The NISPI framework: Analysing collaborative problem-solving from students' physical interactions

Cukurova, Mutlu; Luckin, Rose; Millán, Eva; Mavrikis, Manolis

doi:10.1016/j.compedu.2017.08.007

Cited by 86 publications

(83 citation statements)

References 50 publications

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“…In our case, these features highly correlate with the quality of the students' artefacts in project-based learning. These results align with existing research on PBL activities that show the value of nonverbal indexes of student interaction in estimating their success at learning processes (Cukurova, Luckin, Millan, & Mavrikis, 2018) as well as MMLA research findings that show the potential of hand motion and speed, and the location of the learners to predict student success at various learning outcomes (Blikstein, 2011;Ochoa et al, 2013;Grover et al, 2016). As mentioned in Section 2, there are three main aspects of PBL: students are asking driving questions, doing investigations to answer these questions, and collaborate together to solve these questions (Krajcik, 2010).…”

Section: Traditional Approachsupporting

confidence: 89%

Supervised machine learning in multimodal learning analytics for estimating success in project‐based learning

Spikol

Ruffaldi

Dabisias

et al. 2018

Computer Assisted Learning

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138

107

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Multimodal learning analytics provides researchers new tools and techniques to capture different types of data from complex learning activities in dynamic learning environments. This paper investigates the use of diverse sensors, including computer vision, user‐generated content, and data from the learning objects (physical computing components), to record high‐fidelity synchronised multimodal recordings of small groups of learners interacting. We processed and extracted different aspects of the students' interactions to answer the following question: Which features of student group work are good predictors of team success in open‐ended tasks with physical computing? To answer this question, we have explored different supervised machine learning approaches (traditional and deep learning techniques) to analyse the data coming from multiple sources. The results illustrate that state‐of‐the‐art computational techniques can be used to generate insights into the "black box" of learning in students' project‐based activities. The features identified from the analysis show that distance between learners' hands and faces is a strong predictor of students' artefact quality, which can indicate the value of student collaboration. Our research shows that new and promising approaches such as neural networks, and more traditional regression approaches can both be used to classify multimodal learning analytics data, and both have advantages and disadvantages depending on the research questions and contexts being investigated. The work presented here is a significant contribution towards developing techniques to automatically identify the key aspects of students success in project‐based learning environments, and to ultimately help teachers provide appropriate and timely support to students in these fundamental aspects.

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Section: Traditional Approachsupporting

confidence: 89%

Supervised machine learning in multimodal learning analytics for estimating success in project‐based learning

Spikol

Ruffaldi

Dabisias

et al. 2018

Computer Assisted Learning

Self Cite

138

107

View full text Add to dashboard Cite

show abstract

“…Similarly, Di Mitri et al (2017) investigated the potential of multimodal data from Fitbit wristband and computer logs to predict learners' self-regulation performance. In the context of project-based learning, Cukurova, Luckin, Millán, and Mavrikis (2018), and Spikol, Ruffaldi, Dabisias, and Cukurova (2018) collected data from learners' hand movements and head direction to predict their success in open-ended design tasks. In the area of professional development, Echeverria, Martinez-Maldonado, Power, Hayes, and Shum (2018) used sensor data to capture trainee nurses' interactions during healthcare training and created visualisations of their interaction and movements for effective reflections.…”

mentioning

confidence: 99%

Artificial intelligence and multimodal data in the service of human decision‐making: A case study in debate tutoring

Cukurova¹,

Kent²,

Luckin³

2019

Brit J Educational Tech

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The question: “What is an appropriate role for AI?” is the subject of much discussion and interest. Arguments about whether AI should be a human replacing technology or a human assisting technology frequently take centre stage. Education is no exception when it comes to questions about the role that AI should play, and as with many other professional areas, the exact role of AI in education is not easy to predict. Here, we argue that one potential role for AI in education is to provide opportunities for human intelligence augmentation, with AI supporting us in decision‐making processes, rather than replacing us through automation. To provide empirical evidence to support our argument, we present a case study in the context of debate tutoring, in which we use prediction and classification models to increase the transparency of the intuitive decision‐making processes of expert tutors for advanced reflections and feedback. Furthermore, we compare the accuracy of unimodal and multimodal classification models of expert human tutors' decisions about the social and emotional aspects of tutoring while evaluating trainees. Our results show that multimodal data leads to more accurate classification models in the context we studied.

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“…We explored four constructs from the learning sciences: synchrony, individual accountability, equality and intraindividual variability (Cukurova et al , ). These concepts were interpreted via nonverbal indexes of students' physical interactivity, both at the individual and at the group levels, to identify students' CPS competence.…”

Section: Empirical Case Studiesmentioning

confidence: 99%

“…The first case study, PELARS, demonstrates that nonverbal interactions of learners can be effectively used to interpret complex learning process of collaborative problem-solving (CPS) (Cukurova, Luckin, Millán, & Mavrikis, 2018). Although, CPS has been widely studied in the learning sciences domain, there are very limited number of studies that focus on the nonverbal aspects of it.…”

Section: Empirical Case Studiesmentioning

confidence: 99%

Designing educational technologies in the age of AI: A learning sciences‐driven approach

Luckin¹,

Cukurova²

2019

Brit J Educational Tech

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161

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Interdisciplinary research from the learning sciences has helped us understand a great deal about the way that humans learn, and as a result we now have an improved understanding about how best to teach and train people. This same body of research must now be used to better inform the development of Artificial Intelligence (AI) technologies for use in education and training. In this paper, we use three case studies to illustrate how learning sciences research can inform the judicious analysis, of rich, varied and multimodal data, so that it can be used to help us scaffold students and support teachers. Based on this increased understanding of how best to inform the analysis of data through the application of learning sciences research, we are better placed to design AI algorithms that can analyse rich educational data at speed. Such AI algorithms and technology can then help us to leverage faster, more nuanced and individualised scaffolding for learners. However, most commercial AI developers know little about learning sciences research, indeed they often know little about learning or teaching. We therefore argue that in order to ensure that AI technologies for use in education and training embody such judicious analysis and learn in a learning sciences informed manner, we must develop inter‐stakeholder partnerships between AI developers, educators and researchers. Here, we exemplify our approach to such partnerships through the EDUCATE Educational Technology (EdTech) programme. What is already known about this topic? The progress of AI Technology and learning analytics lags behind the adoption of these approaches and technologies in other fields such as medicine or finance. Data are central to the empirical work conducted in the learning sciences and to the development of machine learning Artificial Intelligence (AI). Education is full of doubts about the value that any technology can bring to the teaching and learning process. What this paper adds? We argue that the learning sciences have an important role to play in the design of educational AI, through their provision of theories that can be operationalised and advanced. Through case studies, we illustrate that the analysis of data appropriately informed by interdisciplinary learning sciences research can be used to power AI educational technology. We provide a framework for inter‐stakeholder, interdisciplinary partnerships that can help educators better understand AI, and AI developers better understand education. Implications for practice and/or policy? AI is here to stay and that it will have an increasing impact on the design of technology for use in education and training. Data, which is the power behind machine learning AI, can enable analysis that can vastly increase our understanding of when and how the teaching and learning process is progressing positively. Inter‐stakeholder, interdisciplinary partnerships must be used to make sure that AI provides some of the educational benefits its application in other areas promise us.

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The NISPI framework: Analysing collaborative problem-solving from students' physical interactions

Cited by 86 publications

References 50 publications

Supervised machine learning in multimodal learning analytics for estimating success in project‐based learning

Supervised machine learning in multimodal learning analytics for estimating success in project‐based learning

Artificial intelligence and multimodal data in the service of human decision‐making: A case study in debate tutoring

Designing educational technologies in the age of AI: A learning sciences‐driven approach

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