ORCID Profile
0000-0003-4062-6035
Current Organisations
University of Technology Sydney
,
NSW Department of Education
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Publisher: Australasian Society for Computers in Learning in Tertiary Education
Date: 10-05-2021
DOI: 10.14742/AJET.6632
Abstract: There is growing demand for online learning activities that offer flexibility for students to study anywhere, anytime, as online students fit study around work and family commitments. We designed a series of online activities and evaluated how, where, and with what devices students used the activities, as well as their levels of engagement and deep learning with the activities. A mixed-methods design was used to explore students’ interactions with the online activities. This method integrated learning analytics data with responses from 63 survey, nine interview, and 16 focus group participants. We found that students used a combination of mobile devices to access the online learning activities across a variety of locations during opportunistic study sessions in order to fit study into their daily routines. The online activities were perceived positively, facilitating affective, cognitive, and behavioural engagement as well as stimulating deep learning. Activities that were authentic, promoted problem-solving, applied theory to real-life scenarios, and increased students’ feelings of being supported were perceived as most beneficial to learning. These findings have implications for the future design of online activities, where activities need to accommodate students’ need for flexibility as students’ study habits become more mobile.
Publisher: Wiley
Date: 23-04-2023
DOI: 10.1111/BJET.13321
Abstract: An extraordinary amount of data is becoming available in educational settings, collected from a wide range of Educational Technology tools and services. This creates opportunities for using methods from Artificial Intelligence and Learning Analytics (LA) to improve learning and the environments in which it occurs. And yet, analytics results produced using these methods often fail to link to theoretical concepts from the learning sciences, making them difficult for educators to trust, interpret and act upon. At the same time, many of our educational theories are difficult to formalise into testable models that link to educational data. New methodologies are required to formalise the bridge between big data and educational theory. This paper demonstrates how causal modelling can help to close this gap. It introduces the apparatus of causal modelling, and shows how it can be applied to well‐known problems in LA to yield new insights. We conclude with a consideration of what causal modelling adds to the theory‐versus‐data debate in education, and extend an invitation to other investigators to join this exciting programme of research. ‘Correlation does not equal causation’ is a familiar claim in many fields of research but increasingly we see the need for a causal understanding of our educational systems. Big data bring many opportunities for analysis in education, but also a risk that results will fail to replicate in new contexts. Causal inference is a well‐developed approach for extracting causal relationships from data, but is yet to become widely used in the learning sciences. An overview of causal modelling to support educational data scientists interested in adopting this promising approach. A demonstration of how constructing causal models forces us to more explicitly specify the claims of educational theories. An understanding of how we can link educational datasets to theoretical constructs represented as causal models so formulating empirical tests of the educational theories that they represent. Causal models can help us to explicitly specify educational theories in a testable format. It is sometimes possible to make causal inferences from educational data if we understand our system well enough to construct a sufficiently explicit theoretical model. Learning Analysts should work to specify more causal models and test their predictions, as this would advance our theoretical understanding of many educational systems.
Publisher: Australasian Society for Computers in Learning in Tertiary Education
Date: 23-07-2022
DOI: 10.14742/AJET.7627
Abstract: Discussion forums are often touted as maximising student participation and learning but concerns around engagement counter any perceived benefits. Often participation is the measure of engagement, and students who do not post are deemed unengaged. To further examine engagement and forums as learning communities, we used analytic data from 270 students enrolled in two online psychology subjects using social network analysis and interviewed 22 students. Both subjects’ forums had greater egalitarian triads, indicative of reciprocal relationships. Furthermore, both active posters and lurkers had a mix of grade bands indicating that some lurkers still achieved academically. Lurking was a key qualitative theme along with checking for assessment information and intimidation. For both subjects, students were engaged with the forums, and differences in connectivity were consistent with the different subject content. However, forum activity indicated that these forums were more like subscription services than communities of practice. We propose that forums can be transformed into dynamic teaching spaces through understanding the multidimensionality of engagement, setting the expectation and tone of the forum as a safe place, and enhancing the platforms so visible indicators show that posts have been read or consumed. We further propose that students who engage but do not post be called quiet participants. Implications for practice or policy: Educators and researchers can improve the understanding of engagement with discussion forums by acknowledging the distinction between participation and engagement. Online providers can assist in transforming these online platforms into dynamic teaching tools through enhancements that indicate a post has been read. Educators can increase engagement by setting the tone and expectations for the forum. Replacing the negative term lurker with quiet participants better reflects the activity of these students.
Publisher: Australasian Society for Computers in Learning in Tertiary Education
Date: 18-11-2022
Abstract: It is known that due to the large, erse sets of data captured in learning analytics, clear display of information is crucial to its success. Here, we describe a concise learning analytics resource, the Cohort Snapshot, that has been developed using a human centred approach, with input from experienced academics from across the institution. Meaningful data is presented as a unit level summary and includes program enrolment, student demographics, grade distributions and LMS activity. The data that was used to develop the resource was accessed via the university’s data warehouse which was synthesised using the R programming language. The Cohort Snapshot provides teaching academics, including sessional staff, access to data, to allow the adaption of teaching pedagogy to meet the needs of increasingly erse student cohorts in a regional Australian university.
Publisher: Society for Learning Analytics Research
Date: 30-08-2023
Abstract: NA
No related grants have been discovered for Ben Hicks.