Wiki-supported collaborative learning in primary education. How a dialogic space is created for thinking together

This paper explores how wikis may be used to support primary education students’ collaborative interaction and how such an interaction process can be characterised. The overall aim of this study is to analyse the collaborative processes of students working together in a wiki environment, in order to see how primary students can actively create a shared context for learning in the wiki. Educational literature has already reported that wikis may support collaborative knowledge-construction processes, but in our study we claim that a dialogic perspective is needed to accomplish this. Students must develop an intersubjective orientation towards each others’ perspectives, to co-construct knowledge about a topic. For this purpose, our project utilised a ‘Thinking Together’ approach to help students develop an intersubjective orientation towards one another and to support the creation of a ‘dialogic space’ to co-construct new understanding in a wiki science project. The students’ asynchronous interaction process in a primary classroom -- which led to the creation of a science text in the wiki -- was analysed and characterised, using a dialogic approach to the study of CSCL practices. Our results illustrate how the Thinking Together approach became embedded within the wiki environment and in the students’ collaborative processes. We argue that a dialogic approach for examining interaction can be used to help design more effective pedagogic approaches related to the use of wikis in education and to equip learners with the competences they need to participate in the global knowledge-construction era.

Machine Learning and Clinical Text. Supporting Health Information Flow

Fluent health information flow is critical for clinical decision-making. However, a considerable part of this information is free-form text and inabilities to utilize it create risks to patient safety and cost-­effective hospital administration. Methods for automated processing of clinical text are emerging. The aim in this doctoral dissertation is to study machine learning and clinical text in order to support health information flow.First, by analyzing the content of authentic patient records, the aim is to specify clinical needs in order to guide the development of machine learning applications.The contributions are a model of the ideal information flow,a model of the problems and challenges in reality, and a road map for the technology development. Second, by developing applications for practical cases,the aim is to concretize ways to support health information flow. Altogether five machine learning applications for three practical cases are described: The first two applications are binary classification and regression related to the practical case of topic labeling and relevance ranking.The third and fourth application are supervised and unsupervised multi-class classification for the practical case of topic segmentation and labeling.These four applications are tested with Finnish intensive care patient records.The fifth application is multi-label classification for the practical task of diagnosis coding. It is tested with English radiology reports.The performance of all these applications is promising. Third, the aim is to study how the quality of machine learning applications can be reliably evaluated.The associations between performance evaluation measures and methods are addressed,and a new hold-out method is introduced.This method contributes not only to processing time but also to the evaluation diversity and quality. The main conclusion is that developing machine learning applications for text requires interdisciplinary, international collaboration. Practical cases are very different, and hence the development must begin from genuine user needs and domain expertise. The technological expertise must cover linguistics,machine learning, and information systems. Finally, the methods must be evaluated both statistically and through authentic user-feedback.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

Mobile learning and video experience in delivering digital content

In recent years, the worldwide distribution of smartphone devices has been growing rapidly. Mobile technologies are evolving fast, a situation which provides new possibilities for mobile learning applications. Along with new delivery methods, this development enables new concepts for learning. This study focuses on the effectiveness and experience of a mobile learning video promoting the key features of a specific device. Through relevant learning theories, mobile technologies and empirical findings, the thesis presents the key elements for a mobile learning video that are essential for effective learning. This study also explores how previous experience with mobile services and knowledge of a mobile handset relate to final learning results. Moreover, this study discusses the optimal delivery mechanisms for a mobile video. The target group for the study consists of twenty employees of a Sanoma Company. The main findings show that the individual experience of learning and the actual learning results may differ and that the design for certain video elements, such as sound and the presentation of technical features, can have an impact on the experience and effectiveness of a mobile learning video. Moreover, a video delivery method based on cloud technologies and HTML5 is suggested to be used in parallel with standalone applications.

The nature of communication structures of university students in an international web-based computer-supported collaborative learning (CSCL) course

Communication, the flow of ideas and information between individuals in a social context, is the heart of educational experience. Constructivism and constructivist theories form the foundation for the collaborative learning processes of creating and sharing meaning in online educational contexts. The Learning and Collaboration in Technology-enhanced Contexts (LeCoTec) course comprised of 66 participants drawn from four European universities (Oulu, Turku, Ghent and Ramon Llull). These participants were split into 15 groups with the express aim of learning about computer-supported collaborative learning (CSCL). The Community of Inquiry model (social, cognitive and teaching presences) provided the content and tools for learning and researching the collaborative interactions in this environment. The sampled comments from the collaborative phase were collected and analyzed at chain-level and group-level, with the aim of identifying the various message types that sustained high learning outcomes. Furthermore, the Social Network Analysis helped to view the density of whole group interactions, as well as the popular and active members within the highly collaborating groups. It was observed that long chains occur in groups having high quality outcomes. These chains were also characterized by Social, Interactivity, Administrative and Content comment-types. In addition, high outcomes were realized from the high interactive cases and high-density groups. In low interactive groups, commenting patterned around the one or two central group members. In conclusion, future online environments should support high-order learning and develop greater metacognition and self-regulation. Moreover, such an environment, with a wide variety of problem solving tools, would enhance interactivity.

Integration of learning supportive applications into the development of the e-portfolio construction process

The portfolio as a means of demonstrating personal skills has lately been gaining prominence among technology students. This is partially due to the introduction of electronic portfolios, or e-portfolios. As platforms for e-portfolio management with different approaches have been introduced, the learning cycle, traditional portfolio pedagogy, and learner centricity have sometimes been forgotten, and as a result, the tools have been used for the most part as data depositories. The purpose of this thesis is to show how the construction of e-portfolios of IT students can be supported by institutions through the usage of different tools that relate to study advising, teaching, and learning. The construction process is presented as a cycle based on learning theories. Actions related to the various phases of the e-portfolio construction process are supported by the implementation of software applications. To maximize learner-centricity and minimize the intervention of the institution, the evaluated and controlled actions for these practices can be separated from the e-portfolios, leaving the construction of the e-portfolio to students. The main contributions of this thesis are the implemented applications, which can be considered to support the e-portfolio construction by assisting in planning, organizing, and reflecting activities. Eventually, this supports the students in their construction of better and more extensive e-portfolios. The implemented tools include 1) JobSkillSearcher to help students’ recognition of the demands of the ICT industry regarding skills, 2) WebTUTOR to support students’ personal study planning, 3) Learning Styles to determine students' learning styles, and 4) MyPeerReview to provide a platform on which to carry out anonymous peer review processes in courses. The most visible outcome concerning the e-portfolio is its representation, meaning that one can use it to demonstrate personal achievements at the time of seeking a job and gaining employment. Testing the tools and the selected open-source e-portfolio application indicates that the degree of richness of e-portfolio content can be increased by using the implemented applications.

Employees’ continuous learning and job satisfaction - effects on productivity

In today’s knowledge intense economy the human capital is a source for competitive advantage for organizations. Continuous learning and sharing the knowledge within the organization are important to enhance and utilize this human capital in order to maximize the productivity. The new generation with different views and expectations of work is coming to work life giving its own characteristics on learning and sharing. Work should offer satisfaction so that the new generation employees would commit to organizations. At the same time organizations have to be able to focus on productivity to survive in the competitive market. The objective of this thesis is to construct a theory based framework of productivity, continuous learning and job satisfaction and further examine this framework and its applications in a global organization operating in process industry. Suggestions for future actions are presented for this case organization. The research is a qualitative case study and the empiric material was gathered by personal interviews concluding 15 employee and one supervisor interview. Results showed that more face to face interaction is needed between employees for learning because much of the knowledge of the process is tacit and so difficult to share in other ways. Offering these sharing possibilities can also impact positively to job satisfaction because they will increase the sense of community among employees which was found to be lacking. New employees demand more feedback to improve their learning and confidence. According to the literature continuous learning and job satisfaction have a relative strong relationship on productivity. The employee’s job description in the case organization has moved towards knowledge work due to continuous automation and expansion of the production process. This emphasizes the importance of continuous learning and means that productivity can be seen also from quality perspective. The normal productivity output in the case organization is stable and by focusing on the quality of work by improving continuous learning and job satisfaction the upsets in production can be handled and prevented more effectively. Continuous learning increases also the free human capital input and utilization of it and this can breed output increasing innovations that can increase productivity in long term. Also job satisfaction can increase productivity output in the end because employees will work more efficiently, not doing only the minimum tasks required. Satisfied employees are also found participating more in learning activities.

Designing gamification for collaborative learning in group work

Computer Supported Collaborative Learning (CSCL) is a teaching and learning approach which is widely adopted. However there are still some problems can be found when CSCL takes place. Studies show that using game-like mechanics can increase motivation, engagement, as well as modelling behaviors of players. Gamification is a rapid growing trend by applying the same mechanics. It refers to use game design elements in non-game contexts. This thesis is about combining gamification concept and computer supported collaborative learning together in software engineering education field. And finally a gamified prototype system is designed.

Collaborative learning in a Japanese as a foreign language classroom /

This study examined the influence of training on Asian learners' beliefs, interaction, and attitudes during collaborative learning (CL) and explored the processes of their CL in pairs. The literature contains few studies on the effect of collaborative training in language learning. In addition, it shows gaps between SLA theory and practice resulting from learners' cultural differences. Although second/subsequent language acquisition (SLA) theory assumes that CL contributes to language learning, implementing CL in a multicultural classroom is often considered to be unsuccessful by teachers. The research questions designed to address this gap explore: (a) the extent to which tra~ng affects Asian learners' attitudes towards and interaction during CL; (b) how Asian learners accomplish collaborative tasks in pairs. In the quasi-experimental research design, the learners in the treatment group received special training in CL for 5 weeks while the learners in the comparison group did not receive similar training. Data were collected from 45 McMaster University students through pre- and posttests, pre- and postintervention questionnaires, student information, and informal classroom observations. To detennine the influence of training, the frequency of communication units (c-units), Language Related Episodes (LREs), Collaborative Dialogue (CD) from audio-taped data, and the fmal draft scores were compared between pre- and posttests. The learners' pre- and postintervention questionnaires were also compared. Transcripts from audio-taped data, students' information, their responses and comments from questionnaires, and informal observations served to investigate the processes of Asian learners' CL. Overall, this study found that training had significant influence on the frequency of c-units and CD, and considerable impact on the draft scores, although little influence on the frequency of LREs was observed. The results from the questionnaires in the treatment group showed positive changes in the learners' beliefs on pair work after training. On the other hand, analyses of the transcription data showed that the learners did not conduct enough discussion for a resolution of problems with peers. In conclusion, results suggested the need for teacher intervention, a longer period of collaborative training, and an implementation of self-evaluation into the course grade to encourage the learners to succeed in collaborative learning.

A study of the role learning and socialization during the transition from teacher to elementary school administrator

This thesis examined the role transition from an elementary teacher to an elementary principal. In particular, the training and socialization process of becoming an elementary principal was explored through the study of the hierarchical and political structure of a southern Ontario school board, and how this influenced the learning experiences of new elementary principals. A qualitative methodology, with a grounded theory design, was employed to investigate this process through interviews with 10 participants to examine their experiences and role learning occurs during their development. Specifically, participants perspective shifts, developmental experiences, understanding of group culture, and expansion of a board profile were highlighted in the data. One of the compelling results of the study was the degree to which principals of aspiring administrators influence the socialization of their subordinates. The beliefs and practices of the school principal determine the socialization orientation that teachers and vice-principals will experience during role learning. The results of this study also imply that role orientation needs to be understood as a continuum between custodial and innovative role assumption. Varying degrees of custodianship or innovation depended on the context of the administrative placement and the personal attributes of administrative candidates. Principals who are willing to share responsibilities, who are good communicators, and who wish to develop a collaborative relationship with their viceprincipals are the individuals the participants in this study described as making the best mentors.

The Effective Use of 21st-Century Learning iPad Applications in the Primary/Junior Literacy Classroom

The purpose of this major research project was to develop a practical tool in the form of a handbook that could facilitate educators’ effective use of technology in primary and junior classrooms. The main goal was to explore the use of iPad devices and applications in the literacy classroom. The study audited available free applications against set criteria and selected only those that promoted 21st-century learning. The researcher used such applications to develop literacy lessons that aligned with curriculum expectations and promoted 21st-century skills and traditional skills alike. The study also created assessment models to evaluate the use of iPads in student work and explored the benefits and limitations of technology usage in student learning.

A Formulation for Active Learning with Applications to Object Detection

We discuss a formulation for active example selection for function learning problems. This formulation is obtained by adapting Fedorov's optimal experiment design to the learning problem. We specifically show how to analytically derive example selection algorithms for certain well defined function classes. We then explore the behavior and sample complexity of such active learning algorithms. Finally, we view object detection as a special case of function learning and show how our formulation reduces to a useful heuristic to choose examples to reduce the generalization error.

Support Vector Machines: Training and Applications

The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT&T Bell Labs. This new learning algorithm can be seen as an alternative training technique for Polynomial, Radial Basis Function and Multi-Layer Perceptron classifiers. An interesting property of this approach is that it is an approximate implementation of the Structural Risk Minimization (SRM) induction principle. The derivation of Support Vector Machines, its relationship with SRM, and its geometrical insight, are discussed in this paper. Training a SVM is equivalent to solve a quadratic programming problem with linear and box constraints in a number of variables equal to the number of data points. When the number of data points exceeds few thousands the problem is very challenging, because the quadratic form is completely dense, so the memory needed to store the problem grows with the square of the number of data points. Therefore, training problems arising in some real applications with large data sets are impossible to load into memory, and cannot be solved using standard non-linear constrained optimization algorithms. We present a decomposition algorithm that can be used to train SVM's over large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of, and also establish the stopping criteria for the algorithm. We present previous approaches, as well as results and important details of our implementation of the algorithm using a second-order variant of the Reduced Gradient Method as the solver of the sub-problems. As an application of SVM's, we present preliminary results we obtained applying SVM to the problem of detecting frontal human faces in real images.