Multimodal learning analytics as a tool for bridging learning theory and complex learning behaviors

Authors:
Worsley, M.
Venue:
Project:
Multimodal Learning Analytics
Project:
Multimodal Learning Analytics
Multimodal Learning Analytics
Year:
2014
Abstract:
The recent emergence of several low-cost, high resolution, multimodal sensors has greatly facilitated the ability for researchers to capture a wealth of data across a variety of contexts. Over the past few years, this multimodal technology has begun to receive greater attention within the learning community. Specifically, the Multimodal Learning Analytics community has been capitalizing on new sensor technology, as well as the expansion of tools for supporting computational analysis, in order to better understand and improve student learning in complex learning environments. However, even as the data collection and analysis tools have greatly eased the process, there remain a number of considerations and challenges in framing research in such a way that it lends to the development of learning theory. Moreover, there are a multitude of approaches that can be used for integrating multimodal data, and each approach has different assumptions and implications. In this paper, I describe three different types of multimodal analyses, and discuss how decisions about data integration and fusion have a significant impact on how the research relates to learning theories.
Citation:

Worsley, M. (2014). Multimodal Learning Analytics as a Tool for Bridging Learning Theory and Complex Learning Behaviors. In Proceedings of the 2014 ACM workshop on Multimodal Learning Analytics Workshop and Grand Challenge (MLA '14). ACM, New York, NY, USA, 1-4.

Using multimodal learning analytics to study learning mechanisms

Authors:
Worsley, M. & Blikstein, P.
Venue:
Project:
Multimodal Learning Analytics
Project:
Multimodal Learning Analytics
Multimodal Learning Analytics
Year:
2014
Abstract:
In this paper, we propose multimodal learning analytics as a new approach for studying the intricacies of different learning mechanisms. More specifically, we conduct two analyses of a hands-on, engineering design study (N=20) in which students received different treatments. In the first analysis, we used machine learning to analyze hand-labeled video data. The findings of this analysis suggest that one of the treatments resulted in students initially engaging in more planning, while the other resulted in students initially engaging in more building. In accordance with prior literature, beginning with dedicated planning tends to be associated with improved success and improved learning. In the second analysis we introduce a completely automated multimodal analysis of speech, actions and stress. This automated analysis uses multimodal states to show that students in the two conditions engaged in different amounts of speech and building during the second half of the activity. These findings mirror prior work on teamwork, expertise and engineering education. They also represent two novel approaches for studying complex, non-computer mediated learning environments and provide new ways to understand learning.
Citation:

Worsley, M. and Blikstein, P. (2014). Using Multimodal Learning Analytics to Study Learning Mechanisms. In Proceedings of the 2014 Educational Data Mining Conference.

Making smarter not harder: Using principle-based reasoning to promote object closeness and improve making

Authors:
Worsley, M. & Blikstein, P.
Venue:
Year:
2014
Abstract:
Constructionism and the Maker Movement are becoming increasingly prevalent and increasingly popular. Makerspaces and Fablabs are being developed in schools, libraries, museums and community centers around the world. However, as this movement grows it is important to continue researching, refining and improving the best practices within these innovative environments. In this paper we present a pair of studies that document 1) common strategies that students use in hands-on learning, and 2) how those strategies impact student performance and learning. Specifically, we show that students who engage in a short principle-based reasoning intervention, outperform their peers who use example-based reasoning both in terms of the quality of their designs, and in terms of knowledge construction. Based on the results of these studies we propose that short, appropriately targeted, generative activities be more broadly used in constructionist learning environments. The generative activities will help promote “object closeness” and improve the current state of making in education.
Citation:

Worsley, M. and Blikstein, P. (2014). Making Smarter not Harder: Using Principle-based Reasoning to Promote Object Closeness and Improve Making. In Proceedings of the 2014 FabLearn Conference.

Educational Data Mining and Learning Analytics: Applications to Constructionist Research

Project:
Multimodal Learning Analytics
Project:
Multimodal Learning Analytics
Year:
2014
doi:
10.1007/s10758-014-9223-7
Abstract:
Constructionism can be a powerful framework for teaching complex content to novices. At the core of constructionism is the suggestion that by enabling learners to build creative artifacts that require complex content to function, those learners will have opportunities to learn this content in contextualized, personally meaningful ways. In this paper, we investigate the relevance of a set of approaches broadly called “educational data mining” or “learning analytics” (henceforth, EDM) to help provide a basis for quantitative research on constructionist learning which does not abandon the richness seen as essential by many researchers in that paradigm. We suggest that EDM may have the potential to support research that is meaningful and useful both to researchers working actively in the constructionist tradition but also to wider communities. Finally, we explore potential collaborations between researchers in the EDM and constructionist traditions; such collaborations have the potential to enhance the ability of constructionist researchers to make rich inferences about learning and learners, while providing EDM researchers with many interesting new research questions and challenges.
Citation:

Berland, M., Baker, R.S. & Blikstein, P. (2014). Educational Data Mining and Learning Analytics: Applications to Constructionist Research. Technology, Knowledge and Learning, Volume 19, Issue 1-2, pp 205-220.

Unraveling students’ interaction around a tangible interface using Multimodal Learning Analytics

Project:
Ear Explorer
Project:
Ear Explorer
Year:
in press
Abstract:
In this paper, we describe techniques to use multimodal learning analytics to analyze data collected around an interactive tangible learning environment. In a previous study [13], we designed and evaluated a Tangible User Interface (TUI) where dyads of students were asked to learn about the human hearing system by reconstructing it. In the current study, we present the analysis of the data collected in form of logs, both from students’ interaction with the tangible interface and as well as from their gestures, and we describe how we extracted meaningful predictors for students’ learning from those two datasets. First we show how Natural Language Processing (NLP) techniques can be used on the tangible interface logs to predict learning. Secondly, we explored how KinectTM data can inform “in-situ” interactions around a tabletop (i.e. using clustering algorithms to find prototypical body positions). Finally, we fed those features to a machine-learning classifier (Support Vector Machine) and split students in two groups after performing a median split on their learning scores. We found that we were able to predict students’ learning gains (i.e. being above or belong the median split) with very high accuracy. We discuss the implications of those results for analyzing data from rich, multimodal learning environments.
Citation:

Schneider, B., & Blikstein, P. (accepted). Unraveling Students’ Interaction Around a Tangible Interface using Multimodal Learning Analytics. In Proceedings of the 7th International Conference on Educational Data Mining, London, UK (Vol. ?, No. ?, p. ??).

LightUp: an augmented, learning platform for electronics

Authors:
Chan J., Pondicherry T., & Blikstein P.
Venue:
Project:
LightUp
Project:
LightUp
LightUp
Year:
2013
doi:
10.1145/2485760.2485812
Abstract:
We present and evaluate the design of LightUp, an augmented, learning platform for electronics. LightUp helps children explore engineering and electronics by foregrounding fundamental concepts and backgrounding the extraneous intricacies of circuit construction. LightUp consists of electronic components (e.g. wire, bulb, motor, microcontroller) mounted on blocks that connect to each other magnetically to form circuits. In addition, LightUp provides an "informational lens" through a mobile app that recognizes the components in a photographed circuit and augments the image with visualizations of otherwise invisible circuit behavior. Our study findings demonstrate the experiential learning made possible by augmenting an intuitive circuit-building platform with information that allows children to learn skills that will help them develop engineering skills and agency.
Citation:

Joshua Chan, Tarun Pondicherry, and Paulo Blikstein. 2013. LightUp: an augmented, learning platform for electronics. In Proceedings of the 12th International Conference on Interaction Design and Children (IDC '13). ACM, New York, NY, USA, 491-494.

Designing for diversely motivated learners

Authors:
Worsley, M. & Blikstein, P.
Venue:
Project:
Multimodal Learning Analytics
Project:
Multimodal Learning Analytics
Multimodal Learning Analytics
Year:
2013
Abstract:
In this paper, I present three case studies of students that represent different phases of interest development and commitment. Based on this analysis, I conclude with four recommendations for better enabling learning across a diverse set of interest levels.
Citation:

Worsley, M. and Blikstein, P. (2013). Designing for Diversely Motivated Learners. Paper Presented at the Digital Fabrication and Making In Education Workshop at the 2013 Interactive Design for Children Conference (IDC 2013), New York, NY, USA.

Digital Fabrication and ‘Making’ in Education: The Democratization of Invention

Authors:
Blikstein, P.
Venue:
Project:
FabLearn Labs
Project:
FabLab@School
FabLearn Labs
Year:
2013
Abstract:
Every few decades or centuries, a new set of skills and intellectual activities become crucial for work, conviviality, and citizenship - often democratizing tasks and skills previously only accessible to experts. Digital fabrication and ‘making’ could be a new and major chapter in this process of bringing powerful ideas, literacies, and expressive tools to children. Today, the range of accepted disciplinary knowledge has expanded to include not only programming, but also engineering and design. In addition, there are calls everywhere for educational approaches that foster creativity and inventiveness. In this chapter, I will first briefly review the history of engineering education to show the rise and fall then rise again of the making and building as curricular foci. I then discuss the theoretical underpinnings of project-based, student-centered, constructionist learning, showing that much of what digital fabrication labs can enact was already predicted and advocated in the theories and writings of John Dewey, Seymour Papert, and Paulo Freire. The following section approaches the educational benefits of digital fabrication and how it could be a unique tool in the hands of progressive educators. In the final part of the chapter I present not only four prototypical episodes that exemplify the advantages and perils of FabLabs in schools, but also some guidelines for the design of learning environments incorporating these types of technologies. See also the related article, "Travels in Troy with Freire: Technology as an Agent of Emancipation" at https://tltl.stanford.edu/publications/papers-or-book-chapters/travels-troy-freire.
Citation:

To appear in: Blikstein, P. (2013). Digital Fabrication and ’Making’ in Education: The Democratization of Invention. In J. Walter-Herrmann & C. Büching (Eds.), FabLabs: Of Machines, Makers and Inventors. Bielefeld: Transcript Publishers.