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Marelie Davel is a Research Professor in the Faculty of Engineering of North-West University (NWU), and the Director of the Multilingual Speech Technologies (MuST) research group. Building on a long-standing interest in the theory and application of machine learning, her current research focuses on the generalisation ability of deep neural networks.
Marelie obtained her undergraduate degrees (Computer Science & Mathematics) from Stellenbosch University, receiving the Dean’s medal as best student in the US Faculty of Science at the end of her Honours degree. Prior to joining NWU, Marelie was a principal researcher and research group leader at the South African CSIR, involved in technology-oriented research and development. Her research group focussed on speech technology development in under-resourced environments; in 2005, she received her PhD from the University of Pretoria (UP), with a thesis on bootstrapping pronunciation models, at the time one of the core ‘missing’ components when developing speech recognition for South African languages.
In 2011, Marelie joined NWU, becoming the Director of MuST in 2014. MuST is a focussed research environment with an emphasis on postgraduate training and delivering on externally-focussed projects. Recent projects include the development of an automatic speech transcription platform for the South African government, development of a new multilingual text-to-speech corpus in collaboration with Internet giant Google, and being part of the winning consortium of the BABEL project: a 5-year internationally collaborative challenge aimed at solving the spoken term detection task for under-resourced languages.
Over the past few years, Marelie has supervised 23 post-graduate students, all producing research related to the theory and applications of machine learning. She frequently participates in various scientific committees both nationally and internationally (AAAI, IJCAI, Interspeech, SLT, MediaEval, ICASSP, SLTU), is the NWU group representative at the national Centre for Artificial Intelligence Research (CAIR), and an NRF-rated researcher. Since 2003, she has published 100 peer-reviewed papers related to machine learning; she has an h-index of 21, and an i10-index of 37.
Marelie obtained her undergraduate degrees (Computer Science & Mathematics) from Stellenbosch University, receiving the Dean’s medal as best student in the US Faculty of Science at the end of her Honours degree. Prior to joining NWU, Marelie was a principal researcher and research group leader at the South African CSIR, involved in technology-oriented research and development. Her research group focussed on speech technology development in under-resourced environments; in 2005, she received her PhD from the University of Pretoria (UP), with a thesis on bootstrapping pronunciation models, at the time one of the core ‘missing’ components when developing speech recognition for South African languages.
In 2011, Marelie joined NWU, becoming the Director of MuST in 2014. MuST is a focussed research environment with an emphasis on postgraduate training and delivering on externally-focussed projects. Recent projects include the development of an automatic speech transcription platform for the South African government, development of a new multilingual text-to-speech corpus in collaboration with Internet giant Google, and being part of the winning consortium of the BABEL project: a 5-year internationally collaborative challenge aimed at solving the spoken term detection task for under-resourced languages.
Over the past few years, Marelie has supervised 23 post-graduate students, all producing research related to the theory and applications of machine learning. She frequently participates in various scientific committees both nationally and internationally (AAAI, IJCAI, Interspeech, SLT, MediaEval, ICASSP, SLTU), is the NWU group representative at the national Centre for Artificial Intelligence Research (CAIR), and an NRF-rated researcher. Since 2003, she has published 100 peer-reviewed papers related to machine learning; she has an h-index of 21, and an i10-index of 37.
Latest Research Publications:
1.
Heymans W, Davel MH, Van Heerden CJ. Efficient acoustic feature transformation in mismatched environments using a Guided-GAN. Speech Communication. 2022;143. doi:https://doi.org/10.1016/j.specom.2022.07.002.
We propose a new framework to improve automatic speech recognition (ASR) systems in resource-scarce environments using a generative adversarial network (GAN) operating on acoustic input features. The GAN is used to enhance the features of mismatched data prior to decoding, or can optionally be used to fine-tune the acoustic model. We achieve improvements that are comparable to multi-style training (MTR), but at a lower computational cost. With less than one hour of data, an ASR system trained on good quality data, and evaluated on mismatched audio is improved by between 11.5% and 19.7% relative word error rate (WER). Experiments demonstrate that the framework can be very useful in under-resourced environments where training data and computational resources are limited. The GAN does not require parallel training data, because it utilises a baseline acoustic model to provide an additional loss term that guides the generator to create acoustic features that are better classified by the baseline.
@article{492,
author = {Walter Heymans, Marelie Davel, Charl Van Heerden},
title = {Efficient acoustic feature transformation in mismatched environments using a Guided-GAN},
abstract = {We propose a new framework to improve automatic speech recognition (ASR) systems in resource-scarce environments using a generative adversarial network (GAN) operating on acoustic input features. The GAN is used to enhance the features of mismatched data prior to decoding, or can optionally be used to fine-tune the acoustic model. We achieve improvements that are comparable to multi-style training (MTR), but at a lower computational cost. With less than one hour of data, an ASR system trained on good quality data, and evaluated on mismatched audio is improved by between 11.5% and 19.7% relative word error rate (WER). Experiments demonstrate that the framework can be very useful in under-resourced environments where training data and computational resources are limited. The GAN does not require parallel training data, because it utilises a baseline acoustic model to provide an additional loss term that guides the generator to create acoustic features that are better classified by the baseline.},
year = {2022},
journal = {Speech Communication},
volume = {143},
pages = {10 - 20},
month = {09/2022},
doi = {https://doi.org/10.1016/j.specom.2022.07.002},
}
1.
Oosthuizen AJ, Davel MH, Helberg A. Multi-Layer Perceptron for Channel State Information Estimation: Design Considerations. In: Southern Africa Telecommunication Networks and Applications Conference (SATNAC). Fancourt, George; 2022.
The accurate estimation of channel state information (CSI) is an important aspect of wireless communications. In this paper, a multi-layer perceptron (MLP) is developed as a CSI estimator in long-term evolution (LTE) transmission conditions. The representation of the CSI data is investigated in conjunction with batch normalisation and the representational ability of MLPs. It is found that discontinuities in the representational feature space can cripple an MLP’s ability to accurately predict CSI when noise is present. Different ways in which to mitigate this effect are analysed and a solution developed, initially in the context of channels that are only affected by additive white
Guassian noise. The developed architecture is then applied to more complex channels with various delay profiles and Doppler spread. The performance of the proposed MLP is shown to be comparable with LTE minimum mean squared error (MMSE), and to outperform least square (LS) estimation over a range of channel conditions.
@{491,
author = {Andrew Oosthuizen, Marelie Davel, Albert Helberg},
title = {Multi-Layer Perceptron for Channel State Information Estimation: Design Considerations},
abstract = {The accurate estimation of channel state information (CSI) is an important aspect of wireless communications. In this paper, a multi-layer perceptron (MLP) is developed as a CSI estimator in long-term evolution (LTE) transmission conditions. The representation of the CSI data is investigated in conjunction with batch normalisation and the representational ability of MLPs. It is found that discontinuities in the representational feature space can cripple an MLP’s ability to accurately predict CSI when noise is present. Different ways in which to mitigate this effect are analysed and a solution developed, initially in the context of channels that are only affected by additive white
Guassian noise. The developed architecture is then applied to more complex channels with various delay profiles and Doppler spread. The performance of the proposed MLP is shown to be comparable with LTE minimum mean squared error (MMSE), and to outperform least square (LS) estimation over a range of channel conditions.},
year = {2022},
journal = {Southern Africa Telecommunication Networks and Applications Conference (SATNAC)},
pages = {94 - 99},
month = {08/2022},
address = {Fancourt, George},
}
1.
Modipa T, Davel MH. Two Sepedi‑English code‑switched speech corpora. Language Resources and Evaluation. 2022;56. doi:https://doi.org/10.1007/s10579-022-09592-6 (Read here: https://rdcu.be/cO6lD).
We report on the development of two reference corpora for the analysis of SepediEnglish code-switched speech in the context of automatic speech recognition. For the first corpus, possible English events were obtained from an existing corpus of transcribed Sepedi-English speech. The second corpus is based on the analysis of radio broadcasts: actual instances of code switching were transcribed and reproduced by a number of native Sepedi speakers. We describe the process to develop and verify both corpora and perform an initial analysis of the newly produced data sets. We find that, in naturally occurring speech, the frequency of code switching is unexpectedly high for this language pair, and that the continuum of code switching (from unmodified embedded words to loanwords absorbed into the matrix language) makes this a particularly challenging task for speech recognition systems.
@article{483,
author = {Thipe Modipa, Marelie Davel},
title = {Two Sepedi‑English code‑switched speech corpora},
abstract = {We report on the development of two reference corpora for the analysis of SepediEnglish code-switched speech in the context of automatic speech recognition. For the first corpus, possible English events were obtained from an existing corpus of transcribed Sepedi-English speech. The second corpus is based on the analysis of radio broadcasts: actual instances of code switching were transcribed and reproduced by a number of native Sepedi speakers. We describe the process to develop and verify both corpora and perform an initial analysis of the newly produced data sets. We find that, in naturally occurring speech, the frequency of code switching is unexpectedly high for this language pair, and that the continuum of code switching (from unmodified embedded words to loanwords absorbed into the matrix language) makes this a particularly challenging task for speech recognition systems.},
year = {2022},
journal = {Language Resources and Evaluation},
volume = {56},
pages = {https://rdcu.be/cO6lD)},
publisher = {Springer},
address = {South Africa},
url = {https://rdcu.be/cO6lD},
doi = {https://doi.org/10.1007/s10579-022-09592-6 (Read here: https://rdcu.be/cO6lD)},
}
1.
Heymans W, Davel MH, Van Heerden CJ. Multi-style Training for South African Call Centre Audio. Communications in Computer and Information Science. 2022;1551. doi:https://doi.org/10.1007/978-3-030-95070-5_8.
Mismatched data is a challenging problem for automatic speech recognition (ASR) systems. One of the most common techniques used to address mismatched data is multi-style training (MTR), a form of data augmentation that attempts to transform the training data to be more representative of the testing data; and to learn robust representations applicable to different conditions. This task can be very challenging if the test conditions are unknown. We explore the impact of different MTR styles on system performance when testing conditions are different from training conditions in the context of deep neural network hidden Markov model (DNN-HMM) ASR systems. A controlled environment is created using the LibriSpeech corpus, where we isolate the effect of different MTR styles on final system performance. We evaluate our findings on a South African call centre dataset that contains noisy, WAV49-encoded audio.
@article{480,
author = {Walter Heymans, Marelie Davel, Charl Van Heerden},
title = {Multi-style Training for South African Call Centre Audio},
abstract = {Mismatched data is a challenging problem for automatic speech recognition (ASR) systems. One of the most common techniques used to address mismatched data is multi-style training (MTR), a form of data augmentation that attempts to transform the training data to be more representative of the testing data; and to learn robust representations applicable to different conditions. This task can be very challenging if the test conditions are unknown. We explore the impact of different MTR styles on system performance when testing conditions are different from training conditions in the context of deep neural network hidden Markov model (DNN-HMM) ASR systems. A controlled environment is created using the LibriSpeech corpus, where we isolate the effect of different MTR styles on final system performance. We evaluate our findings on a South African call centre dataset that contains noisy, WAV49-encoded audio.},
year = {2022},
journal = {Communications in Computer and Information Science},
volume = {1551},
pages = {111 - 124},
publisher = {Southern African Conference for Artificial Intelligence Research},
address = {South Africa},
doi = {https://doi.org/10.1007/978-3-030-95070-5_8},
}
1.
Mouton C, Davel MH. Exploring layerwise decision making in DNNs. Communications in Computer and Information Science. 2022;1551. doi:https://doi.org/10.1007/978-3-030-95070-5_10.
While deep neural networks (DNNs) have become a standard architecture for many machine learning tasks, their internal decision-making process and general interpretability is still poorly understood. Conversely, common decision trees are easily interpretable and theoretically well understood. We show that by encoding the discrete sample activation values of nodes as a binary representation, we are able to extract a decision tree explaining the classification procedure of each layer in a ReLU-activated multilayer perceptron (MLP). We then combine these decision trees with existing feature attribution techniques in order to produce an interpretation of each layer of a model. Finally, we provide an analysis of the generated interpretations, the behaviour of the binary encodings and how these relate to sample groupings created during the training process of the neural network.
@article{479,
author = {Coenraad Mouton, Marelie Davel},
title = {Exploring layerwise decision making in DNNs},
abstract = {While deep neural networks (DNNs) have become a standard architecture for many machine learning tasks, their internal decision-making process and general interpretability is still poorly understood. Conversely, common decision trees are easily interpretable and theoretically well understood. We show that by encoding the discrete sample activation values of nodes as a binary representation, we are able to extract a decision tree explaining the classification procedure of each layer in a ReLU-activated multilayer perceptron (MLP). We then combine these decision trees with existing feature attribution techniques in order to produce an interpretation of each layer of a model. Finally, we provide an analysis of the generated interpretations, the behaviour of the binary encodings and how these relate to sample groupings created during the training process of the neural network.},
year = {2022},
journal = {Communications in Computer and Information Science},
volume = {1551},
pages = {140 - 155},
publisher = {Artificial Intelligence Research (SACAIR 2021)},
doi = {https://doi.org/10.1007/978-3-030-95070-5_10},
}
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Alta de Waal is a senior lecturer at the Department of Statistics at UP and has 19 years’ experience in Statistics and Artificial Intelligence. Her research focuses on causal inference methods such as Bayesian networks modelling as well as natural language processing (NLP). The application domains of her research include environmental and ecological modelling, risk management and short text modelling.
Amongst her other qualifications, she holds a PhD in Engineering Science from the North West University, South Africa.
Amongst her other qualifications, she holds a PhD in Engineering Science from the North West University, South Africa.
Latest Research Publications:
1.
Wolpe Z, de Waal A. Autoencoding variational Bayes for latent Dirichlet allocation. In: Proceedings of the South African Forum for Artificial Intelligence Research. CEUR Workshop Proceedings; 2019. http://ceur-ws.org/Vol-2540/FAIR2019_paper_33.pdf.
Many posterior distributions take intractable forms and thus
require variational inference where analytical solutions cannot be found.
Variational Inference and Monte Carlo Markov Chains (MCMC) are established mechanism to approximate these intractable values. An alternative approach to sampling and optimisation for approximation is a direct mapping between the data and posterior distribution. This is made
possible by recent advances in deep learning methods. Latent Dirichlet
Allocation (LDA) is a model which offers an intractable posterior of this
nature. In LDA latent topics are learnt over unlabelled documents to
soft cluster the documents. This paper assesses the viability of learning
latent topics leveraging an autoencoder (in the form of Autoencoding
variational Bayes) and compares the mimicked posterior distributions to
that achieved by VI. After conducting various experiments the proposed
AEVB delivers inadequate performance. Under Utopian conditions comparable conclusion are achieved which are generally unattainable. Further, model specification becomes increasingly complex and deeply circumstantially dependant - which is in itself not a deterrent but does warrant consideration. In a recent study, these concerns were highlighted and
discussed theoretically. We confirm the argument empirically by dissecting the autoencoder’s iterative process. In investigating the autoencoder,
we see performance degrade as models grow in dimensionality. Visualization of the autoencoder reveals a bias towards the initial randomised
topics.
@{254,
author = {Zach Wolpe, Alta de Waal},
title = {Autoencoding variational Bayes for latent Dirichlet allocation},
abstract = {Many posterior distributions take intractable forms and thus
require variational inference where analytical solutions cannot be found.
Variational Inference and Monte Carlo Markov Chains (MCMC) are established mechanism to approximate these intractable values. An alternative approach to sampling and optimisation for approximation is a direct mapping between the data and posterior distribution. This is made
possible by recent advances in deep learning methods. Latent Dirichlet
Allocation (LDA) is a model which offers an intractable posterior of this
nature. In LDA latent topics are learnt over unlabelled documents to
soft cluster the documents. This paper assesses the viability of learning
latent topics leveraging an autoencoder (in the form of Autoencoding
variational Bayes) and compares the mimicked posterior distributions to
that achieved by VI. After conducting various experiments the proposed
AEVB delivers inadequate performance. Under Utopian conditions comparable conclusion are achieved which are generally unattainable. Further, model specification becomes increasingly complex and deeply circumstantially dependant - which is in itself not a deterrent but does warrant consideration. In a recent study, these concerns were highlighted and
discussed theoretically. We confirm the argument empirically by dissecting the autoencoder’s iterative process. In investigating the autoencoder,
we see performance degrade as models grow in dimensionality. Visualization of the autoencoder reveals a bias towards the initial randomised
topics.},
year = {2019},
journal = {Proceedings of the South African Forum for Artificial Intelligence Research},
pages = {25-36},
month = {12/09},
publisher = {CEUR Workshop Proceedings},
isbn = {1613-0073},
url = {http://ceur-ws.org/Vol-2540/FAIR2019_paper_33.pdf},
}
1.
Weyer VD, de Waal A, Lechner AM, Unger CJ, O’Connor TG, Baumgartl T, Schulze R, Truter WF. Quantifying rehabilitation risks for surface‐strip coal mines using a soil compaction Bayesian network in South Africa and Australia: To demonstrate the R2AIN Framework. Integrated Environmental Assessment and Management. 2019;15(2). doi:10.1002/ieam.4128.
Environmental information is acquired and assessed during the environmental impact assessment process for surface‐strip coal mine approval. However, integrating these data and quantifying rehabilitation risk using a holistic multidisciplinary approach is seldom undertaken. We present a rehabilitation risk assessment integrated network (R2AIN™) framework that can be applied using Bayesian networks (BNs) to integrate and quantify such rehabilitation risks. Our framework has 7 steps, including key integration of rehabilitation risk sources and the quantification of undesired rehabilitation risk events to the final application of mitigation. We demonstrate the framework using a soil compaction BN case study in the Witbank Coalfield, South Africa and the Bowen Basin, Australia. Our approach allows for a probabilistic assessment of rehabilitation risk associated with multidisciplines to be integrated and quantified. Using this method, a site's rehabilitation risk profile can be determined before mining activities commence and the effects of manipulating management actions during later mine phases to reduce risk can be gauged, to aid decision making
@article{253,
author = {Vanessa Weyer, Alta de Waal, Alex Lechner, Corinne Unger, Tim O'Connor, Thomas Baumgartl, Roland Schulze, Wayne Truter},
title = {Quantifying rehabilitation risks for surface‐strip coal mines using a soil compaction Bayesian network in South Africa and Australia: To demonstrate the R2AIN Framework},
abstract = {Environmental information is acquired and assessed during the environmental impact assessment process for surface‐strip coal mine approval. However, integrating these data and quantifying rehabilitation risk using a holistic multidisciplinary approach is seldom undertaken. We present a rehabilitation risk assessment integrated network (R2AIN™) framework that can be applied using Bayesian networks (BNs) to integrate and quantify such rehabilitation risks. Our framework has 7 steps, including key integration of rehabilitation risk sources and the quantification of undesired rehabilitation risk events to the final application of mitigation. We demonstrate the framework using a soil compaction BN case study in the Witbank Coalfield, South Africa and the Bowen Basin, Australia. Our approach allows for a probabilistic assessment of rehabilitation risk associated with multidisciplines to be integrated and quantified. Using this method, a site's rehabilitation risk profile can be determined before mining activities commence and the effects of manipulating management actions during later mine phases to reduce risk can be gauged, to aid decision making},
year = {2019},
journal = {Integrated Environmental Assessment and Management},
volume = {15},
pages = {190-208},
issue = {2},
publisher = {Wiley Online},
doi = {10.1002/ieam.4128},
}
1.
de Waal A, Yoo K. Latent Variable Bayesian Networks Constructed Using Structural Equation Modelling. In: 2018 21st International Conference on Information Fusion (FUSION). IEEE; 2018. https://ieeexplore.ieee.org/abstract/document/8455240.
Bayesian networks in fusion systems often contain latent variables. They play an important role in fusion systems as they provide context which lead to better choices of data sources to fuse. Latent variables in Bayesian networks are mostly constructed by means of expert knowledge modelling.We propose using theory-driven structural equation modelling (SEM) to identify and structure latent variables in a Bayesian network. The linking of SEM and Bayesian networks is motivated by the fact that both methods can be shown to be causal models. We compare this approach to a data-driven approach where latent factors are induced by means of unsupervised learning. We identify appropriate metrics for URREF ontology criteria for both approaches.
@{204,
author = {Alta de Waal, Keunyoung Yoo},
title = {Latent Variable Bayesian Networks Constructed Using Structural Equation Modelling},
abstract = {Bayesian networks in fusion systems often contain latent variables. They play an important role in fusion systems as they provide context which lead to better choices of data sources to fuse. Latent variables in Bayesian networks are mostly constructed by means of expert knowledge modelling.We propose using theory-driven structural equation modelling (SEM) to identify and structure latent variables in a Bayesian network. The linking of SEM and Bayesian networks is motivated by the fact that both methods can be shown to be causal models. We compare this approach to a data-driven approach where latent factors are induced by means of unsupervised learning. We identify appropriate metrics for URREF ontology criteria for both approaches.},
year = {2018},
journal = {2018 21st International Conference on Information Fusion (FUSION)},
pages = {688-695},
month = {10/07-13/07},
publisher = {IEEE},
isbn = {978-0-9964527-6-2},
url = {https://ieeexplore.ieee.org/abstract/document/8455240},
}
1.
de Waal A, Koen H, de Villiers J, Roodt H. An expert-driven causal model of the rhino poaching problem. Ecological Modelling. 2017;347. https://www.sciencedirect.com/science/article/pii/S0304380016307621.
A significant challenge in ecological modelling is the lack of complete sets of high-quality data. This is especially true in the rhino poaching problem where data is incomplete. Although there are many poaching attacks, they can be spread over a vast surface area such as in the case of the Kruger National Park in South Africa, which is roughly the size of Israel. Bayesian networks are useful reasoning tools and can utilise expert knowledge when data is insufficient or sparse. Bayesian networks allow the modeller to incorporate data, expert knowledge, or any combination of the two. This flexibility of Bayesian networks makes them ideal for modelling complex ecological problems. In this paper an expert-driven model of the rhino poaching problem is presented. The development as well as the evaluation of the model is performed from an expert perspective. Independent expert evaluation is performed in the form of queries that test different scenarios. Structuring the rhino poaching problem as a causal network yields a framework that can be used to reason about the problem, as well as inform the modeller of the type of data that has to be gathered.
@article{191,
author = {Alta de Waal, Hildegarde Koen, J.P de Villiers, Henk Roodt},
title = {An expert-driven causal model of the rhino poaching problem},
abstract = {A significant challenge in ecological modelling is the lack of complete sets of high-quality data. This is especially true in the rhino poaching problem where data is incomplete. Although there are many poaching attacks, they can be spread over a vast surface area such as in the case of the Kruger National Park in South Africa, which is roughly the size of Israel. Bayesian networks are useful reasoning tools and can utilise expert knowledge when data is insufficient or sparse. Bayesian networks allow the modeller to incorporate data, expert knowledge, or any combination of the two. This flexibility of Bayesian networks makes them ideal for modelling complex ecological problems. In this paper an expert-driven model of the rhino poaching problem is presented. The development as well as the evaluation of the model is performed from an expert perspective. Independent expert evaluation is performed in the form of queries that test different scenarios. Structuring the rhino poaching problem as a causal network yields a framework that can be used to reason about the problem, as well as inform the modeller of the type of data that has to be gathered.},
year = {2017},
journal = {Ecological Modelling},
volume = {347},
pages = {29-39},
publisher = {Elsevier},
isbn = {0304-3800},
url = {https://www.sciencedirect.com/science/article/pii/S0304380016307621},
}
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Bernd Fischer is professor in the Division of Computer Science at Stellenbosch University, and the Division's post-graduate co-ordinator. His research area is automated software engineering, in particular logic-based (in the broadest sense) techniques. He has worked on specification-based component reuse and program synthesis; his work now focusses on program verification, including annotation inference, software model checking, and human-oriented presentation of verification results. He previously worked at TU Braunschweig, NASA Ames Research Center, and University of Southampton.
Latest Research Publications:
1.
Du Toit T, Berndt J, Britz K, Fischer B. ConceptCloud 2.0: Visualisation and exploration of geolocation-rich semi-structured data sets. ICFCA 2019 Conference and Workshops. 2019. http://ceur-ws.org/Vol-2378/.
ConceptCloud is a flexible interactive tool for exploring, vi- sualising, and analysing semi-structured data sets. It uses a combination of an intuitive tag cloud visualisation with an underlying concept lattice to provide a formal structure for navigation through a data set. Con- ceptCloud 2.0 extends the tool with an integrated map view to exploit the geolocation aspect of data. The tool’s implementation of exploratory search does not require prior knowledge of the structure of the data or compromise on scalability, and provides seamless navigation through the tag cloud and the map viewer.
@misc{227,
author = {Tiaan Du Toit, Joshua Berndt, Katarina Britz, Bernd Fischer},
title = {ConceptCloud 2.0: Visualisation and exploration of geolocation-rich semi-structured data sets},
abstract = {ConceptCloud is a flexible interactive tool for exploring, vi- sualising, and analysing semi-structured data sets. It uses a combination of an intuitive tag cloud visualisation with an underlying concept lattice to provide a formal structure for navigation through a data set. Con- ceptCloud 2.0 extends the tool with an integrated map view to exploit the geolocation aspect of data. The tool’s implementation of exploratory search does not require prior knowledge of the structure of the data or compromise on scalability, and provides seamless navigation through the tag cloud and the map viewer.},
year = {2019},
journal = {ICFCA 2019 Conference and Workshops},
month = {06/2019},
publisher = {CEUR-WS},
isbn = {1613-0073},
url = {http://ceur-ws.org/Vol-2378/},
}
1.
Berndt J, Fischer B, Britz K. Scaling the ConceptCloud browser to large semi-structured data sets. In: 14th African Conference on Research in Computer Science and Applied Mathematics, Stellenbosch, South Africa, Proceedings. HAL archives-ouvertes; 2018. https://hal.inria.fr/hal-01881376.
Semi-structured data sets such as product reviews or event log data are simultaneously becoming more widely used and growing ever larger. This paper describes ConceptCloud, a flexible interactive browser for semi-structured datasets, with a focus on the recent trend of implementing server-based architectures to accommodate ever growing datasets. ConceptCloud makes use of an intuitive tag cloud visualization viewer in combination with an underlying concept lattice to provide a formal structure for navigation through datasets without prior knowledge of the structure of the data or compromising scalability. This is achieved by implementing architectural changes to increase the system’s resource efficiency.
@{185,
author = {Joshua Berndt, Bernd Fischer, Katarina Britz},
title = {Scaling the ConceptCloud browser to large semi-structured data sets},
abstract = {Semi-structured data sets such as product reviews or event log data are simultaneously becoming more widely used and growing ever larger. This paper describes ConceptCloud, a flexible interactive browser for semi-structured datasets, with a focus on the recent trend of implementing server-based architectures to accommodate ever growing datasets. ConceptCloud makes use of an intuitive tag cloud visualization viewer in combination with an underlying concept lattice to provide a formal structure for navigation through datasets without prior knowledge of the structure of the data or compromising scalability. This is achieved by implementing architectural changes to increase the system’s resource efficiency.},
year = {2018},
journal = {14th African Conference on Research in Computer Science and Applied Mathematics, Stellenbosch, South Africa, Proceedings},
pages = {276- 283},
month = {14/10-16/10},
publisher = {HAL archives-ouvertes},
url = {https://hal.inria.fr/hal-01881376},
}
1.
Fischer B, Esterhuizen M, Greene G. Visualizing and Exploring Software Version Control Repositories using Interactive Tag Clouds over Formal Concept Lattices. Elsevier. 2017;87(2017). https://www.sciencedirect.com/science/article/pii/S0950584916304050?via%3Dihub.
Context: version control repositories contain a wealth of implicit information that can be used to answer many questions about a project’s development process. However, this information is not directly accessible in the repositories and must be extracted and visualized.
Objective: the main objective of this work is to develop a flexible and generic interactive visualization engine called ConceptCloud that supports exploratory search in version control repositories.
Method: ConceptCloud is a flexible, interactive browser for SVN and Git repositories. Its main novelty is the combination of an intuitive tag cloud visualization with an underlying concept lattice that provides a formal structure for navigation. ConceptCloud supports concurrent navigation in multiple linked but individually customizable tag clouds, which allows for multi-faceted repository browsing, and scriptable construction of unique visualizations.
Results: we describe the mathematical foundations and implementation of our approach and use ConceptCloud to quickly gain insight into the team structure and development process of three projects. We perform a user study to determine the usability of ConceptCloud. We show that untrained participants are able to answer historical questions about a software project better using ConceptCloud than using a linear list of commits.
Conclusion: ConceptCloud can be used to answer many difficult questions such as “What has happened in this project while I was away?” and “Which developers collaborate?”. Tag clouds generated from our approach provide a visualization in which version control data can be aggregated and explored interactively.
@article{174,
author = {Bernd Fischer, M. Esterhuizen, G.J. Greene},
title = {Visualizing and Exploring Software Version Control Repositories using Interactive Tag Clouds over Formal Concept Lattices},
abstract = {Context: version control repositories contain a wealth of implicit information that can be used to answer many questions about a project’s development process. However, this information is not directly accessible in the repositories and must be extracted and visualized.
Objective: the main objective of this work is to develop a flexible and generic interactive visualization engine called ConceptCloud that supports exploratory search in version control repositories.
Method: ConceptCloud is a flexible, interactive browser for SVN and Git repositories. Its main novelty is the combination of an intuitive tag cloud visualization with an underlying concept lattice that provides a formal structure for navigation. ConceptCloud supports concurrent navigation in multiple linked but individually customizable tag clouds, which allows for multi-faceted repository browsing, and scriptable construction of unique visualizations.
Results: we describe the mathematical foundations and implementation of our approach and use ConceptCloud to quickly gain insight into the team structure and development process of three projects. We perform a user study to determine the usability of ConceptCloud. We show that untrained participants are able to answer historical questions about a software project better using ConceptCloud than using a linear list of commits.
Conclusion: ConceptCloud can be used to answer many difficult questions such as “What has happened in this project while I was away?” and “Which developers collaborate?”. Tag clouds generated from our approach provide a visualization in which version control data can be aggregated and explored interactively.},
year = {2017},
journal = {Elsevier},
volume = {87},
pages = {223-241},
issue = {2017},
url = {https://www.sciencedirect.com/science/article/pii/S0950584916304050?via%3Dihub},
}
1.
Fischer B, Dunaiski M, Greene G. Exploratory Search of Academic Publication and Citation Data using Interactive Tag Cloud Visualizations. Scientometrics (Springer). 2017;110(3). https://link.springer.com/article/10.1007%2Fs11192-016-2236-3.
Acquiring an overview of an unfamiliar discipline and exploring relevant papers and journals is often a laborious task for researchers. In this paper we show how exploratory search can be supported on a large collection of academic papers to allow users to answer complex scientometric questions which traditional retrieval approaches do not support optimally. We use our ConceptCloud browser, which makes use of a combination of concept lattices and tag clouds, to visually present academic publication data (specifically, the ACM Digital Library) in a browsable format that facilitates exploratory search. We augment this dataset with semantic categories, obtained through automatic keyphrase extraction from papers’ titles and abstracts, in order to provide the user with uniform keyphrases of the underlying data collection. We use the citations and references of papers to provide additional mechanisms for exploring relevant research by presenting aggregated reference and citation data not only for a single paper but also across topics, authors and journals, which is novel in our approach. We conduct a user study to evaluate our approach in which we asked 34 participants, from different academic backgrounds with varying degrees of research experience, to answer a variety of scientometric questions using our ConceptCloud browser. Participants were able to answer complex scientometric questions using our ConceptCloud browser with a mean correctness of 73%, with the user’s prior research experience having no statistically significant effect on the results.
@article{173,
author = {Bernd Fischer, M. Dunaiski, G.J. Greene},
title = {Exploratory Search of Academic Publication and Citation Data using Interactive Tag Cloud Visualizations},
abstract = {Acquiring an overview of an unfamiliar discipline and exploring relevant papers and journals is often a laborious task for researchers. In this paper we show how exploratory search can be supported on a large collection of academic papers to allow users to answer complex scientometric questions which traditional retrieval approaches do not support optimally. We use our ConceptCloud browser, which makes use of a combination of concept lattices and tag clouds, to visually present academic publication data (specifically, the ACM Digital Library) in a browsable format that facilitates exploratory search. We augment this dataset with semantic categories, obtained through automatic keyphrase extraction from papers’ titles and abstracts, in order to provide the user with uniform keyphrases of the underlying data collection. We use the citations and references of papers to provide additional mechanisms for exploring relevant research by presenting aggregated reference and citation data not only for a single paper but also across topics, authors and journals, which is novel in our approach. We conduct a user study to evaluate our approach in which we asked 34 participants, from different academic backgrounds with varying degrees of research experience, to answer a variety of scientometric questions using our ConceptCloud browser. Participants were able to answer complex scientometric questions using our ConceptCloud browser with a mean correctness of 73%, with the user’s prior research experience having no statistically significant effect on the results.},
year = {2017},
journal = {Scientometrics (Springer)},
volume = {110},
pages = {1539-1571},
issue = {3},
address = {Netherlands},
isbn = {0138-9130},
url = {https://link.springer.com/article/10.1007%2Fs11192-016-2236-3},
}
1.
Greene G, Fischer B. CVExplorer: Identifying Candidate Developers by Mining and Exploring Their Open Source Contributions. In: Automated Software Engineering. ; 2016.
No Abstract
@{141,
author = {G.J. Greene, Bernd Fischer},
title = {CVExplorer: Identifying Candidate Developers by Mining and Exploring Their Open Source Contributions},
abstract = {No Abstract},
year = {2016},
journal = {Automated Software Engineering},
pages = {804-809},
month = {03/09-07/09},
isbn = {978-1-4503-3845-5},
}
Research Groups:
More Info
Latest Research Publications:
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Research Groups:
More Info
IBS SCHOLARSHIP STUDENT.
DEGREES LINKED TO THIS RESEARCH GROUP:
1) 2019-2020 Masters of Arts (Philosophy): 'The Ethics of Artificially Intelligent Sexbots: A Philosophical Investigation into the Ethical Conditions for Human-Sexbot Interaction';
2) 2018 BA (Honours) (Philosophy): 'The ethics of artificial intelligence: Should artificially intelligent social robots be used as slaves?'.
TALKS:
1) 'The ethics of artificial intelligence: Should artificially intelligent social robots be used as slaves?' (PSSA 2019);
2) 'The application of ethical boundaries for social robots: a human perspective' (4IR: Philosophical, Ethical & Legal Dimensions 2019);
3) 'The application of ethical boundaries for social robots: a human perspective' (PPA 2019);
4) 'Ethical boundaries for android companion robots: a human perspective' (FAIR 2019).
PUBLICATIONS:
1) Friedman, C. 2019. 'Ethical boundaries for android companion robots: A human perspective'. Proceedings of the South African Forum for Artificial Intelligence Research Vol 2540. http://ceur-ws.org/Vol-2540/.
DEGREES LINKED TO THIS RESEARCH GROUP:
1) 2019-2020 Masters of Arts (Philosophy): 'The Ethics of Artificially Intelligent Sexbots: A Philosophical Investigation into the Ethical Conditions for Human-Sexbot Interaction';
2) 2018 BA (Honours) (Philosophy): 'The ethics of artificial intelligence: Should artificially intelligent social robots be used as slaves?'.
TALKS:
1) 'The ethics of artificial intelligence: Should artificially intelligent social robots be used as slaves?' (PSSA 2019);
2) 'The application of ethical boundaries for social robots: a human perspective' (4IR: Philosophical, Ethical & Legal Dimensions 2019);
3) 'The application of ethical boundaries for social robots: a human perspective' (PPA 2019);
4) 'Ethical boundaries for android companion robots: a human perspective' (FAIR 2019).
PUBLICATIONS:
1) Friedman, C. 2019. 'Ethical boundaries for android companion robots: A human perspective'. Proceedings of the South African Forum for Artificial Intelligence Research Vol 2540. http://ceur-ws.org/Vol-2540/.
Latest Research Publications:
1.
Friedman C. Human-Robot Moral Relations: Human Interactants as Moral Patients of Their Own Agential Moral Actions Towards Robots. Communications in Computer and Information Science . 2020;1342. doi:https://doi.org/10.1007/978-3-030-66151-9_1.
This paper contributes to the debate in the ethics of social robots on how or whether to treat social robots morally by way of considering a novel perspective on the moral relations between human interactants and social robots. This perspective is significant as it allows us to circumnavigate debates about the (im)possibility of robot consciousness and moral patiency (debates which often slow down discussion on the ethics of HRI), thus allowing us to address actual and urgent current ethical issues in relation to human-robot interaction. The paper considers the different ways in which human interactants may be moral patients in the context of interaction with social robots: robots as conduits of human moral action towards human moral patients; humans as moral patients to the actions of robots; and human interactants as moral patients of their own agential moral actions towards social robots. This third perspective is the focal point of the paper. The argument is that due to perceived robot consciousness, and the possibility that the immoral treatment of social robots may morally harm human interactants, there is a unique moral relation between humans and social robots wherein human interactants are both the moral agents of their actions towards robots, as well as the actual moral patients of those agential moral actions towards robots. Robots, however, are no more than perceived moral patients. This discussion further adds to debates in the context of robot moral status, and the consideration of the moral treatment of robots in the context of human-robot interaction.
@article{385,
author = {Cindy Friedman},
title = {Human-Robot Moral Relations: Human Interactants as Moral Patients of Their Own Agential Moral Actions Towards Robots},
abstract = {This paper contributes to the debate in the ethics of social robots on how or whether to treat social robots morally by way of considering a novel perspective on the moral relations between human interactants and social robots. This perspective is significant as it allows us to circumnavigate debates about the (im)possibility of robot consciousness and moral patiency (debates which often slow down discussion on the ethics of HRI), thus allowing us to address actual and urgent current ethical issues in relation to human-robot interaction. The paper considers the different ways in which human interactants may be moral patients in the context of interaction with social robots: robots as conduits of human moral action towards human moral patients; humans as moral patients to the actions of robots; and human interactants as moral patients of their own agential moral actions towards social robots. This third perspective is the focal point of the paper. The argument is that due to perceived robot consciousness, and the possibility that the immoral treatment of social robots may morally harm human interactants, there is a unique moral relation between humans and social robots wherein human interactants are both the moral agents of their actions towards robots, as well as the actual moral patients of those agential moral actions towards robots. Robots, however, are no more than perceived moral patients. This discussion further adds to debates in the context of robot moral status, and the consideration of the moral treatment of robots in the context of human-robot interaction.},
year = {2020},
journal = {Communications in Computer and Information Science},
volume = {1342},
pages = {3-20},
publisher = {Springer},
isbn = {978-3-030-66151-9},
url = {https://link.springer.com/chapter/10.1007/978-3-030-66151-9_1},
doi = {https://doi.org/10.1007/978-3-030-66151-9_1},
}
Research Groups:
More Info
Associate Professor
Department of Informatics
Physical address: Information Technology Building, room 5-61,
University of Pretoria, Corner of Lynnwood Road and Roper Street,
Hatfield, Pretoria, 0083
Postal address: Private Bag X20, Hatfield, 0028
Tel: +27-(0)12-4203798
Department of Informatics
Physical address: Information Technology Building, room 5-61,
University of Pretoria, Corner of Lynnwood Road and Roper Street,
Hatfield, Pretoria, 0083
Postal address: Private Bag X20, Hatfield, 0028
Tel: +27-(0)12-4203798
Latest Research Publications:
1.
Gerber A. The Detection of Conversation Patterns in South African Political Tweets through Social Network Analysis. Communications in Computer and Information Science. 2022;1551. doi:10.1007/978-3-030-95070-5_2.
Within complex societies, social communities are distinguishable based on social interactions. The interactions can be between members or communities and can range from simple conversations between family members and friends to complex interactions that represent the flow of money, information, or power. In our modern digital society, social media platforms present unique opportunities to study social networks through social network analysis (SNA). Social media platforms are usually representative of a specific user group, and Twitter, a microblogging platform, is characterised by the fast distribution of news and often provocative opinions, as well as social mobilizing, which makes it popular for political interactions. The nature of Twitter generates a valuable SNA data source for investigating political conversations and communities, and in related research, specific archetypal conversation patterns between communities were identified that allow for unique interpretations of conversations about a topic. This paper reports on a study where social network analysis (SNA) was performed on Twitter data about political events in 2021 in South Africa. The purpose was to determine which distinct conversation patterns could be detected in datasets collected, as well as what could be derived from these patterns given the South African political landscape and perceptions. The results indicate that conversations in the South African political landscape are less polarized than expected. Conversations often manifest broadcast patterns from key influencers in addition to tight crowds or community clusters. Tight crowds or community clusters indicate intense conversation across communities that exhibits diverse opinions and perspectives on a topic. The results may be of value for researchers that aim to understand social media conversations within the South African society.
@article{434,
author = {Aurona Gerber},
title = {The Detection of Conversation Patterns in South African Political Tweets through Social Network Analysis},
abstract = {Within complex societies, social communities are distinguishable based on social interactions. The interactions can be between members or communities and can range from simple conversations between family members and friends to complex interactions that represent the flow of money, information, or power. In our modern digital society, social media platforms present unique opportunities to study social networks through social network analysis (SNA). Social media platforms are usually representative of a specific user group, and Twitter, a microblogging platform, is characterised by the fast distribution of news and often provocative opinions, as well as social mobilizing, which makes it popular for political interactions. The nature of Twitter generates a valuable SNA data source for investigating political conversations and communities, and in related research, specific archetypal conversation patterns between communities were identified that allow for unique interpretations of conversations about a topic. This paper reports on a study where social network analysis (SNA) was performed on Twitter data about political events in 2021 in South Africa. The purpose was to determine which distinct conversation patterns could be detected in datasets collected, as well as what could be derived from these patterns given the South African political landscape and perceptions. The results indicate that conversations in the South African political landscape are less polarized than expected. Conversations often manifest broadcast patterns from key influencers in addition to tight crowds or community clusters. Tight crowds or community clusters indicate intense conversation across communities that exhibits diverse opinions and perspectives on a topic. The results may be of value for researchers that aim to understand social media conversations within the South African society.},
year = {2022},
journal = {Communications in Computer and Information Science},
volume = {1551},
pages = {15-31},
publisher = {Springer},
address = {Cham},
isbn = {978-3-030-95070-5},
url = {https://link.springer.com/chapter/10.1007/978-3-030-95070-5_2},
doi = {10.1007/978-3-030-95070-5_2},
}
1.
Eybers S, Gerber A. A Preliminary Investigation into the Role of Virtual Sport Training Technology as Emotional Coping Mechanism During a National Pandemic Lockdown. Lecture Notes in Networks and Systems. 2021;186. doi:10.1007/978-3-030-66093-2_18.
The COVID-19 pandemic and the subsequent response by governments to introduce national lockdown regulations have confined individuals to their residential premises. As a result, no recreational or sport activities are allowed outside the (often small) boundaries of family homes, a situation often rapidly introducing social isolation. Research has proven that emotional coping mechanisms, such as sport, can lower the stressful and uncertainty burden on individuals. However, without the availability of this coping mechanism, many individuals have been forced to use virtual sport training technology to keep active. This preliminary quantitative study investigated the role of technology, in particular virtual sport training technology (if any) by cyclists as emotional coping mechanism during a period of national lockdown. The results of an online survey indicated that sport, in general, has always been an emotional coping mechanism during normal challenging situations but that slightly more respondents used sport as mechanism during the lockdown period. Respondents indicated that virtual cycling training technology enabled them to continue with using their normal coping mechanism even in a period of national lockdown. One of the benefits of a virtual training environment is the ability to socialize by riding with virtual team members. Surprisingly, the number of cyclists who preferred riding alone in the virtual cycling environment was slightly more than the cyclists who preferred to join scheduled rides with virtual team members. The research is the first step towards an in-depth investigation into the adoption of technology as an emotional coping mechanism in stressful environments.
@article{439,
author = {Sunet Eybers, Aurona Gerber},
title = {A Preliminary Investigation into the Role of Virtual Sport Training Technology as Emotional Coping Mechanism During a National Pandemic Lockdown},
abstract = {The COVID-19 pandemic and the subsequent response by governments to introduce national lockdown regulations have confined individuals to their residential premises. As a result, no recreational or sport activities are allowed outside the (often small) boundaries of family homes, a situation often rapidly introducing social isolation. Research has proven that emotional coping mechanisms, such as sport, can lower the stressful and uncertainty burden on individuals. However, without the availability of this coping mechanism, many individuals have been forced to use virtual sport training technology to keep active. This preliminary quantitative study investigated the role of technology, in particular virtual sport training technology (if any) by cyclists as emotional coping mechanism during a period of national lockdown. The results of an online survey indicated that sport, in general, has always been an emotional coping mechanism during normal challenging situations but that slightly more respondents used sport as mechanism during the lockdown period. Respondents indicated that virtual cycling training technology enabled them to continue with using their normal coping mechanism even in a period of national lockdown. One of the benefits of a virtual training environment is the ability to socialize by riding with virtual team members. Surprisingly, the number of cyclists who preferred riding alone in the virtual cycling environment was slightly more than the cyclists who preferred to join scheduled rides with virtual team members. The research is the first step towards an in-depth investigation into the adoption of technology as an emotional coping mechanism in stressful environments.},
year = {2021},
journal = {Lecture Notes in Networks and Systems},
volume = {186},
pages = {186-194},
publisher = {Springer},
address = {Cham},
isbn = {978-3-030-66093-2},
url = {https://link.springer.com/chapter/10.1007/978-3-030-66093-2_18},
doi = {10.1007/978-3-030-66093-2_18},
}
1.
Gerber A, Strachan S. Network Patterns in South African Election Tweets. Lecture Notes in Networks and Systems. 2021;186. doi:10.1007/978-3-030-66093-2_1.
Social communities play a significant role in understanding complex societies, from communities formed by support interactions between friends and family to community structures that depict the flow of information, money and power. With the emergence of the internet, the nature of social networks changed because communities could form disassociated from physical location, and social network analysis (SNA) on social media such as Twitter and Facebook emerged as a distinct research field. Studies suggest that Twitter feeds have a significant influence on the views and opinions of society, and subsequently the formation of communities. This paper reports on a study where social network analysis was performed on Twitter feeds in South Africa around the 2019 elections to detect distinct patterns within the overall network. In the datasets that were analysed, a specific network pattern namely Broadcast Networks were observed. A Broadcast Network typically reflects central hubs such as media houses, political parties or influencers whose messages are repeated without interaction or discussion. Our results indicate that there were few discussions and interactions and that messages were broadcasted from central nodes even though the general experience of Twitter users during this time was of intense discussions and differences in opinion.
@article{438,
author = {Aurona Gerber, Stephanie Strachan},
title = {Network Patterns in South African Election Tweets},
abstract = {Social communities play a significant role in understanding complex societies, from communities formed by support interactions between friends and family to community structures that depict the flow of information, money and power. With the emergence of the internet, the nature of social networks changed because communities could form disassociated from physical location, and social network analysis (SNA) on social media such as Twitter and Facebook emerged as a distinct research field. Studies suggest that Twitter feeds have a significant influence on the views and opinions of society, and subsequently the formation of communities. This paper reports on a study where social network analysis was performed on Twitter feeds in South Africa around the 2019 elections to detect distinct patterns within the overall network. In the datasets that were analysed, a specific network pattern namely Broadcast Networks were observed. A Broadcast Network typically reflects central hubs such as media houses, political parties or influencers whose messages are repeated without interaction or discussion. Our results indicate that there were few discussions and interactions and that messages were broadcasted from central nodes even though the general experience of Twitter users during this time was of intense discussions and differences in opinion.},
year = {2021},
journal = {Lecture Notes in Networks and Systems},
volume = {186},
pages = {3-13},
publisher = {Springer},
address = {Cham},
isbn = {978-3-030-66093-2},
url = {https://link.springer.com/chapter/10.1007/978-3-030-66093-2_1},
doi = {10.1007/978-3-030-66093-2_1},
}
1.
Marimuthu T, van der Merwe A, Gerber A. A systematic literature review of essential enterprise architecture management dimensions. Lecture Notes in Networks and Systems. 2021;235. doi:10.1007/978-981-16-2377-6_36.
Many organisations turn to enterprise architecture (EA) to assist with the alignment of business and information technology. While some of these organisations succeed in the development and implementation of EA, many of them fail to manage EA after implementation. Because of the specific focus on the management of EA during and after the initial implementation, the enterprise architecture management (EAM) field is developed. EAM is characterised by many dimensions or elements. It is a challenge to select the dimensions that should be managed and that are vital for successful EA practice. In this study, we executed a systematic literature review (SLR) of primary EA and EAM literature with the aim of identifying dimensions regarded as key areas of EAM. The main contribution of this work is a concept map of the essential EAM dimensions with their relationships. The results of the SLR indicate that dimensions that used to be considered important or seemed to be the most essential for EA, such as frameworks, EA principles and reference models, are no longer emphasised as strongly and more focus is placed on people, skills, communication and governance when considering EAM literature and EAM maturity.
@article{437,
author = {Trishan Marimuthu, Alta van der Merwe, Aurona Gerber},
title = {A systematic literature review of essential enterprise architecture management dimensions},
abstract = {Many organisations turn to enterprise architecture (EA) to assist with the alignment of business and information technology. While some of these organisations succeed in the development and implementation of EA, many of them fail to manage EA after implementation. Because of the specific focus on the management of EA during and after the initial implementation, the enterprise architecture management (EAM) field is developed. EAM is characterised by many dimensions or elements. It is a challenge to select the dimensions that should be managed and that are vital for successful EA practice. In this study, we executed a systematic literature review (SLR) of primary EA and EAM literature with the aim of identifying dimensions regarded as key areas of EAM. The main contribution of this work is a concept map of the essential EAM dimensions with their relationships. The results of the SLR indicate that dimensions that used to be considered important or seemed to be the most essential for EA, such as frameworks, EA principles and reference models, are no longer emphasised as strongly and more focus is placed on people, skills, communication and governance when considering EAM literature and EAM maturity.},
year = {2021},
journal = {Lecture Notes in Networks and Systems},
volume = {235},
pages = {381-391},
publisher = {Springer},
address = {Singapore},
isbn = {978-981-16-2377-6},
url = {https://link.springer.com/chapter/10.1007/978-981-16-2377-6_36},
doi = {10.1007/978-981-16-2377-6_36},
}
1.
Gerber A, Eybers S. Converting to Inclusive Online Flipped Classrooms in Response to Covid-19 Lockdown. South African Journal of Higher Education. 2021;35(4). doi:10.20853/35-4-4285.
The global Covid-19 pandemic caused havoc in higher education teaching routines and several residential institutions encouraged instructors to convert existing modules to flipped classrooms as part of an online, blended learning strategy. Even though this seems a reasonable request, instructors straightaway encountered challenges which include a vague concept of what an online flipped classroom entails within a higher education context, a lack of guidelines for converting an existing module, facilitating learner engagement as well as unique challenges for inclusion of all learners in a digitally divided developing country in Covid-19 lockdown. In order to respond, we embarked on a study to identify the distinguishing characteristics of flipped classrooms to understand the as-is and to-be scenarios using a systematic literature review. The characteristics were used to develop of design considerations to convert to an online flipped classroom for higher education taking our diverse learner profiles into account. We subsequently converted a short module in an information systems department and shortly report on our experience.
@article{436,
author = {Aurona Gerber, Sunet Eybers},
title = {Converting to Inclusive Online Flipped Classrooms in Response to Covid-19 Lockdown},
abstract = {The global Covid-19 pandemic caused havoc in higher education teaching routines and several residential institutions encouraged instructors to convert existing modules to flipped classrooms as part of an online, blended learning strategy. Even though this seems a reasonable request, instructors straightaway encountered challenges which include a vague concept of what an online flipped classroom entails within a higher education context, a lack of guidelines for converting an existing module, facilitating learner engagement as well as unique challenges for inclusion of all learners in a digitally divided developing country in Covid-19 lockdown. In order to respond, we embarked on a study to identify the distinguishing characteristics of flipped classrooms to understand the as-is and to-be scenarios using a systematic literature review. The characteristics were used to develop of design considerations to convert to an online flipped classroom for higher education taking our diverse learner profiles into account. We subsequently converted a short module in an information systems department and shortly report on our experience.},
year = {2021},
journal = {South African Journal of Higher Education},
volume = {35},
pages = {34-57},
issue = {4},
isbn = {1753-5913},
url = {https://journals.co.za/doi/10.20853/35-4-4285},
doi = {10.20853/35-4-4285},
}
