Thermo-Tutor: An Intelligent Tutoring System for thermodynamics

Mitrović, Antonija; Williamson, Chris; Bebbington, Aidan; Mathews, Moffat; Suraweera, Pramuditha; Martin, Brent; Thomson, David; Holland, Jay

doi:10.1109/educon.2011.5773164

Cited by 10 publications

(6 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A constraint consists of three components: (a) a relevance condition that indicates when the constraint is applicable, (b) a satisfaction condition that tests the current state of the student’s solution, and (c) a feedback message that, when the solution state fails the satisfaction condition, advises the student of the error and reminds them of the principle that was violated by the error (Mitrovic, Martin, & Suraweera, 2007). To explain by analogy, if the relevance condition is “cooking a pot roast,” the satisfaction condition might be “oven temperature below 120 degrees Celsius” and the feedback message might be “when cooking a pot roast remember to keep the oven temperature below 120 degrees Celsius.” Thus, when a student’s behavior violates a constraint, an error is detected and appropriate feedback is provided (Mitrovic et al, 2011). If no constraint is violated, the student is considered to be on the right solution path.…”

Section: Types Of Itsmentioning

confidence: 99%

Intelligent tutoring systems and learning outcomes: A meta-analysis.

Adesope

Nesbit

et al. 2014

Journal of Educational Psychology

470

252

View full text Add to dashboard Cite

Intelligent Tutoring Systems (ITS) are computer programs that model learners' psychological states to provide individualized instruction. They have been developed for diverse subject areas (e.g., algebra, medicine, law, reading) to help learners acquire domain-specific, cognitive and metacognitive knowl edge. A meta-analysis was conducted on research that compared the outcomes from students learning from ITS to those learning from non-ITS learning environments. The meta-analysis examined how effect sizes varied with type of ITS, type of comparison treatment received by learners, type of learning outcome, whether knowledge to be learned was procedural or declarative, and other factors. After a search of major bibliographic databases, 107 effect sizes involving 14,321 participants were extracted and analyzed. The use of ITS was associated with greater achievement in comparison with teacher-led, large-group instruction (g = .42), non-ITS computer-based instruction (g = .57), and textbooks or workbooks (g = .35). There was no significant difference between learning from ITS and learning from individualized human tutoring (g = -.11) or small-group instruction (g = .05). Significant, positive mean effect sizes were found regardless of whether the ITS was used as the principal means of instruction, a supplement to teacher-led instruction, an integral component of teacher-led instruction, or an aid to homework. Significant, positive effect sizes were found at all levels of education, in almost all subject domains evaluated, and whether or not the ITS provided feedback or modeled student misconceptions. The claim that ITS are relatively effective tools for learning is consistent with our analysis of potential publication bias.

show abstract

Section: Types Of Itsmentioning

confidence: 99%

Intelligent tutoring systems and learning outcomes: A meta-analysis.

Adesope

Nesbit

et al. 2014

Journal of Educational Psychology

470

252

View full text Add to dashboard Cite

show abstract

“…A tutor that allowed the student to solve thermodynamic problems is reported in [12]. The student can select a problem and solve it in the workspace.…”

Section: Intelligent Tutorsmentioning

confidence: 99%

Supporting the review of student proposal drafts in information technologies

López

2012

Proceedings of the 13th Annual Conference on Information Technology Education

View full text Add to dashboard Cite

In many cases, academic programs or courses conclude with a thesis or research proposal text, elaborated by students. The review of such texts is a heavy load, especially at initial stages of drafting. This paper proposes a model that allows linguistic and structural review of some essential elements in proposal drafts of undergraduate students. The model aims to support the review from vocabulary to the argumentation in the draft, and is part of an intelligent tutor to monitor student progress. This work presents the initial results in terms of lexical and global coherence analysis of proposal drafts of students. Lexical analysis is done in terms of lexical density, lexical diversity, and sophistication. Global coherence is evaluated using the Latent Semantic Analysis technique. Our results show that the level reached so far by the analyzer is adequate to support the review, taking into account for one section the level of agreement with human reviewers.

show abstract

“…The authors in Mitrovic and Suraweera(2004), presented a problem-solving environment for the university level students in which they can practice conceptual database design using the entityrelationship data model. This work presents an intelligent tutor called KERMIT (Knowledge-based Entity-Relationship Modeling IntelligentTutor).…”

Section: Related Workmentioning

confidence: 99%

System-Based Ontology for Assessing Learner's Programming Practical Works Activities (S_Onto_ALPPWA)

Boussaha

Mokhati

Hanneche

2021

International Journal of Web-Based Learning and Teaching Technologies

View full text Add to dashboard Cite

This article introduces a new learner's self-assessment environment as CEHL that allows comparison of learners' programs with those elaborated by the teacher. The subjacent idea is to indirectly compare programs through their graphical representations described by ontologies. So, CEHL developed so-called S_Onto_ALPPWA which allows comparing learners' productions with those elaborated by the teacher. The tool allows essentially (1) generating two ontologies from the learner's program and the teacher's one, (2) applies some matching algorithms for measuring degrees of similarity and dissimilarity between learner's program and teacher's one, and (3) assessing the learners by giving them a list of semantic and syntactic errors detected in their programs. The present work is an extension of the authors' previous work, which did not take into account semantics errors. In the present work, they have managed to detect syntactic and semantic errors by using ontologies. To demonstrate the effectiveness of the system, two prospective experiments were conducted. The obtained results were very encouraging.

show abstract

Thermo-Tutor: An Intelligent Tutoring System for thermodynamics

Cited by 10 publications

References 16 publications

Intelligent tutoring systems and learning outcomes: A meta-analysis.

Intelligent tutoring systems and learning outcomes: A meta-analysis.

Supporting the review of student proposal drafts in information technologies

System-Based Ontology for Assessing Learner's Programming Practical Works Activities (S_Onto_ALPPWA)

Contact Info

Product

Resources

About