Submission portal is located at: https://www.editorialmanager.com/ijcci/default.aspx
Please select “VSI: LA–ED” when you reach the “Article Type” step in the submission process.
Dor Abrahamson (University of California Berkeley)
Marcelo Worsley (Northwestern University)
Zach Pardos (University of California Berkeley)
Lu Ou (ACTNext by ACT)
Arthur Glenberg (Arizona State University)
Anthony Chemero (University of Cincinnati)
Two thriving efforts in the field of educational research could benefit, we believe, from greater philosophical, theoretical, and methodological synergy.
Embodied design (ED) is an educational framework oriented primarily on children’s study of STEM concepts (science, technology, engineering, mathematics; Abrahamson, 2009, 2014, 2015, 2019; Abrahamson & Lindgren, 2014; Nathan et al., 2019). ED learning activities, heuristic principles, and theoretical models are developed through design-based research studies (Cobb et al., 2003) of cognition, teaching, and learning. Drawing on embodied-cognition theory, ED’s development creates empirical contexts for investigating the micro-processes of children’s educational interaction with a variety of technological media. In ED activities, students work initially with non-symbolic objects. In their attempts to perform assigned tasks, students draw on their innate sensorimotor capacity. For example, to solve a motor-control problem, students develop new sensory perceptions of the environment that enable them to coordinate the bimanual enactment of a complex movement instantiating a mathematical concept. Only later are disciplinary resources introduced into the situation that afford students perceptual shifts into formal conceptions of the situation. ED research tackles philosophical and theoretical questions related to the epistemic function of movement in the cognition, teaching, and learning of curricular content (Abrahamson & Bakker, 2016; Abrahamson & Sánchez–García, 2016). Combining clinical, action, and eye-tracking data, ED studies have documented the emergence of new perceptual structures that enable students to enact complex movements; these perceptual structures then become accessible to students’ explicit reasoning as representable ontologies (Abrahamson, Shayan, Bakker, & van der Schaaf, 2016; Shvarts & Abrahamson, 2017). As such, ED is theoretically resonant with recent calls to favorably consider tenets of Piaget’s (1968) genetic epistemology (Allen & Bickhard, 2013; Arsalidou & Pascual-Leone, 2016) as well as enactivist philosophy (Maturana & Varela, 1992) in making sense of children’s movement-based conceptual learning.
The call for LAK 2011—the 1st international Conference on Learning Analytics and Knowledge—states that “Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs.” One way that Learning Analytics (LA) and ED have begun to interface is in the form of multimodal learning analytics (MMLA). Inspired by micro-ethnographic and interaction-analysis methodologies, MMLA aims to harness the affordances of multimodal sensors and computational analysis to better understand and support student learning. MMLA involves principled, yet emerging, methods for gathering, analyzing, coordinating, and presenting visual, aural, gestural, spatial, linguistic, and other data of students’ cognitive, affective, behavioral, and physiological processes in online and offline learning environments, as they engage in instructional tasks (Worsley & Blikstein, 2014b; Worsley et al., 2016). MMLA is conceptualized as aligning with the embodied and multimodal nature of learning as well as the complementary diversity in contexts where students may individually or collectively experience learning. Importantly, MMLA represents a variety of strategies for drawing inferences about student learning experience. For example, some prior work has demonstrated how computational algorithms can surface hard-to-see patterns within human annotated data (Worsley & Blikstein, 2014a). Other research takes a more automated approach, explicitly introducing researcher inference only after the computational techniques have annotated and clustered the data (Huang, Bryant & Schneider, 2019; Worsley & Blikstein, 2018). Still others utilize multimodal features and artificial intelligence to augment video analysis (i.e., overlaying eye-tracking, gestures or electro-dermal activation data in videos) or to segment continuous data into meaningful segments (Worsley, Scherer, Morency & Blikstein, 2015). Collectively, these approaches welcome a careful and thoughtful application of multimodal sensor data and computing to enhance student and researcher sense-making.
Granted, there have been a number of collaborative efforts, where educational designers, often design-based researchers of STEM teaching and learning, worked with learning analytics specialists to evaluate artifacts and activities (Pardos & Horodyskyj, 2019). For example, educational designers of movement-based learning have shared their data with learning analytics experts who applied machine-learning algorithms and statistical methods to detect and classify micro-processes of skill development (e.g., Pardos et al., 2018). Also, data-driven quantitative and qualitative insights about students’ behavior can inform the development of new ED applications for personalized learning (Ou et al., 2020). Still, it is our reading of the field that ED and LA communities mostly operate from distinct, often non-overlapping intellectual bases, with their own associations, special interest groups, conferences, journals, and online activities. When they do collaborate, it is often across a professional divide, where each researcher appreciates but may not completely understand the other’s intellectual foundation. As such, LA techniques may be applied to ED data only after research design has been charted out and implemented. In like vein, graduate-school course offerings often compartmentalize design-oriented seminars as satisfying requirements of cognition, curriculum, and/or theories of learning, whereas learning-analytics and/or educational data mining (Fischer et al., 2020) courses fall under the division of quantitative methods.
The vision of this special issue is to stimulate the field to foster “organic” relations between the camps, for the edification of all stakeholders. Thus, we are looking to boost a conception of LA–ED no longer as interdisciplinary but as disciplinary. One way forward, we believe, is integrating LA considerations and utilities into ED technology to build research designs optimized for the technical capacity, rigor, and scope of MMLA.
The special issue calls for state-of-the-art articles that:
1. report on empirical research projects where multimodal interaction data were collected to investigate the micro-process of teaching and learning grounded in movement;
2. present philosophical, theoretical, and/or critical-pedagogy review work that contemplates, evaluates, elaborates, and/or challenges premisses of LA–ED; or
3. offer reflections from researchers who analyze their project procedure to understand opportunities, challenges, and solutions for this line of collaborative work.
We are particularly seeking articles that would bridge the learning design and learning analytics camps. That is, projects from LA experts should be presented so as to be accessible to learning-science readers interested primarily in design-based research of theoretically informed learning environments, and projects from ED experts should, in turn, be presented so as to be accessible to ED-curious LA readers. As such, we are less interested in computationally-heavy LA papers that use learning designs only as their host context and would be inscrutable and perhaps irrelevant to ED scholars, just as we are equally less interested in ED papers that include a LA facet as an afterthought. Ideally, contributions to the SI would come from LA–ED collaborations.
As such, this call encourages the submission of outstanding articles concerned with integrating some of the following LA–ED themes, though we are open to other themes:
● Learning design that includes movement-oriented digital technology
● Research design incorporating, and perhaps combining, utilities for monitoring a wide span of cognitive, affective, behavioral, physiological, and other data
● Underrepresented content domain (on beyond STEM, e.g., literacy, the arts, history)
● Underrepresented theoretical foundations (on beyond Piaget and Vygotsky, such as dynamic systems theory)
● Underrepresented participants, such differently abled students, remote rural students
● Underrepresented settings, such as remote learning for a/synchronous learning
To emphasize, we are looking for contributions demonstrating the possibility of deep dialogue among these not-yet-quite-convergent tributaries of educational research, LA and ED. Submitted papers would ideally help the field answer questions such as these:
● How does applying LA methodologies to ED contribute to the evaluation of contemporary paradigms in the cognitive sciences?
● What new disciplinary constructs come forth as instrumental to the productive collaboration of experts in the respective areas of ED and LA?
● What graduate-level courses train learning sciences students to understand and use learning analytics in the DBR of learning environments?
➔ Manuscript submission deadline: November 1, 2020
➔ First reviews are back and initial decisions on papers: February 1, 2021
➔ Resubmission deadline: April 1
➔ Final paper decisions: May 15
➔ Publication: July 2021