Wissenschaftliche Arbeiten

Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Abschlussarbeiten

2017

Kopeinik Simone

array(37) { ["Start"]=> string(10) "18.04.2012" ["year"]=> int(2017) ["title"]=> string(94) "Applying Cognitive Learner Models for Recommender Systems in Small-Scale Learning Environments" ["Abstract de"]=> string(2119) "In recent years, various recommendation algorithms have been proposed to support learners in technology-enhanced learning environments. Such algorithms have proven to be quite effective in big-data learning settings (massive open online courses), yet successful applications in other informal and formal learning settings are rare. Common challenges include data sparsity, the lack of sufficiently flexible learner and domain models, and the difficulty of including pedagogical goals into recommendation strategies. Computational models of human cognition and learning are, in principle, well positioned to help meet these challenges, yet the effectiveness of cognitive models in educational recommender systems remains poorly understood to this date. This thesis contributes to this strand of research by investigating i) two cognitive learner models (CbKST and SUSTAIN) for resource recommendations that qualify for sparse user data by following theory-driven top down approaches, and ii) two tag recommendation strategies based on models of human cognition (BLL and MINERVA2) that support the creation of learning content meta-data. The results of four online and offline experiments in different learning contexts indicate that a recommendation approach based on the CbKST, a well-founded structural model of knowledge representation, can improve the users' perceived learning experience in formal learning settings. In informal settings, SUSTAIN, a human category learning model, is shown to succeed in representing dynamic, interest based learning interactions and to improve Collaborative Filtering for resource recommendations. The investigation of the two proposed tag recommender strategies underlined their ability to generate accurate suggestions (BLL) and in collaborative settings, their potential to promote the development of shared vocabulary (MINERVA2). This thesis shows that the application of computational models of human cognition holds promise for the design of recommender mechanisms and, at the same time, for gaining a deeper understanding of interaction dynamics in virtual learning systems." ["AutorId"]=> string(0) "" ["author"]=> string(15) "Kopeinik Simone" ["Autor_extern_Geschlecht"]=> string(8) "weiblich" ["BetreuerId"]=> string(3) "214" ["Betreuer"]=> string(20) "Lindstaedt Stefanie " ["Option_Betreuer_extern_intern"]=> string(6) "intern" ["Betreuer_extern"]=> string(0) "" ["BetreuerAffiliation"]=> string(3) "TUG" ["Zweitbetreuer"]=> string(15) "Lex Elisabeth; " ["Zweitbetreuer1_ID"]=> string(3) "110" ["Option_Zweitbetreuer1_extern_intern"]=> string(6) "intern" ["Zweitbetreuer1_extern"]=> string(0) "" ["ZweitBetreuer1Affiliation"]=> string(3) "TUG" ["Zweitbetreuer2_ID"]=> string(0) "" ["Option_Zweitbetreuer2_extern_intern"]=> string(6) "intern" ["Zweitbetreuer2_extern"]=> string(0) "" ["ZweitBetreuer2Affiliation"]=> string(0) "" ["meta"]=> string(1) "4" ["Dont Publish"]=> string(0) "" ["Keywords"]=> string(245) "Personalized Learning Resource Recommendations, Personalized Tag Recommendations, Recommender Systems, Online Studies, Offline Studies, Cognitive Learner Models, CbKST, SUSTAIN, Base-level Learning Equation, Minerva, Technology-enhanced Learning" ["Link"]=> string(0) "" ["ID"]=> string(3) "267" ["angestellt bei"]=> string(21) "TUG-IWT Wiss. Partner" ["Text_intern_extern"]=> string(0) "" ["Anzahl_Wissenschaftliche_Arbeiten"]=> string(3) "118" ["Kombifeld_Autoren"]=> string(15) "Kopeinik Simone" ["Kombifeld_AutorIntern_Autor_Extern_geschlecht"]=> string(8) "weiblich" ["Erstelldatum"]=> string(0) "" ["Letzter_Aufruf"]=> string(10) "11.06.2018" ["Letzte_Änderung_Person"]=> string(10) "alangmaier" ["Wissenschaftliche Arbeiten_Art::ID"]=> string(1) "4" ["organ"]=> string(17) "PhD/ Dissertation" ["thesis"]=> string(3) "PhD" }

Applying Cognitive Learner Models for Recommender Systems in Small-Scale Learning Environments i

PhD/ Dissertation

PhD
In recent years, various recommendation algorithms have been proposed to support learners in technology-enhanced learning environments. Such algorithms have proven to be quite effective in big-data learning settings (massive open online courses), yet successful applications in other informal and formal learning settings are rare. Common challenges include data sparsity, the lack of sufficiently flexible learner and domain models, and the difficulty of including pedagogical goals into recommendation strategies. Computational models of human cognition and learning are, in principle, well positioned to help meet these challenges, yet the effectiveness of cognitive models in educational recommender systems remains poorly understood to this date. This thesis contributes to this strand of research by investigating i) two cognitive learner models (CbKST and SUSTAIN) for resource recommendations that qualify for sparse user data by following theory-driven top down approaches, and ii) two tag recommendation strategies based on models of human cognition (BLL and MINERVA2) that support the creation of learning content meta-data. The results of four online and offline experiments in different learning contexts indicate that a recommendation approach based on the CbKST, a well-founded structural model of knowledge representation, can improve the users' perceived learning experience in formal learning settings. In informal settings, SUSTAIN, a human category learning model, is shown to succeed in representing dynamic, interest based learning interactions and to improve Collaborative Filtering for resource recommendations. The investigation of the two proposed tag recommender strategies underlined their ability to generate accurate suggestions (BLL) and in collaborative settings, their potential to promote the development of shared vocabulary (MINERVA2). This thesis shows that the application of computational models of human cognition holds promise for the design of recommender mechanisms and, at the same time, for gaining a deeper understanding of interaction dynamics in virtual learning systems.
2017

Kowald Dominik

array(37) { ["Start"]=> string(10) "07.06.2013" ["year"]=> int(2017) ["title"]=> string(107) "Modellierung von Aktivierungsprozessen im menschlichen Gedächtnis zur Verbesserung von Tag Recommendations" ["Abstract de"]=> string(1971) "Social tagging systems enable users to collaboratively assign freely chosen keywords (i.e., tags) to resources (e.g., Web links). In order to support users in finding descrip- tive tags, tag recommendation algorithms have been proposed. One issue of current state-of-the-art tag recommendation algorithms is that they are often designed in a purely data-driven way and thus, lack a thorough understanding of the cognitive processes that play a role when people assign tags to resources. A prominent exam- ple is the activation equation of the cognitive architecture ACT-R, which formalizes activation processes in human memory to determine if a specific memory unit (e.g., a word or tag) will be needed in a specific context. It is the aim of this thesis to investigate if a cognitive-inspired approach, which models activation processes in human memory, can improve tag recommendations. For this, the relation between activation processes in human memory and usage practices of tags is studied, which reveals that (i) past usage frequency, (ii) recency, and (iii) semantic context cues are important factors when people reuse tags. Based on this, a cognitive-inspired tag recommendation approach termed BLL AC +MP r is developed based on the activation equation of ACT-R. An extensive evaluation using six real-world folksonomy datasets shows that BLL AC +MP r outperforms current state-of-the-art tag recommendation algorithms with respect to various evaluation metrics. Finally, BLL AC +MP r is utilized for hashtag recommendations in Twitter to demonstrate its generalizability in related areas of tag-based recommender systems. The findings of this thesis demonstrate that activation processes in human memory can be utilized to improve not only social tag recommendations but also hashtag recommendations. This opens up a number of possible research strands for future work, such as the design of cognitive-inspired resource recommender systems" ["AutorId"]=> string(0) "" ["author"]=> string(14) "Kowald Dominik" ["Autor_extern_Geschlecht"]=> string(9) "männlich" ["BetreuerId"]=> string(3) "214" ["Betreuer"]=> string(20) "Lindstaedt Stefanie " ["Option_Betreuer_extern_intern"]=> string(6) "intern" ["Betreuer_extern"]=> string(0) "" ["BetreuerAffiliation"]=> string(3) "TUG" ["Zweitbetreuer"]=> string(25) "Lex Elisabeth; Ley Tobias" ["Zweitbetreuer1_ID"]=> string(3) "110" ["Option_Zweitbetreuer1_extern_intern"]=> string(6) "intern" ["Zweitbetreuer1_extern"]=> string(0) "" ["ZweitBetreuer1Affiliation"]=> string(3) "TUG" ["Zweitbetreuer2_ID"]=> string(0) "" ["Option_Zweitbetreuer2_extern_intern"]=> string(6) "extern" ["Zweitbetreuer2_extern"]=> string(10) "Ley Tobias" ["ZweitBetreuer2Affiliation"]=> string(17) "Talinn University" ["meta"]=> string(1) "4" ["Dont Publish"]=> string(0) "" ["Keywords"]=> string(66) "dissertation, tag recommendation, recommendation evaluation, ACT-R" ["Link"]=> string(72) "http://www.dominikkowald.info/documents/others/2017dissertation_bllac.pd" ["ID"]=> string(3) "206" ["angestellt bei"]=> string(24) "TUG-IWT KC Wiss. Partner" ["Text_intern_extern"]=> string(0) "" ["Anzahl_Wissenschaftliche_Arbeiten"]=> string(3) "118" ["Kombifeld_Autoren"]=> string(14) "Kowald Dominik" ["Kombifeld_AutorIntern_Autor_Extern_geschlecht"]=> string(9) "männlich" ["Erstelldatum"]=> string(0) "" ["Letzter_Aufruf"]=> string(10) "11.06.2018" ["Letzte_Änderung_Person"]=> string(10) "alangmaier" ["Wissenschaftliche Arbeiten_Art::ID"]=> string(1) "4" ["organ"]=> string(17) "PhD/ Dissertation" ["thesis"]=> string(3) "PhD" }

Modellierung von Aktivierungsprozessen im menschlichen Gedächtnis zur Verbesserung von Tag Recommendations i

PhD/ Dissertation

PhD
Social tagging systems enable users to collaboratively assign freely chosen keywords (i.e., tags) to resources (e.g., Web links). In order to support users in finding descrip- tive tags, tag recommendation algorithms have been proposed. One issue of current state-of-the-art tag recommendation algorithms is that they are often designed in a purely data-driven way and thus, lack a thorough understanding of the cognitive processes that play a role when people assign tags to resources. A prominent exam- ple is the activation equation of the cognitive architecture ACT-R, which formalizes activation processes in human memory to determine if a specific memory unit (e.g., a word or tag) will be needed in a specific context. It is the aim of this thesis to investigate if a cognitive-inspired approach, which models activation processes in human memory, can improve tag recommendations. For this, the relation between activation processes in human memory and usage practices of tags is studied, which reveals that (i) past usage frequency, (ii) recency, and (iii) semantic context cues are important factors when people reuse tags. Based on this, a cognitive-inspired tag recommendation approach termed BLL AC +MP r is developed based on the activation equation of ACT-R. An extensive evaluation using six real-world folksonomy datasets shows that BLL AC +MP r outperforms current state-of-the-art tag recommendation algorithms with respect to various evaluation metrics. Finally, BLL AC +MP r is utilized for hashtag recommendations in Twitter to demonstrate its generalizability in related areas of tag-based recommender systems. The findings of this thesis demonstrate that activation processes in human memory can be utilized to improve not only social tag recommendations but also hashtag recommendations. This opens up a number of possible research strands for future work, such as the design of cognitive-inspired resource recommender systems
2017

Geigl Florian

array(37) { ["Start"]=> string(10) "01.01.2016" ["year"]=> int(2017) ["title"]=> string(55) "Random Surfers as Models of Human Navigation on the Web" ["Abstract de"]=> string(1876) "The Web is a central part of modern everyday life. Many people access it on a daily basis for a variety of reasons such as to retrieve news, watch videos, engage in social networks, buy goods in online shops or simply to procrastinate. Yet, we are still uncertain about how humans navigate the Web and the potential of factors influencing this process. To shed light on this topic, this thesis deals with modeling aspects of human navigation on the Web and the effects arising due to manipulations of this process. Mainly, this work provides a solid theoretical framework which allows to examine the potential effects of two different strategies aiming to guide visitors of a website. The framework builds upon the random surfer model, which is shown to be a sufficiently accurate model of human navigation on the Web in the first part of this work. In a next step, this thesis examines to which extent various click biases influence the typical whereabouts of the random surfer. Based on this analysis, this work demonstrates that exploiting common human cognitive biases exhibits a high potential of manipulating the frequencies with which the random surfer visits certain webpages. However, besides taking advantage of these biases, there exist further possibilities to steer users who navigate a website. Specifically, simply inserting new links to a webpage opens up new routes for visitors to explore a website. To investigate which of the two guiding strategies bears the higher potential, this work applies both of them to webgraphs of several websites and provides a detailed comparison of the emerging effects. The results presented in this thesis lead to actionable insights for website administrators and further broaden our understanding of how humans navigate the Web. Additionally, the presented model builds the foundation for further research in this field. " ["AutorId"]=> string(0) "" ["author"]=> string(13) "Geigl Florian" ["Autor_extern_Geschlecht"]=> string(9) "männlich" ["BetreuerId"]=> string(3) "232" ["Betreuer"]=> string(11) "Helic Denis" ["Option_Betreuer_extern_intern"]=> string(6) "intern" ["Betreuer_extern"]=> string(0) "" ["BetreuerAffiliation"]=> string(3) "TUG" ["Zweitbetreuer"]=> string(0) "" ["Zweitbetreuer1_ID"]=> string(0) "" ["Option_Zweitbetreuer1_extern_intern"]=> string(6) "intern" ["Zweitbetreuer1_extern"]=> string(0) "" ["ZweitBetreuer1Affiliation"]=> string(0) "" ["Zweitbetreuer2_ID"]=> string(0) "" ["Option_Zweitbetreuer2_extern_intern"]=> string(6) "intern" ["Zweitbetreuer2_extern"]=> string(0) "" ["ZweitBetreuer2Affiliation"]=> string(0) "" ["meta"]=> string(1) "4" ["Dont Publish"]=> string(0) "" ["Keywords"]=> string(48) "random surfer; web; human navigation; biases; " ["Link"]=> string(74) "https://online.tugraz.at/tug_online/webnav.ini?pUrl=anmeldung.durchfuehren" ["ID"]=> string(3) "897" ["angestellt bei"]=> string(7) "Student" ["Text_intern_extern"]=> string(0) "" ["Anzahl_Wissenschaftliche_Arbeiten"]=> string(3) "118" ["Kombifeld_Autoren"]=> string(13) "Geigl Florian" ["Kombifeld_AutorIntern_Autor_Extern_geschlecht"]=> string(9) "männlich" ["Erstelldatum"]=> string(10) "08.11.2017" ["Letzter_Aufruf"]=> string(10) "11.06.2018" ["Letzte_Änderung_Person"]=> string(10) "alangmaier" ["Wissenschaftliche Arbeiten_Art::ID"]=> string(1) "4" ["organ"]=> string(17) "PhD/ Dissertation" ["thesis"]=> string(3) "PhD" }

Random Surfers as Models of Human Navigation on the Web i

PhD/ Dissertation

PhD
The Web is a central part of modern everyday life. Many people access it on a daily basis for a variety of reasons such as to retrieve news, watch videos, engage in social networks, buy goods in online shops or simply to procrastinate. Yet, we are still uncertain about how humans navigate the Web and the potential of factors influencing this process. To shed light on this topic, this thesis deals with modeling aspects of human navigation on the Web and the effects arising due to manipulations of this process. Mainly, this work provides a solid theoretical framework which allows to examine the potential effects of two different strategies aiming to guide visitors of a website. The framework builds upon the random surfer model, which is shown to be a sufficiently accurate model of human navigation on the Web in the first part of this work. In a next step, this thesis examines to which extent various click biases influence the typical whereabouts of the random surfer. Based on this analysis, this work demonstrates that exploiting common human cognitive biases exhibits a high potential of manipulating the frequencies with which the random surfer visits certain webpages. However, besides taking advantage of these biases, there exist further possibilities to steer users who navigate a website. Specifically, simply inserting new links to a webpage opens up new routes for visitors to explore a website. To investigate which of the two guiding strategies bears the higher potential, this work applies both of them to webgraphs of several websites and provides a detailed comparison of the emerging effects. The results presented in this thesis lead to actionable insights for website administrators and further broaden our understanding of how humans navigate the Web. Additionally, the presented model builds the foundation for further research in this field.
2016

Fessl Angela

array(37) { ["Start"]=> string(10) "28.04.2009" ["year"]=> int(2016) ["title"]=> string(69) "Individual Reflection Guidance to Support Reflective Learning at Work" ["Abstract de"]=> string(3838) "Reflective learning can be seen as the conscious re-evaluation of past situations or experiences with the goal to learn from them and to use the gained insights to guide future behaviour. Reflective learning in the context of workplace learning has been identified as a core process which aims at getting new insights, deriving better practices and finally improving own work. Reflective learning, which is a cognitive process based on the individual’s intrinsic motivation, cannot be directly enforced, but guidance techniques like prompts, journals or diary writing, and visuals can foster reflection while using tools or software applications during work. The goal of this thesis is to conceptualise reflection guidance as adaptive software components that provide technologically supported guidance independent of the application and the working environment. In order to achieve this, a literature review was conducted to identify key challenges necessary to provide meaningful technological support for guiding reflective learning at work. Based on those challenges, technologies were investigated and analysed to extract those technologies that are the most suitable ones for providing reflection guidance and are able to trigger reflective learning. Finally, core components and architecture were derived to present a general applicable reflection guidance framework. The theoretical underpinning is grounded in existing reflective learning theory and theoretical models and processes supporting reflective learning. The design science research methodology is used as underlying research method to thoroughly present the conducted research. Altogether fifteen field studies consisting of one focus group, two design studies, six formative field studies and six summative field studies were conducted in different work-related settings. The field studies together with an extensive literature research led to the development of five iteration cycles of two different reflective learning applications to trigger reflective learning. Finally the thesis resulted in 9 publications (7 accepted and 2 under major revision). The research conducted was divided into three different phases. First, form the extensive literature the following key challenges emerged: (i) the timing of reflection (when to motivate to reflect: during an activity or after an activity), (ii) the appropriate tool used to motivate for reflection ( prompts vs. diaries vs. visuals vs. contextualisation) and (iii) the work-related context of reflection (to not disrupt the work-flow). Second, an in-app reflection guidance concept was developed, which provides reflection guidance in form of adaptive components. To illustrate how the concept can be instantiated in work-related settings, different components of the concept were implemented in three applications adopting various approaches to support reflective learning. The results showed that (i) prompts, diaries, and contextualisation are effective tools for initiating reflection when presented at the right time and in the right place and (ii) their integration in the work processes needs to be carefully considered in order to not interrupt or annoy the user during work. Third, a general applicable conceptual reflection guidance framework called “Reflector” has been elaborated including requirements, lessons learned and necessary features for providing meaningful technologically supported reflection guidance. This framework can be seen as a kind of a technical summary of the insights gained from the literature review and the implemented and evaluated reflection guidance concept. This thesis contributes scientifically to the area of technology-enhanced learning and provides a novel approach on how to provide meaningful technologically supported individual reflection guidance at work." ["AutorId"]=> string(2) "93" ["author"]=> string(0) "" ["Autor_extern_Geschlecht"]=> string(0) "" ["BetreuerId"]=> string(3) "214" ["Betreuer"]=> string(20) "Lindstaedt Stefanie " ["Option_Betreuer_extern_intern"]=> string(6) "intern" ["Betreuer_extern"]=> string(0) "" ["BetreuerAffiliation"]=> string(3) "TUG" ["Zweitbetreuer"]=> string(27) "Pammer-Schindler Viktoria; " ["Zweitbetreuer1_ID"]=> string(3) "212" ["Option_Zweitbetreuer1_extern_intern"]=> string(6) "intern" ["Zweitbetreuer1_extern"]=> string(0) "" ["ZweitBetreuer1Affiliation"]=> string(3) "TUG" ["Zweitbetreuer2_ID"]=> string(0) "" ["Option_Zweitbetreuer2_extern_intern"]=> string(6) "intern" ["Zweitbetreuer2_extern"]=> string(0) "" ["ZweitBetreuer2Affiliation"]=> string(0) "" ["meta"]=> string(1) "4" ["Dont Publish"]=> string(0) "" ["Keywords"]=> string(115) "reflective learning, reflection guidance, workplace learning, reflection guidance concept, framework for reflection" ["Link"]=> string(0) "" ["ID"]=> string(3) "197" ["angestellt bei"]=> string(2) "KC" ["Text_intern_extern"]=> string(2) "KC" ["Anzahl_Wissenschaftliche_Arbeiten"]=> string(3) "118" ["Kombifeld_Autoren"]=> string(12) "Fessl Angela" ["Kombifeld_AutorIntern_Autor_Extern_geschlecht"]=> string(8) "weiblich" ["Erstelldatum"]=> string(0) "" ["Letzter_Aufruf"]=> string(10) "05.04.2018" ["Letzte_Änderung_Person"]=> string(14) "dhinterleitner" ["Wissenschaftliche Arbeiten_Art::ID"]=> string(1) "4" ["organ"]=> string(17) "PhD/ Dissertation" ["thesis"]=> string(3) "PhD" }

Individual Reflection Guidance to Support Reflective Learning at Work i

PhD/ Dissertation

PhD
Reflective learning can be seen as the conscious re-evaluation of past situations or experiences with the goal to learn from them and to use the gained insights to guide future behaviour. Reflective learning in the context of workplace learning has been identified as a core process which aims at getting new insights, deriving better practices and finally improving own work. Reflective learning, which is a cognitive process based on the individual’s intrinsic motivation, cannot be directly enforced, but guidance techniques like prompts, journals or diary writing, and visuals can foster reflection while using tools or software applications during work. The goal of this thesis is to conceptualise reflection guidance as adaptive software components that provide technologically supported guidance independent of the application and the working environment. In order to achieve this, a literature review was conducted to identify key challenges necessary to provide meaningful technological support for guiding reflective learning at work. Based on those challenges, technologies were investigated and analysed to extract those technologies that are the most suitable ones for providing reflection guidance and are able to trigger reflective learning. Finally, core components and architecture were derived to present a general applicable reflection guidance framework. The theoretical underpinning is grounded in existing reflective learning theory and theoretical models and processes supporting reflective learning. The design science research methodology is used as underlying research method to thoroughly present the conducted research. Altogether fifteen field studies consisting of one focus group, two design studies, six formative field studies and six summative field studies were conducted in different work-related settings. The field studies together with an extensive literature research led to the development of five iteration cycles of two different reflective learning applications to trigger reflective learning. Finally the thesis resulted in 9 publications (7 accepted and 2 under major revision). The research conducted was divided into three different phases. First, form the extensive literature the following key challenges emerged: (i) the timing of reflection (when to motivate to reflect: during an activity or after an activity), (ii) the appropriate tool used to motivate for reflection ( prompts vs. diaries vs. visuals vs. contextualisation) and (iii) the work-related context of reflection (to not disrupt the work-flow). Second, an in-app reflection guidance concept was developed, which provides reflection guidance in form of adaptive components. To illustrate how the concept can be instantiated in work-related settings, different components of the concept were implemented in three applications adopting various approaches to support reflective learning. The results showed that (i) prompts, diaries, and contextualisation are effective tools for initiating reflection when presented at the right time and in the right place and (ii) their integration in the work processes needs to be carefully considered in order to not interrupt or annoy the user during work. Third, a general applicable conceptual reflection guidance framework called “Reflector” has been elaborated including requirements, lessons learned and necessary features for providing meaningful technologically supported reflection guidance. This framework can be seen as a kind of a technical summary of the insights gained from the literature review and the implemented and evaluated reflection guidance concept. This thesis contributes scientifically to the area of technology-enhanced learning and provides a novel approach on how to provide meaningful technologically supported individual reflection guidance at work.
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