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Cited by 14Intelligent Tutoring Systems and Adaptive LearningScience Education and PedagogyOnline Learning and Analytics

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Digital Assessment Environments for Scientific Inquiry Practices

Gobert, Janice D., Sao Pedro, Michael A.·2016·The Handbook of Cognition and Assessment
DOI 10.1002/9781118956588.ch21
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Abstract

In this chapter, we provide an overview of the design, data-collection, and data-analysis efforts for a digital learning and assessment environment for scientific inquiry/science practices called Inq-ITS (Inquiry Intelligent Tutoring System). We first present a brief literature review on current science standards, learning sciences research on students’ difficulties with scientific inquiry practices, and modern assessment design frameworks. We then describe how we used pilot data from four case studies with hands-on inquiry tasks for middle school students to better understand these difficulties and design various components of the Inq-ITS system to support students’ inquiry accordingly. Lastly, we describe how we used key computational techniques from knowledge-engineering and educational data mining to analyze data from students’ log files in this environment to (1) automatically score students’ inquiry skills, (2) provide teachers with fine-grained, rich, classroom-based formative assessment data on these practices, and (3) react in real time to scaffold students as they engage in inquiry.

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  • Data-intensive machine learning methods are positioned at the intersection of big data and AI and are capable of improving the services and productivity of education.

    “These data-intensive, machine learning methods are positioned at the intersection of big data and AI, and are capable of improving the services and productivity of education”

    From Challenges and Future Directions of Big Data and Artificial Intelligence in Education (2020) · cites this paper as Gobert and Sao Pedro, 2017

  • Machine learning algorithms can provide new insights, predictions, and solutions to customize the needs and circumstances of each individual.

    “Machine learning algorithms can provide new insights, predictions, and solutions to customize the needs and circumstances of each individual.”

    From Challenges and Future Directions of Big Data and Artificial Intelligence in Education (2020) · cites this paper as Gobert and Sao Pedro, 2017

  • With the availability of large quantity and high-quality input training data, machine learning processes can achieve accurate results and facilitate informed decision making.

    “With the availability of large quantity and high-quality input training data, machine learning processes can achieve accurate results and facilitate informed decision making”

    From Challenges and Future Directions of Big Data and Artificial Intelligence in Education (2020) · cites this paper as Gobert and Sao Pedro, 2017

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Metadata

Authors
Gobert, J. D., & Sao Pedro, M. A.
Affiliations
Relay Graduate School of Education (US)
ORCID
Gobert, Janice D.
Venue
The Handbook of Cognition and Assessment
Publisher
John Wiley & Sons, Inc.
Published
2016-11-26
Pages
508-534
DOI
10.1002/9781118956588.ch21
OpenAlex
W2550861584
Cited by
14
References
9
Citations by year
2025: 3 · 2024: 1 · 2023: 1 · 2022: 2 · 2021: 3 · 2020: 3 · 2018: 1
FWCI
0.58
Type
book chapter
Language
en
Open access
closed
Locations
John Wiley & Sons, Inc. eBooks (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Social Sciences › Education › Science Education and Pedagogy
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
Keywords
intelligent tutoring systems, science education, junior high school students, formative assessment, scaffolding, educational data mining, knowledge engineering

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