Constraint-Based Tutors: A Success Story

Mitrović, Antonija; Mayo, Michael; Suraweera, Pramuditha; Martin, Brent

doi:10.1007/3-540-45517-5_103

Cited by 74 publications

(46 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As the success of intelligent learning environments grow, and as some of them become used in real-life settings (see for eg., VanLehn et al, Mitrovic et al, 2005Mitrovic et al, , 2001, researchers in the field have come to focus attention on the need to build rapid, cost-effective means of developing these environments (Aleven et al, 2006;Martin and Mitrovic, 2001;Kodaganallur et al, 2005). This is currently a major issue.…”

Section: Tradeoffs Between Student Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Learned student models with item to item knowledge structures

Desmarais

Meshkinfam

Gagnon

2006

User Model User-Adap Inter

View full text Add to dashboard Cite

Abstract. Probabilistic and learned approaches to student modeling are attractive because of the uncertainty surrounding the student skills assessment and because of the need to automatize the process. Item to item structures readily lend themselves to probabilistic and fully learned models because they are solely composed of observable nodes, like answers to test questions. Their structure is also well grounded in the cognitive theory of knowledge spaces. We study the effectiveness of two Bayesian frameworks to learn item to item structures and to use the induced structures to predict item outcome from a subset of evidence. One approach, POKS, relies on a naive Bayes framework whereas the other is based on the Bayesian network learning and inference framework. Both approaches are assessed over their predictive ability and their computational efficiency in different experimental simulations. The results from simulations over three data sets show that they both can effectively perform accurate predictions, but POKS generally displays higher predictive power than the Bayesian network. Moreover, the simplicity of POKS translates to a time efficiency of one to three orders of magnitude greater than the Bayesian network runs. We furhter explore the use of the item to item approach for handling concepts mastery assessment. The approach investigated consist in augmenting an initial set of observations, based on inferences with the item to item structure, and feed the augmented set to a Bayesian network containing a number of concepts. The results show that augmented set can effectively improve predictive power of a Bayesian network for item outcome, but that improvement does not transfer to the concept assessment in this particular experiment. We discuss different explanations for the results and outline future research avenues.

show abstract

Section: Tradeoffs Between Student Modelsmentioning

confidence: 99%

“…Beyond graphical models, other approaches based on using constraints to learn domain rules have also been investigated with encouraging results (Suraweera et al, 2005;Mitrovic et al, 2001). They can yield detailed diagnostics while relying on rules induced from data.…”

Section: Tradeoffs Between Student Modelsmentioning

confidence: 99%

Learned student models with item to item knowledge structures

Desmarais

Meshkinfam

Gagnon

2006

User Model User-Adap Inter

View full text Add to dashboard Cite

show abstract

“…KERMIT [12] is a problem-solving environment, in which students construct ER schemas that satisfy a given set of requirements. The system provides feedback tailored towards each student's knowledge.…”

Section: Ke E E Er R R Rmit: a Knowledge-based Er Modelling Tutormentioning

confidence: 99%

KERMIT: A Constraint-Based Tutor for Database Modeling

Suraweera

Mitrović

2002

Intelligent Tutoring Systems

Self Cite

View full text Add to dashboard Cite

Abstract:KERMIT is an intelligent tutoring system that teaches conceptual database design using the Entity-Relationship data model. Database design is an open-ended task: although there is an outcome defined in abstract terms, there is no procedure to use to find that outcome. So far, constraint based modelling has been used in a tutor that teaches a database language (SQL-Tutor) and a system that teaches punctuation and capitalisation rules (CAPIT). Both systems have proved to be extremely effective in evaluations performed in real classrooms. In this paper, we present experiences in using CBM in an open-ended domain. We describe system's architecture and functionality. KERMIT has also been evaluated in the context of genuine teaching activities. We present the results of an evaluation study with students taking a database course, which show that KERMIT is an effective system. The students enjoyed the system's adaptability and found it a valuable asset to their learning.

show abstract

“…The basic rationale underlying traditional ITS systems is that by using formalized domain knowledge and dynamic student models, comparing student's problem solving steps to the explicit domain models, it is possible to adapt instruction to the needs of specific students and effectively support their learning. Two prominent approaches that both use such explicit domain models have demonstrated impressive practical success in teaching domains such as algebra, geometry, programming, database design or UML [13,14]:…”

Section: Introductionmentioning

confidence: 99%

Learning Feedback in Intelligent Tutoring Systems

et al. 2015

View full text Add to dashboard Cite

Intelligent Tutoring Systems (ITSs) are adaptive learning systems that aim to support learners by providing one-on-one individualized instruction. Typically, instructing learners in ITSs is build on formalized domain knowledge and, thus, the applicability is restricted to well-defined domains where knowledge about the domain being taught can be explicitly modeled. For ill-defined domains, human tutors still by far outperform the performance of ITSs, or the latter are not applicable at all. As part of the DFG priority programme "Autonomous Learning", the FIT project has been conducted over a period of 3 years pursuing the goal to develop novel ITS methods, that are also applicable for ill-defined problems, based on implicit domain knowledge extracted from educational data sets. Here, machine learning techniques have been used to autonomously infer structures from given learning data (e.g., student solutions) and, based on these structures, to develop strategies for instructing learners.

show abstract

Constraint-Based Tutors: A Success Story

Cited by 74 publications

References 12 publications

Learned student models with item to item knowledge structures

Learned student models with item to item knowledge structures

KERMIT: A Constraint-Based Tutor for Database Modeling

Learning Feedback in Intelligent Tutoring Systems

Contact Info

Product

Resources

About