A mathematics rendering model to support chat-based tutoring
- Authors: Haskins, Bertram Peter
- Date: 2014
- Subjects: Intelligent tutoring systems , Educational innovations , Tutors and tutoring
- Language: English
- Type: Thesis , Doctoral , PhD
- Identifier: vital:9822 , http://hdl.handle.net/10948/d1020567
- Description: Dr Math is a math tutoring service implemented on the chat application Mxit. The service allows school learners to use their mobile phones to discuss mathematicsrelated topics with human tutors. Using the broad user-base provided by Mxit, the Dr Math service has grown to consist of tens of thousands of registered school learners. The tutors on the service are all volunteers and the learners far outnumber the available tutors at any given time. School learners on the service use a shorthand language-form called microtext, to phrase their queries. Microtext is an informal form of language which consists of a variety of misspellings and symbolic representations, which emerge spontaneously as a result of the idiosyncrasies of a learner. The specific form of microtext found on the Dr Math service contains mathematical questions and example equations, pertaining to the tutoring process. Deciphering the queries, to discover their embedded mathematical content, slows down the tutoring process. This wastes time that could have been spent addressing more learner queries. The microtext language thus creates an unnecessary burden on the tutors. This study describes the development of an automated process for the translation of Dr Math microtext queries into mathematical equations. Using the design science research paradigm as a guide, three artefacts are developed. These artefacts take the form of a construct, a model and an instantiation. The construct represents the creation of new knowledge as it provides greater insight into the contents and structure of the language found on a mobile mathematics tutoring service. The construct serves as the basis for the creation of a model for the translation of microtext queries into mathematical equations, formatted for display in an electronic medium. No such technique currently exists and therefore, the model contributes new knowledge. To validate the model, an instantiation was created to serve as a proof-of-concept. The instantiation applies various concepts and techniques, such as those related to natural language processing, to the learner queries on the Dr Math service. These techniques are employed in order to translate an input microtext statement into a mathematical equation, structured by using mark-up language. The creation of the instantiation thus constitutes a knowledge contribution, as most of these techniques have never been applied to the problem of translating microtext into mathematical equations. For the automated process to have utility, it should perform on a level comparable to that of a human performing a similar translation task. To determine how closely related the results from the automated process are to those of a human, three human participants were asked to perform coding and translation tasks. The results of the human participants were compared to the results of the automated process, across a variety of metrics, including agreement, correlation, precision, recall and others. The results from the human participants served as the baseline values for comparison. The baseline results from the human participants were compared with those of the automated process. Krippendorff’s α was used to determine the level of agreement and Pearson’s correlation coefficient to determine the level of correlation between the results. The agreement between the human participants and the automated process was calculated at a level deemed satisfactory for exploratory research and the level of correlation was calculated as moderate. These values correspond with the calculations made as the human baseline. Furthermore, the automated process was able to meet or improve on all of the human baseline metrics. These results serve to validate that the automated process is able to perform the translation at a level comparable to that of a human. The automated process is available for integration into any requesting application, by means of a publicly accessible web service.
- Full Text:
- Date Issued: 2014
- Authors: Haskins, Bertram Peter
- Date: 2014
- Subjects: Intelligent tutoring systems , Educational innovations , Tutors and tutoring
- Language: English
- Type: Thesis , Doctoral , PhD
- Identifier: vital:9822 , http://hdl.handle.net/10948/d1020567
- Description: Dr Math is a math tutoring service implemented on the chat application Mxit. The service allows school learners to use their mobile phones to discuss mathematicsrelated topics with human tutors. Using the broad user-base provided by Mxit, the Dr Math service has grown to consist of tens of thousands of registered school learners. The tutors on the service are all volunteers and the learners far outnumber the available tutors at any given time. School learners on the service use a shorthand language-form called microtext, to phrase their queries. Microtext is an informal form of language which consists of a variety of misspellings and symbolic representations, which emerge spontaneously as a result of the idiosyncrasies of a learner. The specific form of microtext found on the Dr Math service contains mathematical questions and example equations, pertaining to the tutoring process. Deciphering the queries, to discover their embedded mathematical content, slows down the tutoring process. This wastes time that could have been spent addressing more learner queries. The microtext language thus creates an unnecessary burden on the tutors. This study describes the development of an automated process for the translation of Dr Math microtext queries into mathematical equations. Using the design science research paradigm as a guide, three artefacts are developed. These artefacts take the form of a construct, a model and an instantiation. The construct represents the creation of new knowledge as it provides greater insight into the contents and structure of the language found on a mobile mathematics tutoring service. The construct serves as the basis for the creation of a model for the translation of microtext queries into mathematical equations, formatted for display in an electronic medium. No such technique currently exists and therefore, the model contributes new knowledge. To validate the model, an instantiation was created to serve as a proof-of-concept. The instantiation applies various concepts and techniques, such as those related to natural language processing, to the learner queries on the Dr Math service. These techniques are employed in order to translate an input microtext statement into a mathematical equation, structured by using mark-up language. The creation of the instantiation thus constitutes a knowledge contribution, as most of these techniques have never been applied to the problem of translating microtext into mathematical equations. For the automated process to have utility, it should perform on a level comparable to that of a human performing a similar translation task. To determine how closely related the results from the automated process are to those of a human, three human participants were asked to perform coding and translation tasks. The results of the human participants were compared to the results of the automated process, across a variety of metrics, including agreement, correlation, precision, recall and others. The results from the human participants served as the baseline values for comparison. The baseline results from the human participants were compared with those of the automated process. Krippendorff’s α was used to determine the level of agreement and Pearson’s correlation coefficient to determine the level of correlation between the results. The agreement between the human participants and the automated process was calculated at a level deemed satisfactory for exploratory research and the level of correlation was calculated as moderate. These values correspond with the calculations made as the human baseline. Furthermore, the automated process was able to meet or improve on all of the human baseline metrics. These results serve to validate that the automated process is able to perform the translation at a level comparable to that of a human. The automated process is available for integration into any requesting application, by means of a publicly accessible web service.
- Full Text:
- Date Issued: 2014
A framework to enhance the mobile user experience in an Mlearning interaction
- Authors: Botha, Adele
- Date: 2011
- Subjects: Educational technology , Information technology , Educational innovations , Teaching -- Aids and devices
- Language: English
- Type: Thesis , Doctoral , PhD
- Identifier: vital:9753 , http://hdl.handle.net/10948/d1008163 , Educational technology , Information technology , Educational innovations , Teaching -- Aids and devices
- Description: The new millennium is witness to a telecommunications world that is vastly different from even the recent past with developments in the mobile sector having dramatically changed the Information and Communication Technology (ICT) landscape. Mobile cellular technology has proliferated faster than any previous technology and is now the most ubiquitous technology in the world. The focus of this thesis is the development of a framework to enhance the Mobile User Experience in an Mlearning interaction. This research is contextualised by the goal-oriented use of mobile cellular technologies in a formal educational environment. As such the study, although residing in the field of Human-Computer Interaction (HCI), acknowledges issues arising in the Education Domain as a specific field of application. The aim of the research was to investigate the components of a framework to enhance the Mobile User Experience in an Mlearning interaction. The development of the framework was facilitated by the exploration of: the Mobile User Experience factors and their impact, on the Mobile User Experience of learners participating in a goal-oriented Mlearning interaction. These critical factors were documented in terms of the Mobile User Experience components, and the relationships of these components to each other as well as the Mobile User Experience of an Mlearning interaction. The research, grounded in a phenomenological research philosophy, applied an inductive reasoning approach, and was operationalised through a single case study methodology. A qualitative research strategy was considered appropriate, as the phenomenon of User Experience is linked to the hedonistic attributes of the interaction. This study was conducted in four phases with focus on three embedded units of analysis. The three units of analysis were identified as: The learner as end user in an Mlearning interaction; The educator as designer of the Mlearning interaction; and The Mlearning interaction. The research revealed that the Mobile User Experience of an Mlearning interaction is affected by the mobile user, mobile use, mobile device, mobile business practices, mobile networks, mobile interaction and mobile context. Within the Mlearning interaction the significant components are the learners as mobile users, the enhance interactions, removal of barriers to the interaction, goal-oriented nature of the interaction and the ducational context. Identifying these components and their associated Mobile User Experience factors and impacts, present the main contribution of this thesis. In conclusion, the limitations of the study are documented and topics for future research are outlined.
- Full Text:
- Date Issued: 2011
- Authors: Botha, Adele
- Date: 2011
- Subjects: Educational technology , Information technology , Educational innovations , Teaching -- Aids and devices
- Language: English
- Type: Thesis , Doctoral , PhD
- Identifier: vital:9753 , http://hdl.handle.net/10948/d1008163 , Educational technology , Information technology , Educational innovations , Teaching -- Aids and devices
- Description: The new millennium is witness to a telecommunications world that is vastly different from even the recent past with developments in the mobile sector having dramatically changed the Information and Communication Technology (ICT) landscape. Mobile cellular technology has proliferated faster than any previous technology and is now the most ubiquitous technology in the world. The focus of this thesis is the development of a framework to enhance the Mobile User Experience in an Mlearning interaction. This research is contextualised by the goal-oriented use of mobile cellular technologies in a formal educational environment. As such the study, although residing in the field of Human-Computer Interaction (HCI), acknowledges issues arising in the Education Domain as a specific field of application. The aim of the research was to investigate the components of a framework to enhance the Mobile User Experience in an Mlearning interaction. The development of the framework was facilitated by the exploration of: the Mobile User Experience factors and their impact, on the Mobile User Experience of learners participating in a goal-oriented Mlearning interaction. These critical factors were documented in terms of the Mobile User Experience components, and the relationships of these components to each other as well as the Mobile User Experience of an Mlearning interaction. The research, grounded in a phenomenological research philosophy, applied an inductive reasoning approach, and was operationalised through a single case study methodology. A qualitative research strategy was considered appropriate, as the phenomenon of User Experience is linked to the hedonistic attributes of the interaction. This study was conducted in four phases with focus on three embedded units of analysis. The three units of analysis were identified as: The learner as end user in an Mlearning interaction; The educator as designer of the Mlearning interaction; and The Mlearning interaction. The research revealed that the Mobile User Experience of an Mlearning interaction is affected by the mobile user, mobile use, mobile device, mobile business practices, mobile networks, mobile interaction and mobile context. Within the Mlearning interaction the significant components are the learners as mobile users, the enhance interactions, removal of barriers to the interaction, goal-oriented nature of the interaction and the ducational context. Identifying these components and their associated Mobile User Experience factors and impacts, present the main contribution of this thesis. In conclusion, the limitations of the study are documented and topics for future research are outlined.
- Full Text:
- Date Issued: 2011
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