| 1. |
- Dabisias, Giacomo, et al.
(författare)
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A Learning Analytics Framework for Practice-Based Learning
- 2015
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Ingår i: Exploring the Material Conditions of Learning. - : International Society of the Learning Sciences. - 9780990355076 ; , s. 740-742
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Konferensbidrag (refereegranskat)abstract
- The role of the PELARS Learning Analytics System (LAS) system is to collect information from students performing project-based tasks, reason on such information and provide visualization to teachers and students, that is usable for understanding the learning process. The information collected by the LAS comprises pieces of information collected directly by the Students, and other collected by the System automatically. In this work we will provide a comprehensive description of the framework and the motivations behind the various decisions. The software framework will be described starting from the broad vision of the context and then the different components will be described in detail.
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| 2. |
- Ruffaldi, Emanuele, et al.
(författare)
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Data collection and processing for a multimodal learning analytic system
- 2016
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Ingår i: Proceedings of 2016 SAI Computing Conference (SAI). - : IEEE. - 9781467384605 - 9781467384612 ; , s. 858-863
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Konferensbidrag (refereegranskat)abstract
- Learning Analytic (LA) systems are aimed at supporting teachers in understanding the learning process by analyzing the information and the interaction of students with computer systems. In the case of a project-based learning process there is a need of introducing measure the student’ activity as acquired via multiple modalities and then processed. The acquisition and processing needs to take into account the specificities of the learning context and deployment at schools, in particular in terms of system architecture. The paper proposes an architecture for the acquisition and processing of data for project-based LA designed to be interoperable and scalable. System design, details of the solutions and brief examples of acquired data are presented.
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| 3. |
- Spikol, Daniel, et al.
(författare)
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CSCL Opportunities with Digital Fabrication through Learning Analytics
- 2015
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Ingår i: Exploring the Material Conditions of Learning. - : International Society of the Learning Sciences. ; , s. 697-698
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This paper presents a recently started research project that aims to generate, analyze, use, and provide feedback for analytics derived from hands-on, project-based and experiential learning scenarios. The project draws heavy influence from digital fabrication activities and related inquiry-based learning. The intention of the poster is to raise the discussion about how learning analytics from the project can be used to support and enhance learning for tangible technologies, These activities include physical computing and other lab work for small group work in higher education and high school settings.
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| 4. |
- Spikol, Daniel, 1965-, et al.
(författare)
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Current and Future Multimodal Learning Analytics Data Challenges
- 2017
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Ingår i: Seventh International Learning Analytics & Knowledge Conference (LAK'17). - New York, NY, USA : ACM Digital Library. ; , s. 518-519
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Konferensbidrag (refereegranskat)abstract
- Multimodal Learning Analytics (MMLA) captures, integrates and analyzes learning traces from different sources in order to obtain a more holistic understanding of the learning process, wherever it happens. MMLA leverages the increasingly widespread availability of diverse sensors, high-frequency data collection technologies and sophisticated machine learning and artificial intelligence techniques. The aim of this workshop is twofold: first, to expose participants to, and develop, different multimodal datasets that reflect how MMLA can bring new insights and opportunities to investigate complex learning processes and environments; second, to collaboratively identify a set of grand challenges for further MMLA research, built upon the foundations of previous workshops on the topic.
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| 5. |
- Spikol, Daniel, et al.
(författare)
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Estimation of Success in Collaborative Learning Based on Multimodal Learning Analytics Features
- 2017
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Ingår i: Proceedings 17th International Conference on Advanced Learning Technologies - ICALT 2017. - : IEEE. ; , s. 269-273
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Konferensbidrag (refereegranskat)abstract
- Abstract: 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 high-fidelity synchronised multimodal recordings of small groups of learners interacting from diverse sensors that include computer vision, user generated content, and data from the learning objects (like physical computing components or laboratory equipment). 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? The answer to the question provides ways to automatically identify the students' performance during the learning activities.
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| 6. |
- Spikol, Daniel, et al.
(författare)
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Exploring the interplay between human and machine annotated multimodal learning analytics in hands-on STEM Activities
- 2016
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Ingår i: Proceedings of LAK '16 6th International Conference on Learning Analytics and Knowledge. - New York, New York, USA : ACM Digital Library. ; , s. 522-523
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Konferensbidrag (refereegranskat)abstract
- This poster explores how to develop a working framework for STEM education that uses both human annotated and machine data across a purpose-built learning environment. Our dual approach is to develop a robust framework for analysis and investigate how to design a learning analytics system to support hands-on engineering design tasks. Data from the first user tests are presented along with the framework for discussion.
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| 7. |
- Spikol, Daniel, 1965-, et al.
(författare)
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Supervised machine learning in multimodal learning analytics for estimating success in project-based learning
- 2018
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Ingår i: Journal of Computer Assisted Learning. - : John Wiley & Sons. - 0266-4909 .- 1365-2729. ; 34:4, s. 366-377
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 8. |
- Spikol, Daniel, et al.
(författare)
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Using Multimodal Learning Analytics to Identify Aspects of Collaboration in Project-Based Learning
- 2017
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Ingår i: Making aDifference: Prioritizing Equity and Access in CSCL, 12th International Conference onComputer Supported Collaborative Learning (CSCL). - : International Society of the Learning Sciences.. - 9780990355007 ; , s. 263-270
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Konferensbidrag (refereegranskat)abstract
- Collaborative learning activities are a key part of education and are part of many common teaching approaches including problem-based learning, inquiry-based learning, and project-based learning. However, in open-ended collaborative small group work where learners make unique solutions to tasks that involve robotics, electronics, programming, and design artefacts evidence on the effectiveness of using these learning activities are hard to find. The paper argues that multimodal learning analytics (MMLA) can offer novel methods that can generate unique information about what happens when students are engaged in collaborative, project-based learning activities. Through the use of multimodal learning analytics platform, we collected various streams of data, processed and extracted multimodal interactions to answer the following question: which features of MMLA are good predictors of collaborative problem-solving in open-ended tasks in project-based learning? Manual entered scores of CPS were regressed using machine-learning methods. The answer to the question provides potential ways to automatically identify aspects of collaboration in project-based learning.
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