7,254 research outputs found
Discourse-centric learning analytics
Drawing on sociocultural discourse analysis and argumentation theory, we motivate a focus on learners' discourse as a promising site for identifying patterns of activity which correspond to meaningful learning and knowledge construction. However, software platforms must gain access to qualitative information about the rhetorical dimensions to discourse contributions to enable such analytics. This is difficult to extract from naturally occurring text, but the emergence of more-structured annotation and deliberation platforms for learning makes such information available. Using the Cohere web application as a research vehicle, we present examples of analytics at the level of individual learners and groups, showing conceptual and social network patterns, which we propose as indicators of meaningful learning
An Exploratory Study to Determine the Effects Conversational Repetition Has on Perceived Workload and User Experience Quality in an Online Human-Robot Interaction
Human-robot interaction studies in the Caribbean currently face two challenges. First, the robots used in these studies have difficulty understanding many of the regional accents spoken study participants. Secondly, the global pandemic has made in-person HRI studies in the Caribbean more challenging due to the physical and social distancing mandates. This paper reports on our exploratory study to determine what kind of impact these two challenges have on HRI by evaluating the effect conversational repetition has on a human-robot conversation done using video conferencing software. Using network analysis, the results obtained suggest that conversational repetition has several subtle relationships on perceived workload. One interesting finding is that frustration and effort are indirectly affected by conversational repetition. Results from the short User Experience Questionnaire indicate that the overall quality of the user experience is perceived as positive-neutral. This encouraging result indicates that video conferencing may be a suitable interaction modality for HRI studies in the Caribbean
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
Analyzing the Semantic Relatedness of Paper Abstracts: An Application to the Educational Research Field
International audienceEach domain, along with its knowledge base, changes over time and every timeframe is centered on specific topics that emerge from different ongoing research projects. As searching for relevant resources is a time-consuming process, the automatic extraction of the most important and relevant articles from a domain becomes essential in supporting researchers in their day-today activities. The proposed analysis extends other previous researches focused on extracting co-citations between the papers, with the purpose of comparing their overall importance within the domain from a semantic perspective. Our method focuses on the semantic analysis of paper abstracts by using Natural Language Processing (NLP) techniques such as Latent Semantic Analysis, Latent Dirichlet Allocation or specific ontology distances, i.e., WordNet. Moreover, the defined mechanisms are enforced on two different subdomains from the corpora generated around the keywords " e-learning " and " computer ". Graph visual representations are used to highlight the keywords of each subdomain, links among concepts and between articles, as well as specific document similarity views, or scores reflecting the keyword-abstract overlaps. In the end, conclusions and future improvements are presented, emphasizing nevertheless the key elements of our research support framework
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