The Experts below are selected from a list of 34989 Experts worldwide ranked by ideXlab platform
Sarah K. Howard - One of the best experts on this subject based on the ideXlab platform.
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Investigating live streaming data for Student Behaviour modelling
2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017Co-Authors: Jie Yang, Sarah K. HowardAbstract:Modelling technology integration in the teaching and learning environment is a complex, uncertain and dynamic practice. A large amount of Student Behaviour data has been gathered literately for different processing purposes. Yet, considerable questions are still remaining due to the huge data volume, diversification and uncertainty. In this work, we implement a big-data analytical framework for online Behaviour modelling, particularly taking streaming data of Students' online activity from their laptop usage as an illustrative example. The proposed framework covers details from accessing streaming records to storing heterogeneous data. Furthermore, the work also demonstrates the use of a TF-IDF based feature generation and fuzzy representation strategy to discover critical patterns via this Behaviour data. The accuracy of the modelling work is evaluated using Students' score on a national-wide test. Experimental results show that the employed TF-IDF feature is much stabler than other traditional features, thereby achieving a better modelling performance. In summary, the simulation result demonstrates the flexibility and applicability of the proposed framework for processing complex Behaviour data, and revealing important patterns for decision making.
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FUZZ-IEEE - Investigating live streaming data for Student Behaviour modelling
2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017Co-Authors: Jie Yang, Sarah K. HowardAbstract:Modelling technology integration in the teaching and learning environment is a complex, uncertain and dynamic practice. A large amount of Student Behaviour data has been gathered literately for different processing purposes. Yet, considerable questions are still remaining due to the huge data volume, diversification and uncertainty. In this work, we implement a big-data analytical framework for online Behaviour modelling, particularly taking streaming data of Students' online activity from their laptop usage as an illustrative example. The proposed framework covers details from accessing streaming records to storing heterogeneous data. Furthermore, the work also demonstrates the use of a TF-IDF based feature generation and fuzzy representation strategy to discover critical patterns via this Behaviour data. The accuracy of the modelling work is evaluated using Students' score on a national-wide test. Experimental results show that the employed TF-IDF feature is much stabler than other traditional features, thereby achieving a better modelling performance. In summary, the simulation result demonstrates the flexibility and applicability of the proposed framework for processing complex Behaviour data, and revealing important patterns for decision making.
Lisette Toetenel - One of the best experts on this subject based on the ideXlab platform.
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the impact of learning design on Student Behaviour satisfaction and performance
Computers in Human Behavior, 2016Co-Authors: Bart Rienties, Lisette ToetenelAbstract:Pedagogically informed designs of learning are increasingly of interest to researchers in blended and online learning, as learning design is shown to have an impact on Student Behaviour and outcomes. Although learning design is widely studied, often these studies are individual courses or programmes and few empirical studies have connected learning designs of a substantial number of courses with learning Behaviour. In this study we linked 151 modules and 111.256 Students with Students' Behaviour (<400 million minutes of online Behaviour), satisfaction and performance at the Open University UK using multiple regression models. Our findings strongly indicate the importance of learning design in predicting and understanding Virtual Learning Environment Behaviour and performance of Students in blended and online environments. In line with proponents of social learning theories, our primary predictor for academic retention was the time learners spent on communication activities, controlling for various institutional and disciplinary factors. Where possible, appropriate and well designed communication tasks that align with the learning objectives of the course may be a way forward to enhance academic retention. Pedagogically informed learning designs (LD) are increasingly of interest.Few empirical studies have connected LD with Behaviour, satisfaction and retention.Using regression analyses we linked LDs of 151 modules and 111?K Students.LD has strong impact on Behaviour, satisfaction, and performance.Primary predictor for academic retention was communication activities.
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The impact of learning design on Student Behaviour, satisfaction and performance
Computers in Human Behavior, 2016Co-Authors: Bart Rienties, Lisette ToetenelAbstract:Pedagogically informed designs of learning are increasingly of interest to researchers in blended and online learning, as learning design is shown to have an impact on Student Behaviour and outcomes. Although learning design is widely studied, often these studies are individual courses or programmes and few empirical studies have connected learning designs of a substantial number of courses with learning Behaviour. In this study we linked 151 modules and 111.256 Students with Students' Behaviour (
L. Horodyskyj - One of the best experts on this subject based on the ideXlab platform.
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Analysis of Student Behaviour in "Habitable" Worlds Using Continuous Representation Visualization.
Journal of Learning Analytics, 2019Co-Authors: Zachary A. Pardos, L. HorodyskyjAbstract:We introduce a novel approach to visualizing temporal clickstream Behaviour in the context of a degree-satisfying online course, Habitable Worlds, offered through Arizona State University. The current practice for visualizing Behaviour within a digital learning environment is to generate plots based on hand-engineered or coded features using domain knowledge. While this approach has been effective in relating Behaviour to known phenomena, features crafted from domain knowledge are not likely well suited to making unfamiliar phenomena salient and thus can preclude discovery. We introduce a methodology for organically surfacing Behavioural regularities from clickstream data, conducting an expert in-the-loop hyperparameter search, and identifying anticipated as well as newly discovered patterns of Behaviour. While these visualization techniques have been used before in the broader machine-learning community to better understand neural networks and relationships between word vectors, we apply them to online Behavioural learner data and go a step further, exploring the impact of the parameters of the model on producing tangible, non-trivial observations of Behaviour that suggest pedagogical improvement to the course designers and instructors. The methodology introduced in this paper led to an improved understanding of passing and non-passing Student Behaviour in the course and is applicable to other datasets of clickstream activity where investigators and stakeholders wish to organically surface principal patterns of Behaviour.
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analysis of Student Behaviour in habitable worlds using continuous representation visualization
arXiv: Human-Computer Interaction, 2017Co-Authors: Zachary A. Pardos, L. HorodyskyjAbstract:We introduce a novel approach to visualizing temporal clickstream Behaviour in the context of a degree-satisfying online course, Habitable Worlds, offered through Arizona State University. The current practice for visualizing Behaviour within a digital learning environment has been to utilize state space graphs and other plots of descriptive statistics on resource transitions. While these forms can be visually engaging, they rely on conditional frequency tabulations which lack contextual depth and require assumptions about the patterns being sought. Skip-grams and other representation learning techniques position elements into a vector space which can capture a wide scope of regularities in the data. These regularities can then be projected onto a two-dimensional perceptual space using dimensionality reduction techniques designed to retain relationships information encoded in the learned representations. While these visualization techniques have been used before in the broader machine learning community to better understand the makeup of a neural network hidden layer or the relationship between word vectors, we apply them to online behavioral learner data and go a step further; exploring the impact of the parameters of the model on producing tangible, non-trivial observations of Behaviour that are illuminating and suggestive of pedagogical improvement to the course designers and instructors. The methodology introduced in this paper led to an improved understanding of passing and non-passing Student behavior in the course and is widely applicable to other datasets of clickstream activity where investigators and stakeholders wish to organically surface principal behavioral patterns.
Jie Yang - One of the best experts on this subject based on the ideXlab platform.
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Investigating live streaming data for Student Behaviour modelling
2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017Co-Authors: Jie Yang, Sarah K. HowardAbstract:Modelling technology integration in the teaching and learning environment is a complex, uncertain and dynamic practice. A large amount of Student Behaviour data has been gathered literately for different processing purposes. Yet, considerable questions are still remaining due to the huge data volume, diversification and uncertainty. In this work, we implement a big-data analytical framework for online Behaviour modelling, particularly taking streaming data of Students' online activity from their laptop usage as an illustrative example. The proposed framework covers details from accessing streaming records to storing heterogeneous data. Furthermore, the work also demonstrates the use of a TF-IDF based feature generation and fuzzy representation strategy to discover critical patterns via this Behaviour data. The accuracy of the modelling work is evaluated using Students' score on a national-wide test. Experimental results show that the employed TF-IDF feature is much stabler than other traditional features, thereby achieving a better modelling performance. In summary, the simulation result demonstrates the flexibility and applicability of the proposed framework for processing complex Behaviour data, and revealing important patterns for decision making.
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FUZZ-IEEE - Investigating live streaming data for Student Behaviour modelling
2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017Co-Authors: Jie Yang, Sarah K. HowardAbstract:Modelling technology integration in the teaching and learning environment is a complex, uncertain and dynamic practice. A large amount of Student Behaviour data has been gathered literately for different processing purposes. Yet, considerable questions are still remaining due to the huge data volume, diversification and uncertainty. In this work, we implement a big-data analytical framework for online Behaviour modelling, particularly taking streaming data of Students' online activity from their laptop usage as an illustrative example. The proposed framework covers details from accessing streaming records to storing heterogeneous data. Furthermore, the work also demonstrates the use of a TF-IDF based feature generation and fuzzy representation strategy to discover critical patterns via this Behaviour data. The accuracy of the modelling work is evaluated using Students' score on a national-wide test. Experimental results show that the employed TF-IDF feature is much stabler than other traditional features, thereby achieving a better modelling performance. In summary, the simulation result demonstrates the flexibility and applicability of the proposed framework for processing complex Behaviour data, and revealing important patterns for decision making.
Shane Dawson - One of the best experts on this subject based on the ideXlab platform.
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seeing the learning community an exploration of the development of a resource for monitoring online Student networking
British Journal of Educational Technology, 2010Co-Authors: Shane DawsonAbstract:The trend to adopt more online technologies continues unabated in the higher education sector. This paper elaborates the means by which such technologies can be employed for pedagogical purposes beyond simply providing virtual spaces for bringing learners together. It shows how data about Student ‘movement’ within and across a learning community can be captured and analysed for the purposes of making strategic interventions in the learning of ‘at risk’ Students in particular, through the application of social network analysis to the engagement data. The study that is set out in the paper indicates that online technologies bring with them an unprecedented opportunity for educators to visualise changes in Student Behaviour and their learning network composition, including the interventions teachers make in those networks over time. To date, these evaluative opportunities have been beyond the reach of the everyday practitioner—they can now be integrated into every teaching and learning plan. [ABSTRACT FROM AUTHOR]
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Social networks adapting pedagogical practice
2009Co-Authors: Aneesha Bakharia, Shane DawsonAbstract:This poster details the development of a tool that integrates with common commercial and open source Learning Management Systems (LMS) to deliver real-time social network visualisations of discussion forum activity. The tool has aptly been named Social Networks Adapting Pedagogical Practice (SNAPP), because it allows academic staff to identify patterns of Student Behaviour and facilitate appropriate interventions as required. SNAPP has been designed with the aim of mainstreaming the use of social network analysis as a real-time diagnostic instrument. A unique and non-conventional Web 2.0 approach to LMS extension development has been employed to enable cross system (Blackboard, WebCT and Moodle), web browser (Firefox, Safari and Internet Explorer) and platform (PC and Mac) support. Current features and future enhancements are outlined
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The impact of institutional surveillance technologies on Student Behaviour
Surveillance & Society, 2002Co-Authors: Shane DawsonAbstract:Contemporary education institutions are increasingly investing fiscal and human resources to further develop their online infrastructure in order to enhance flexible learning options and the overall Student learning experience. Coinciding with the implementation of these technologies has been the centralisation of data and the emergence of online activities that have afforded the capacity for more intimate modes of surveillance by both the institution and education practitioner. This study offers an initial investigation into the impact of such modes of surveillance on Student Behaviours. Both internal and external Students surveyed indicated that their browsing Behaviours, the range of topics discussed and the writing style of their contributions made to asynchronous discussion forums are influenced by the degree to which such activities are perceived to be surveyed by both the institution and teaching staff. The analyses deriving from this data are framed within Foucault’s works on surveillance and self governance. This paper discusses the implications of this new mode of governance for learning and teaching and suggests areas of further investigations.
