3 research outputs found
The use and application of learning theory in learning analytics: a scoping review
Since its inception in 2011, Learning Analytics has matured and expanded in terms of reach (e.g., primary and K-12 education) and in having access to a greater variety, volume and velocity of data (e.g. collecting and analyzing multimodal data). Its roots in multiple disciplines yield a range and richness of theoretical influences resulting in an inherent theoretical pluralism. Such multi-and interdisciplinary origins and influences raise questions around which learning theories inform learning analytics research, and the implications for the field should a particular theory dominate. In establishing the theoretical influences in learning analytics, this scoping review focused on the Learning Analytics and Knowledge Conference (LAK) Proceedings (2011–2020) and the Journal of Learning Analytics (JLA) (2014–2020) as data sources. While learning analytics research is published across a range of scholarly journals, at the time of this study, a significant part of research into learning analytics had been published under the auspices of the Society of Learning Analytics (SoLAR), in the proceedings of the annual LAK conference and the field’s official journal, and as such, provides particular insight into its theoretical underpinnings. The analysis found evidence of a range of theoretical influences. While some learning theories have waned since 2011, others, such as Self-Regulated Learning (SRL), are in the ascendency. We discuss the implications of the use of learning theory in learning analytics research and conclude that this theoretical pluralism is something to be treasured and protected.publishedVersio
How do we model learning at scale?:A systematic review of research on MOOCs
Despite a surge of empirical work on student participation in online learning environments, the causal links between the learning-related factors and processes with the desired learning outcomes remain unexplored. This study presents a systematic literature review of approaches to model learning in Massive Open Online Courses offering an analysis of learning-related constructs used in the prediction and measurement of student engagement and learning outcome. Based on our literature review, we identify current gaps in the research, including a lack of solid frameworks to explain learning in open online setting. Finally, we put forward a novel framework suitable for open online contexts based on a well-established model of student engagement. Our model is intended to guide future work studying the association between contextual factors (i.e., demographic, classroom, and individual needs), student engagement (i.e., academic, behavioral, cognitive, and affective engagement metrics), and learning outcomes (i.e., academic, social, and affective). The proposed model affords further interstudy comparisons as well as comparative studies with more traditional education models. </jats:p
Analytics-based approach to the study of learning networks in digital education settings
Investigating howgroups communicate, build knowledge and expertise, reach consensus or collaboratively
solve complex problems, became one of the main foci of contemporary research in learning and
social sciences. Emerging models of communication and empowerment of networks as a form of social
organization further reshaped practice and pedagogy of online education, bringing research on learning
networks into the mainstream of educational and social science research. In such conditions, massive
open online courses (MOOCs) emerged as one of the promising approaches to facilitating learning
in networked settings and shifting education towards more open and lifelong learning. Nevertheless,
this most recent educational turn highlights the importance of understanding social and technological
(i.e., material) factors as mutually interdependent, challenging the existing forms of pedagogy and
practice of assessment for learning in online environments.
On the other hand, the main focus of the contemporary research on networked learning is primarily
oriented towards retrospective analysis of learning networks and informing design of future
tasks and recommendations for learning. Although providing invaluable insights for understanding
learning in networked settings, the nature of commonly applied approaches does not necessarily allow
for providing means for understanding learning as it unfolds. In that sense, learning analytics, as
a multidisciplinary research field, presents a complementary research strand to the contemporary research
on learning networks. Providing theory-driven and analytics-based methods that would allow
for comprehensive assessment of complex learning skills, learning analytics positions itself either as
the end point or a part of the pedagogy of learning in networked settings.
The thesis contributes to the development of learning analytics-based research in studying learning
networks that emerge fromthe context of learning with MOOCs. Being rooted in the well-established
evidence-centered design assessment framework, the thesis develops a conceptual analytics-based
model that provides means for understanding learning networks from both individual and network
levels. The proposed model provides a theory-driven conceptualization of the main constructs, along
with their mutual relationships, necessary for studying learning networks. Specifically, to provide
comprehensive understanding of learning networks, it is necessary to account for structure of learner
interactions, discourse generated in the learning process, and dynamics of structural and discourse
properties. These three elements – structure, discourse, and dynamics – should be observed as mutually
dependent, taking into account learners’ personal interests, motivation, behavior, and contextual
factors that determine the environment in which a specific learning network develops. The thesis also
offers an operationalization of the constructs identified in the model with the aim at providing learning analytics-methods for the implementation of assessment for learning. In so doing, I offered a redefinition
of the existing educational framework that defines learner engagement in order to account
for specific aspects of learning networks emerging from learning with MOOCs. Finally, throughout
the empirical work presented in five peer-reviewed studies, the thesis provides an evaluation of the
proposed model and introduces novel learning analytics methods that provide different perspectives
for understanding learning networks. The empirical work also provides significant theoretical and
methodological contributions for research and practice in the context of learning networks emerging
from learning with MOOCs