Evaluating the Transfer of Scaffolded Inquiry: What Sticks and Does It Last?

Li, Haiying; Gobert, Janice D.; Dickler, Rachel

doi:10.1007/978-3-030-23207-8_31

Cited by 7 publications

(5 citation statements)

References 17 publications

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“…Only a small number of studies have focused on the effectiveness of learning analytics programs and AI applications. However, recent findings have revealed encouraging results in terms of improving students' academic performance and retention, as well as supporting teachers in learning design and teaching strategy refinement (Viberg et al, 2018;Li et al, 2019;Sonderlund et al, 2019;Mislevy et al, 2020).…”

Section: The Position Formulationmentioning

confidence: 98%

Challenges and Future Directions of Big Data and Artificial Intelligence in Education

et al. 2020

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We discuss the new challenges and directions facing the use of big data and artificial intelligence (AI) in education research, policy-making, and industry. In recent years, applications of big data and AI in education have made significant headways. This highlights a novel trend in leading-edge educational research. The convenience and embeddedness of data collection within educational technologies, paired with computational techniques have made the analyses of big data a reality. We are moving beyond proof-of-concept demonstrations and applications of techniques, and are beginning to see substantial adoption in many areas of education. The key research trends in the domains of big data and AI are associated with assessment, individualized learning, and precision education. Model-driven data analytics approaches will grow quickly to guide the development, interpretation, and validation of the algorithms. However, conclusions from educational analytics should, of course, be applied with caution. At the education policy level, the government should be devoted to supporting lifelong learning, offering teacher education programs, and protecting personal data. With regard to the education industry, reciprocal and mutually beneficial relationships should be developed in order to enhance academia-industry collaboration. Furthermore, it is important to make sure that technologies are guided by relevant theoretical frameworks and are empirically tested. Lastly, in this paper we advocate an in-depth dialog between supporters of "cold" technology and "warm" humanity so that it can lead to greater understanding among teachers and students about how technology, and specifically, the big data explosion and AI revolution can bring new opportunities (and challenges) that can be best leveraged for pedagogical practices and learning.

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Section: The Position Formulationmentioning

confidence: 98%

Challenges and Future Directions of Big Data and Artificial Intelligence in Education

et al. 2020

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“…Video-worked examples seem especially efficient by providing problem-solving schema, which helps students to reduce mental effort and to have more working-memory capacity when applying this scientific skill to different inquiry topics (Kaiser & Mayer, 2019). Moreover, the continuous scaffolding within an ongoing activity that provides video examples is a particular feature that, according to some authors, benefits the formulation of scientific arguments (Chen et al, 2016;Li et al, 2019). However, it is important not to underestimate the role of the teacher.…”

Section: Collecting Organizing Representing and Analyzing Data And Dr...mentioning

confidence: 99%

The Effect of an Instructional Intervention Based on the Use of Video-Worked Examples to Promote Elementary Students’ Science Process Skills

Solé-Llussà

Camaño

Plana

2022

IJEMST

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Several reforms in national and international curricula have been implemented to introduce inquiry-based activities to promote different science process skills. Science skills require high cognitive effort for students and, thus, they need supports to develop them in an inquiry process. Among these supports, video-worked examples demonstrate how to perform these skills and provide students with a scheme for an inquiry activity. Recently, video-examples has been explored in primary and secondary education with promising results. However, to gain a broader perspective on their effectiveness, this article aims to evaluate the consistent improvement of each science skill from a comparison with a control group carrying out two consecutive classroom interventions. This research included 44 students in fifth and sixth grades and a quasi-experimental research design with a quantitative method has been applied. In the experimental group, video examples had a positive influence on the participants’ inquiry behavior. For most of the science skills, students showed a consistent improvement in both the first and second inquiries supported by these videos. However, the control group showed less predictable outcomes, as some skills improved only during the first or the second intervention, but not throughout the whole study.

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“…There is an increasing amount of research that shows the positive impact of using Artificial Intelligence (AI) applications to support students' academic performance [1,2], their affective engagement [3][4][5], and metacognitive development [6][7][8]. In the design of effective AI in Education (AIED) tools, most available research highlights the significance of robust technical approaches and the use of learning sciences principles [9,10].…”

Section: Introductionmentioning

confidence: 99%

Investigating Students’ Experiences with Collaboration Analytics for Remote Group Meetings

Zhou

Suraworachet

Pozdniakov

et al. 2021

Lecture Notes in Computer Science

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Remote meetings have become the norm for most students learning synchronously at a distance during the ongoing coronavirus pandemic. This has motivated the use of artificial intelligence in education (AIED) solutions to support the teaching and learning practice in these settings. However, the use of such solutions requires new research particularly with regards to the human factors that ultimately shape the future design and implementations. In this paper, we build on the emerging literature on human-centred AIED and explore students' experiences after interacting with a tool that monitors their collaboration in remote meetings (i.e., using Zoom) during 10 weeks. Using the social translucence framework, we probed into the feedback provided by twenty students regarding the design and implementation requirements of the system after their exposure to the tool in their course. The results revealed valuable insights in terms of visibility (what should be made visible to students via the system), awareness (how can this information increase students' understanding of collaboration performance), and accountability (to what extent students take responsibility of changing their behaviours based on the system's feedback); as well as the ethical and privacy aspects related to the use of collaboration analytics tools in remote meetings. This study provides key suggestions for the future design and implementations of AIED systems for remote meetings in educational settings.

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Evaluating the Transfer of Scaffolded Inquiry: What Sticks and Does It Last?

Cited by 7 publications

References 17 publications

Challenges and Future Directions of Big Data and Artificial Intelligence in Education

Challenges and Future Directions of Big Data and Artificial Intelligence in Education

The Effect of an Instructional Intervention Based on the Use of Video-Worked Examples to Promote Elementary Students’ Science Process Skills

Investigating Students’ Experiences with Collaboration Analytics for Remote Group Meetings

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