ORCID Profile
0000-0001-9782-816X
Current Organisations
University of South Australia
,
Technical University of Munich
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Publisher: Elsevier BV
Date: 2018
Publisher: Wiley
Date: 19-05-2021
DOI: 10.1111/BJET.13123
Abstract: Due to the ongoing digitalisation of workplaces and educational settings, human activity underpinning learning and work is increasingly mediated by technology. The advancement of artificial intelligence (AI) and its integration into everyday technologies influences how people are exposed to information, interact, learn and make decisions. We argue that technology, data and evolving AI applications affect how humans enact and experience life and work, changing the context for learning. Hence, as this paper argues, the current notion of lifelong learning needs a revisit to embrace technology at its foundation. To bring freely chosen goals and ownership in one's learning to the fore, in the context of the coming AI age, we argue for the telos of learning to shift from human capital to human development, with the spotlight on capabilities. The paper draws on the capability approach to inform in iduals and organisations of how they can support human development throughout lifelong learning. We then move to provide ex les of how technologies underpinning workplace practices can be seen with the focus on capabilities as in iduals learn to create value. What is known about the topic? The primary notion of lifelong learning refers to adult learning processes. The policy perspective that dominates organisation of lifelong learning opportunities focuses on human capital development. Technologies mediate learning and work. What this paper adds Technology is not explicitly addressed in meanings associated with lifelong learning. AI‐based technologies dynamically interact with human cognitive and social practices. The paper argues for a stronger focus on human development instead of human capital in the telos of lifelong learning opportunities. Capability approach is a viable alternative to human capital perspective on LLL. Data used to support learning can focus on learner agency and systemic factors that enable and constrain lifelong learning. Implications for practice and/or policy LLL interventions should promote systemic support for learner agency and ownership. LLL interventions should focus on negotiated value creation. Workplaces should embrace human‐machine integration but in ways that support capability and human development, not human capital.
Publisher: Elsevier BV
Date: 04-2018
Publisher: Elsevier BV
Date: 10-2020
Publisher: Center for Open Science
Date: 03-03-2021
Abstract: Discussion forums are the primary medium for supporting in-course student interactions in digital learning settings. Despite the significant uptake of discussion forums, questions remain as to how the tool can be used to initiate, maintain, and support interpersonal student connections. Large-scale patterns of student online interactions in forums derived from across the university are under-explored. Most studies have relied on data derived from a single course. This study presents a multi-site analysis of student interactions in online course forums at the university-level. Digital interactions of 14,643 students were analysed across several years in three universities located in the North American, South-East Asian, and Pacific regions. Descriptive results indicate that students with similar grades tend to co-participate in learning discussions. We applied exponential random graph modelling and regression analysis to further understand this observed similarity. Results suggest that this phenomenon can be explained by social processes of selection only to a small extent, and even less so by peer influence mechanisms. The study suggests that occurrence of similarity stems from other factors, such as course and forum designs. The implications of these results raise questions regarding learning designs and the benefits linked to the formation of student connections based on grade similarity.
Publisher: ACM
Date: 23-03-2020
Publisher: Center for Open Science
Date: 27-12-2019
Abstract: The mission of learning analytics (LA) is to improve learner experiences using the insights from digitally collected learner data. While some areas of LA are maturing, this is not consistent across all LA specialisations. For instance, LA for social learning lack validated approaches to account for the effects of cross-course variability in learner behavior. Although the associations between network structure and learning outcomes have been examined in the context of online forums, it remains unclear whether such associations represent bona fide social effects, or merely reflect heterogeneity in in idual posting behavior, leading to seemingly complex but artefactual social network structures. We argue that to start addressing this issue, posting activity should be explicitly included and modelled in forum network representations. To gain insight to what extent learner degree and edge weight are merely derivatives of learner activity, we construct random models that control for the level of posting and post properties, such as popularity and thread hierarchy level. Analysis of forum networks in twenty online courses presented in this paper demonstrates that in idual posting behavior is highly predictive of both the breadth (degree) and frequency (strength) in forum communication networks. This implies that, in the context of forum-based modelling, degree and frequency may not reflect the social dynamics. However, results suggest that clustering of the network structure is not a derivative of in idual posting behaviour. Hence, weighted local clustering coefficient may be a better proxy for social relationships. The empirical results are relevant to scientists interested in social interactions and learner networks in digital learning, and more generally to researchers interested in deriving informative social network models from online forums.
Publisher: ACM
Date: 23-03-2020
Publisher: ACM
Date: 23-03-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Publisher: Athabasca University Press
Date: 11-07-2018
DOI: 10.19173/IRRODL.V19I3.3370
Abstract: The capacity to foster interpersonal interactions in massive open online courses (MOOCs) has frequently been contested, particularly when learner interactions are limited to MOOC forums. The establishment of social presence—a perceived sense of somebody being present and “real”—is among the strategies to tackle the challenges of online learning and could be applied in MOOCs. Thus far, social presence in MOOCs has been under-researched. Studies that previously examined social presence in MOOCs did not account for the peculiar nature of open online learning. In contrast to the existing work, this study seeks to understand how learners perceive social presence, and the different nuances of social presence in erse MOOC populations. In particular, we compare perceptions of social presence across the groups of learners with different patterns of forum participation in three edX MOOCs. The findings reveal substantial differences in how learners with varying forum activity perceive social presence. Perceptions of social presence also differed in courses with the varying volume of forum interaction and duration. Finally, learners with sustained forum activity generally reported higher social presence scores that included low affectivity and strong group cohesion perceptions. With this in mind, this study is significant because of the insights into brings to the current body of knowledge around social presence in MOOCs. The study’s findings also raise questions about the effectiveness of transferring existing socio-constructivist constructs into the MOOC contexts.
Publisher: Auckland University of Technology (AUT) Library
Date: 08-02-2022
Abstract: Learning is a social experience and having meaningful connections with peers and instructors is important for student learning. The interpersonal relationships between students and their instructor can positively influence students’ well-being, motivation and self-efficacy (Aguilera-Hermida, 2020 Almendingen et al., 2021 Gillis & Krull, 2020 Kim & Sax, 2009 Marković et al., 2021 Parpala et al., 2021 Pitsick, 2018). Creating productive interpersonal relationships with peers contributes to students’ beliefs of being supported, respected, and valued, and increases the likelihood of students asking their peers for help (Mäkitalo-Siegl & Fischer, 2011). When students feel connected to their peers they are more likely to engage with their peers in ways that support their learning and deepen their knowledge as a result (Shim et al., 2013). Interaction with instructors can also positively influence learning outcomes and student well-being (Pitsick, 2018), and instructors can be a valuable source of help and guidance (Ryan et al., 2001). However, during the COVID-19 pandemic and the shift to emergency remote teaching and learning, students’ relationship with peers was significantly impacted (Motz et al., 2022) and forcing peer-to-peer interaction through mandating camera feeds on during live synchronous video classes disproportionately affected students from disadvantaged backgrounds and those experiencing anxiety or depression (Castelli & Sarvary, 2021). As students were adapting to learn during the pandemic, they increased their reliance on their instructor and highly ranked instructor engagement as a factor that positively influenced their motivation (Nguyen, 2021). As motivation increases, so does self-efficacy, and when students feel supported, engaged, connected and valued by their peers and instructors, they are more likely to be successful students (Zepke, 2018). This study examines students’ experiences in using technology to connect with peers and their instructors during the COVID-19 pandemic when learning remotely. The research inquiry focusses on the second-year cohort as prior research has revealed that this group of learners tend to struggle with their learning (Kyndt et al., 2017 Milsom, 2015 Milsom & Yorke, 2015 Southgate et al., 2014 Virtue et al., 2017 Webb & Cotton, 2019) and experience higher levels of anxiety and depression compared to students in other years of university study prior to the COVID 19 pandemic (Liu et al., 2019). To examine their experience in peer-to-peer networks and their interactions with instructors for help seeking, interviews were undertaken at a large metropolitan Australian University in 2021 with 26 second-year students across different disciplines who had experienced emergency remote teaching in their first and second year of study. The findings reveal that students resist using the discussion board in the Learning Management System because of perceptions of exposure and embarrassment in asking questions when they feel they are expected to know the answer. Students report that synchronous video classes using technology such as Zoom, increase feelings of isolation and they reach out to their peers via social media technology instead. Students are intentional in their choice of technology in connecting with peers, however in the absence of physical connections, there remains a gap in productive engagement with peers. The findings show that second-year students are reluctant to reach out to their instructor when technology is their only mode of interaction, and students report that they would have been more likely to ask for help during a face-to-face class.
Publisher: Elsevier BV
Date: 06-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Elsevier BV
Date: 03-2022
Publisher: Elsevier BV
Date: 2021
Publisher: Society for Learning Analytics Research (SoLAR)
Date: 05-2017
DOI: 10.18608/HLA17.014
Publisher: Elsevier BV
Date: 2022
Publisher: ACM
Date: 13-03-2017
Publisher: Wiley
Date: 16-07-2019
DOI: 10.1111/BJET.12846
Publisher: ACM
Date: 04-03-2019
Publisher: ACM
Date: 13-03-2023
Publisher: ACM Press
Date: 2016
Publisher: ACM
Date: 13-03-2017
Publisher: ACM
Date: 13-03-2023
Publisher: ACM
Date: 20-07-2023
Publisher: Informa UK Limited
Date: 02-10-2017
Publisher: ACM
Date: 02-07-2201
Publisher: Springer International Publishing
Date: 2018
Publisher: ACM
Date: 12-04-2021
Publisher: Queensland University of Technology
Date: 16-10-2023
DOI: 10.5204/SSJ.3008
Publisher: ACM
Date: 07-03-2018
Publisher: ACM
Date: 07-03-2018
Publisher: Masaryk University Press
Date: 2012
DOI: 10.5817/SP2012-1-7
Publisher: Center for Open Science
Date: 30-05-2022
Abstract: Recommendations for network studies in learning analytics (LA) emphasize that network construction requires careful definitions of nodes, relationships between them, and network boundaries. Thus far, LA researchers have discussed how to operationalizeinterpersonal networks in learning settings. Analytical choices used in constructing networks of text have not been examined as much. By reviewing ex les of text network analysis in LA, we demonstrate that convenience-based decisions for network construction are common, particularly when the ties in the text networks are defined as the co-occurrences of words or ideas. We argue that such an approach is limited in its potential to contribute to theory or generalize across studies. This submission presents an alternative approach to network representations of the text in learning settings, using the concept of Forma Mentis Networks (FMN). As reported in previous studies, FMNs are network representations either (1) elicited from in iduals through free association tasks that capture valence or (2) constructed by analysts creating shared mental maps derived from text. FMN is a theory-based and scalable approach complementary to the existing set of tools available for the analysis of teaching and learning.
Publisher: Center for Open Science
Date: 30-01-2023
Abstract: Large language models represent a significant advancement in the field of AI. The underlying technology is key to further innovations and, despite critical views and even bans within communities and regions, large language models are here to stay. This position paper presents the potential benefits and challenges of educational applications of large language models, from student and teacher perspectives. We briefly discuss the current state of large language models and their applications. We then highlight how these models can be used to create educational content, improve student engagement and interaction, and personalize learning experiences. With regard to challenges, we argue that large language models in education require teachers and learners to develop sets of competencies and literacies necessary to both understand the technology as well as their limitations and unexpected brittleness of such systems. In addition, a clear strategy within educational systems and a clear pedagogical approach with a strong focus on critical thinking and strategies for fact checking are required to integrate and take full advantage of large language models in learning settings and teaching curricula. Other challenges such as the potential bias in the output, the need for continuous human oversight, and the potential for misuse are not unique to the application of AI in education. But we believe that, if handled sensibly, these challenges can offer insights and opportunities in education scenarios to acquaint students early on with potential societal biases, criticalities, and risks of AI applications. We conclude with recommendations for how to address these challenges and ensure that such models are used in a responsible and ethical manner in education.
Publisher: Athabasca University Press
Date: 19-06-2015
DOI: 10.19173/IRRODL.V16I3.2170
Abstract: Distributed Massive Open Online Courses (MOOCs) are based on the premise that online learning occurs through a network of interconnected learners. The teachers’ role in distributed courses extends to forming such a network by facilitating communication that connects learners and their separate personal learning environments scattered around the Internet. The study reported in this paper examined who fulfilled such an influential role in a particular distributed MOOC – a connectivist course (cMOOC) offered in 2011. Social network analysis was conducted over a socio-technical network of the Twitter-based course interactions, comprising both human course participants and hashtags where the latter represented technological affordances for scaling course communication. The results of the week-by-week analysis of the network of interactions suggest that the teaching function becomes distributed among influential actors in the network. As the course progressed, both human and technological actors comprising the network subsumed the teaching functions, and exerted influence over the network formation. Regardless, the official course facilitators preserved a high level of influence over the flow of information in the investigated cMOOC.
Publisher: Springer Science and Business Media LLC
Date: 28-06-2023
DOI: 10.1007/S11423-023-10262-9
Abstract: Interpersonal online interactions are key to digital learning pedagogies and student experiences. Researchers use learner log and text data collected by technologies that mediate learner interactions online to provide indicators about interpersonal interactions. However, analytical approaches used to derive these indicators face conceptual, methodological, and practical challenges. Existing analytical approaches are not well aligned with the theories of digital learning, lack rigor, and are not easily replicable. To address these challenges, we put forward a multi-level framework linking indicators of in idual posting with group-level communication and emergent relational structures. We exemplify the use of the framework by analyzing twenty online and blended courses. Empirical insights demonstrate how indicators at these three levels relate to each other and to potential instructor decisions. Our conclusion highlights current gaps in the framework and the areas for future work.
Publisher: American Educational Research Association (AERA)
Date: 14-11-2017
Abstract: 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 in idual 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.
Publisher: Society for Learning Analytics Research
Date: 11-03-2022
Abstract: Network analysis has contributed to the emergence of learning analytics. In this editorial, we briefly introduce network science as a field and situate it within learning analytics. Drawing on the Learning Analytics Cycle, we highlight that effective application of network science methods in learning analytics involves critical considerations of learning processes, data, methods and metrics, and interventions, as well as ethics and value systems surrounding these areas. Careful work must meaningfully situate network methods and interventions within the theoretical assumptions explaining learning, as well as within pedagogical and technological factors shaping learning processes. The five empirical papers in the special section demonstrate erse applications of network analysis, and the invited commentaries from cognitive network science and physics education research further discuss potential synergies between learning analytics and other sister fields with a shared interest in leveraging network science. We conclude by discussing opportunities to strengthen the rigour of network-based learning analytics projects, expand current work into nascent areas, and achieve more impact by holistically addressing the full cycle of learning analytics.
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