ORCID Profile
0000-0001-9265-1908
Current Organisation
Monash University
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Educational Technology and Computing | Specialist Studies in Education | Learning Sciences |
Teaching and Instruction Technologies | Learner and Learning Processes | Application Software Packages (excl. Computer Games) | Workforce Transition and Employment
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2014
DOI: 10.1109/MITP.2014.85
Publisher: Routledge
Date: 03-08-2023
Publisher: Elsevier BV
Date: 04-2018
Publisher: Society for Learning Analytics Research
Date: 05-08-2018
Abstract: The widespread adoption of digital e-learning environments and other learning technology has provided researchers with ready access to large quantities of data. Much of this data comes from discussion forums and has been studied with analytical methods drawn from social network analysis. However, within this large body of research there exists considerable variation in the definition of what constitutes a social tie, and the consequences of this choice are rarely described or examined. This paper presents findings from two distinct learning environments regarding different social tie extraction methods and their influence on the structural and statistical properties of the induced networks, and the association between measures of centrality and academic performance. Our findings indicate that social tie definitions play an important role in shaping the results of our analyses. The primary purpose of this paper is to raise awareness of the consequences that such methodological choices may have, and to promote transparency in future research.
Publisher: Springer New York
Date: 06-08-2013
Publisher: IGI Global
Date: 10-2012
Abstract: Method Engineering (ME) aims to improve software development methods by creating and proposing adaptation frameworks whereby methods are created to provide suitable matches with the requirements of the organization and address project concerns and fit specific situations. Therefore, methods are defined and modularized into components stored in method repositories. The assembly of appropriate methods depends on the particularities of each project, and rapid method construction is inevitable in the reuse and management of existing methods. The ME discipline aims at providing engineering capability for optimizing, reusing, and ensuring flexibility and adaptability of methods there are three key research challenges which can be observed in the literature: 1) the lack of standards and tooling support for defining, publishing, discovering, and retrieving methods which are only locally used by their providers without been largely adapted by other organizations 2) dynamic adaptation and assembly of methods with respect to imposed continuous changes or evolutions of the project lifecycle and 3) variability management in software methods in order to enable rapid and effective construction, assembly and adaptation of existing methods with respect to particular situations. The authors propose semantically-enabled families of method-oriented architecture by applying service-oriented product line engineering principles and employing Semantic Web technologies.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Publisher: Oxford University Press (OUP)
Date: 05-11-2015
Publisher: ACM Press
Date: 2016
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: Wiley
Date: 20-11-2022
DOI: 10.1111/JCAL.12755
Abstract: Online learning has grown significantly during the past two decades, and COVID‐19 pandemic has expedited this process. However, previous research has shown how academic dishonesty is more prevalent under these modalities. Therefore, there is the challenge of performing trustworthy remote assessments, in order to obtain valid and reliable measures of students' knowledge. The research question that drove this research was: what actions have been proposed in contemporary research to improve remote assessment trustworthiness from a technological and pedagogical perspective? We analysed the papers accepted for the special issue titled ‘Trustworthy Assessment and Academic Integrity in Remote Learning’ following a deductive qualitative category coding methodology to find the main approaches. We identified eight approaches to improve trustworthiness in remote assessment: four for exams and high‐stake tests, one exclusively for performance‐based assessments, and three for any type of assessment. Our findings shift attention from academic dishonesty to trustworthy assessment, integrating recent findings of papers accepted to this special issue. Our findings deepen current understanding of trustworthy remote assessments, inviting practitioners and researchers to explore different types of assessment methods and different moments related to assessing learning.
Publisher: Australasian Society for Computers in Learning in Tertiary Education
Date: 31-12-2020
DOI: 10.14742/AJET.6853
Abstract: The field of learning analytics has evolved over the past decade to provide new ways to view, understand and enhance learning activities and environments in higher education. It brings together research and practice traditions from multiple disciplines to provide an evidence base to inform student support and effective design for learning. This has resulted in a plethora of ideas and research exploring how data can be analysed and utilised to not only inform educators, but also to drive online learning systems that offer personalised learning experiences and/or feedback for students. However, a core challenge that the learning analytics community continues to face is how the impact of these innovations can be demonstrated. Where impact is positive, there is a case for continuing or increasing the use of learning analytics, however, there is also the potential for negative impact which is something that needs to be identified quickly and managed. As more institutions implement strategies to take advantage of learning analytics as part of core business, it is important that impact can be evaluated and addressed to ensure effectiveness and sustainability. In this editorial of the AJET special issue dedicated to the impact of learning analytics in higher education, we consider what impact can mean in the context of learning analytics and what the field needs to do to ensure that there are clear pathways to impact that result in the development of systems, analyses, and interventions that improve the educational environment.
Publisher: JMIR Publications Inc.
Date: 09-12-2021
DOI: 10.2196/27984
Abstract: There is an increasing amount of electronic data sitting within the health system. These data have untapped potential to improve clinical practice if extracted efficiently and harnessed to change the behavior of health professionals. Furthermore, there is an increasing expectation from the government and peak bodies that both in idual health professionals and health care organizations will use electronic data for a range of applications, including improving health service delivery and informing clinical practice and professional accreditation. The aim of this research program is to make eHealth data captured within tertiary health care organizations more actionable to health professionals for use in practice reflection, professional development, and other quality improvement activities. A multidisciplinary approach was used to connect academic experts from core disciplines of health and medicine, education and learning sciences, and engineering and information communication technology with government and health service partners to identify key problems preventing the health care industry from using electronic data to support health professional learning. This multidisciplinary approach was used to design a large-scale research program to solve the problem of making eHealth data more accessible to health professionals for practice reflection. The program will be delivered over 5 years by doctoral candidates undertaking research projects with discrete aims that run in parallel to achieving this program’s objectives. The process used to develop the research program identified 7 doctoral research projects to answer the program objectives, split across 3 streams. This research program has the potential to successfully unpack electronic data siloed within clinical sites and enable health professionals to use them to reflect on their practice and deliver informed and improved care. The program will contribute to current practices by fostering stronger connections between industry and academia, interlinking doctoral research projects to solve complex problems, and creating new knowledge for clinical sites on how data can be used to understand and improve performance. Furthermore, the program aims to affect policy by developing insights on how professional development programs may be strengthened to enhance their alignment with clinical practice. The key contributions of this paper include the introduction of a new conceptualized research program, Practice Analytics in Health care, by describing the foundational academic disciplines that the program is formed of and presenting scientific methods for its design and development. PRR1-10.2196/27984
Publisher: Elsevier BV
Date: 02-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Elsevier BV
Date: 06-2016
Publisher: Society for Learning Analytics Research
Date: 07-12-2015
Abstract: This issue is a call for researchers and practitioners to reflect on progress to date and understand the criticality of theory – how it facilitates interpretation of findings but also how it can also restrict and confine our thinking through the assumptions many theoretical models bring. As education paradigms further shift and juxtapose informal and formal learning settings there is a need to re-visit any underlying theoretical assumptions.
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: Australasian Society for Computers in Learning in Tertiary Education
Date: 24-08-2019
DOI: 10.14742/AJET.4314
Abstract: Despite the recent surge of interest in learning analytics (LA), their adoption in everyday classroom practice is still slow. Knowledge gaps and lack of inter-stakeholder communication (particularly with educational practitioners) have been posited as critical factors for previous LA adoption failures. Yet, what issues should researchers, practitioners and other actors communicate about, when considering the adoption of an LA innovation in a particular context? We reviewed and synthesised existing literature on four focus areas related to LA, their adoption, implications for practice, and more general factors that have emerged as crucial when studying everyday classroom adoption of technologies (i.e., classroom orchestration). This synthesis resulted in two conversational frameworks and an inter-stakeholder communication tool. These can be used to guide and support conversations and decision-making about the adoption of LA innovations. We illustrate their usefulness with ex les of use in ongoing LA adoption processes in Australia, Spain and Estonia.
Publisher: Elsevier BV
Date: 07-2014
Publisher: Elsevier BV
Date: 09-2014
Publisher: Springer Nature Switzerland
Date: 2023
DOI: 10.1007/978-3-031-42682-7_27
Abstract: Peer feedback has been widely used in computer-supported collaborative learning (CSCL) setting to improve students’ engagement with massive courses. Although the peer feedback process increases students’ self-regulatory practice, metacognition, and academic achievement, instructors need to go through large amounts of feedback text data which is much more time-consuming. To address this challenge, the present study proposes an automated content analysis approach to identify relevant categories in peer feedback based on traditional and sequence-based classifiers using TF-IDF and content-independent features. We use a data set from an extensive course (N = 231 students) in the setting of engineering higher education. In particular, a total of 2,444 peer feedback messages were analyzed. The CRF classification model based on the TF-IDF features achieved the best performance. The results illustrate that the ability to scale up the automatic analysis of peer feedback provides new opportunities for student-improved learning and improved teacher support in higher education at scale.
Publisher: Society for Learning Analytics Research
Date: 16-12-2022
Abstract: As a research field geared toward understanding and improving learning, Learning Analytics (LA) must be able to provide empirical support for causal claims. However, as a highly applied field, tightly controlled randomized experiments are not always feasible nor desirable. Instead, researchers often rely on observational data, based on which they may be reluctant to draw causal inferences. The past decades have seen much progress concerning causal inference in the absence of experimental data. This paper introduces directed acyclic graphs (DAGs), an increasingly popular tool to visually determine the validity of causal claims. Based on this, three basic pitfalls are outlined: confounding bias, overcontrol bias, and collider bias. Further, the paper shows how these pitfalls may be present in the published LA literature alongside possible remedies. Finally, this approach is discussed in light of practical constraints and the need for theoretical development.
Publisher: Society for Learning Analytics Research
Date: 24-12-2014
Abstract: This paper describes a doctoral research that focuses on the development of a learning analytics framework for inquiry-based digital learning. This research builds on the the Community of Inquiry model (CoI) as a foundation commonly used in research and practice of digital learning and teaching. Specifically, the main contributions of this research are: i) the development of a novel text classification algorithm for (semi)automated message classification which enables for easier adoption of the CoI model, ii) understanding of the relationships between different socio-technological interactions and the dimensions of the CoI model.
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Society for Learning Analytics Research
Date: 24-12-2014
Abstract: Teaching and learning in networked setting has attained a significant amount of attention recently. The central topic of networked learning research is human-human and human-information interactions that occur within a networked learning environment. The nature of these interactions is highly complex and usually requires a multi-dimensional approach in analyzing their effects. Therefore, the main goal of this research is the development of a theoretical model that allows for a comprehensive and scalable analysis of how and why learners engage into collaboration in networked communities. The proposed research method, anticipated research outcomes and contributions to the learning analytics field are discussed.
Publisher: Elsevier BV
Date: 07-2013
Publisher: ACM
Date: 10-2012
Publisher: ACM
Date: 13-03-2023
Publisher: ACM
Date: 13-03-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2012
Publisher: Springer International Publishing
Date: 2019
Publisher: Elsevier BV
Date: 03-2013
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Society for Learning Analytics Research
Date: 03-11-2021
Abstract: Learning analytics dashboards commonly visualize data about students with the aim of helping students and educators understand and make informed decisions about the learning process. To assist with making sense of complex and multidimensional data, many learning analytics systems and dashboards have relied strongly on AI algorithms based on predictive analytics. While predictive models have been successful in many domains, there is an increasing realization of the inadequacies of using predictive models in decision-making tasks that affect in iduals without human oversight. In this paper, we employ a suite of state-of-the-art algorithms, from the online analytics processing, data mining, and process mining domains, to present an alternative human-in-the-loop AI method to enable educators to identify, explore, and use appropriate interventions for subpopulations of students with the highest deviation in performance or learning process compared to the rest of the class. We demonstrate an application of our proposed approach in an existing learning analytics dashboard (LAD) and explore the recommended drill-downs in a course with 875 students. The demonstration provides an ex le of the recommendations from real course data and shows how recommendations can lead the user to interesting insights. Furthermore, we demonstrate how our approach can be employed to develop intelligent LADs.
Publisher: Elsevier BV
Date: 03-2011
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: IEEE
Date: 08-2011
DOI: 10.1109/EDOC.2011.25
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Springer International Publishing
Date: 2021
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: ACM
Date: 13-03-2023
Publisher: IEEE
Date: 09-2012
DOI: 10.1109/EDOCW.2012.6
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Society for Learning Analytics Research
Date: 18-02-2015
Abstract: This final issue for 2015 includes a special section of invited papers from the recent Learning Analytics and Knowledge conference (LAK15). The collected papers connect with the conference theme of “Scaling up: Big data to Big Impact” and reflect the emerging trends and future directions of learning analytics research.
Publisher: IEEE
Date: 11-2011
Publisher: Springer Science and Business Media LLC
Date: 22-02-2016
Publisher: Informa UK Limited
Date: 20-10-2018
Publisher: ACM
Date: 13-03-2023
Publisher: Springer International Publishing
Date: 2011
Publisher: Elsevier BV
Date: 07-2014
Publisher: Springer Science and Business Media LLC
Date: 21-07-2017
Publisher: Routledge
Date: 30-10-2013
Publisher: ACM Press
Date: 2016
Publisher: ACM Press
Date: 2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2011
DOI: 10.1109/TLT.2011.21
Publisher: Springer Science and Business Media LLC
Date: 21-12-2015
Publisher: Australasian Society for Computers in Learning in Tertiary Education
Date: 28-03-2018
DOI: 10.14742/AJET.3207
Abstract: The rapid growth of blended and online learning models in higher education has resulted in a parallel increase in the use of audio-visual resources among students and teachers. Despite the heavy adoption of video resources, there have been few studies investigating their effect on learning processes and even less so in the context of academic development. This paper uses learning analytic techniques to examine how academic teaching staff engage with a set of prescribed videos and video annotations in a professional development course. The data was collected from two offerings of the course at a large research-intensive university in Australia. The data was used to identify patterns of activity and transition states as users engaged with the course videos and video annotations. Latent class analysis and hidden Markov models were used to characterise the evolution of engagement throughout the course. The results provide a detailed description of the evolution of learner engagement that can be readily translated into action aimed at increasing the quality of the learning experience.
Publisher: IEEE
Date: 07-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2022
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: IEEE
Date: 07-2010
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer International Publishing
Date: 2015
Publisher: Society for Learning Analytics Research
Date: 17-12-2020
Abstract: Programming is a complex learning activity that involves coordination of cognitive processes and affective states. These aspects are often considered in idually in computing education research, demonstrating limited understanding of how and when students learn best. This issue confines researchers to contextualize evidence-driven outcomes when learning behaviour deviates from pedagogical intentions. Multimodal learning analytics (MMLA) captures data essential for measuring constructs (e.g., cognitive load, confusion) that are posited in the learning sciences as important for learning, and cannot effectively be measured solely with the use of programming process data (IDE-log data). Thus, we augmented IDE-log data with physiological data (e.g., gaze data) and participants’ facial expressions, collected during a debugging learning activity. The findings emphasize the need for learning analytics that are consequential for learning, rather than easy and convenient to collect. In that regard, our paper aims to provoke productive reflections and conversations about the potential of MMLA to expand and advance the synergy of learning analytics and learning design among the community of educators from a post-evaluation design-aware process to a permanent monitoring process of adaptation.
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: ACM
Date: 13-03-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2020
Publisher: ACM
Date: 07-03-2018
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Elsevier BV
Date: 03-2013
Publisher: Springer International Publishing
Date: 2012
Publisher: Australasian Society for Computers in Learning in Tertiary Education
Date: 23-12-2020
DOI: 10.14742/AJET.6370
Abstract: Although technological advances have brought about new opportunities for scaling feedback to students, there remain challenges in how such feedback is presented and interpreted. There is a need to better understand how students make sense of such feedback to adapt self-regulated learning processes. This study examined students’ sense-making of learning analytics–based personalised feedback across four courses. Results from a combination of thematic analysis and epistemic network analysis show an association between student perceptions of their personalised feedback and how these map to subsequent self-described self-regulated learning processes. Most notably, the results indicate that personalised feedback, elaborated by personal messages from course instructors, helps students refine or strengthen important forethought processes of goal-setting, as well as to reduce procrastination. The results highlight the need for instructors to increase the dialogic element in personalised feedback in order to reduce defensive reactions from students who hold to their own learning strategies. This approach may prompt reflection on the suitability of students’ current learning strategies and achievement of associated learning goals. Implications for practice or policy: Personalised feedback based on learning analytics should be informed by an understanding of students’ self-regulated learning. Instructors implementing personalised feedback should align this closely with the course curriculum. Instructors implementing personalised feedback in their courses should consider the relational element of feedback by using a positive tone. Personalised feedback can be further enhanced by increasing the dialogic element and by including more information about learning strategies.
Publisher: Elsevier BV
Date: 10-2014
Publisher: Springer Science and Business Media LLC
Date: 13-05-2016
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: ACM
Date: 16-03-2015
Publisher: Informa UK Limited
Date: 30-06-2021
Publisher: ACM
Date: 16-03-2015
Publisher: ACM
Date: 24-03-2014
Publisher: Elsevier BV
Date: 04-2017
Publisher: Society for Learning Analytics Research
Date: 31-08-2021
Abstract: By participating in asynchronous course discussion forums, students can work together to refine their ideas and construct knowledge collaboratively. Typically, some messages simply repeat or paraphrase course content, while others bring in new material, demonstrate reasoning, integrate concepts, and develop solutions. Through the messages they send, students thus display different levels of intellectual engagement with the topic and the course. We refer to this as cognitive quality. The work presented here used two widely studied frameworks for assessing critical discourse and cognitive engagement: the ICAP and Community of Inquiry frameworks. The constructs of the frameworks were used as proxy measures for cognitive quality. Predictive classifiers were trained for both frameworks on the same data in order to discover which attributes of the dialogue were most informative and how those attributes were correlated with framework constructs. We found that longer and more complex messages were associated with indicators of greater quality in both frameworks, and that the threaded reply structure mattered more than chronological order. By including the framework labels as additional model features, we also assessed the links between frameworks. The empirical results provide evidence that the two frameworks measure different aspects of student behaviour relating to cognitive quality.
Publisher: Oxford University Press (OUP)
Date: 13-03-2013
Publisher: Wiley
Date: 02-03-2021
DOI: 10.1111/JCAL.12542
Abstract: Multimodal data have the potential to explore emerging learning practices that extend human cognitive capacities. A critical issue stretching in many multimodal learning analytics (MLA) systems and studies is the current focus aimed at supporting researchers to model learner behaviours, rather than directly supporting learners. Moreover, many MLA systems are designed and deployed without learners' involvement. We argue that in order to create MLA interfaces that directly support learning, we need to gain an expanded understanding of how multimodal data can support learners' authentic needs. We present a qualitative study in which 40 computer science students were tracked in an authentic learning activity using wearable and static sensors. Our findings outline learners' curated representations about multimodal data and the non‐technical challenges in using these data in their learning practice. The paper discusses 10 dimensions that can serve as guidelines for researchers and designers to create effective and ethically aware student‐facing MLA innovations.
Publisher: JMIR Publications Inc.
Date: 16-02-2021
Abstract: here is an increasing amount of electronic data sitting within the health system. These data have untapped potential to improve clinical practice if extracted efficiently and harnessed to change the behavior of health professionals. Furthermore, there is an increasing expectation from the government and peak bodies that both in idual health professionals and health care organizations will use electronic data for a range of applications, including improving health service delivery and informing clinical practice and professional accreditation. he aim of this research program is to make eHealth data captured within tertiary health care organizations more actionable to health professionals for use in practice reflection, professional development, and other quality improvement activities. multidisciplinary approach was used to connect academic experts from core disciplines of health and medicine, education and learning sciences, and engineering and information communication technology with government and health service partners to identify key problems preventing the health care industry from using electronic data to support health professional learning. This multidisciplinary approach was used to design a large-scale research program to solve the problem of making eHealth data more accessible to health professionals for practice reflection. The program will be delivered over 5 years by doctoral candidates undertaking research projects with discrete aims that run in parallel to achieving this program’s objectives. he process used to develop the research program identified 7 doctoral research projects to answer the program objectives, split across 3 streams. his research program has the potential to successfully unpack electronic data siloed within clinical sites and enable health professionals to use them to reflect on their practice and deliver informed and improved care. The program will contribute to current practices by fostering stronger connections between industry and academia, interlinking doctoral research projects to solve complex problems, and creating new knowledge for clinical sites on how data can be used to understand and improve performance. Furthermore, the program aims to affect policy by developing insights on how professional development programs may be strengthened to enhance their alignment with clinical practice. The key contributions of this paper include the introduction of a new conceptualized research program, Practice Analytics in Health care, by describing the foundational academic disciplines that the program is formed of and presenting scientific methods for its design and development. RR1-10.2196/27984
Publisher: Springer International Publishing
Date: 2018
Publisher: Informa UK Limited
Date: 26-05-2015
Publisher: ACM
Date: 13-03-2017
Publisher: Springer International Publishing
Date: 2019
Publisher: ACM
Date: 12-04-2017
Publisher: Elsevier BV
Date: 2019
Publisher: Elsevier BV
Date: 09-0012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2015
Publisher: Society for Learning Analytics Research
Date: 24-12-2014
Abstract: This study aims to investigate how students’ motivated strategies of learning and their achievement goal orientations relate to their academic behaviours and performance in the context of online leaning systems. The study also develops and validates a relational model between students’ learning strategies and achievement goals.
Publisher: ACM
Date: 16-03-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2021
Publisher: Elsevier BV
Date: 2016
Publisher: Society for Learning Analytics Research
Date: 16-07-2015
Abstract: This editorial discusses events that marked the period since the publication of the previous issue – the 5th International Conference on Learning Analytics and Knowledge (LAK 2015), Learning Analytics Summer Institutes (LASIs 2015), and Learning Analytics Policy Briefing in the European Parliament. This period saw releases of two important publications for system-wide implementation of learning analytics in higher education published by Jisc and the Australian Government’s Office for Learning and Teaching. An important recognition of the maturation of the field of learning analytics is the recent publication of the 2015 Google Scholar Metrics identifying the LAK proceedings as the only conference proceedings among the 20 most cited publication venues in educational technology. Building bridges for enhancing impact is another important activity for the field maturation through developing linkages of learning analytics with educational data mining, user modeling, the learning sciences, technology enhanced learning, cyber-learning, and learning at scale. This editorial also introduces a special section published in this issue dedicated to the exploration of connections between self-regulated learning and learning analytics, introduces two regular research papers featured in this issue and describes several special sections that will be published in future issues of the journal.
Publisher: ACM
Date: 07-03-2018
Publisher: Emerald
Date: 05-08-2019
DOI: 10.1108/IJILT-02-2019-0024
Abstract: The analysis of data collected from user interactions with educational and information technology has attracted much attention as a promising approach to advancing our understanding of the learning process. This promise motivated the emergence of the field of learning analytics and supported the education sector in moving toward data-informed strategic decision making. Yet, progress to date in embedding such data-informed processes has been limited. The purpose of this paper is to address a commonly posed question asked by educators, managers, administrators and researchers seeking to implement learning analytics – how do we start institutional adoption of learning analytics? A narrative review is performed to synthesize the existing literature on learning analytics adoption in higher education. The synthesis is based on the established models for the adoption of business analytics and finding two projects performed in Australia and Europe to develop and evaluate approaches to adoption of learning analytics in higher education. The paper first defines learning analytics and touches on lessons learned from some well-known case studies. The paper then reviews the current state of institutional adoption of learning analytics by examining evidence produced in several studies conducted worldwide. The paper next outlines an approach to learning analytics adoption that could aid system-wide institutional transformation. The approach also highlights critical challenges that require close attention in order for learning analytics to make a long-term impact on research and practice of learning and teaching. The paper proposed approach that can be used by senior leaders, practitioners and researchers interested in adoption of learning analytics in higher education. The proposed approach highlights the importance of the socio-technical nature of learning analytics and complexities pertinent to innovation adoption in higher education institutions.
Publisher: IEEE
Date: 09-2012
DOI: 10.1109/EDOC.2012.6
Publisher: ACM
Date: 07-03-2018
Publisher: Springer International Publishing
Date: 2015
Publisher: Wiley
Date: 14-01-2020
DOI: 10.1111/JCAL.12401
Publisher: ACM
Date: 13-03-2017
Publisher: ACM
Date: 21-03-2011
Publisher: ACM
Date: 21-03-2011
Publisher: ACM
Date: 16-03-2015
Publisher: Elsevier BV
Date: 02-2016
Publisher: ACM Press
Date: 2016
Publisher: Springer Science and Business Media LLC
Date: 11-08-2011
Publisher: Springer International Publishing
Date: 2022
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Elsevier BV
Date: 11-2011
Publisher: SAGE Publications
Date: 12-03-2013
Abstract: This article presents results from an investigation of the association between student academic performance and social ties. Based on social capital and networked learning research, we hypothesized that (a) students’ social capital accumulated through their course progression is positively associated with their academic performance and (b) students with more social capital have significantly higher academic performance (operationalized as grade point average). Both hypotheses were supported by results of an empirical study that analyzed 10 years of student course enrolment records ( N = 505) in a master’s degree program offered through distance education at a Canadian university. These results are consistent with previous studies that looked at social networks built through student interaction in classrooms or computer-mediated communication environments. The significance of this research lies in the simplicity of the method used to establish student social networks from existing course registration records readily available through an institution’s student information system. Direct implications of this research are that (a) study plans for students should consider investment in building new social ties in each course during degree programs and (b) readily available data about cross-class networks can be used in software systems supporting study planning.
Publisher: Society for Learning Analytics Research
Date: 05-2014
Abstract: This article introduces the inaugural issue for the Journal of Learning Analytics. The article outlines the Journal’s aims and scope and summarises the research and Hot Spot papers for the issue.
Publisher: ACM
Date: 25-04-2016
Publisher: ACM
Date: 23-06-2013
Publisher: Informa UK Limited
Date: 14-05-2018
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Wiley
Date: 16-07-2019
DOI: 10.1111/BJET.12846
Publisher: ACM
Date: 13-03-2017
Publisher: Springer International Publishing
Date: 2018
Publisher: JMIR Publications Inc.
Date: 17-05-2023
DOI: 10.2196/41671
Abstract: Digital education has expanded since the COVID-19 pandemic began. A substantial amount of recent data on how students learn has become available for learning analytics (LA). LA denotes the “measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.” This scoping review aimed to examine the use of LA in health care professions education and propose a framework for the LA life cycle. We performed a comprehensive literature search of 10 databases: MEDLINE, Embase, Web of Science, ERIC, Cochrane Library, PsycINFO, CINAHL, ICTP, Scopus, and IEEE Explore. In total, 6 reviewers worked in pairs and performed title, abstract, and full-text screening. We resolved disagreements on study selection by consensus and discussion with other reviewers. We included papers if they met the following criteria: papers on health care professions education, papers on digital education, and papers that collected LA data from any type of digital education platform. We retrieved 1238 papers, of which 65 met the inclusion criteria. From those papers, we extracted some typical characteristics of the LA process and proposed a framework for the LA life cycle, including digital education content creation, data collection, data analytics, and the purposes of LA. Assignment materials were the most popular type of digital education content (47/65, 72%), whereas the most commonly collected data types were the number of connections to the learning materials (53/65, 82%). Descriptive statistics was mostly used in data analytics in 89% (58/65) of studies. Finally, among the purposes for LA, understanding learners’ interactions with the digital education platform was cited most often in 86% (56/65) of papers and understanding the relationship between interactions and student performance was cited in 63% (41/65) of papers. Far less common were the purposes of optimizing learning: the provision of at-risk intervention, feedback, and adaptive learning was found in 11, 5, and 3 papers, respectively. We identified gaps for each of the 4 components of the LA life cycle, with the lack of an iterative approach while designing courses for health care professions being the most prevalent. We identified only 1 instance in which the authors used knowledge from a previous course to improve the next course. Only 2 studies reported that LA was used to detect at-risk students during the course’s run, compared with the overwhelming majority of other studies in which data analysis was performed only after the course was completed.
Publisher: Society for Learning Analytics Research
Date: 19-12-2016
Abstract: This paper is a guest editorial into a special section that offers a collection of tutorials on methods that can be used in learning analytics. The special section is prepared as a response to the growing need of learning analytics practitioners and researchers to learn and use novel methods. In spite of this need, papers that systematically introduce some of the methods have been underrepresented in the literature. Specifically, the special section features papers that introduce epistemic network analysis, automated content and network analysis of social media, text coherence analysis with Coh-Metrix, microgenetic analysis with sequence pattern mining, and design of visual learning analytics guided by educational theory informed goals.
Publisher: Elsevier BV
Date: 2012
Publisher: Society for Learning Analytics Research
Date: 19-12-2016
Abstract: This issue of the Journal of Learning Analytics features seven research papers, complemented by a practitioner research paper (Dvorak & Jia). Papers by McCoy and Shih, and Knight, Brozina, and Novoselich discuss the important topic of educators working with educational data, alongside (in the latter paper) student perspectives on learning analytics. Douglas, Bermel, Alam, and Madhavan and Waddington, Nam, Lonn, and Teasley offer empirical insight on developing a richer perspective on learning material interaction and engagement in online learning contexts (MOOCs, and LMS’ respectively). Dvorak and Jia bring a practitioner perspective to the issue in their discussion of approaches to analyzing online work habits via timeliness, regularity, and intensity. Sutherland and White, and Vieira, Goldstein, Purzer, and Magana offer focus on specific subject-based learning activities (algebra learning, and student experimentation strategies in engineering design, respectively). Finally, Howley and Rosé discuss the complex interactions of theory and method in computational modeling of group learning processes. The issue also features a special section on learning analytics tutorials, edited by Gašević and Pechenizkiy. The editorial concludes with a report of the recent ‘hot spots section’ consultation from the editorial team of the journal.
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer Science and Business Media LLC
Date: 19-01-2012
Publisher: Wiley
Date: 04-06-2015
DOI: 10.1111/JCAL.12107
Publisher: ACM Press
Date: 2016
Publisher: Wiley
Date: 15-02-2012
DOI: 10.1002/SMR.534
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2020
Publisher: ACM
Date: 25-04-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2012
DOI: 10.1109/TLT.2012.9
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Publisher: IEEE
Date: 04-2010
DOI: 10.1109/ITNG.2011.58
Publisher: Informa UK Limited
Date: 25-11-2019
Publisher: Informa UK Limited
Date: 12-03-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Elsevier BV
Date: 02-2022
Publisher: Springer International Publishing
Date: 2012
Publisher: ACM
Date: 13-03-2017
Publisher: IEEE
Date: 12-2011
Publisher: Society for Learning Analytics Research
Date: 17-09-2016
Abstract: This issue of the Journal of Learning Analytics features three special sections that look into topics of learning analytics for 21st century skills, multimodal learning analytics, and sharing of datasets for learning analytics. The issue also features a paper that looks at models for early detection of students at risk in tertiary education. The editorial concludes with a summary of the changes in the editorial team of the journal.
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: McMaster University Library
Date: 10-05-2022
Abstract: An industrial revolution based on digital technologies and data is rapidly transforming most human activities. In the case of higher education, research on learning analytics has experienced significant expansion and evolution in only a few years. However, there is still a dearth of literature on analytics tools designed to support student representatives, which could generate growing informational and technological asymmetries in higher education. To address this critical gap, this study explored the potential key data required by student representatives for their effective participation as partners in educational improvement and the main benefits that associated analytics tools could offer. To do this, this study used a micro design ethnography and a dialogic approach with participants from a Scottish university. Findings suggest that access to data and analytics could influence the participation of the student body as egalitarian partners. These results reinforce the need for further research.
Publisher: Elsevier BV
Date: 2022
Publisher: ACM
Date: 29-04-2012
Publisher: Society for Learning Analytics Research (SoLAR)
Date: 05-2017
DOI: 10.18608/HLA17
Publisher: ACM
Date: 13-03-2023
Publisher: ACM
Date: 21-03-2011
Publisher: Elsevier BV
Date: 11-2015
Publisher: IEEE
Date: 10-2010
Publisher: Elsevier BV
Date: 07-2019
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: 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: ACM
Date: 13-03-2023
Publisher: Society for Learning Analytics Research
Date: 14-03-2017
Abstract: This first issue of the Journal of Learning Analytics in 2017 features a special section of invited papers from the recent Learning Analytics and Knowledge conference (LAK'16). The theme of the conference, and this special section, relates to the need for Learning Analytics research to challenge our methodological and theoretical assumptions and build new interdisciplinary connections to further our thinking.
Publisher: Informa UK Limited
Date: 07-2020
Publisher: Springer International Publishing
Date: 2019
Publisher: ACM
Date: 21-03-2011
Publisher: Wiley
Date: 07-06-2023
DOI: 10.1111/BJET.13343
Publisher: Springer Science and Business Media LLC
Date: 19-03-2016
Publisher: IEEE
Date: 06-2012
DOI: 10.1109/SCC.2012.6
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2019
Publisher: Elsevier BV
Date: 04-2016
Publisher: ACM
Date: 24-03-2014
Publisher: Society for Learning Analytics Research
Date: 13-10-2021
Abstract: The adoption of learning analytics (LA) in complex educational systems is woven into sociocultural and technical challenges that have induced distrust in data and difficulties in scaling LA. This paper presents a study that investigated areas of distrust and threats to trustworthy LA through a series of consultations with teaching staff and students at a large UK university. Surveys and focus groups were conducted to explore participant expectations of LA. The observed distrust is broadly attributed to three areas: the subjective nature of numbers, the fear of power diminution, and approaches to design and implementation of LA. The paper highlights areas to maintain existing trust with policy procedures and areas to cultivate trust by engaging with tensions arising from the social process of LA.
Publisher: IEEE
Date: 09-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Wiley
Date: 10-10-2019
DOI: 10.1111/JCAL.12392
Publisher: Elsevier BV
Date: 06-2015
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Society for Learning Analytics Research
Date: 09-02-2015
Abstract: This issue of the Journal of Learning Analytics comprises two special issue sections. The first of which presents five papers from the 4th International Learning Analytics and Knowledge conference held in Indianapolis. The second showcases the current or recent work of doctoral students who attended the 2nd Learning Analytics Summer Institute at Harvard University, Boston. The issue also includes two articles in the Hot Spots section, discussing the application of learning analytics initiatives in higher education institutions from different perspectives – broad-scale initiatives to in idual course design. The breadth and ersity of the articles covered in this issue demonstrate how the discipline has matured and moved towards understanding student learning to inform pedagogical practice and curricular redesign coupled with strategies for the application and adoption of LA strategies across institutions
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Elsevier BV
Date: 2012
Publisher: ACM Press
Date: 2016
Publisher: Society for Learning Analytics Research
Date: 23-04-2016
Abstract: This issue of the Journal of Learning Analytics features a special section on ethics and privacy that is guest edited by a team of researchers involved in the European Learning Analytics Community Exchange (LACE) project. The issue also features a paper that looks at the use of new methods for the measurement of self-regulated learning. This editorial concludes with a summary of the future changes in the editorial team of the journal.
Publisher: ACM
Date: 13-03-2023
Publisher: IEEE
Date: 09-2013
DOI: 10.1109/EDOCW.2013.5
Publisher: Wiley
Date: 27-04-2023
DOI: 10.1111/BJET.13333
Abstract: Researchers have demonstrated that dialogue‐based intelligent tutoring systems (ITS) can be effective in assisting students in learning. However, little research has attempted to explore the necessity of equipping dialogue‐based ITS with one of the most important capabilities of human tutors, that is, maintaining polite interactions with students, which is essential to provide students with a pleasant learning experience. In this study, we examined the role of politeness by analysing a large‐scale real‐world dataset consisting of over 14K online human–human tutorial dialogues. Specifically, we employed linguistic theories of politeness to characterise the politeness levels of tutor–student‐generated utterances, investigated the correlation between the politeness levels of tutors' utterances and students' problem‐solving performance and quantified the power of politeness in predicting students' problem‐solving performance by applying Gradient Tree Boosting. The study results showed that: (i) in the effective tutorial sessions (ie, sessions in which students successfully solved problems), tutors tended to be very polite at the start of a tutorial session and become more direct to guide students as the session progressed (ii) students with better performance in solving problems tended to be more polite at the beginning and the end of a tutorial session than their counterparts who failed to solve problems (iii) the correlation between tutors' polite expressions and students' performance was not evident in non‐instructional communication and (iv) politeness alone cannot adequately reveal students' problem‐solving performance, and thus other factors (eg, sentiment contained in utterances) should also be taken into account. What is already known about this topic Human–human tutoring is acknowledged as an effective instructional method. Polite expression can help strengthen the relationship between tutors and students. Polite expression can promote students' learning achievements in many educational contexts. What this paper adds By considering the students' prior progress on a problem‐based learning task, we demonstrated the extent to which tutors and students display politeness in tutoring dialogues. Tutors' polite expressions might not correlate with students' problem‐solving performance in online human–human tutoring dialogues. Politeness alone was insufficient to predict the students' performance. Implications for practice Tutors might consider using words with positive sentiment values to express politeness to students with prior progress, which might encourage those students to make a further effort. The polite strategy of expressing indirect requests could help tutors mitigate the sense of directness, but this strategy should be carefully used in delivering instructional hints, especially for students without prior progress. To better assist students without prior progress, tutors might consider using more direct expression to explicitly guide students.
Publisher: ACM
Date: 24-03-2014
Publisher: ACM
Date: 02-09-2012
Publisher: Springer Nature Switzerland
Date: 2022
Publisher: ACM
Date: 13-03-2017
Publisher: Informa UK Limited
Date: 02-01-2017
Publisher: Elsevier BV
Date: 06-2019
Publisher: IEEE
Date: 07-2011
Publisher: ACM Press
Date: 2016
Publisher: Springer Science and Business Media LLC
Date: 2011
Publisher: Elsevier BV
Date: 2021
Publisher: Informa UK Limited
Date: 29-04-2020
Publisher: JMIR Publications Inc.
Date: 04-08-2022
Abstract: igital education has expanded since the COVID-19 pandemic began. A substantial amount of recent data on how students learn has become available for learning analytics (LA). LA denotes the “measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.” his scoping review aimed to examine the use of LA in health care professions education and propose a framework for the LA life cycle. e performed a comprehensive literature search of 10 databases: MEDLINE, Embase, Web of Science, ERIC, Cochrane Library, PsycINFO, CINAHL, ICTP, Scopus, and IEEE Explore. In total, 6 reviewers worked in pairs and performed title, abstract, and full-text screening. We resolved disagreements on study selection by consensus and discussion with other reviewers. We included papers if they met the following criteria: papers on health care professions education, papers on digital education, and papers that collected LA data from any type of digital education platform. e retrieved 1238 papers, of which 65 met the inclusion criteria. From those papers, we extracted some typical characteristics of the LA process and proposed a framework for the LA life cycle, including digital education content creation, data collection, data analytics, and the purposes of LA. Assignment materials were the most popular type of digital education content (47/65, 72%), whereas the most commonly collected data types were the number of connections to the learning materials (53/65, 82%). Descriptive statistics was mostly used in data analytics in 89% (58/65) of studies. Finally, among the purposes for LA, understanding learners’ interactions with the digital education platform was cited most often in 86% (56/65) of papers and understanding the relationship between interactions and student performance was cited in 63% (41/65) of papers. Far less common were the purposes of optimizing learning: the provision of at-risk intervention, feedback, and adaptive learning was found in 11, 5, and 3 papers, respectively. e identified gaps for each of the 4 components of the LA life cycle, with the lack of an iterative approach while designing courses for health care professions being the most prevalent. We identified only 1 instance in which the authors used knowledge from a previous course to improve the next course. Only 2 studies reported that LA was used to detect at-risk students during the course’s run, compared with the overwhelming majority of other studies in which data analysis was performed only after the course was completed.
Publisher: Elsevier BV
Date: 2018
Publisher: Elsevier BV
Date: 10-2020
Publisher: ACM
Date: 13-03-2023
Publisher: Wiley
Date: 23-07-0004
DOI: 10.1111/BJET.12277
Publisher: Elsevier BV
Date: 2015
Publisher: Springer Berlin Heidelberg
Date: 2011
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: IEEE
Date: 10-2010
DOI: 10.1109/EDOC.2010.18
Publisher: Elsevier BV
Date: 11-2013
Publisher: Elsevier BV
Date: 10-2021
Publisher: Society for Learning Analytics Research
Date: 21-08-2014
Abstract: This article introduces the special issue from SoLAR’s Learning Analytics and Knowledge conference. Learning analytics is an emerging field incorporating theory and practice from numerous disciplines to investigate how learner interactions in digital environments can provide actionable data about the learning process. As the field continues to expand there is a timely opportunity to evaluate its ongoing maturation. This evaluation could be in part informed by regular scientometric analyses from both the Journal and Conference publications. These analyses can collectively provide insight into the development of learning analytics more broadly and assist with the allocation of resources to under-represented areas for ex le.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: ACM
Date: 21-08-2011
Publisher: Society for Learning Analytics Research
Date: 05-07-2017
Abstract: The use of analytic methods for extracting learning strategies from trace data has attracted considerable attention in the literature. However, there is a paucity of research examining any association between learning strategies extracted from trace data and responses to well-established self-report instruments and performance scores. This paper focuses on the link between the learning strategies identified in the trace data and student reported approaches to learning. The paper reports on the findings of a study conducted in the scope of an undergraduate engineering course (N=144) that followed a flipped classroom design. The study found that learning strategies extracted from trace data can be interpreted in terms of deep and surface approaches to learning. The detected significant links with self-report measures are with small effect sizes for both the overall deep approach to learning scale and the deep strategy scale. However, there was no observed significance linking the surface approach to learning and surface strategy nor were there significant associations with motivation scales of approaches to learning. The significant effects on academic performance were found, and consistent with the literature that used self-report instruments showing that students who followed a deep approach to learning had a significantly higher performance.
Publisher: Springer International Publishing
Date: 2018
Publisher: Elsevier BV
Date: 10-2023
Publisher: Springer International Publishing
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer International Publishing
Date: 2019
Publisher: ACM
Date: 13-03-2023
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Informa UK Limited
Date: 13-05-2020
Publisher: Society for Learning Analytics Research
Date: 06-02-2014
Abstract: This section offers a compilation of 16 extended abstracts summarizing research of the doctoral students who participated in the Second Learning Analytics Summer Institute (LASI 2014) held at Harvard University in July 2014. The abstracts highlight the motivation, main goals and expected contributions to the field from the ongoing learning analytics doctoral research around the globe. These works cover several major topics in learning analytics including novel methods for automated annotations, longitudinal analytic studies, networking analytics, multi-modal analytics, dashboards, and data-driven feedback and personalization. The assumed settings include the traditional classroom, online and mobile learning, blended learning, and massive open online course education models.
Publisher: IEEE
Date: 08-2011
Publisher: IEEE
Date: 08-2011
Publisher: Society for Learning Analytics Research
Date: 09-04-2021
Abstract: Using data to generate a deeper understanding of collaborative learning is not new, but automatically analyzing log data has enabled new means of identifying key indicators of effective collaboration and teamwork that can be used to predict outcomes and personalize feedback. Collaboration analytics is emerging as a new term to refer to computational methods for identifying salient aspects of collaboration from multiple group data sources for learners, educators, or other stakeholders to gain and act upon insights. Yet, it remains unclear how collaboration analytics go beyond previous work focused on modelling group interactions for the purpose of adapting instruction. This paper provides a conceptual model of collaboration analytics to help researchers and designers identify the opportunities enabled by such innovations to advance knowledge in, and provide enhanced support for, collaborative learning and teamwork. We argue that mapping from low-level data to higher-order constructs that are educationally meaningful, and that can be understood by educators and learners, is essential to assessing the validity of collaboration analytics. Through four cases, the paper illustrates the critical role of theory, task design, and human factors in the design of interfaces that inform actionable insights for improving collaboration and group learning.
Publisher: Springer New York
Date: 04-09-2014
Publisher: Springer Science and Business Media LLC
Date: 15-08-2012
Publisher: Springer International Publishing
Date: 2021
Publisher: Wiley
Date: 18-06-2019
DOI: 10.1111/JCAL.12366
Publisher: Wiley
Date: 07-03-2023
DOI: 10.1111/JCAL.12802
Abstract: Learning Analytics (LA) is an emerging field concerned with measuring, collecting, and analysing data about learners and their contexts to gain insights into learning processes. As the technology of Learning Analytics is evolving, many systems are being implemented. In this context, it is essential to understand stakeholders' expectations of LA across Higher Education Institutions (HEIs) for large‐scale implementations that take their needs into account. This study aims to contribute to knowledge about in idual LA expectations of European higher education students. It may facilitate the strategy of stakeholder buy‐in, the transfer of LA insights across HEIs, and the development of international best practices and guidelines. To this end, the study employs a ‘Student Expectations of Learning Analytics Questionnaire’ (SELAQ) survey of 417 students at the Goethe University Frankfurt (Germany) Based on this data, Multiple Linear Regressions are applied to determine how these students position themselves compared to students from Madrid (Spain), Edinburgh (United Kingdom) and the Netherlands, where SELAQ had already been implemented at HEIs. The results show that students' expectations at Goethe University Frankfurt are rather homogeneous regarding ‘LA Ethics and Privacy’ and ‘LA Service Features’. Furthermore, we found that European students generally show a consistent pattern of expectations of LA with a high degree of similarity across the HEIs examined. European HEIs face challenges more similar than anticipated. The HEI experience with implementing LA can be more easily transferred to other HEIs, suggesting standardized LA rather than tailor‐made solutions designed from scratch.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2023
Publisher: Elsevier BV
Date: 10-2015
Location: United Kingdom of Great Britain and Northern Ireland
Start Date: 07-2021
End Date: 08-2024
Amount: $229,171.00
Funder: Australian Research Council
View Funded ActivityStart Date: 08-2022
End Date: 07-2025
Amount: $389,011.00
Funder: Australian Research Council
View Funded Activity