Lina Petrakieva - Academia.edu (original) (raw)
Uploads
Papers by Lina Petrakieva
Journal of Further and Higher Education, Dec 22, 2016
Transition support for international students has traditionally adopted deficit models which atte... more Transition support for international students has traditionally adopted deficit models which attempt to 'fix' assumed academic literacy problems (Hughes, 2013). This study explores a more culturally inclusive initiative which supported international students at a UK university in a holistic and developmental way. The initiative was delivered across an academic year and a mix of focus groups and semi-structured interviews were undertaken to understand students' experiences of participating in it. Although small-scale, the initiative emerged as a lively learning community which supported both academic and sociocultural transition. Qualitative data illuminate a number of fruitful methodological foci, including informality of the learning space and exploration of intercultural learning and teaching practices. Findings indicate that these cultural explorations were instrumental in helping students navigate the new learning and teaching system and forge a stronger sense of academic and social belonging. These outcomes were cultivated within an ethos that valued and enhanced the diverse skills, identities and attributes that students brought, rather than one that suppressed their previous learning practices. Findings thus demonstrate how transition and academic success can be facilitated in ways that do not problematize international students and highlight the need for more holistic and inclusive ways of supporting them.
Facet eBooks, Jul 5, 2018
The Third European Conference on Education - Official Conference Proceedings, Sep 5, 2015
M-learning is often approached as an innovative method to teach, but quite often without the prop... more M-learning is often approached as an innovative method to teach, but quite often without the proper planning of the actual learning process and proper understanding of the implications on the pedagogy of the learning process in such a setting. Because of the multiple stakeholders in the process-the institution, the learners, the educators, the policy makers, etc.-it is very difficult to encourage educators to engage with something so different that will require a rethink of their teaching practices. In addition, with so many different technical elements and challenges, it is often simply just too daunting a prospect. It is also unfortunate that m-learning is often only limited to simply mobile access. A good m-pedagogy will not just transfer the learning process to a mobile device, but incorporate the very nature of mobile, flexible, user-guided, bite-sized learning. A real m-learning needs to have a real purpose and the stakeholders need to see the value in it for it to have a chance to be a success. With all the correct
International Journal of Approximate Reasoning, Mar 1, 2004
There has been much interest in applying techniques that incorporate knowledge from unlabelled da... more There has been much interest in applying techniques that incorporate knowledge from unlabelled data into a supervised learning system but less effort has been made to compare the effectiveness of different approaches on real world problems and to analyse the behaviour of the learning system when using different amount of unlabelled data. In this paper an analysis of the performance of supervised methods enforced by unlabelled data and some semisupervised approaches using different ratios of labelled to unlabelled samples is presented. The experimental results show that when supported by unlabelled samples much less labelled data is generally required to build a classifier without compromising the classification performance. If only a very limited amount of labelled data is available the results show high variability and the performance of the final classifier is more dependant on how reliable the labelled data samples are rather than use of additional unlabelled data. Semi-supervised clustering utilising both labelled and unlabelled data have been shown to offer most significant improvements when natural clusters are present in the considered problem.
The Journal of Educational Innovation, Partnership and Change, 2017
This is a research article supported by a video. To read the article, please click on the pdf. To... more This is a research article supported by a video. To read the article, please click on the pdf. To play the video, click on the link at the bottom of this page.The Learning Development Centre (LDC) in the School of Health & Life Sciences at Glasgow Caledonian University serves over 6000 students, of whom over 70% have off-campus placement requirements. Provision of high-quality online support materials is therefore a priority. The LDC team has developed a suite of contextualised online resources to assist students in their development of academic and digital literacies. The LDC Team undertook a project to evaluate and enhance the resources and the LDC website. This involved the use of traditional evaluation tools in addition to the more innovative use of the 'Think Aloud Method' (Ericsson and Simon, 1984), to assess interface usability and online community content. A key to this project was the Team’s partnership with students, as ‘Student Learning Developers’ who played a pi...
Journal of Further and Higher Education, 2016
Transition support for international students has traditionally adopted deficit models which atte... more Transition support for international students has traditionally adopted deficit models which attempt to 'fix' assumed academic literacy problems (Hughes, 2013). This study explores a more culturally inclusive initiative which supported international students at a UK university in a holistic and developmental way. The initiative was delivered across an academic year and a mix of focus groups and semi-structured interviews were undertaken to understand students' experiences of participating in it. Although small-scale, the initiative emerged as a lively learning community which supported both academic and sociocultural transition. Qualitative data illuminate a number of fruitful methodological foci, including informality of the learning space and exploration of intercultural learning and teaching practices. Findings indicate that these cultural explorations were instrumental in helping students navigate the new learning and teaching system and forge a stronger sense of academic and social belonging. These outcomes were cultivated within an ethos that valued and enhanced the diverse skills, identities and attributes that students brought, rather than one that suppressed their previous learning practices. Findings thus demonstrate how transition and academic success can be facilitated in ways that do not problematize international students and highlight the need for more holistic and inclusive ways of supporting them.
Applications and Science in Soft Computing, 2004
This paper examines the problem of selecting a suitable subset of data to be labelled when buildi... more This paper examines the problem of selecting a suitable subset of data to be labelled when building pattern classifiers from labelled and unlabelled data. The selection of representative set is guided by a clustering information and various options of allocating a number of samples within clusters and their distributions are investigated. The experimental results show that hybrid methods like Semi-supervised clustering with selective sampling can result in building a classifier which requires much less labelled data in order to achieve a comparable classification performance to classifiers built only on the basis of labelled data.
In this paper, we apply the combination method of bagging which has been developed in the context... more In this paper, we apply the combination method of bagging which has been developed in the context of supervised learning of classifiers and regressors to the unsupervised artificial neural network known as the Self Organising Map. We show that various initialisation techniques can be used to create maps which are comparable by humans by eye. We then use a semi-supervised version of the SOM to classify data sets and show how bagging may be used to improve classification. We then compare bumping and bagging on this data set.
International Journal of Approximate Reasoning, 2004
There has been much interest in applying techniques that incorporate knowledge from unlabelled da... more There has been much interest in applying techniques that incorporate knowledge from unlabelled data into a supervised learning system but less effort has been made to compare the effectiveness of different approaches on real world problems and to analyse the behaviour of the learning system when using different amount of unlabelled data. In this paper an analysis of the performance of supervised methods enforced by unlabelled data and some semisupervised approaches using different ratios of labelled to unlabelled samples is presented. The experimental results show that when supported by unlabelled samples much less labelled data is generally required to build a classifier without compromising the classification performance. If only a very limited amount of labelled data is available the results show high variability and the performance of the final classifier is more dependant on how reliable the labelled data samples are rather than use of additional unlabelled data. Semi-supervised clustering utilising both labelled and unlabelled data have been shown to offer most significant improvements when natural clusters are present in the considered problem.
Handbook of Mobile Teaching and Learning, 2015
M-learning is often approached as an innovative method to teach, but quite often without the prop... more M-learning is often approached as an innovative method to teach, but quite often without the proper planning of the actual learning process and proper understanding of the implications on the pedagogy of the learning process in such a setting. Because of the multiple stakeholders in the process-the institution, the learners, the educators, the policy makers, etc.-it is very difficult to encourage educators to engage with something so different that will require a rethink of their teaching practices. In addition, with so many different technical elements and challenges, it is often simply just too daunting a prospect. It is also unfortunate that m-learning is often only limited to simply mobile access. A good m-pedagogy will not just transfer the learning process to a mobile device, but incorporate the very nature of mobile, flexible, user-guided, bite-sized learning. A real m-learning needs to have a real purpose and the stakeholders need to see the value in it for it to have a chance to be a success. With all the correct
Journal of Further and Higher Education, Dec 22, 2016
Transition support for international students has traditionally adopted deficit models which atte... more Transition support for international students has traditionally adopted deficit models which attempt to 'fix' assumed academic literacy problems (Hughes, 2013). This study explores a more culturally inclusive initiative which supported international students at a UK university in a holistic and developmental way. The initiative was delivered across an academic year and a mix of focus groups and semi-structured interviews were undertaken to understand students' experiences of participating in it. Although small-scale, the initiative emerged as a lively learning community which supported both academic and sociocultural transition. Qualitative data illuminate a number of fruitful methodological foci, including informality of the learning space and exploration of intercultural learning and teaching practices. Findings indicate that these cultural explorations were instrumental in helping students navigate the new learning and teaching system and forge a stronger sense of academic and social belonging. These outcomes were cultivated within an ethos that valued and enhanced the diverse skills, identities and attributes that students brought, rather than one that suppressed their previous learning practices. Findings thus demonstrate how transition and academic success can be facilitated in ways that do not problematize international students and highlight the need for more holistic and inclusive ways of supporting them.
Facet eBooks, Jul 5, 2018
The Third European Conference on Education - Official Conference Proceedings, Sep 5, 2015
M-learning is often approached as an innovative method to teach, but quite often without the prop... more M-learning is often approached as an innovative method to teach, but quite often without the proper planning of the actual learning process and proper understanding of the implications on the pedagogy of the learning process in such a setting. Because of the multiple stakeholders in the process-the institution, the learners, the educators, the policy makers, etc.-it is very difficult to encourage educators to engage with something so different that will require a rethink of their teaching practices. In addition, with so many different technical elements and challenges, it is often simply just too daunting a prospect. It is also unfortunate that m-learning is often only limited to simply mobile access. A good m-pedagogy will not just transfer the learning process to a mobile device, but incorporate the very nature of mobile, flexible, user-guided, bite-sized learning. A real m-learning needs to have a real purpose and the stakeholders need to see the value in it for it to have a chance to be a success. With all the correct
International Journal of Approximate Reasoning, Mar 1, 2004
There has been much interest in applying techniques that incorporate knowledge from unlabelled da... more There has been much interest in applying techniques that incorporate knowledge from unlabelled data into a supervised learning system but less effort has been made to compare the effectiveness of different approaches on real world problems and to analyse the behaviour of the learning system when using different amount of unlabelled data. In this paper an analysis of the performance of supervised methods enforced by unlabelled data and some semisupervised approaches using different ratios of labelled to unlabelled samples is presented. The experimental results show that when supported by unlabelled samples much less labelled data is generally required to build a classifier without compromising the classification performance. If only a very limited amount of labelled data is available the results show high variability and the performance of the final classifier is more dependant on how reliable the labelled data samples are rather than use of additional unlabelled data. Semi-supervised clustering utilising both labelled and unlabelled data have been shown to offer most significant improvements when natural clusters are present in the considered problem.
The Journal of Educational Innovation, Partnership and Change, 2017
This is a research article supported by a video. To read the article, please click on the pdf. To... more This is a research article supported by a video. To read the article, please click on the pdf. To play the video, click on the link at the bottom of this page.The Learning Development Centre (LDC) in the School of Health & Life Sciences at Glasgow Caledonian University serves over 6000 students, of whom over 70% have off-campus placement requirements. Provision of high-quality online support materials is therefore a priority. The LDC team has developed a suite of contextualised online resources to assist students in their development of academic and digital literacies. The LDC Team undertook a project to evaluate and enhance the resources and the LDC website. This involved the use of traditional evaluation tools in addition to the more innovative use of the 'Think Aloud Method' (Ericsson and Simon, 1984), to assess interface usability and online community content. A key to this project was the Team’s partnership with students, as ‘Student Learning Developers’ who played a pi...
Journal of Further and Higher Education, 2016
Transition support for international students has traditionally adopted deficit models which atte... more Transition support for international students has traditionally adopted deficit models which attempt to 'fix' assumed academic literacy problems (Hughes, 2013). This study explores a more culturally inclusive initiative which supported international students at a UK university in a holistic and developmental way. The initiative was delivered across an academic year and a mix of focus groups and semi-structured interviews were undertaken to understand students' experiences of participating in it. Although small-scale, the initiative emerged as a lively learning community which supported both academic and sociocultural transition. Qualitative data illuminate a number of fruitful methodological foci, including informality of the learning space and exploration of intercultural learning and teaching practices. Findings indicate that these cultural explorations were instrumental in helping students navigate the new learning and teaching system and forge a stronger sense of academic and social belonging. These outcomes were cultivated within an ethos that valued and enhanced the diverse skills, identities and attributes that students brought, rather than one that suppressed their previous learning practices. Findings thus demonstrate how transition and academic success can be facilitated in ways that do not problematize international students and highlight the need for more holistic and inclusive ways of supporting them.
Applications and Science in Soft Computing, 2004
This paper examines the problem of selecting a suitable subset of data to be labelled when buildi... more This paper examines the problem of selecting a suitable subset of data to be labelled when building pattern classifiers from labelled and unlabelled data. The selection of representative set is guided by a clustering information and various options of allocating a number of samples within clusters and their distributions are investigated. The experimental results show that hybrid methods like Semi-supervised clustering with selective sampling can result in building a classifier which requires much less labelled data in order to achieve a comparable classification performance to classifiers built only on the basis of labelled data.
In this paper, we apply the combination method of bagging which has been developed in the context... more In this paper, we apply the combination method of bagging which has been developed in the context of supervised learning of classifiers and regressors to the unsupervised artificial neural network known as the Self Organising Map. We show that various initialisation techniques can be used to create maps which are comparable by humans by eye. We then use a semi-supervised version of the SOM to classify data sets and show how bagging may be used to improve classification. We then compare bumping and bagging on this data set.
International Journal of Approximate Reasoning, 2004
There has been much interest in applying techniques that incorporate knowledge from unlabelled da... more There has been much interest in applying techniques that incorporate knowledge from unlabelled data into a supervised learning system but less effort has been made to compare the effectiveness of different approaches on real world problems and to analyse the behaviour of the learning system when using different amount of unlabelled data. In this paper an analysis of the performance of supervised methods enforced by unlabelled data and some semisupervised approaches using different ratios of labelled to unlabelled samples is presented. The experimental results show that when supported by unlabelled samples much less labelled data is generally required to build a classifier without compromising the classification performance. If only a very limited amount of labelled data is available the results show high variability and the performance of the final classifier is more dependant on how reliable the labelled data samples are rather than use of additional unlabelled data. Semi-supervised clustering utilising both labelled and unlabelled data have been shown to offer most significant improvements when natural clusters are present in the considered problem.
Handbook of Mobile Teaching and Learning, 2015
M-learning is often approached as an innovative method to teach, but quite often without the prop... more M-learning is often approached as an innovative method to teach, but quite often without the proper planning of the actual learning process and proper understanding of the implications on the pedagogy of the learning process in such a setting. Because of the multiple stakeholders in the process-the institution, the learners, the educators, the policy makers, etc.-it is very difficult to encourage educators to engage with something so different that will require a rethink of their teaching practices. In addition, with so many different technical elements and challenges, it is often simply just too daunting a prospect. It is also unfortunate that m-learning is often only limited to simply mobile access. A good m-pedagogy will not just transfer the learning process to a mobile device, but incorporate the very nature of mobile, flexible, user-guided, bite-sized learning. A real m-learning needs to have a real purpose and the stakeholders need to see the value in it for it to have a chance to be a success. With all the correct