Phases of inquiry-based learning: Definitions and the inquiry cycle

Pedaste, Margus; Mäeots, Mario; Siiman, Leo; Jong, Ton de; Riesen, Siswa A. N. van; Kamp, Ellen T.; Manoli, Constantinos C.; Zacharia, Zacharias C.; Tsourlidaki, Eleftheria

doi:10.1016/j.edurev.2015.02.003

Cited by 1,340 publications

(1,258 citation statements)

References 47 publications

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“…In science inquiries, learning oft en begins with a phenomenon, a topic, or a learning challenge that requires scientifi c investigations to provide explanation, discovers properties of a given domain, or address a certain challenge (Pedaste et al, 2015). To proceed, learners need to conceptualize the problem by questioning and generating hypotheses; design and conduct experiments; record, analyze, and evaluate data; and infer or induce conclusions from data (Pedaste et al, 2015;van Joolingen & de Jong, 1997).…”

Section: Toward a Theoretical Framework Of Selfregulation In Ill-strumentioning

confidence: 99%

“…To proceed, learners need to conceptualize the problem by questioning and generating hypotheses; design and conduct experiments; record, analyze, and evaluate data; and infer or induce conclusions from data (Pedaste et al, 2015;van Joolingen & de Jong, 1997). Science inquiries often end with communication as well as reflections of findings (Pedaste et al, 2015).…”

Section: Toward a Theoretical Framework Of Selfregulation In Ill-strumentioning

confidence: 99%

“…The planning phase is characterized by (1) reading task materials, (2) activating prior knowledge and motivational beliefs, and (3) forming initial goals for problem representation. For each of the three problemsolving contexts, the learner would first engage in an attempt to closely read an IPS task (Brand-Gruwel et al, 2005), a historical document (Poitras & Lajoie, 2013), or a challenge or topic in science (Pedaste et al, 2015). Based on the readings, the learner may recall relevant prior knowledge, which may include both content knowledge related to the task and learner's conceptualization of domain standards.…”

Section: Self-regulation In Problem Representationmentioning

confidence: 99%

“…In historical inquiries, learners may apply prior knowledge or refer to additional resources on hand to examine the historical document and may identify events depicted in the document that has unknown, uncertain, or unreported causes (Poitras & Lajoie, 2013). In the case of science inquiry, learners may apply prior knowledge to identify the variables involved in a science problem and subsequently formulate preliminary research questions and hypotheses regarding the relationships among the variables (Pedaste et al, 2015;van Joolingen & de Jong, 1997). While such execution processes may not be externalized or observed from the outside, the identified key components of the problem and their interrelationships become "an internal model of the task or problem at hand" (Lazonder & Rouet, 2008, p. 755), or the representation of the problem.…”

Section: Self-regulation In Problem Representationmentioning

confidence: 99%

“…In science inquiry, the learner may continuously engage in a mindful coordination between hypotheses and evidence gathered from experiments and draw evidence-based conclusions (Klahr & Dunbar, 1998;Zhang, Chen, Sun, & Reid, 2004). At times, the process may lead back to the planning and execution of another experiment (Pedaste et al, 2015).…”

Section: Self-regulation In Solution Generationmentioning

confidence: 99%

See 4 more Smart Citations

Detangling the Interrelationships Between Self-Regulation and Ill-Structured Problem Solving in Problem-Based Learning

Ge¹,

Law²,

Huang³

2016

Interdisciplinary Journal of Problem-Based Learning

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Section: Toward a Theoretical Framework Of Selfregulation In Ill-strumentioning

confidence: 99%

Section: Toward a Theoretical Framework Of Selfregulation In Ill-strumentioning

confidence: 99%

Section: Self-regulation In Problem Representationmentioning

confidence: 99%

Section: Self-regulation In Problem Representationmentioning

confidence: 99%

Section: Self-regulation In Solution Generationmentioning

confidence: 99%

See 3 more Smart Citations

Detangling the Interrelationships Between Self-Regulation and Ill-Structured Problem Solving in Problem-Based Learning

Ge¹,

Law²,

Huang³

2016

Interdisciplinary Journal of Problem-Based Learning

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Modeling and simulation practices in engineering education

Magana

Jong

2018

Comp Applic In Engineering

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Much can be learned from the vast work on the use of computer simulations for inquiry learning for the integration of modeling and simulation practices in engineering education. This special issue presents six manuscripts that take steps toward evidence‐based teaching and learning practices. These six studies present learning designs that align learning objectives, with evidence of the learning, and pedagogy. Here we highlight the main contributions from each paper individually, but also themes identified across all of them. These themes include (a) approaches for modeling‐and‐simulation‐centric course design; (b) teaching practices and pedagogies for modeling and simulation implementation; and (c) evidence of learning with and about modeling and simulation practices. We conclude our introduction by highlighting desirable characteristics of studies that report on the effectiveness of modeling and simulation in engineering education, and with that we provide some recommendations for improving the scholarship of teaching and learning in this field.

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Gamifying research in software engineering

Nowostawski

McCallum

Mishra

2018

Comp Applic In Engineering

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It is a non‐trivial task for research‐centric courses in the software engineering curriculum to compete and engage students on the same level as the practical, software development courses. Practical software development courses and projects are inherently motivating to students, as they provide necessary elements such as agency, relatedness, and the strong sense of competence upon completing software engineering tasks. In contrast, reading research articles and technical white papers feels dry and non‐engaging. Nevertheless, a well‐balanced MSc programme curriculum covers both, learning through construction and practical courses as well as research courses. The main motivation for the development of game‐centric approaches to the research aspects of curriculum is to improve students’ interest and engagement with those courses. In this paper, we present the methodology and initial evaluations from three gamification strategies used in the Master's degree programme. These are: the game of reading and discussing research articles (GoRaD), the game of arguing and counter‐arguing, and combining research and practice. The paper presents our experimentation and initial evaluations of the use of those strategies, as well as plans for future development and enhancements.

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Phases of inquiry-based learning: Definitions and the inquiry cycle

Cited by 1,340 publications

References 47 publications

Detangling the Interrelationships Between Self-Regulation and Ill-Structured Problem Solving in Problem-Based Learning

Detangling the Interrelationships Between Self-Regulation and Ill-Structured Problem Solving in Problem-Based Learning

Modeling and simulation practices in engineering education

Gamifying research in software engineering

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