Evaluation of a Meta-tutor for Constructing Models of Dynamic Systems

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

doi:10.1007/978-3-642-39112-5_84

Cited by 7 publications

(8 citation statements)

References 2 publications

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“…The research community has identified several types of strategies based on the tasks for which they are designed. For example, strategies may be (1) cognitive (e.g., a strategy for solving an addition problem); (2) metacognitive (e.g., a strategy for monitoring one's own cognitive operations when working on an equation-solving task), (3) focused on management (e.g., managing one's environment to promote focused attention); (4) directed toward learning (e.g., a strategy that facilitates learning of feedback processes); or (5) a combination of the four strategy types discussed above [29], [30]. For example, a metacognitive learning strategy may involve activating prior knowledge before learning about a topic by consciously bringing to mind information one already knows about the topic [31].…”

Section: Background: Metacognitionmentioning

confidence: 99%

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Integrating Model-Driven and Data-Driven Techniques for Analyzing Learning Behaviors in Open-Ended Learning Environments

Kinnebrew

Segedy

Biswas

2017

IEEE Trans. Learning Technol.

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Research in computer-based learning environments has long recognized the vital role of adaptivity in promoting effective, individualized learning among students. Adaptive scaffolding capabilities are particularly important in open-ended learning environments, which provide students with opportunities for solving authentic and complex problems, and the choice to adopt a variety of strategies and approaches to solving these problems. To help students overcome their difficulties and become effective learners and problem solvers, we have to develop methods that can track and interpret students' open-ended learning and problem-solving behaviors. The complexity of the problems and the open-ended nature of the solution processes pose considerable challenges to accurately interpret and evaluate student behaviors and performance as they work on the system. In this paper, we develop a framework that combines model-driven strategy detection with data-driven pattern discovery for analyzing students' learning activity data in open-ended environments. We present results from an in-depth case study of multiple activity patterns identified in data from the Betty's Brain learning environment. The results illustrate the benefits of combining model-and data-driven techniques to precisely characterize the learning behavior of students in an open-ended environment.!

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Section: Background: Metacognitionmentioning

confidence: 99%

“…For example, Zhang and colleagues [30] instructed students in the use of a target node strategy while constructing system dynamics models and then measured their use of that strategy. Results showed that in one experiment, 34% and 39% of students' steps (in two different groups) were consistent with the strategy.…”

Section: Background: Strategy Understandingmentioning

confidence: 99%

Integrating Model-Driven and Data-Driven Techniques for Analyzing Learning Behaviors in Open-Ended Learning Environments

Kinnebrew

Segedy

Biswas

2017

IEEE Trans. Learning Technol.

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“…A first task model was created to represent learner's activity on software without the presence of the meta-tutor. This corresponds to the first version of software, which was evaluated against the interface including the meta-tutor in [18]. This second software interface includes a text-based agent that intervenes as the students engage in modeling to help them achieve deeper modeling behaviors, by applying constraints to the user's actions and giving meta-cognitive feedback.…”

Section: Conditions On Tasksmentioning

confidence: 99%

“…The meta-tutor requires students to follow a goal-reduction problem solving strategy, the Target Node Strategy [18]. The basic idea is to focus on one node at a time (the target node) and completely define it before working on any other node.…”

mentioning

confidence: 99%

Using HCI Task Modeling Techniques to Measure How Deeply Students Model

Girard

Zhang

Hidalgo-Pontet

et al. 2013

Lecture Notes in Computer Science

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User modeling in AIED has been extended in the past decades to include affective and motivational aspects of learner's interaction in intelligent tutoring systems. An issue in such systems is researchers' ability to understand and detect students' cognitive and meta-cognitive processes while they learn. In order to study those factors, various detectors have been created that classify episodes in log data as gaming, high/low effort on task, robust learning, etc. When simulating students' learning processes in an ITS, a question remains as to how to create those detectors, and how reliable their simulation of the user's learning processes can be. In this article, we present our method for creating a detector of shallow modeling practices within a meta-tutor instructional system. The detector was defined using HCI (human-computer interaction) task modeling as well as a coding scheme defined by human coders from past users' screen recordings of software use. The detector produced classifications of student behavior that were highly similar to classifications produced by human coders with a kappa of .925.

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“…This paper describes the design, development, implementation and evaluation of an intelligent online tutoring system (TS) (Chrysafiadi and Virvou ; Kularbphettong et al ; Zhang et al ) 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 e‐assessments 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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Evaluation of a Meta-tutor for Constructing Models of Dynamic Systems

Cited by 7 publications

References 2 publications

Integrating Model-Driven and Data-Driven Techniques for Analyzing Learning Behaviors in Open-Ended Learning Environments

Integrating Model-Driven and Data-Driven Techniques for Analyzing Learning Behaviors in Open-Ended Learning Environments

Using HCI Task Modeling Techniques to Measure How Deeply Students Model

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

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