During the past few decades, there has been increasing attention to multimodal adaptive language learning interface design. The purpose of this study was to examine users’ experiences with a chatbot language learning interface through the lens of cognitive emotions and emotions in learning. A particular focus of this study was on users’ interactions with a chatbot in a public setting and in a private environment. Focusing on the event of users’ interaction with a chatbot interface, seventy-five interactions were videotaped in this study, in which fifteen users were asked to interact with the chatbot “Lucy” for their language learning. The video-stimulated post interaction interviews with participants provided complementary data for understanding their experiences with the language learning system. Analysis of twenty-five interactions selected from a total of seventy-five revealed five main factors of chatbot language tutor interface design and their relative significance in the process of users’ meaning making and knowledge construction. Findings showed that users’ sensory, emotional, cultural, linguistic and relational engagement influenced their responses to the chatbot interface, which in turn, shaped their learning processes. Building on a theoretical framework of cognitive emotions and emotions in learning, this study documented users’ language learning processes with the chatbot language learning interface by investigating users’ experiences. The findings and techniques resulting from this study will help designers and researchers achieve a better understanding of users’ experiences with technology and the role of emotions in the processes of learning when using technology and assist them to improve the design of language learning environments.

During the past few decades, there has been increasing attention to multimodal adaptive language learning interface design. The purpose of this study was to examine users’ experiences with a chatbot language learning interface through the lens of cognitive emotions and emotions in learning. A particular focus of this study was on users’ interactions with a chatbot in a public setting and in a private environment. Focusing on the event of users’ interaction with a chatbot interface, seventy-five interactions were videotaped in this study, in which fifteen users were asked to interact with the chatbot “Lucy” for their language learning. The video-stimulated post interaction interviews with participants provided complementary data for understanding their experiences with the language learning system. Analysis of twenty-five interactions selected from a total of seventy-five revealed five main factors of chatbot language tutor interface design and their relative significance in the process of users’ meaning making and knowledge construction. Findings showed that users’ sensory, emotional, cultural, linguistic and relational engagement influenced their responses to the chatbot interface, which in turn, shaped their learning processes. Building on a theoretical framework of cognitive emotions and emotions in learning, this study documented users’ language learning processes with the chatbot language learning interface by investigating users’ experiences. The findings and techniques resulting from this study will help designers and researchers achieve a better understanding of users’ experiences with technology and the role of emotions in the processes of learning when using technology and assist them to improve the design of language learning environments.

During the past few decades, there has been increasing attention to multimodal adaptive language learning interface design. The purpose of this study was to examine users’ experiences with a chatbot language learning interface through the lens of cognitive emotions and emotions in learning. A particular focus of this study was on users’ interactions with a chatbot in a public setting and in a private environment. Focusing on the event of users’ interaction with a chatbot interface, seventy-five interactions were videotaped in this study, in which fifteen users were asked to interact with the chatbot “Lucy” for their language learning. The video-stimulated post interaction interviews with participants provided complementary data for understanding their experiences with the language learning system. Analysis of twenty-five interactions selected from a total of seventy-five revealed five main factors of chatbot language tutor interface design and their relative significance in the process of users’ meaning making and knowledge construction. Findings showed that users’ sensory, emotional, cultural, linguistic and relational engagement influenced their responses to the chatbot interface, which in turn, shaped their learning processes. Building on a theoretical framework of cognitive emotions and emotions in learning, this study documented users’ language learning processes with the chatbot language learning interface by investigating users’ experiences. The findings and techniques resulting from this study will help designers and researchers achieve a better understanding of users’ experiences with technology and the role of emotions in the processes of learning when using technology and assist them to improve the design of language learning environments. / Education, Faculty of / Curriculum and Pedagogy (EDCP), Department of / Graduate

The use of machine learning to predict output from data, using a model, is a well studied area. There are, however, a number of real-world applications that require a model to be produced but have little or no data available of the specific environment. These situations are prominent in Intelligent Environments (IEs). The sparsity of the data can be a result of the physical nature of the implementation, such as sensors placed into disaster recovery scenarios, or where the focus of the data acquisition is on very defined user groups, in the case of disabled individuals. Standard machine learning approaches focus on a need for training data to come from the same domain. The restrictions of the physical nature of these environments can severely reduce data acquisition making it extremely costly, or in certain situations, impossible. This impedes the ability of these approaches to model the environments. It is this problem, in the area of IEs, that this thesis is focussed. To address complex and uncertain environments, humans have learnt to use previously acquired information to reason and understand their surroundings. Knowledge from different but related domains can be used to aid the ability to learn. For example, the ability to ride a road bicycle can help when acquiring the more sophisticated skills of mountain biking. This humanistic approach to learning can be used to tackle real-world problems where a-priori labelled training data is either difficult or not possible to gain. The transferral of knowledge from a related, but differing context can allow for the reuse and repurpose of known information. In this thesis, a novel composition of methods are brought together that are broadly based on a humanist approach to learning. Two concepts, Transfer Learning (TL) and Fuzzy Logic (FL) are combined in a framework, Fuzzy Transfer Learning (FuzzyTL), to address the problem of learning tasks that have no prior direct contextual knowledge. Through the use of a FL based learning method, uncertainty that is evident in dynamic environments is represented. By combining labelled data from a contextually related source task, and little or no unlabelled data from a target task, the framework is shown to be able to accomplish predictive tasks using models learned from contextually different data. The framework incorporates an additional novel five stage online adaptation process. By adapting the underlying fuzzy structure through the use of previous labelled knowledge and new unlabelled information, an increase in predictive performance is shown. The framework outlined is applied to two differing real-world IEs to demonstrate its ability to predict in uncertain and dynamic environments. Through a series of experiments, it is shown that the framework is capable of predicting output using differing contextual data.

In this master graduation work there are analyzed and described the principles of creation an intelligent component of adaptive e-learning system, by using Kohonen self organized map neural networks. There created intelligent component, which was realized by using agent technologies, distinguishes by abilities to make a decision in respect of concrete user needs. In this work there are also described common structure of adaptive e-learning system, by pointing the place, which takes intelligent component, and its relationships with other parts of such adaptive system.

資訊科技對學習引發了全面性的變革，網路無遠弗屆、快速便利的特性，更使得網路學習、線上學習、數位學習等利用網路與資訊科技的結合的學習方式為大家所重視。未來邁入知識經濟後，人們更需要不斷的學習來提昇自己的競爭力，透過學習獲取知識。資訊科技與網路的結合，為數位學習帶來許多的優勢與好處，但是雖然數位學習的網站很多，大多數仍無法達到如傳統學習的學習效果，學習者通常瀏覽過一兩次後，便失去學習的興趣，因此數位學習系統仍缺乏整合性的規劃與開發。 　　目前網路上許多的數位學習系統提供的互動機制不足，學習者缺乏人際互動的工具與活動，除此之外，許多互動工具無法得到參與學習的使用者之青睞，學習者甚少使用，如同虛設。因此本篇論文企圖設計出一套整合性與互動的數位學習之設計，解決目前網路學習在互動中所遭遇的瓶頸。 　　本研究從適性學習(Adaptive Learning)、合作學習(Collaborative and Cooperative Learning)與建構學習(Constructive Learning)三方面進行設計，互動機制可以達到此三個目的，進一步讓系統可以支援達成合作學習與建構學習的活動設計。 / Information technology and Internet technology have brought the revolution of learning, training and education. The knowledge economy in the 21st century has made the knowledge become the most important critical success factor. We must continuously learn in order to maintain the competitive advantage. Many corporations and educational institutions have developed their own e-Learning web sites. Because the lack of adequate interactive mechanisms, most of them cannot provide the efficient and effective learning results. Our research aims to develop an interaction-centric e-Learning design and implementation. Based on the adaptive learning, collaborative and cooperative learning, constructive learning concepts, we design an integrated interaction-centric model to develop a set of toolbox to enhance the interactive activities of e-Learning.

The Master’s thesis deals with the topic of the use of artificial inteligence for managerial decision making in the firm. This thesis contains an aplication of fuzzy logic system for firm’s supplier evaluation that provides informations for more efficient collaboration with suppliers.

Es besteht eine starke Nachfrage nach einer positiven Applikation zum Lernen, um den strategischen Plan des indonesischen Ministeriums für Bildung und Kultur zu fördern, dass die Ratio von Berufsschule höher als die allgemeinbildende Schule werden kann. Die rasante entwicklung der Informations- und Kommunikationstechnologie könnte es ermöglichen, den Lernenden ein computergestütztes, personalisiertes E-Learning-System zur Verfügung zu stellen, um die Tatsache zu überwinden, dass jeder Lernende seine eigene Präferenz hat. Diese Studie bietet ein adaptives E-Learning-System, bei dem zwei Quellen der Personalisierung berücksichtigt werden: der Lernstil des Schülers und das Vorwissen. Um die Wirksamkeit des vorgeschlagenen E-Learning-Programms zu untersuchen, werden die Leistungen der Schüler bezüglich der drei niedrigsten Ebenen im kognitiven Bereich (Wissen, Verständnis und Anwendung) in der E-Learning-Gruppe mit denen der traditionellen Unterrichtsgruppe verglichen. Ein weiterer interessanter Bereich ist die sogannte schülerperspektive Usability-Bewertung und die Beziehung zwischen den Usability-Fragebogen angegebenen Aspekten zu erforschen. Der Entwurfs- und Entwicklungsprozess des adaptiven E-Learning-Systems in dieser Studie berücksichtigte sowohl das Instruktionsdesign als auch das Software-Engineering. Die erste Phase begann mit der Analyse des Kandidaten der Teilnehmer, des Fachkurses und des Online-Liefermediums. Der nächste Schritt bestand darin, die Prozedur, die Regelwerk der Adaptation und die Benutzeroberfläche zu entwerfen. Dann wurde Entwicklungsprozess des Lehrsystems auf der Grundlage der aus den vorherigen Phasen gesammelten Daten durchgeführt. Die nächste Phase war die Implementierung des Unterrichtsprogramms für die Schüler in einer kleinen Gruppe. Schließlich wurde die E-Learning-Anwendung in drei verschiedenen Teststrategien bewertet: Funktionsbasiertes Testen, Expertenbasierte Bewertung und benutzerperspektivische Bewertung. Die nächste Aktion ist eine experimentelle Studie, bei der das adaptive E-Learning-System im Lernprozess angewendet wird. An diesem Experiment waren zwei Gruppen beteiligt. Die Experimentalgruppe bestand aus 21 Studenten, die den Unterrichtsfach Digital Simulation mithilfe des adaptiven E-Learning-Systems lernten. Eine andere Gruppe war die Kontrollgruppe, die 21 Schüler umfasste, die dasselbe Unterrichtsfach in der traditionellen Klasse lernten. Es wurden zwei Instrumente verwendet, um die erforderlichen Daten zu erheben. Das erste Instrument bestand aus 30 Multiple-Choice-Fragen, die die kognitiven Ebenen von Wissen, Verstehen und Anwendung enthielten. Dieses Instrument wurde verwendet, um die Schülerleistung bei dem obengeschriebenen Unterrichtsfach zu bewerten. Das zweite Instrument war der Usability-Fragebogen, der aus 30 4-Punkte-Likert Aussagen bestand. Dieser Fragebogen bestand aus vier Dimensionen nämlich Nützlichkeit, Benutzerfreundlichkeit, Lernfreundlichkeit und Zufriedenheit. Mit diesem Fragebogen wurde die Usability der adaptiven E-Learning-Applikation basierend auf die Perspektive des Schülers bewertet. Der Befund dieser Studie ergab ein ungewöhnliches Phänomen, bei dem das Ergebnis des Pre-Tests der Kontrollgruppe signifikant höher als Experimentalgruppe. Zum Post-Test Vergleich, obwohl die Leistung der E-Learning Gruppe höher als der von der regulären war, war der Unterschied zwischen den beiden statistisch nicht signifikant. Der Vergleich der Punktzahlsteigerung wurde gemacht, um zu untersuchen, welche Behandlungsgruppe effektiver war. Die Ergebnisse zeigten, dass die gesamte Punktzahlsteigerung von der Experimentalgruppe signifikant höher als die von der Kontrollgruppe war. Diese Beweise waren auch im Hinblick auf das Wissen, das Verständnis und die Anwendungsebene des kognitiven Bereichs gültig. Diese Ergebnisse bestätigten, dass die Gruppe des adaptiven E-Learning-Systems bezüglich ihrer Leistung effektiver war als die Gruppe der Studenten, die in der traditionellen Klasse lernten. Ein weiterer wichtiger Befund betraf die Bewertung der Usability. Die Punktzahl der Messung wurde anhand verschiedener Ansätze analysiert und ergab, dass der Usability-Score in allen Aspekten (Nützlichkeit, Benutzerfreundlichkeit, Lernfreundlichkeit und Zufriedenheit) den akzeptablen Kriterien zuzuordnen ist. Darüber hinaus wurde die Regressionsanalyse durchgeführt, um die Beziehung zwischen den Variablen zu untersuchen. Der erste Befund ergab, dass die unabhängigen Variablen (Nützlichkeit, Benutzerfreundlichkeit und Lernfreundlichkeit) gleichzeitig die abhängige Variable (Zufriedenheit) beeinflussten. In der Zwischenzeit ergab der Teil t-Test unterschiedliche Ergebnisse. Die Ergebnisse zeigten, dass die variable Benutzerfreundlichkeit die variable Zufriedenheit signifikant beeinflusste. Der variable Nützlichkeit und die Lernfreundlichkeit wirkten sich indessen nicht signifikant auf die variable Zufriedenheit aus. / There is a strong demand for a positive instructional application in order to address the strategic plan of the Ministry of Education and Culture in Indonesia to change the ratio of vocational secondary school to be higher than the general school one. The immense growth of information and communication technology may be possible to provide a computer-based personalized e-learning system to the learners in order to overcome the fact that each student has their own preferences in learning. This study offers an adaptive e-learning system by considering two sources of personalization: the student’s learning style and initial knowledge. In order to investigate the effectiveness of the proposed e-learning program, the students’ achievement in terms of three lowest levels in the cognitive domain (knowledge, comprehension, and application) in the e-learning group is compared with the traditional classroom group. Another area that is interesting to explore is the usability evaluation based on the students’ perspective and the relationship between aspects specified in the usability questionnaire. The design and development process of the adaptive e-learning system in this study was considering both the instructional system design and software engineering. The first phase was started by analyzing the participants’ candidate, the subject course, and the online delivery medium. The next step was designing the procedure, the adaptation set of rules, and the user interface. Then, the process to develop the instructional system based on the data collected from the previous phases was conducted. The next stage was implemented the instructional program to the students in a small group setting. Finally, the e-learning application was evaluated in three different settings: functional-based testing, experts-based assessment, and user-perspective evaluation. The next action is an experimental study by applying the adaptive e-learning system to the learning process. There were two groups involved in this experiment. The experimental group that consisted of 21 students who learned the Digital Simulation course by utilizing the adaptive e-learning system. Another group was the control group that included 21 students who studied the same course through the traditional classroom setting. There were two instruments used to collect the required data. The first instrument contained 30 multiple-choice questions that considered the cognitive levels of knowledge, comprehension, and application. This instrument was used to assess the student achievement of the intended course. The second instrument was the usability questionnaire that consisted of 30 4-point Likert scale statements. This questionnaire was composed of four dimensions, namely usefulness, ease of use, ease of learning, and satisfaction. This questionnaire aimed to evaluate the usability of the adaptive e-learning application based on the student’s perspective. The finding in this study revealed an unusual phenomenon which the pre-test result of the control group was significantly exceeding those of the experimental group. For the post-test score comparison, although there was a higher achievement in the e-learning group than in the regular group, the difference between both achievements was not statistically significant. The comparison in terms of the gain score was conducted in order to investigate which treatment group was more effective. The results indicated that the total gain score achieved by the experimental group was significantly higher than those recorded by the control group. This evidence was also valid with regard to the knowledge, comprehension, and application-level of the cognitive domain. These findings confirmed that the group who utilized the adaptive e-learning system was reported more effective in terms of the achievement score than the group of students who studied in the traditional setting. Another important finding was related to usability evaluation. The measurement score was analyzed through different approaches and revealed that the usability score categorized in the acceptable criteria in all aspects (usefulness, ease of use, ease of learning, and satisfaction). Furthermore, the regression analysis was conducted in order to explore the relation between the variables. The first finding reported that the independent variables (usefulness, ease of use, and ease of learning) simultaneously influenced the dependent variable (satisfaction). In the meantime, the partial t-Test found varying results. The results indicated that the variable ease of use was significantly influenced variable satisfaction. Meanwhile, variable usefulness and ease of learning were not significantly affected variable satisfaction.

Enrollment in online courses is increasing at a much higher rate than enrollment in on campus courses. Initially, online systems were developed by moving course content from in-class courses as is to an online platform. Later, Web 2.0 technology was implemented in order to improve students’ online engagement. These systems considered all students as one homogeneous group and ignored the fact that different students learn in different ways and at different speeds. Later, adaptive online learning systems were developed based on the assumption that if the instructional approach matches the student learning style, student performance and experience will improve. The use of these systems yielded mixed results because there is no agreement on what, how, and when to adapt instructions. The problem is that there is still a lack of empirical evidence about which online learning system’ design is the most effective, efficient, and engaging. There were two goals for this study. The first was to develop a new instructional theory and design model suitable for personalizing and adapting online learning. The first goal was achieved by developing student personalized, adaptive, and comprehensive e-learning spaces instructional theory and design model. This theory is based on finding the best fit among student characteristics, knowledge domain objectives, and technology used in delivering the online course. The second goal was to implement the newly developed theory and design model in an e-learning system prototype. This goal was achieved by developing and internally validating the e-learning system prototype by utilizing a panel of five instructional design experts. The Delphi method was used to solicit input from the expert panel in three rounds of validation. The validation process resulted in the experts’ consensus that the prototype incorporated the instructional theory and design model well and that this instructional theory holds the promise of increasing online learning courses’ effectiveness, efficiency, and student engagement.