(2016). Multimodal learning analytics and education data mining: Using computational technologies to measure complex learning tasks. Journal of Learning Analytics, 3(2), 220-238. http://dx.doi.org/10.18608/jla.2016220-238. http://dx.doi.org/10.18608/jla. .32.11 ISSN 1929
Marcelo Worsley Learning Sciences & Computer ScienceNorthwestern University, USA marcelo.worsley@northwestern.edu ABSTRACT: New high-frequency multimodal data collection technologies and machine learning analysis techniques could offer new insights into learning, especially when students have the opportunity to generate unique, personalized artifacts, such as computer programs, robots, and solutions engineering challenges. To date most of the work on learning analytics and educational data mining has been focused on online courses and cognitive tutors, both of which provide a high degree of structure to the tasks, and are restricted to interactions that occur in front of a computer screen. In this paper, we argue that multimodal learning analytics can offer new insights into student learning trajectories in more complex and open-ended learning environments. We present several examples of this work and its educational applications.
Keywords: Learning analytics, multimodal interaction, constructivism, constructionism, assessment
INTRODUCTIONThe same battle is fought in every field of educational research and practice: the champions of the direct instruction of well-defined content pitted against those who encourage student-centred exploration of ill-defined domains. These wars have taken place repeatedly over past decades, and partisans on each side have been reborn in multiple incarnations. The first tradition tends to be aligned with behaviourist or neo-behaviourist approaches, while the second favours constructivist-inspired pedagogies. In language arts, the battle has been between phonics and the whole word approach. In math, war is wagged between teaching algorithms versus instruction in how to think mathematically. In history, they clash over the relative merits of critical interpretations and the memorization of historical facts. In science, they clash about inquiry-based approaches versus direct instruction of formulas and principles.
The goal of Learning Analytics is to understand and improve learning. However, learning does not always occur through or mediated by a technological system that can collect digital traces. To be able to study learning in non-technology centered environments, several signals, such as video and audio, should be captured, processed and analyzed to produce traces of the actions and interactions of the actors of the learning process. The use and integration of the different modalities present in those signals is known as Multimodal Learning Analytics. This editorial presents a brief introduction to this new variation of Learning Analytics and summarizes the four representative articles included in this special issue. The editorial closes with a small discussion about the current opportunities and challenges in multimodal learning analytics.
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