| SMART: Student modeling approach for responsive tutoring | | BIBAK | Full-Text | 1-44 | |
| Valerie J. Shute | |||
| This paper describes a new student modeling paradigm called SMART. The
premise is that a single, principled approach to student modeling, involving
both theoretical and empirical methods, can render automated instruction more
efficacious across a broad array of instructional domains. After defining key
terms and discussing limitations to previous student modeling paradigms, I
describe the SMART approach, as embedded within a statistics tutor called Stat
Lady (Shute and Gluck, 1994). SMART works in conjunction with a tutor design
where low-level knowledge and skills (i.e., curricular elements) are identified
and separated into three main outcome types. Throughout the tutor, curricular
elements with values below a pre-set mastery criterion are instructed,
evaluated, and remediated, if necessary. The diagnostic part of the student
model is driven by a series of regression equations based on the level of
assistance the computer gives each person, per curriculum element. Remediation
on a given element occurs when a subject fails to achieve mastery during
assessment, which follows instruction. Remediation is precise because each
element knows its location within the tutor where it is instructed and
assessed. I end with a summary of results from two controlled evaluations of
SMART examining the following research issues: (a) diagnostic validity, (b)
individual differences in learning from Stat Lady, (c) affective perceptions of
the tutorial experience, and (d) contributions of mastery and remediation to
learning outcome and efficiency. Comments about related and future research
with this paradigm are offered. Keywords: Aptitudes; cognitive diagnosis; learning outcomes; mastery learning;
macroadaptation; microadaptation; remediation | |||
| Extending the scope of the student model | | BIBAK | Full-Text | 45-65 | |
| Susan Bull; Paul Brna; Helen Pain | |||
| In this paper we maintain that there are benefits to extending the scope of
student models to include additional information as part of the explicit
student model. We illustrate our argument by describing a student model which
focuses on 1. performance in the domain; 2. acquisition order of the target
knowledge; 3. analogy; 4. learning strategies; 5. awareness and reflection. The
first four of these issues are explicitly represented in the student model.
Awareness and reflection should occur as the student model is transparent; it
is used to promote learner reflection by encouraging the learner to view, and
even negotiate changes to the model. Although the architecture is transferable
across domains, each instantiation of the student model will necessarily be
domain specific due to the importance of factors such as the relevant
background knowledge for analogy, and typical progress through the target
material. As an example of this approach we describe the student model of an
intelligent computer assisted language learning system which was based on
research findings on the above five topics in the field of second language
acquisition. Throughout we address the issue of the generality of this model,
with particular reference to the possibility of a similar architecture
reflecting comparable issues in the domain of learning about electrical
circuits. Keywords: Student model; intelligent learning environment; reflection; learning
strategies; analogy; second language acquisition | |||
| An adaptive Student Centered Curriculum for an intelligent training system | | BIBAK | Full-Text | 67-86 | |
| Chris Eliot; Beverly Park Woolf | |||
| An intelligent tutoring system customizes its presentation of knowledge to
the individual needs of each student based on a model of the student. Student
models are more complex than other user models because the student is likely to
have misconceptions. We have addressed several difficult issues in reasoning
about a student's knowledge and skills within a real-time simulation-based
training system. Our conceptual framework enables important aspects of the
tutor's reasoning to be based upon simple, comprehensible representations that
are the basis for a Student Centered Curriculum. We have built a system for
teaching cardiac resuscitation techniques in which the decisions about how to
teach are separated from the decisions about what to teach. The training
context (i.e., choice of topics) is changed based on a tight interaction
between student modeling techniques and simulation management. Although complex
student models are still required to support detailed reasoning about how to
teach, we argue that the decision about what to teach can be adequately
supported by qualitatively simpler techniques, such as overlay models. This
system was evaluated in formative studies involving medical school faculty and
students. Construction of the student model involves monitoring student actions
during a simulation and evaluating these actions in comparison with an expert
model encoded as a multi-agent plan. The plan recognition techniques used in
this system are novel and allow the expert knowledge to be expressed in a form
that is natural for domain experts. Keywords: Adaptive; planning; planning recognition; simulation; multi-agent;
multimedia; tutoring; artificial intelligence; knowledge representation | |||
| Student diagnosis in practice; bridging a gap | | BIBAK | Full-Text | 93-116 | |
| Eva L. Ragnemalm | |||
| This paper presents a novel framework for looking at the problem of
diagnosing a student's knowledge in an Intelligent Tutoring System. It is
indicated that the input and the conceptualisation of the student model are
significant for the choice of modeling technique. The framework regards student
diagnosis as the process of bridging the gap between the student's input to the
tutoring system, and the system's conception and representation of correct
knowledge. The process of bridging the gap can be subdivided into three phases,
data acquisition, transformation and evaluation, which are studied further. A
number of published student modeling techniques are studied with respect to how
they bridge the gap. Keywords: Student diagnosis; student modeling; intelligent tutoring systems | |||
| Feature Based Modelling: A methodology for producing coherent, consistent, dynamically changing models of agents' competencies | | BIBAK | Full-Text | 117-150 | |
| Geoffrey I. Webb; Mark Kuzmycz | |||
| 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. Keywords: Student modelling; machine learning; modelling competency | |||
| Student Modelling: The Key to Individualized Knowledge-based Instruction, Jim E. Greer and Gordon I. McCalla (eds) | | BIB | Full-Text | 151-155 | |
| Judy Kay | |||
| Using dynamic user models in the recognition of the plans of the user | | BIBAK | Full-Text | 157-190 | |
| Liliana Ardissono; Dario Sestero | |||
| This paper is concerned with information-seeking dialogues in a restricted
domain (we consider a consultation system for a Computer Science Department,
delivering information about the various tasks that the users may want to
perform: for example, how to access the library, get information about the
courses of the Department, etc.) and presents a framework where a plan
recognition and a user modeling component are integrated to cooperate in the
task of identifying the user's plans and goals. The focus of the paper is
centered on the techniques used for building the user model and exploiting it
in the determination of the user's intentions. For this task, we use
stereotypes and we propose some inference rules for expanding the user model by
inferring the user's beliefs from both the sentences s/he utters and the
information stored in the plan library of the system, that describes the
actions in the domain. Moreover, we introduce some disambiguation rules that
are applied to the information in the user model for restricting the set of
ambiguous hypotheses on the user's plans and goals to the most plausible ones.
This also simplifies a further clarification dialogue if it is necessary for a
precise identification of the user's intentions. Keywords: User modeling; plan recognition; information-seeking dialogue; natural
language; pragmatics | |||
| Preface | | BIB | Full-Text | iii-iv | |
| Anthony Jameson | |||
| Numerical uncertainty management in user and student modeling: An overview of systems and issues | | BIBAK | Full-Text | 193-251 | |
| Anthony Jameson | |||
| A rapidly growing number of user and student modeling systems have employed
numerical techniques for uncertainty management. The three major paradigms are
those of Bayesian networks, the Dempster-Shafer theory of evidence, and fuzzy
logic. In this overview, each of the first three main sections focuses on one
of these paradigms. It first introduces the basic concepts by showing how they
can be applied to a relatively simple user modeling problem. It then surveys
systems that have applied techniques from the paradigm to user or student
modeling, characterizing each system within a common framework. The final main
section discusses several aspects of the usability of these techniques for user
or student modeling, such as their knowledge engineering requirements, their
need for computational resources, and the communicability of their results. Keywords: numerical uncertainty management; Bayesian networks; Dempster-Shafer theory;
fuzzy logic; user modeling; student modeling | |||
| The role of probability-based inference in an intelligent tutoring system | | BIBAK | Full-Text | 253-282 | |
| Robert J. Mislevy; Drew H. Gitomer | |||
| Probability-based inference in complex networks of interdependent variables
is an active topic in statistical research, spurred by such diverse
applications as forecasting, pedigree analysis, troubleshooting, and medical
diagnosis. This paper concerns the role of Bayesian inference networks for
updating student models in intelligent tutoring systems (ITSs). Basic concepts
of the approach are briefly reviewed, but the emphasis is on the considerations
that arise when one attempts to operationalize the abstract framework of
probability-based reasoning in a practical ITS context. The discussion revolves
around HYDRIVE, an ITS for learning to troubleshoot an aircraft hydraulics
system. HYDRIVE supports generalized claims about aspects of student
proficiency through probability-based combination of rule-based evaluations of
specific actions. The paper highlights the interplay among inferential issues,
the psychology of learning in the domain, and the instructional approach upon
which the ITS is based. Keywords: Bayesian inference networks; cognitive diagnosis; HYDRIVE; intelligent
tutoring systems; probability-based inference; student models | |||
| User-expertise modeling with empirically derived probabilistic implication networks | | BIBAK | Full-Text | 283-315 | |
| Michel C. Desmarais; Ameen Maluf; Jiming Liu | |||
| The application of user-expertise modeling for adaptive interfaces is
confronted with a number of difficult challenges, namely, efficiency and
reliability, the cost-benefit ratio, and the practical usability of user
modeling techniques. We argue that many of these obstacles can be overcome by
standard, automatic means of performing knowledge assessment. Within this
perspective, we present the basis of a probabilistic user modeling approach,
the POKS technique, which could serve as a standard user-expertise modeling
tool.
The POKS technique is based on the cognitive theory of knowledge structures: a formalism for the representation of the order in which we learn knowledge units (KU). The technique permits the induction of knowledge structures from a small number of empirical data cases. It uses an evidence propagation scheme within these structures to infer an individual's knowledge state from a sample of KU. The empirical induction technique is based, in part, on statistical hypothesis testing over conditional probabilities that are determined by the KUs' learning order. Experiments with this approach show that the technique is successful in partially inferring an individual's knowledge state, either through the monitoring of a user's behavior, or through a selective questioning process. However, the selective process, based on entropy minimization, is shown to be much more effective in reducing the standard error score of knowledge assessment than random sampling. Keywords: knowledge assessment; knowledge spaces; automatic knowledge structure
induction; Bayesian network inferences; computer assisted testing; user
modeling | |||
| A Dempster-Shafer approach to modeling agent preferences for plan recognition | | BIBAK | Full-Text | 317-348 | |
| Mathias Bauer | |||
| Plan recognition is an important task whenever a system has to take into
account an agent's actions and goals in order to be able to react adequately.
Most plan recognizers work by merely maintaining a set of equally plausible
plan hypotheses each of which proved compatible with recent observations
without taking into account individual preferences of the currently observed
agent. Such additional information provides a basis for ranking the hypotheses
so that the "best" one can be selected whenever the system is forced to react
(e.g., to provide help to the user of a software system to accomplish his
goals). Furthermore, hypotheses with low valuations can be excluded from
considerations at an early stage. In this paper, an approach to the
quantitative modeling of the required agent-related data and their use in plan
recognition is presented. It relies on the Dempster-Shafer Theory and provides
mechanisms for the initialization and update of corresponding numerical values. Keywords: plan recognition; user preferences; quantitative user modeling;
Dempster-Shafer Theory | |||
| Fuzzy techniques and user modeling in Sales Assistants | | BIBAK | Full-Text | 349-370 | |
| Heribert Popp; Dieter Lödel | |||
| Uncertainty and fuzziness are ubiquitous in the field of computerized
selling. Therefore the mastery of these domains might be a key factor for the
success of electronic selling. In this paper the Sales Assistant is introduced.
It employs user models in the problem solving and dialog control layers, and
fuzzy techniques for the management of imprecision. Fuzzy Multiple Criteria
Analysis has proven its usefulness in product evaluation if there are no severe
interdependencies among the product attributes. The user model in the Sales
Assistant is constructed unobtrusively on the basis of user behavior, and it
uses short-term information. It increases the transparency and usability of a
large hypertext-like information system. Keywords: stereotypes; product rating; fuzzy techniques; soft computing; requirements
analysis; sales assistants; user modeling | |||