Providing Adaptive Support in an Interactive Simulation for Learning

Kardan, Samad; Conati, Cristina

doi:10.1145/2702123.2702424

Cited by 36 publications

(35 citation statements)

References 22 publications

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“…to identify what approach the learner is using from a number of options. In this sense, our approach is driven by domain-specific knowledge, unlike [23] and [55], who focus on a data-driven approach to user modelling for supporting exploration in Massive Open Online Courses (MOOCs). While the data-driven approach may be suitable for MOOCs, our approach was designed for 11 to 14 year old students with the purpose of addressing particular known issues in the domain of mathematical generalisation.…”

Section: Discussionmentioning

confidence: 99%

Design and Evaluation of a Case-Based System for Modelling Exploratory Learning Behavior of Math Generalization

Cocea

Magoulas

2017

IEEE Trans. Learning Technol.

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Exploratory learning environments (ELEs) promote a view of learning that encourages students to construct and/or explore models and observe the effects of modifying their parameters. The freedom given to learners in this exploration context leads to a variety of learner approaches for constructing models and makes modelling of learner behaviour a challenging task. To address this issue, we propose a learner modelling mechanism for monitoring learners' actions when constructing/exploring models by modelling sequences of actions reflecting different strategies in solving a task. This is based on a modified version of case-based reasoning for problems with multiple solutions. In our formulation, approaches to explore the task are represented as sequences of simple cases linked by temporal and dependency relations, which are mapped to the learners' behaviour in the system by means of appropriate similarity metrics. This paper presents the development and validation of the modelling mechanism. The model was validated in the context of an ELE for mathematical generalisation using data from classroom sessions and pedagogically-driven learning scenarios.

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Section: Discussionmentioning

confidence: 99%

Design and Evaluation of a Case-Based System for Modelling Exploratory Learning Behavior of Math Generalization

Cocea

Magoulas

2017

IEEE Trans. Learning Technol.

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“…These improved diagnostic techniques may form the basis for adoptive scaffolding. Kardan and Conati (), for example, diagnosed differences in behaviour between low and high learners in an interactive simulation, designed prompts for learners to stimulate them to change their way of learning if necessary, and found that this positively influenced their learning results. Zhu et al () used c‐rater‐ML as a basis in providing students with automatic feedback on open‐ended written scientific explanations they gave for their choices in online multiple‐choice quizzes related to a virtual laboratory on climate change.…”

Section: The Next Steps In Technology‐based Guidance Of the Inquiry Pmentioning

confidence: 99%

Moving towards engaged learning in STEM domains; there is no simple answer, but clearly a road ahead

Jong

2019

Computer Assisted Learning

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What is the best approach to educating students is, evidently, the pivotal question in educational research. In the general debate on this question, clear positions are often taken―for example, whether teacher‐led instruction or more student‐directed approaches should be followed. In the current narrative review, this central question and the different stances taken are explored for a specific field of learning, STEM (science, technology, engineering, and mathematics) domains. The current article starts with an argument as to why the traditional, more directive teacher‐led educational approach to STEM learning may not always lead to deep conceptual knowledge and proposes exploring more engaged forms of learning. More specifically, a particular arrangement for engaged learning, inquiry learning, is taken as an example in this exploration. It is argued that the effects of educational methods are influenced by a multitude of (interacting) factors related to student and teacher characteristics, context, domain, frequency of use, timing, and probably more. Therefore, this article tries to outline a more nuanced and balanced stance towards choosing educational approaches. A picture is presented to show that adopting and combining different approaches, at both the lesson and the curricular levels, may be necessary to reach optimal levels of learning. Existing and upcoming technologies may play a decisive role in realizing this more complex, but also more realistic view of learning in STEM.

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“…Some tools have been developed to offer "part-task practice," including worked examples and games. Simulation has the advantage of being capable of addressing whole complex tasks and has been used in multiple domains as a teaching tool, including areas of knowledge like mathematics [3], [4], physics [5], and engineering [6]- [8]. During the development of learning tasks, unlike real-life tasks, simulation gives teachers more control over the complexity of the tasks, the amount of student support provided, and mitigation of the risks associated with failed task execution.…”

Section: Introductionmentioning

confidence: 99%

Learning UI Functional Design Principles Through Simulation With Feedback

Ruiz

Serral

Snoeck

2020

IEEE Trans. Learning Technol.

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The User interface (UI) is a key component of an interactive software application; therefore, it is important to provide software developers with basic UI design skills. However, teaching UI design is challenging, even at a basic level, and there is little teaching support. In this paper, we investigate the benefits of the feedback-enriched simulation environment (FENIkS) for learning fundamental UI design principles toward designing the functional aspects of a UI. FENIkS is a model-driven educational environment making use of simulation as learning support by generating a UI and the underlying application starting from conceptual domain and presentation models. The generated application and UI contain feedback elements for the learners. This feedback shows if the generated prototype is compliant with certain key design principles and why the principles are considered to be well applied or not. We conducted an experiment using bachelor-level courses to observe the effects of FENIkS on the learning of UI design principles by novice UI designers. The effectiveness of FENIkS was measured by comparing the results of students on a test performed without and with the use of FENIkS, using statistical methods. The findings show an improvement in student's learning of UI design principles when using the FENIkS approach.

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Providing Adaptive Support in an Interactive Simulation for Learning

Cited by 36 publications

References 22 publications

Design and Evaluation of a Case-Based System for Modelling Exploratory Learning Behavior of Math Generalization

Design and Evaluation of a Case-Based System for Modelling Exploratory Learning Behavior of Math Generalization

Moving towards engaged learning in STEM domains; there is no simple answer, but clearly a road ahead

Learning UI Functional Design Principles Through Simulation With Feedback

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