title: Towards an understanding of existing e-learning for university science education in Taiwan

authors: Wang, Su-Chen

subjects: e-learning

abstract: E-learning is a fast growing trend worldwide but it is still not universally accepted and practice does not always reach national government and tertiary institution expectations, especially in Taiwan. While issues around the effective implementation of e-learning to produce high quality education are being raised internationally, very little research has been undertaken in Taiwanese tertiary institutions, particularly for science education. No research was found that addressed the various perspectives of the stakeholders involved in blended courses which had both face-to-face and online learning components. The link between e-learning practice and views of learning had also received little attention.

This study investigated how e-learning practice was perceived and experienced at a national research-based university in Taiwan. The main focus was to identify the challenges, benefits and related success factors of e-learning practice as part of blended learning courses from the perspectives of university administrators, support people, instructors and students. An interpretative methodology using questionnaires and interviews was employed to generate data from these participant groups. Both quantitative and qualitative data were collected. 

This study provides empirical evidence that e-learning practice is perceived and experienced as a technology-mediated and collaborative practice that is socially and culturally situated. The study supports the view that e-learning practice as a whole is a socio-cultural system, although when looking at instructor and student preferences for instructional design and learning processes there is a fit with both behaviorist and constructivist approaches to teaching and learning. However, instructors and students need to be active and self-managed to find e-learning efficient and effective. Students, instructors, support people and administrators held very similar perceptions of the benefits of and influences on lecturer and student use of e-learning as a component of blended learning.

Based on the findings, an explanatory model for the influences on e-learning practice as part of blended learning in a Taiwan university context was developed. E-learning teaching and learning approaches are initiated by and created within a multi-layered context. At the first level, e-learning practice is accomplished via instructor and student engagement in day to day teaching and learning and as an educational reform it cannot separated from the ICT technologies which mediate their interaction. Put another way, because instructor and student participation in e-learning as part of blended learning is voluntary students are included with instructors and the technology in the core enactment zone for practice. At the next level this three-way instructor-student-technology interaction is affected by and nested within the university instructor professional community and student peer community, which in turn is shaped by and nested in university-wide policies and practices. These three levels are nested in and influenced by the national policy context, external professionals, private enterprise and the public at large.

The model and associated suggestions presented in this study are expected to assist governments and universities to play a more constructive role in the development and implementation of e-learning education to improve the quality of courses for students and instructors. The hope is that the findings will contribute to enhanced teaching and learning supported by better administrator decision-making regarding institutional policies and practices including investment in learning technologies and support services for e-learning.

text: Towards an understanding of existing e-learning for university science education in Taiwan E-learning is a fast growing trend worldwide but it is still not universally accepted and practice does not always reach national government and tertiary institution expectations, especially in Taiwan. While issues around the effective implementation of e-learning to produce high quality education are being raised internationally, very little research has been undertaken in Taiwanese tertiary institutions, particularly for science education. No research was found that addressed the various perspectives of the stakeholders involved in blended courses which had both face-to-face and online learning components. The link between e-learning practice and views of learning had also received little attention.

This study investigated how e-learning practice was perceived and experienced at a national research-based university in Taiwan. The main focus was to identify the challenges, benefits and related success factors of e-learning practice as part of blended learning courses from the perspectives of university administrators, support people, instructors and students. An interpretative methodology using questionnaires and interviews was employed to generate data from these participant groups. Both quantitative and qualitative data were collected. 

This study provides empirical evidence that e-learning practice is perceived and experienced as a technology-mediated and collaborative practice that is socially and culturally situated. The study supports the view that e-learning practice as a whole is a socio-cultural system, although when looking at instructor and student preferences for instructional design and learning processes there is a fit with both behaviorist and constructivist approaches to teaching and learning. However, instructors and students need to be active and self-managed to find e-learning efficient and effective. Students, instructors, support people and administrators held very similar perceptions of the benefits of and influences on lecturer and student use of e-learning as a component of blended learning.

Based on the findings, an explanatory model for the influences on e-learning practice as part of blended learning in a Taiwan university context was developed. E-learning teaching and learning approaches are initiated by and created within a multi-layered context. At the first level, e-learning practice is accomplished via instructor and student engagement in day to day teaching and learning and as an educational reform it cannot separated from the ICT technologies which mediate their interaction. Put another way, because instructor and student participation in e-learning as part of blended learning is voluntary students are included with instructors and the technology in the core enactment zone for practice. At the next level this three-way instructor-student-technology interaction is affected by and nested within the university instructor professional community and student peer community, which in turn is shaped by and nested in university-wide policies and practices. These three levels are nested in and influenced by the national policy context, external professionals, private enterprise and the public at large.

The model and associated suggestions presented in this study are expected to assist governments and universities to play a more constructive role in the development and implementation of e-learning education to improve the quality of courses for students and instructors. The hope is that the findings will contribute to enhanced teaching and learning supported by better administrator decision-making regarding institutional policies and practices including investment in learning technologies and support services for e-learning.

year: 2008

title: Improving Hoeffding Trees

authors: Kirkby, Richard Brendon

subjects: machine learning

abstract: Modern information technology allows information to be collected at a far greater rate than ever before. So fast, in fact, that the main problem is making sense of it all. Machine learning offers promise of a solution, but the field mainly focusses on achieving high accuracy when data supply is limited. While this has created sophisticated classification algorithms, many do not cope with increasing data set sizes. When the data set sizes get to a point where they could be considered to represent a continuous supply, or data stream, then incremental classification algorithms are required. In this setting, the effectiveness of an algorithm cannot simply be assessed by accuracy alone. Consideration needs to be given to the memory available to the algorithm and the speed at which data is processed in terms of both the time taken to predict the class of a new data sample and the time taken to include this sample in an incrementally updated classification model.

The Hoeffding tree algorithm is a state-of-the-art method for inducing decision trees from data streams. The aim of this thesis is to improve this algorithm. To measure improvement, a comprehensive framework for evaluating the performance of data stream algorithms is developed. Within the framework memory size is fixed in order to simulate realistic application scenarios. In order to simulate continuous operation, classes of synthetic data are generated providing an evaluation on a large scale. Improvements to many aspects of the Hoeffding tree algorithm are demonstrated. First, a number of methods for handling continuous numeric features are compared. Second, tree prediction strategy is investigated to evaluate the utility of various methods. Finally, the possibility of improving accuracy using ensemble methods is explored.

The experimental results provide meaningful comparisons of accuracy and processing speeds between different modifications of the Hoeffding tree algorithm under various memory limits. The study on numeric attributes demonstrates that sacrificing accuracy for space at the local level often results in improved global accuracy. The prediction strategy shown to perform best adaptively chooses between standard majority class and Naive Bayes prediction in the leaves. The ensemble method investigation shows that combining trees can be worthwhile, but only when sufficient memory is available, and improvement is less likely than in traditional machine learning. In particular, issues are encountered when applying the popular boosting method to streams.

text: Improving Hoeffding Trees Modern information technology allows information to be collected at a far greater rate than ever before. So fast, in fact, that the main problem is making sense of it all. Machine learning offers promise of a solution, but the field mainly focusses on achieving high accuracy when data supply is limited. While this has created sophisticated classification algorithms, many do not cope with increasing data set sizes. When the data set sizes get to a point where they could be considered to represent a continuous supply, or data stream, then incremental classification algorithms are required. In this setting, the effectiveness of an algorithm cannot simply be assessed by accuracy alone. Consideration needs to be given to the memory available to the algorithm and the speed at which data is processed in terms of both the time taken to predict the class of a new data sample and the time taken to include this sample in an incrementally updated classification model.

The Hoeffding tree algorithm is a state-of-the-art method for inducing decision trees from data streams. The aim of this thesis is to improve this algorithm. To measure improvement, a comprehensive framework for evaluating the performance of data stream algorithms is developed. Within the framework memory size is fixed in order to simulate realistic application scenarios. In order to simulate continuous operation, classes of synthetic data are generated providing an evaluation on a large scale. Improvements to many aspects of the Hoeffding tree algorithm are demonstrated. First, a number of methods for handling continuous numeric features are compared. Second, tree prediction strategy is investigated to evaluate the utility of various methods. Finally, the possibility of improving accuracy using ensemble methods is explored.

The experimental results provide meaningful comparisons of accuracy and processing speeds between different modifications of the Hoeffding tree algorithm under various memory limits. The study on numeric attributes demonstrates that sacrificing accuracy for space at the local level often results in improved global accuracy. The prediction strategy shown to perform best adaptively chooses between standard majority class and Naive Bayes prediction in the leaves. The ensemble method investigation shows that combining trees can be worthwhile, but only when sufficient memory is available, and improvement is less likely than in traditional machine learning. In particular, issues are encountered when applying the popular boosting method to streams.

year: 2007

title: Mā te hangarau te oranga o te reo Māori e tautoko ai? Can technology support the long-term health of the Māori language?

authors: Mato, Paora James

subjects: Te reo Māori

abstract: Can contemporary technologies be used to support the ongoing health of an endangered language? Since the late twentieth century unprecedented and substantial social changes have been propelled by advances in contemporary technologies. Continuous connectivity harnessing digital communication, mass media and social networking has enabled instant diffusions of ideas, viewpoints and values. Unrestrained broadcasting and publishing by the general populace and the absence of the traditional media gatekeepers has been linked to the weakening of traditional cultural and linguistic ties as the private, more intimate oral domains inherent to minority indigenous cultures are being opened up to more public modes of consumption. Additionally, significant numbers of minority language speakers switch from the use of their own languages in digital environments to a language that is globally more recognisable. This type of digital language switching emulates the historical occurrence of language shift as minority language speakers opt to use a language that they perceive to be of more benefit to themselves and to their children.

Ironically, language revitalisation initiatives are embracing the ubiquitous attributes of digital technology. A joint venture between the Microsoft Corporation and the University of Waikato resulted in Māori-language interfaces for Microsoft Office and Windows. These and similar interfaces for a range of other applications were investigated. The testing groups were largely unaware the translated interfaces existed and expressed pleasure and pride to see te reo Māori within the various technologies. Following engagement the feedback was less positive. New words, unfamiliar uses of words and poor translations were cited as significant difficulties. Ensuing navigation was often hesitant and uncertain. Were the translations unsuitable? Is te reo Māori unusable within these technologies? The high incidence of task completion suggests difficulties may have arisen from a previous competence with the technologies in another language - English in this case. Future initiatives would do well to consider that perceived difficulties might arise from factors other than the design of the interfaces themselves.

 
Te reo Māori is being used on Twitter. Particular events significantly impacted the use of tweeting in te reo. During Māori Language Week the number of tweeters increased. During the Matatini Festival the volume of tweets increased. While creating an online Māori-language community, a range of strategies became necessary to maintain and prolong some conversations. At times, bilingual tweets extended faltering conversations. Sometimes forwarding tweets drew success. Furthermore, a significant number of people preferred to watch without engaging directly – perhaps a target area for future strategies.

Te reo Māori is a viable option within various technologies as evidenced by the increasing use of the language on a range of contemporary platforms. Translated interfaces provide opportunities for language promotion and extensive language visibility. The development of an online language forum has shown that conversations in te reo Māori can be successfully fostered using social media. Understanding the differences between the static visibility of translated interfaces and the dynamic nature of online conversations can be a huge asset for language strategies once issues of awareness, perception and extended engagement are successfully addressed.

text: Mā te hangarau te oranga o te reo Māori e tautoko ai? Can technology support the long-term health of the Māori language? Can contemporary technologies be used to support the ongoing health of an endangered language? Since the late twentieth century unprecedented and substantial social changes have been propelled by advances in contemporary technologies. Continuous connectivity harnessing digital communication, mass media and social networking has enabled instant diffusions of ideas, viewpoints and values. Unrestrained broadcasting and publishing by the general populace and the absence of the traditional media gatekeepers has been linked to the weakening of traditional cultural and linguistic ties as the private, more intimate oral domains inherent to minority indigenous cultures are being opened up to more public modes of consumption. Additionally, significant numbers of minority language speakers switch from the use of their own languages in digital environments to a language that is globally more recognisable. This type of digital language switching emulates the historical occurrence of language shift as minority language speakers opt to use a language that they perceive to be of more benefit to themselves and to their children.

Ironically, language revitalisation initiatives are embracing the ubiquitous attributes of digital technology. A joint venture between the Microsoft Corporation and the University of Waikato resulted in Māori-language interfaces for Microsoft Office and Windows. These and similar interfaces for a range of other applications were investigated. The testing groups were largely unaware the translated interfaces existed and expressed pleasure and pride to see te reo Māori within the various technologies. Following engagement the feedback was less positive. New words, unfamiliar uses of words and poor translations were cited as significant difficulties. Ensuing navigation was often hesitant and uncertain. Were the translations unsuitable? Is te reo Māori unusable within these technologies? The high incidence of task completion suggests difficulties may have arisen from a previous competence with the technologies in another language - English in this case. Future initiatives would do well to consider that perceived difficulties might arise from factors other than the design of the interfaces themselves.

 
Te reo Māori is being used on Twitter. Particular events significantly impacted the use of tweeting in te reo. During Māori Language Week the number of tweeters increased. During the Matatini Festival the volume of tweets increased. While creating an online Māori-language community, a range of strategies became necessary to maintain and prolong some conversations. At times, bilingual tweets extended faltering conversations. Sometimes forwarding tweets drew success. Furthermore, a significant number of people preferred to watch without engaging directly – perhaps a target area for future strategies.

Te reo Māori is a viable option within various technologies as evidenced by the increasing use of the language on a range of contemporary platforms. Translated interfaces provide opportunities for language promotion and extensive language visibility. The development of an online language forum has shown that conversations in te reo Māori can be successfully fostered using social media. Understanding the differences between the static visibility of translated interfaces and the dynamic nature of online conversations can be a huge asset for language strategies once issues of awareness, perception and extended engagement are successfully addressed.

year: 2018