I.},
Booktitle = {Computers in Learning in Tertiary education: Proceedings of the Sixth Annual Conference of the Australian Society for Computers in Learning in Tertiary Education (ASCILITE-88)},
Year = {1988},
Editor = {Fielden, K. and Hicks, F. and Scott, N.},
Pages = {502-514},
Abstract = {This paper details an approach to cognitive diagnosis that enables the inference of detailed models of a student's conceptualisation of a domain. This model is constructed be examining the attributes of the problems that the student has tackled and the student's performance while tackling those problems. A feature network is used to represent educationally relevant domain knowledge. This approach has low implementation and operational overheads; It provides a detailed model of the student's conceptualisation of the subject domain in terms of elements of knowledge from that domain; Student models are not restricted to overlays of predefined correct and/or incorrect knowledge; It does not require that the instructional designer anticipate the possible forms of error that may occur; It is robust in the face of partial evaluation of student performance; It is also robust in the face of the instructional designer's failure to incorporate relevant aspects of the subject domain in the knowledge-base; The student models can be executed; It supports accurate diagnosis of multiple viewpoints of the domain even when those viewpoints are not anticipated by the instructional designer; It can support multiple teaching styles in the one lesson.},
Keywords = {Feature Based Modeling and Computer Based Learning},
Location = {Canberra, Australia},
Related = {feature-based-modeling}
}

ABSTRACT This paper details an approach to cognitive diagnosis that enables the inference of detailed models of a student's conceptualisation of a domain. This model is constructed be examining the attributes of the problems that the student has tackled and the student's performance while tackling those problems. A feature network is used to represent educationally relevant domain knowledge. This approach has low implementation and operational overheads; It provides a detailed model of the student's conceptualisation of the subject domain in terms of elements of knowledge from that domain; Student models are not restricted to overlays of predefined correct and/or incorrect knowledge; It does not require that the instructional designer anticipate the possible forms of error that may occur; It is robust in the face of partial evaluation of student performance; It is also robust in the face of the instructional designer's failure to incorporate relevant aspects of the subject domain in the knowledge-base; The student models can be executed; It supports accurate diagnosis of multiple viewpoints of the domain even when those viewpoints are not anticipated by the instructional designer; It can support multiple teaching styles in the one lesson.