Situating multimodal learning analytics

The digital age has introduced a host of new challenges and opportunities for the learning sciences community. These challenges and opportunities are particularly abundant in multimodal learning analytics (MMLA), a research methodology that aims to extend work from Educational Data Mining (EDM) and Learning Analytics (LA) to multimodal learning environments by treating multimodal data. Recognizing the short-term opportunities and longterm challenges will help develop proof cases and identify grand challenges that will help propel the field forward. To support the field's growth, we use this paper to describe several ways that MMLA can potentially advance learning sciences research and touch upon key challenges that researchers who utilize MMLA have encountered over the past few years

Exploring children's designs for maker technologies

There is growing interest in maker technologies around how they can be included in school curriculums to engage children with science subjects and about their use to explore new creative possibilities. Given that maker technologies are currently unfamiliar to most children across the world this work sought to use these technologies to investigate whether technology experience has an influence on design within a making context. A study was carried out with 29 participants aged 8-9 that involved a design task and a scaffolded making task based around a physical game using Arduino. Half of the participants completed the making task first then the design task, the other half completed the design task first then the making task. The design ideas created were then coded on 5-point scales for complexity of construction and novelty of concept, the coders also looked for evidence of transference from the making task to the design ideas. Results indicated that completing the making task prior to the design task increased the mean complexity of construction score. No clear evidence was found of elements from the making task being transferred into the design ideas. In addition to the specific findings about technology influence on design, the paper offers more general insights for those working within this space

Quantifying Collaboration Quality in Face-to-Face Classroom Settings Using MMLA

Producción CientíficaThe estimation of collaboration quality using manual observation and coding is a tedious and difficult task. Researchers have proposed the automation of this process by estimation into few categories (e.g., high vs. low collaboration). However, such categorical estimation lacks in depth and actionability, which can be critical for practitioners. We present a case study that evaluates the feasibility of quantifying collaboration quality and its multiple sub-dimensions (e.g., collaboration flow) in an authentic classroom setting. We collected multimodal data (audio and logs) from two groups collaborating face-to-face and in a collaborative writing task. The paper describes our exploration of different machine learning models and compares their performance with that of human coders, in the task of estimating collaboration quality along a continuum. Our results show that it is feasible to quantitatively estimate collaboration quality and its sub-dimensions, even from simple features of audio and log data, using machine learning. These findings open possibilities for in-depth automated quantification of collaboration quality, and the use of more advanced features and algorithms to get their performance closer to that of human coders.European Union via the European Regional Development Fund and in the context of CEITER and Next-Lab (Horizon 2020 Research and Innovation Programme, grant agreements no. 669074 and 731685)Junta de Castilla y León (Project VA257P18)Ministerio de Ciencia, Innovación y Universidades (Project TIN2017-85179-C3-2-R

Machine and human observable differences in groups’ collaborative problem-solving behaviours

This paper contributes to our understanding of how to design learning analytics to capture and analyse collaborative problem-solving (CPS) in practice-based learning activities. Most research in learning analytics focuses on student interaction in digital learning environments, yet still most learning and teaching in schools occurs in physical environments. Investigation of student interaction in physical environments can be used to generate observable differences among students, which can then be used in the design and implementation of Learning Analytics. Here, we present several original methods for identifying such differences in groups CPS behaviours. Our data set is based on human observation, hand position (fiducial marker) and heads direction (face recognition) data from eighteen students working in six groups of three. The results show that the high competent CPS groups spend an equal distribution of time on their problem-solving and collaboration stages. Whereas, the low competent CPS groups spend most of their time in identifying knowledge and skill deficiencies only. Moreover, as machine observable data shows, high competent CPS groups present symmetrical contributions to the physical tasks and present high synchrony and individual accountability values. The findings have significant implications on the design and implementation of future learning analytics systems

Motive-demand dynamics creating a social context for students’ learning experiences in a making and design environment

Making and design environments, often referred to as makerspaces, have aroused recent educational interest. These environments typically consist of spaces that support interest-driven engagement in hands-on creative activities with a range of digital artefacts. Although a variety of benefits from participating in making and design activities have been proposed, we currently have limited understanding of students’ learning experiences in makerspaces situated in schools. Following Hedegaards’ conceptualisations, we investigate motive-demand dynamics in students’ social activity in a school-based digital making and design environment, ‘The FUSE Studio’. We highlight our findings via vignettes selected from 65 h of video recordings of 94 students (aged between 9 and 12 years old) carrying out activities; the recordings were collected intermittently from an elective course over one semester. Our study illustrates how the students’ learning experiences were shaped through tension-laden interplay between the motives and demands of their activity situated across personal, relational and institutional contexts. The findings make visible how established ways of working and being at school interacted and came into tension with the students’ motive orientations, thereby limiting and at times transforming the social context of their learning. Our work also demonstrates how the analysis of motive-demand dynamics offers one useful conceptual tool to unpack students’ learning experiences in novel learning environments.Peer reviewe

Collaborative Design Work in Technology Education

Peer reviewe

Culture, technology and local networks: towards a sociology of ‘making’ in education

This article is about ‘making’ in education. Often associated with software programming (as in ‘digital making’), making can also involve creating or modifying physical technological artefacts. In this paper, making is examined as a phenomenon that occurs at the intersection of culture, the economy, technology and education. The focus is not on the effects on cognitive gains or motivations, but on locating making in a social, historical and economic context. Making is also described as a form of ‘material connotation’, where connotation refers to the process through which the technical structure of artefacts is altered by culture and society. In the second part of the paper, the theoretical discussion is complemented by a case study in which making is described as a networked phenomenon where technology companies, consultants, volunteers, schools, and students were all implicated in turning a nebulous set of practices and discourses into an educational reality