ORCID Profile
0000-0001-9694-6033
Current Organisation
University of South Australia
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer International Publishing
Date: 2018
Publisher: Elsevier BV
Date: 04-2018
Publisher: Society for Learning Analytics Research
Date: 16-12-2022
Abstract: The editorial looks back at the journal in 2022 and forward to 2023. For this editorial, we analysed all ‘Notes for Practice’ published in the journal from when they first appeared in issue 5(1) to the end of November, 2022. Our goals were to examine critically the ways in which these notes have been used to foster collaboration between researchers and practitioners, and also to summarise key findings that practitioners can use to inform their work. Our analysis covers 434 Notes for Practice from 130 different papers. The full dataset used for this analysis is provided as a supplementary file.
Publisher: IEEE
Date: 12-2018
Publisher: Wiley
Date: 26-05-2023
DOI: 10.1111/BJET.13341
Abstract: This paper discusses a three‐level model that synthesizes and unifies existing learning theories to model the roles of artificial intelligence (AI) in promoting learning processes. The model, drawn from developmental psychology, computational biology, instructional design, cognitive science, complexity and sociocultural theory, includes a causal learning mechanism that explains how learning occurs and works across micro, meso and macro levels. The model also explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels. Fourteen roles for AI in education are proposed, aligned with the model's features: four roles at the in idual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity. Implications for research and practice, evaluation criteria and a discussion of limitations are included. Armed with the proposed model, AI developers can focus their work with learning designers, researchers and practitioners to leverage the proposed roles to improve in idual learning, team performance and building knowledge communities. What is already known about this topic Numerous learning theories exist with significant cross‐over of concepts, duplication and redundancy in terms and structure that offer partial explanations of learning. Frameworks concerning learning have been offered from several disciplines such as psychology, biology and computer science but have rarely been integrated or unified. Rethinking learning theory for the age of artificial intelligence (AI) is needed to incorporate computational resources and capabilities into both theory and educational practices. What this paper adds A three‐level theory (ie, micro, meso and macro) of learning that synthesizes and unifies existing theories is proposed to enhance computational modelling and further develop the roles of AI in education. A causal model of learning is defined, drawing from developmental psychology, computational biology, instructional design, cognitive science and sociocultural theory, which explains how learning occurs and works across the levels. The model explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels. Fourteen roles for AI in education are aligned with the model's features: four roles at the in idual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity. Implications for practice and policy Researchers may benefit from referring to the new theory to situate their work as part of a larger context of the evolution and complexity of in idual and organizational learning and learning systems. Mechanisms newly discovered and explained by future researchers may be better understood as contributions to a common framework unifying the scientific understanding of learning theory.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Publisher: ACM
Date: 23-03-2020
Publisher: ACM
Date: 13-03-2023
Publisher: Elsevier BV
Date: 02-2022
Publisher: Society for Learning Analytics Research
Date: 30-08-2023
Abstract: NA
Publisher: Wiley
Date: 21-10-2022
DOI: 10.1002/WIDM.1479
Abstract: The advent of technological developments is allowing to gather large amounts of data in several research fields. Learning analytics (LA)/educational data mining has access to big observational unstructured data captured from educational settings and relies mostly on unsupervised machine learning (ML) algorithms to make sense of such type of data. Generalized additive models for location, scale, and shape (GAMLSS) are a supervised statistical learning framework that allows modeling all the parameters of the distribution of the response variable with respect to the explanatory variables. This article overviews the power and flexibility of GAMLSS in relation to some ML techniques. Also, GAMLSS' capability to be tailored toward causality via causal regularization is briefly commented. This overview is illustrated via a data set from the field of LA. This article is categorized under: Application Areas Education and Learning Algorithmic Development Statistics Technologies Machine Learning
Publisher: ACM
Date: 23-03-2020
Publisher: Elsevier BV
Date: 03-2022
Publisher: Elsevier BV
Date: 12-2022
Publisher: ACM Press
Date: 2016
Publisher: Elsevier BV
Date: 06-2021
Publisher: ACM Press
Date: 2016
Publisher: Wiley
Date: 31-03-2022
DOI: 10.1111/BJET.13218
Abstract: Technological affordances have shown promising potential in advancing the delivery of corporate learning programmes designed for professional leadership development. However, there is a considerable challenge in evaluating learners' skill acquisition, with most of the past research relying on pre‐ and post‐tests or other forms of self‐reports to measure leadership development. In that sense, these approaches measure leadership development before and after the programme, while being inefficient for measuring the development during the learning process. This study collected self‐reflection answers from a professional development MOOC that allows learners to express their stepwise learning and reflect on their professional experience on leadership fronts. We developed a novel methodology and an automated system for the evaluation of leadership skills' mastery based on the depth of reflection exhibited during the learning process. We identified four groups of learners based on their course content mastery and explored the differences within groups. The results also highlight relevant insights about instructional design and provide promising avenues for future research. What is already known about this topic Professional leadership programmes have become increasingly common in workplace learning. Programmes mainly use manual/introspective measures to assess skill acquisition. What this paper adds An automated assessment system to evaluate leadership skill mastery. Evidence‐based and leadership driven inferences about skill acquisition. Use of a novel multidisciplinary methodology for complex skills assessment. Implications of practice and/or policy Assessing leadership development should include more than course grades. Assessing differences in content mastery requires evaluation of various skills. Developed assessment system provides promise for other similar domains.
Publisher: ACM
Date: 13-03-2017
Publisher: Wiley
Date: 24-05-2023
DOI: 10.1111/JCAL.12829
Abstract: Maintaining cohesion is critical for teams to achieve shared goals and performance outcomes within a work‐integrated learning (WIL) environment. Cohesion is an emergent state that develops over time, representing the synchrony of different behavioural interactions. Cohesive teams will exhibit such phenomena by their temporal coordination of micro‐level relations. The primary aim of this study is to examine the cohesion of teams in learning environments using a learning analytics approach. This study examines teams from higher education who participate in a WIL environment platform working in teams to develop their collaborative problem‐solving skills. Here we show that temporal network motifs can be used as a proxy to measure cohesion. We illustrate three clusters represented by team learning behaviours and found that each cluster has distinctive interactions with learning resources, performance outcomes, temporal network motif group characteristics and emergence over time using learning analytics. Applying temporal motifs as an analytics‐based measure of cohesion is a starting point for understanding how cohesion develops over time without relying on surveys. We anticipate that the same approaches can be applied in most learning management systems containing trace data of teams and their interactions with learning resources to understand cohesion.
Publisher: ACM
Date: 23-03-2020
DOI: 10.1145/3375462
Publisher: ACM Press
Date: 2017
Publisher: Informa UK Limited
Date: 02-01-2017
Publisher: Springer International Publishing
Date: 2023
Publisher: ACM
Date: 16-03-2015
Publisher: Springer International Publishing
Date: 2023
Publisher: Springer International Publishing
Date: 2023
Publisher: Springer International Publishing
Date: 2018
Publisher: Wiley
Date: 04-06-2015
DOI: 10.1111/JCAL.12107
Publisher: ACM Press
Date: 2016
Publisher: Elsevier BV
Date: 2018
Publisher: Elsevier BV
Date: 2022
Publisher: Elsevier BV
Date: 04-2021
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: 10-11-0003
Publisher: Springer International Publishing
Date: 2019
Publisher: Wiley
Date: 23-07-0004
DOI: 10.1111/BJET.12277
Publisher: ACM
Date: 13-03-2023
Publisher: Elsevier BV
Date: 2015
Publisher: ACM
Date: 25-04-2016
Publisher: The Online Learning Consortium
Date: 03-2018
Abstract: Dual-layer MOOCs are an educational framework designed to create customizable modality pathways through a learning experience. The basic premise is to design two framework choices through a course - one that is instructor guided and the other that is student-determined and open. Learners have the option to create their own customized pathway by choosing or combining both modalities as they see fit at any given time in the course. This mixed-methods study sought to understand the patterns that learners engaged in during a course designed with this pathway framework. The results of the quantitative examination of the course activity are presented, as well as the categories and themes that arose from the qualitative research. The results of the analysis indicates that learners value the ability to choose the pathway that they engage the course in. Additional research is needed to improve the technical and design aspects of the framework.
Publisher: ACM
Date: 12-04-2017
Publisher: Elsevier BV
Date: 09-0012
Publisher: ACM
Date: 21-03-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Publisher: SOLAR
Date: 2022
DOI: 10.18608/HLA22.011
Abstract: The broadening adoption of technology enhanced learning environments has substantially altered the manner in which educational communication takes place, with most people engaging in some form of online asynchronous or synchronous conversation every day. The language and discourse artifacts emerging from these technological environments is a rich source of information into learning processes and outcomes. This chapter describes the current landscape of natural language processing (NLP) tools and approaches available to researchers and practitioners to computationally discern patterns in large quantities of text-based conversations that take place across a variety of educational technology platforms. The capabilities of NLP are particularly important as, in the field of learning analytics, we desire to effectively and efficiently learn about the process of learning by observing learners, and then subsequently use that information to improve learning. We conclude the chapter with a discussion around the emerging applications (i.e., sensing technologies, breakthroughs in AI, and cloud computing) and challenges of NLP tools to educational discourse.
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: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Springer International Publishing
Date: 2022
Publisher: Wiley
Date: 03-11-2021
DOI: 10.1111/JCAL.12614
Abstract: The COVID‐19 outbreak came with an unprecedented opportunity to investigate how the new reality of social distancing and limited international travel will affect the organization of academic conferences. Drawing on conceptualization of academic conferences as professional learning spaces, in this study, we examine the factors associated with the perceived value of purely virtual academic conferences and how such perceptions differ between participants from different research fields. The aim was to gain knowledge about factors that should be considered when designing a virtual conference. Survey data from participants of three different virtual conferences were collected ( N = 311). Kendall's rank correlation and χ 2 ‐analyses were performed. Results show satisfaction with social interaction, the extent to which presentations met participants' topics of interest and the perceived importance of learning and getting an overview on the research topic to be related to the value rating. Researchers from different research fields differ significantly in their opinion about the most appropriate conference format regarding getting an overview on the research topic. For some researchers, virtual participation might be a valuable alternative to attending a conference in person. The study serves as a first attempt to understand how and for which target groups virtual conferences serve as a valuable learning event. Further research on this conference format is needed.
Publisher: Edward Elgar Publishing
Date: 21-04-2023
Publisher: Society for Learning Analytics Research
Date: 18-02-2015
Abstract: With the widespread adoption of Learning Management Systems (LMS) and other learning technology, large amounts of data – commonly known as trace data – are being recorded and are readily accessible to educational researchers. Among different uses of trace data, it has been extensively used to calculate time that students spent on different learning activities – commonly referred to as student time-on-task. Extracted time-on-task measures are then used to build predictive models of student learning in order to understand and improve learning processes. While time-on-task measures have been extensively used in Learning Analytics research, the details of their estimation are rarely described and the consequences that this process entails are not fully examined.This paper presents findings from two experiments that looked at the different time-on-task estimation methods and how they influence the final research findings. Based on modeling different student performance measures with popular statistical methods in two datasets (one online and one blended), our findings indicate that time-on-task estimation methods play an important role in shaping the final study results. This is particularly true for online setting where the amount of interaction with LMS is typically higher. The primary goal of this paper is to raise awareness and initiate a debate on the important issue of time-on-task estimation within a broader learning analytics community. Finally, the paper provides an overview of commonly adopted time-on-task estimation methods in educational and related research fields.
Publisher: IEEE
Date: 07-2019
Publisher: American Educational Research Association (AERA)
Date: 24-09-2016
Abstract: In recent years, a wide array of tools have emerged for the purposes of conducting educational data mining (EDM) and/or learning analytics (LA) research. In this article, we hope to highlight some of the most widely used, most accessible, and most powerful tools available for the researcher interested in conducting EDM/LA research. We will highlight the utility that these tools have with respect to common data preprocessing and analysis steps in a typical research project as well as more descriptive information such as price point and user-friendliness. We will also highlight niche tools in the field, such as those used for Bayesian knowledge tracing (BKT), data visualization, text analysis, and social network analysis. Finally, we will discuss the importance of familiarizing oneself with multiple tools—a data analysis toolbox—for the practice of EDM/LA research.
Publisher: Society for Learning Analytics Research
Date: 05-11-2021
Abstract: One of the major factors affecting student learning is feedback. Although the importance of feedback has been recognized in educational institutions, dramatic changes - such as bigger class sizes and a more erse student population - challenged the provision of effective feedback. In light of these changes, educators have increasingly been using new digital tools to provide student feedback, given the broader adoption and availability of these new technologies. However, despite these efforts, most educators have limited insight into the recipience of their feedback and wonder which students engage with feedback. This problem is referred to as the "feedback gap," which is the difference between the potential and actual use of feedback, preventing educators and instructional designers from understanding feedback recipience among students. In this study, a set of trackable call-to-action (CTA) links were embedded in feedback messages focused on learning processes and self-regulation of learning in one fully online marketing course and one blended bioscience course. These links helped us examine the association between feedback engagement and course success. We also conducted two focus groups with students from one of the courses to further examine student perceptions of feedback messages. Our results across both courses revealed that early engagement with feedback is positively associated with passing the course and that most students considered feedback messages helpful in their learning. Our study also found some interesting demographic differences between students regarding their engagement with the feedback messages. Such insight enables instructors to ask "why" questions, support students' learning, improve feedback processes, and narrow the gap between potential and actual use of feedback. The practical implications of our findings are further discussed.
Publisher: ACM
Date: 07-03-2018
Publisher: Society for Learning Analytics Research
Date: 03-09-2021
Abstract: Over the past decade, the increasing use of learning analytics opened the possibility of making data-driven decisions for improving student learning. Driven by the strong university adoption of learning analytics, most early learning analytics research focused on issues specific to tertiary education. With the broader adoption of educational technologies in primary and secondary education and the emergence of new classroom-focused technologies, there has been a growing awareness of the potentials of learning analytics for supporting students and diagnosing their learning progress in pre-university contexts. This special section focused on investigating, developing, and evaluating state-of-the-art learning analytics approaches within primary and secondary school settings. In this editorial, we summarize the papers of the special section and discuss the challenges and opportunities for learning analytics within the school context. We conclude with the discussion around the opportunities for future work and the implications of this special section for the field of learning analytics.
Publisher: Elsevier BV
Date: 07-2014
Publisher: Informa UK Limited
Date: 02-01-2022
Publisher: Elsevier BV
Date: 08-2022
Publisher: ACM
Date: 13-03-2017
Publisher: Society for Learning Analytics Research (SoLAR)
Date: 05-2017
DOI: 10.18608/HLA17.007
Publisher: Elsevier BV
Date: 12-2021
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: Informa UK Limited
Date: 19-04-2022
Publisher: ACM
Date: 16-03-2015
Publisher: Elsevier BV
Date: 10-2015
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Vitomir Kovanović.