Using the Internet to improve university education

Up to this point, university education has largely remained unaffected by the developments of novel approaches to web-based learning. The paper presents a principled approach to the design of problem-oriented, web-based learning at the university level. The principles include providing authentic contexts with multimedia, supporting collaborative knowledge construction, making thinking visible with dynamic visualisation, quick access to content resources via information and communication technologies, and flexible support by tele-tutoring. These principles are used in the MUNICS learning environment, which is designed to support students of computer science to apply their factual knowledge from the lectures to complex real-world problems. For example, students may model the knowledge management in an educational organisation with a graphical simulation tool. Some more general findings from a formative evaluation study with the MUNICS prototype are reported and discussed. For example, the students' ignorance of the additional content resources is discussed in the light of the well-known finding of insufficient use of help systems in software applications

On the Design of an Advanced Web-Based System for Supporting Thesis Research Process and Knowledge Sharing

Recently many universities have adopted web-based systems to manage the supervision and administration of postgraduate programs. The existing web-based systems mainly focus on monitoring the thesis management process rather than on supporting thesis research itself. This study aims on designing an advanced web-based system to support the Master’s degree thesis research process and the knowledge sharing. Master’s degree. This study firstly identifies the main steps of the thesis research process. It then presents an instructional model based on the analysis of practical thesis research workflow and relevant instructional approaches, such as problem-based learning, cognitive apprenticeship learning and collaborative learning. Based on this instructional model and the relevant literature, six principles were adopted to develop an advanced web-based system for the supporting thesis research process. This system includes three key modules: research process, research group and knowledge sharing. A preliminary evaluation of the system was conducted and the results showed that the system is effective to support the thesis research by providing multi-supervision in the research process. Moreover, a literature resources database plays an important role in knowledge sharing

Using the Internet to improve university education: Problem-oriented web-based learning and the MUNICS environment

Up to this point, university education has largely remained unaffected by the developments of novel approaches to web-based learning. The paper presents a principled approach to the design of problem-oriented, web-based learning at the university level. The principles include providing authentic contexts with multimedia, supporting collaborative knowledge construction, making thinking visible with dynamic visualisation, quick access to content resources via Information and Communication Technologies (ICT), and flexible support by tele-tutoring. These principles are used in the Munich Net-based Learning In Computer Science (MUNICS) learning environment, which is designed to support students of computer science to apply their factual knowledge from the lectures to complex real-world problems. For example, students can model the knowledge management in an educational organisation with a graphical simulation tool. Some more general findings from a formative evaluation study with the MUNICS prototype are reported and discussed. E.g., the students' ignorance of the additional content resources is discussed in the light of the well-known finding of insufficient use of help systems in software applicationsBislang wurden neuere Ansätze zum web-basierten Lernen in nur geringem Maße zur Verbesserung des Universitätsstudiums genutzt. Es werden theoretisch begründete Prinzipien für die Gestaltung problemorientierter, web-basierter Lernumgebungen an der Universität formuliert. Zu diesen Prinzipien gehören die Nutzung von Multimedia-Technologien für die Realisierung authentischer Problemkontexte, die Unterstützung der gemeinsamen Wissenskonstruktion, die dynamische Visualisierung, der schnelle Zugang zu weiterführenden Wissensressourcen mit Hilfe von Informations- und Kommunikationstechnologien sowie die flexible Unterstützung durch Teletutoring. Diese Prinzipien wurden bei der Gestaltung der MUNICS Lernumgebung umgesetzt. MUNICS soll Studierende der Informatik bei der Wissensanwendung im Kontext komplexer praktischer Problemstellungen unterstützen. So können die Studierenden u.a. das Wissensmanagement in einer Bildungsorganisation mit Hilfe eines graphischen Simulationswerkzeugs modellieren. Es werden Ergebnisse einer formativen Evaluationsstudie berichtet und diskutiert. Beispielsweise wird die in der Studie festgestellte Ignoranz der Studierenden gegenüber den weiterführenden Wissensressourcen vor dem Hintergrund des häufig berichteten Befunds der unzureichenden Nutzung von Hilfesystemen beleuchte

A Framework for Personalized Content Recommendations to Support Informal Learning in Massively Diverse Information WIKIS

Personalization has proved to achieve better learning outcomes by adapting to specific learners’ needs, interests, and/or preferences. Traditionally, most personalized learning software systems focused on formal learning. However, learning personalization is not only desirable for formal learning, it is also required for informal learning, which is self-directed, does not follow a specified curriculum, and does not lead to formal qualifications. Wikis among other informal learning platforms are found to attract an increasing attention for informal learning, especially Wikipedia. The nature of wikis enables learners to freely navigate the learning environment and independently construct knowledge without being forced to follow a predefined learning path in accordance with the constructivist learning theory. Nevertheless, navigation on information wikis suffer from several limitations. To support informal learning on Wikipedia and similar environments, it is important to provide easy and fast access to relevant content. Recommendation systems (RSs) have long been used to effectively provide useful recommendations in different technology enhanced learning (TEL) contexts. However, the massive diversity of unstructured content as well as user base on such information oriented websites poses major challenges when designing recommendation models for similar environments. In addition to these challenges, evaluation of TEL recommender systems for informal learning is rather a challenging activity due to the inherent difficulty in measuring the impact of recommendations on informal learning with the absence of formal assessment and commonly used learning analytics. In this research, a personalized content recommendation framework (PCRF) for information wikis as well as an evaluation framework that can be used to evaluate the impact of personalized content recommendations on informal learning from wikis are proposed. The presented recommendation framework models learners’ interests by continuously extrapolating topical navigation graphs from learners’ free navigation and applying graph structural analysis algorithms to extract interesting topics for individual users. Then, it integrates learners’ interest models with fuzzy thesauri for personalized content recommendations. Our evaluation approach encompasses two main activities. First, the impact of personalized recommendations on informal learning is evaluated by assessing conceptual knowledge in users’ feedback. Second, web analytics data is analyzed to get an insight into users’ progress and focus throughout the test session. Our evaluation revealed that PCRF generates highly relevant recommendations that are adaptive to changes in user’s interest using the HARD model with rank-based mean average precision (MAP@k) scores ranging between 100% and 86.4%. In addition, evaluation of informal learning revealed that users who used Wikipedia with personalized support could achieve higher scores on conceptual knowledge assessment with average score of 14.9 compared to 10.0 for the students who used the encyclopedia without any recommendations. The analysis of web analytics data show that users who used Wikipedia with personalized recommendations visited larger number of relevant pages compared to the control group, 644 vs 226 respectively. In addition, they were also able to make use of a larger number of concepts and were able to make comparisons and state relations between concepts

Evaluating students’ experiences in self-regulated smart learning environment

The increasing development in smart and mobile technologies transforms a learning environment into a smart learning environment that can support diverse learning styles and skills development. An online learner needs to be supported for an engaging and active learning experience. Previously, this progressive research developed and implemented a self-regulated smart learning environment (mobile app) among final-year undergraduate students to support online learning experiences. To understand students' experiences, there is a need to evaluate the mobile app. However, there is a lack of a well-documented study investigating students' experiences in terms of usability, challenges, and factors influencing satisfaction to inform a decision regarding future implementation. This study attempts to fill these gaps by exploring these experiences for sustainable future implementation. The study used cyclical mixed-method evaluations to explore the experiences of 85 final-year undergraduate students. The quantitative data were collected using a survey on the constructs of the research model previously developed to evaluate factors influencing students' satisfaction, and the qualitative used focus group discussions to explore usability experiences and challenges of implementations. The quantitative data were analyzed using SPSS 25 to confirm the structural equation model's relationship. The qualitative data were analyzed using a thematic process to understand students' experiences. The findings from the first mixed-method evaluation show that students were able to follow the learning process, and the application supported their online learning experiences. However, a student expressed the need to improve user functionalities to motivate and engage them in the learning process. The suggestions were incorporated into the mobile app development for the second evaluation. The findings from the second evaluation revealed similar support. However, students suggested a web-based version to support different operating systems and improve interactions. Furthermore, the information system qualities and moderating factors investigated supported students' satisfaction. Future research could explore facilitators' experiences in the mobile app for sustainable development and implementation for engaging online learning experiences and skills development

Quality assurance practice in online (web-supported) learning in higher education : an exploratory study

The fields of quality assurance in higher education and e-learning, or technology-enhanced learning, are current and topical, yet seldom overlap (Reid, 2003). Higher education institutions are experiencing pressure to become more client focused and compete on the global stage, especially with respect to technology-enhanced learning. We are on the brink of a genuine pedagogical revolution (Moon, 2003) and calls for quality promotion, accountability, self-evaluation, value for money and client satisfaction cannot go unheeded. Three knowledge domains provide the context for this study: quality assurance, higher education and web-supported learning. Their intersection locates the research problem that was investigated, namely the quality assurance of web-supported learning in higher education. The research design is an instrumental case study, focusing on web-supported learning as a supportive medium in a flexible, blended learning model at the University of Pretoria, South Africa. The research methods include the literature survey, case analysis meetings, a student survey, lecturer interviews, expert consultation and task teaming. The conceptual framework for this study (Figure 2.5) is based on the confluence of the existing theories: quality assurance theory, instructional systems design and systems theory. The updated conceptual framework (Figure 7.1) and the synthesized findings (Table 7.1) reflect the holistic nature of the process-based quality management system for web-supported learning that characterises this study. The value of this study to the academic community is in the findings, which include a taxonomy of critical success factors for web-supported learning, the identification of factors which promote student and lecturer satisfaction (or frustration) with web-supported learning experiences, and lessons learnt by applying standard quality assurance theory to the instructional design process. The self-evaluation exercise in an academic support unit provides a precedent and contributes criteria that will be useful to the Higher Education Quality Committee in South Africa, as well as to other higher education institutions.Thesis (PhD (Computer Integrated Education))--University of Pretoria, 2006.Curriculum Studiesunrestricte

Evaluation of topic-based adaptation and student modeling in QuizGuide

This paper presents an in-depth analysis of a nonconventional topic-based personalization approach for adaptive educational systems (AES) that we have explored for a number of years in the context of university programming courses. With this approach both student modeling and adaptation are based on coarse-grained knowledge units that we called topics. Our motivation for the topic-based personalization was to enhance AES transparency for both teachers and students by utilizing typical topic-based course structures as the foundation for designing all aspects of an AES from the domain model to the end-user interface. We illustrate the details of the topic-based personalization technology, with the help of the Web-based educational service QuizGuide—the first system to implement it. QuizGuide applies the topic-based personalization to guide students to the right learning material in the context of an undergraduate C programming course. While having a number of architectural and practical advantages, the suggested coarse-grained personalization approach deviates from the common practices toward knowledge modeling in AES. Therefore, we believe that several aspects of QuizGuide required a detailed evaluation—from modeling accuracy to the effectiveness of adaptation. The paper discusses how this new student modeling approach can be evaluated, and presents our attempts to evaluate it from multiple different prospects. The evaluation of QuizGuide across several consecutive semesters demonstrates that, although topics do not always support precise user modeling, they can provide a basis for successful personalization in AESs

Using machine learning to infer reasoning provenance from user interaction log data: based on the data/frame theory of sensemaking

The reconstruction of analysts’ reasoning processes (reasoning provenance) during complex sensemaking tasks can support reflection and decision making. One potential approach to such reconstruction is to automatically infer reasoning from low-level user interaction logs. We explore a novel method for doing this using machine learning. Two user studies were conducted in which participants performed similar intelligence analysis tasks. In one study, participants used a standard web browser and word processor; in the other, they used a system called INVISQUE (Interactive Visual Search and Query Environment). Interaction logs were manually coded for cognitive actions based on captured think-aloud protocol and posttask interviews based on Klein, Phillips, Rall, and Pelusos’s data/frame model of sensemaking as a conceptual framework. This analysis was then used to train an interaction frame mapper, which employed multiple machine learning models to learn relationships between the interaction logs and the codings. Our results show that, for one study at least, classification accuracy was significantly better than chance and compared reasonably to a reported manual provenance reconstruction method. We discuss our results in terms of variations in feature sets from the two studies and what this means for the development of the method for provenance capture and the evaluation of sensemaking systems

Using Machine Learning to Infer Reasoning Provenance from User Interaction Log Data

The reconstruction of analysts’ reasoning processes (reasoning provenance) during complex sensemaking tasks can support reflection and decision making. One potential approach to such reconstruction is to automatically infer reasoning from low-level user interaction logs. We explore a novel method for doing this using machine learning. Two user studies were conducted in which participants performed similar intelligence analysis tasks. In one study, participants used a standard web browser and word processor; in the other, they used a system called INVISQUE (Interactive Visual Search and Query Environment). Interaction logs were manually coded for cognitive actions based on captured think-aloud protocol and posttask interviews based on Klein, Phillips, Rall, and Pelusos’s data/frame model of sensemaking as a conceptual framework. This analysis was then used to train an interaction frame mapper, which employed multiple machine learning models to learn relationships between the interaction logs and the codings. Our results show that, for one study at least, classification accuracy was significantly better than chance and compared reasonably to a reported manual provenance reconstruction method. We discuss our results in terms of variations in feature sets from the two studies and what this means for the development of the method for provenance capture and the evaluation of sensemaking systems

Using machine learning to infer reasoning provenance from user interaction log data: based on the data/frame theory of sensemaking

The reconstruction of analysts’ reasoning processes (reasoning provenance) during complex sensemaking tasks can support reflection and decision making. One potential approach to such reconstruction is to automatically infer reasoning from low-level user interaction logs. We explore a novel method for doing this using machine learning. Two user studies were conducted in which participants performed similar intelligence analysis tasks. In one study, participants used a standard web browser and word processor; in the other, they used a system called INVISQUE (Interactive Visual Search and Query Environment). Interaction logs were manually coded for cognitive actions based on captured think-aloud protocol and posttask interviews based on Klein, Phillips, Rall, and Pelusos’s data/frame model of sensemaking as a conceptual framework. This analysis was then used to train an interaction frame mapper, which employed multiple machine learning models to learn relationships between the interaction logs and the codings. Our results show that, for one study at least, classification accuracy was significantly better than chance and compared reasonably to a reported manual provenance reconstruction method. We discuss our results in terms of variations in feature sets from the two studies and what this means for the development of the method for provenance capture and the evaluation of sensemaking systems