Evaluation of a meta-tutor for constructing models of dynamic systems

Zhang, Lishan; VanLehn, Kurt; Girard, Sylvie; Burleson, Winslow; Chavez-Echeagaray, Maria Elena; Gonzalez-Sanchez, Javier; Hidalgo-Pontet, Yoalli

doi:10.1016/j.compedu.2014.02.015

Cited by 21 publications

(10 citation statements)

References 26 publications

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“…Drawing on previous work on model progression (Mulder et al, 2011), it was deemed more effective to provide students with a global outline of the entire model instead of with a fully specified part of the larger model. This top-down approach also mirrors what experts and successful learners do in the model formulation stage: they first determine the overall structure of the model before going into the model details (Wu et al, 2013;Zhang et al, 2006). Another, more practical reason for providing these model components was that participants in our previous studies often struggled with the concept of inflow and outflow, and found it difficult to determine to which variables these relation arrows should be attached.…”

Section: Methodsmentioning

confidence: 86%

“…For instance, Löhner, van Joolingen, and Savelsbergh () showed that students benefit from a graphical modelling language that makes use of the traditional “stock and flow” language (Forrester, ) compared to a text‐based language. Students’ model formulation also benefits from constraining scaffolding approaches that organize the task according to a simple‐to‐complex sequence in order to prevent them from getting overwhelmed by the complexity of the task (e.g., Mulder et al, , ; Zhang et al, ). The success of such incremental approaches might derive from the fact that students can focus on the model structure outline first before considering the models’ behavior.…”

Section: Supporting Learning By Modellingmentioning

confidence: 99%

“…The more successful students take the overall model structure into account and include the most salient parts of the model first. Studies comparing novice and expert modelling behavior indicate that experts spend a long time thinking through the entire model in the model formulation stage (Wu, Wu, Zhang, & Hsu, 2013;Zhang, Liu, & Krajcik, 2006). They thoroughly consider which elements to include before creating the relations between these elements.…”

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confidence: 99%

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Scaffolding learning by modelling: The effects of partially worked‐out models

et al. 2015

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Creating executable computer models is a potentially powerful approach to science learning. Learning by modelling is also challenging because students can easily get overwhelmed by the inherent complexities of the task. This study investigated whether offering partially worked-out models can facilitate students' modelling practices and promote learning. Partially worked-out models were expected to aid model construction by revealing the overall structure of the model, and thus enabling student to create better models and learn from the experience. This assumption was tested in high school biology classes where students modelled the human glucose-insulin regulatory system. Students either received support in the form of a partial model that outlined the basic structure of the glucose-insulin system (PM condition; n ¼ 26), an extended partial model that also contained a set of variables students could use to complete the model (PMþ condition; n ¼ 21), or no support (control condition; n ¼ 23). Results showed a significant knowledge increase from pretest to posttest in all conditions. Consistent with expectations, knowledge gains were higher in the two partial model conditions than in the control condition. Students in both partial model conditions also ran their model more often to check its accuracy, and eventually built better models than students from the control condition. Comparison between the PM and PMþ conditions showed that more extensive support further increased knowledge acquisition, model quality, and model testing activities. Based on these findings, it was concluded that partial solutions can support learning by modelling, and that offering both a structure of a model and a list of variables yields the best results. © 2015 Wiley Periodicals, Inc. J Res Sci Teach 53: 502-523, 2016 Keywords: learning by modelling; completion problems; worked-out examples Dutch summary (Nederlandse samenvatting): Modelleren is een veelbelovende manier voor het leren van natuurwetenschappelijke principes en verschijnselen. Leren door te modelleren is echter niet eenvoudig: het maken van een werkend computermodel blijkt voor veel leerlingen een struikelblok te zijn. Het aanbieden van een gedeeltelijk uitgewerkt model zou deze problemen kunnen doen verminderen. In een gedeeltelijk uitgewerkt model wordt de algemene modelstructuur gegeven, wat de leerlingen kan helpen om een beter model te maken en de inhoud ervan beter te begrijpen. Deze hypotheses zijn onderzocht tijdens een biologiepracticum waarin leerlingen uit de bovenbouw van het VWO een computermodel van het glucoseinsuline regulatieproces moesten maken. Een deel van de leerlingen had hierbij de beschikking over een gedeeltelijk uitgewerkt model (PM conditie; n ¼ 26). De tweede groep kreeg hetzelfde uitgewerkte model plus een lijst met variabelen die zij aan het model konden toevoegen (PMþ conditie; n ¼ 21), terwijl de

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Section: Methodsmentioning

confidence: 86%

Section: Supporting Learning By Modellingmentioning

confidence: 99%

mentioning

confidence: 99%

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Scaffolding learning by modelling: The effects of partially worked‐out models

et al. 2015

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“…Paiva et al (2015) present the project design features, its development and implementation, evaluation of the tools used and results of studies with the students involved in the project. This paper describes the design, development, implementation and evaluation of an intelligent online tutoring system (TS) (Chrysafiadi and Virvou 2013;Kularbphettong et al 2015;Zhang et al 2014) designed to teach or to remind math concepts essential for the integration of students of engineering, economics, management, among others, in mathematics higher education. The TS, which we developed in MITO platform, consists of small self-paced modularized units of educational contents including tutorial videos, notes and formative eassessments with personalized feedback.…”

Section: Introductionmentioning

confidence: 99%

Intelligent tutorial system based on personalized system of instruction to teach or remind mathematical concepts

Paiva

Ferreira

Frade

2017

Computer Assisted Learning

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The growth of the higher education population and different school paths to access an academic degree has increased the heterogeneity of students inside the classroom. Consequently, the effectiveness of traditional teaching methods has reduced. This paper describes the design, development, implementation and evaluation of a tutoring system (TS) to improve student's engagement in higher mathematics. The TS design was based on the Personalized System of Instruction of the Mastery Learning pedagogical approach and can be implemented in any higher education course with mathematics needs. The TS consists on small self‐paced modularized units of educational contents, including tutorial videos, notes and formative e‐assessment with personalized feedback. The TS ensures that the student is only allowed to proceed to the next unit after he or she achieves the required mastery criterion of the current unit. The TS was implemented in the Quantitative Methods course of an undergraduate degree and received good acceptance from students. It was also recognized that TS contributed to learning and engagement with the discipline. Through an experimental research experience, it has been shown that the imposition of restrictions on the advance to the next level by a mastery criterion leads to a significant improvement in student's engagement and performance.

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“…If the knowledge concerns domain facts, skills and concepts, then the knowledge is traditionally called Bcognitive.^The six ITS that were used as running examples in Behavior of Tutoring Systems were primarily coaching systems for cognitive knowledge. If the knowledge concerns how to do self-regulation, then the knowledge is traditionally called Bmeta-cognitive.^A meta-tutor (e.g., Azevedo and Cromley 2004;Conati and VanLehn 2000;Jackson et al 2010;Roll et al 2011;Zhang et al 2014) is a coaching system for meta-cognitive knowledge. If the knowledge concerns how to collaborate effectively, then the knowledge doesn't have a traditional name, but could be called Bcollaboration skill.^The Soller et al article provides many examples of systems intended to increase collaborative skill.…”

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confidence: 99%

Regulative Loops, Step Loops and Task Loops

VanLehn

2015

Int J Artif Intell Educ

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This commentary suggests a generalization of the conception of the behavior of tutoring systems, which the target article characterized as having an outer loop that was executed once per task and an inner loop that was executed once per step of the task. A more general conception sees these two loops as instances of regulative loops, which repeatedly compare students' performances to a gold standard and give advice on how to bring that performance closer to the standard.

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Evaluation of a meta-tutor for constructing models of dynamic systems

Cited by 21 publications

References 26 publications

Scaffolding learning by modelling: The effects of partially worked‐out models

Scaffolding learning by modelling: The effects of partially worked‐out models

Intelligent tutorial system based on personalized system of instruction to teach or remind mathematical concepts

Regulative Loops, Step Loops and Task Loops

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