Emotions in a Web-based Learning Environment

The aim of this thesis was to examine emotions in a web-based learning environment (WBLE). Theoretically, the thesis was grounded on the dimensional model of emotions. Four empirical studies were conducted. Study I focused on students’ anxiety and their self-efficacy in computer-using situations. Studies II and III examined the influence of experienced emotions on students’ collaborative visible and non-collaborative invisible activities and lurking in a WBLE. Study II also focused on the antecedents of the emotions students experience in a web-based learning environment. Study IV concentrated on clarifying the differences between emotions experienced in face-to-face and web-based collaborative learning. The results of these studies are reported in four original research articles published in scientific journals. The present studies demonstrate that emotions are important determinants of student behaviour in a web-based learning, and justify the conclusion that interactions on the web can and do have an emotional content. Based on the results of these empirical studies, it can be concluded that the emotions students experience during the web-based learning result mostly from the social interactions rather than from the technological context. The studies indicate that the technology itself is not the only antecedent of students’ emotional reactions in the collaborative web-based learning situations. However, the technology itself also exerted an influence on students’ behaviour. It was found that students’ computer anxiety was associated with their negative expectations of the consequences of using technology-based learning environments in their studies. Moreover, the results also indicated that student behaviours in a WBLE can be divided into three partially overlapping classes: i) collaborative visible ii) non-collaborative invisible activities, and iii) lurking. What is more, students’ emotions experienced during the web-based learning affected how actively they participated in such activities in the environment. Especially lurkers, i.e. students who seldom participated in discussions but frequently visited the online environment, experienced more negatively valenced emotions during the courses than did the other students. This result indicates that such negatively toned emotional experiences can make the lurking individuals less eager to participate in other WBLE courses in the future. Therefore, future research should also focus more precisely on the reasons that cause individuals to lurk in online learning groups, and the development of learning tasks that do not encourage or permit lurking or inactivity. Finally, the results from the study comparing emotional reactions in web-based and face-to-face collaborative learning indicated that the learning by means of web-based communication resulted in more affective reactivity when compared to learning in a face-to-face situation. The results imply that the students in the web-based learning group experienced more intense emotions than the students in the face-to-face learning group.The interpretations of this result are that the lack of means for expressing emotional reactions and perceiving others’ emotions increased the affectivity in the web-based learning groups. Such increased affective reactivity could, for example, debilitate individual’s learning performance, especially in complex learning tasks. Therefore, it is recommended that in the future more studies should be focused on the possibilities to express emotions in a text-based web environment to ensure better means for communicating emotions, and subsequently, possibly decrease the high level of affectivity. However, we do not yet know whether the use of means for communicating emotional expressions via the web (for example, “smileys” or “emoticons”) would be beneficial or disadvantageous in formal learning situations. Therefore, future studies should also focus on assessing how the use of such symbols as a means for expressing emotions in a text-based web environment would affect students’ and teachers’ behaviour and emotional state in web-based learning environments.

Printed platforms for paper-based analytical applications

Paper-based analytical technologies enable quantitative and rapid analysis of analytes from various application areas including healthcare, environmental monitoring and food safety. Because paper is a planar, flexible and light weight substrate, the devices can be transported and disposed easily. Diagnostic devices are especially valuable in resourcelimited environments where diagnosis as well as monitoring of therapy can be made even without electricity by using e.g. colorimetric assays. On the other hand, platforms including printed electrodes can be coupled with hand-held readers. They enable electrochemical detection with improved reliability, sensitivity and selectivity compared with colorimetric assays. In this thesis, different roll-to-roll compatible printing technologies were utilized for the fabrication of low-cost paper-based sensor platforms. The platforms intended for colorimetric assays and microfluidics were fabricated by patterning the paper substrates with hydrophobic vinyl substituted polydimethylsiloxane (PDMS) -based ink. Depending on the barrier properties of the substrate, the ink either penetrates into the paper structure creating e.g. microfluidic channel structures or remains on the surface creating a 2D analog of a microplate. The printed PDMS can be cured by a roll-ro-roll compatible infrared (IR) sintering method. The performance of these platforms was studied by printing glucose oxidase-based ink on the PDMS-free reaction areas. The subsequent application of the glucose analyte changed the colour of the white reaction area to purple with the colour density and intensity depending on the concentration of the glucose solution. Printed electrochemical cell platforms were fabricated on paper substrates with appropriate barrier properties by inkjet-printing metal nanoparticle based inks and by IR sintering them into conducting electrodes. Printed PDMS arrays were used for directing the liquid analyte onto the predetermined spots on the electrodes. Various electrochemical measurements were carried out both with the bare electrodes and electrodes functionalized with e.g. self assembled monolayers. Electrochemical glucose sensor was selected as a proof-of-concept device to demonstrate the potential of the printed electronic platforms.

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

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.

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.

Yrityspelin Kehittäminen Ongelmalähtöisen Oppimisen Välineeksi

Työn tavoitteena on luoda poikkileikkaus ongelmalähtöiseen pedagogiikkaan ja erityisesti sen käyttömahdollisuuksiin Yrityspeli-kurssin opettamisessa oppimislähtöisen opetustavan tukena. Toisena tavoitteena on kurssin pelisovelluksen dokumentointi käyttökelpoiseksi viiteteokseksi. Lopuksi työssä tutkitaan sovelluksen keskeisten parametrien ja pelistrategioiden vaikutusta kysyntään. Ongelmalähtöistä oppimista on tutkittu lähinnä kirjallisuuteen perustuen ja täydennetty tietämystä haastattelututkimuksella. Sovelluksen rakenne on havainnoitu käyttämällä sovellusta. Sovelluksen kriittisiä ominaisuuksia on saatu selville ajamalla sovellusta tarkoitusta varten valikoidulla numeerisella aineistolla. Ongelmalähtöinen pedagogiikka osoittautui soveltuvin osin hyväksi opetusmenetelmäksi Yrityspeli-kurssilla mallintamisen ja käytännön ongelmanratkaisutaitojen kehittämisessä. Kurssin pelisovellus näyttäisi toimivan luotettavasti, tosin se ei ole rakenteeltaan erityisen joustava. Sovelluksen keskeisistä parametreista hinnan vaikutus kysyntään on huomattavasti markkinointipanosta suurempi. Testien mukaan pelissä on mahdollista pärjätä niin kustannusjohtajan kuin hintadifferoijankin strategialla.

Towards adoption of eLearning technologies at Lappeenranta University of Technology: Understanding Teachers‟ Adoption of Blackboard Learning System

The electronic learning has become crucial in higher education with increased usage of learning management systems as a key source of integration on distance learning. The objective of this study is to understand how university teachers are influenced to use and adopt web-based learning management systems. Blackboard, as one of the systems used internationally by various universities is applied as a case. Semi-structured interviews were made with professors and lecturers who are using Blackboard at Lappeenranta University of Technology. The data collected were categorized under constructs adapted from Unified Theory of Acceptance and Use of Technology (UTAUT) and interpretation and discussion were based on reviewed literature. The findings suggest that adoption of learning management systems by LUT teachers is highly influenced by perceived usefulness, facilitating conditions and gained experience. The findings also suggest that easiness of using the system and social influence appear as medium influence of adoption for teachers at LUT.

A bridge between engineering and language learning : automation and German in an online course

Artikkeli luettavissa osassa: Part 2. - ISBN 9789522163172(PDF). - Liitteenä työpaperi

University students’ regulation of learning and text processing – examples from medical and teacher education

The general aim of the thesis was to study university students’ learning from the perspective of regulation of learning and text processing. The data were collected from the two academic disciplines of medical and teacher education, which share the features of highly scheduled study, a multidisciplinary character, a complex relationship between theory and practice and a professional nature. Contemporary information society poses new challenges for learning, as it is not possible to learn all the information needed in a profession during a study programme. Therefore, it is increasingly important to learn how to think and learn independently, how to recognise gaps in and update one’s knowledge and how to deal with the huge amount of constantly changing information. In other words, it is critical to regulate one’s learning and to process text effectively. The thesis comprises five sub-studies that employed cross-sectional, longitudinal and experimental designs and multiple methods, from surveys to eye tracking. Study I examined the connections between students’ study orientations and the ways they regulate their learning. In total, 410 second-, fourth- and sixth-year medical students from two Finnish medical schools participated in the study by completing a questionnaire measuring both general study orientations and regulation strategies. The students were generally deeply oriented towards their studies. However, they regulated their studying externally. Several interesting and theoretically reasonable connections between the variables were found. For instance, self-regulation was positively correlated with deep orientation and achievement orientation and was negatively correlated with non-commitment. However, external regulation was likewise positively correlated with deep orientation and achievement orientation but also with surface orientation and systematic orientation. It is argued that external regulation might function as an effective coping strategy in the cognitively loaded medical curriculum. Study II focused on medical students’ regulation of learning and their conceptions of the learning environment in an innovative medical course where traditional lectures were combined wth problem-based learning (PBL) group work. First-year medical and dental students (N = 153) completed a questionnaire assessing their regulation strategies of learning and views about the PBL group work. The results indicated that external regulation and self-regulation of the learning content were the most typical regulation strategies among the participants. In line with previous studies, self-regulation wasconnected with study success. Strictly organised PBL sessions were not considered as useful as lectures, although the students’ views of the teacher/tutor and the group were mainly positive. Therefore, developers of teaching methods are challenged to think of new solutions that facilitate reflection of one’s learning and that improve the development of self-regulation. In Study III, a person-centred approach to studying regulation strategies was employed, in contrast to the traditional variable-centred approach used in Study I and Study II. The aim of Study III was to identify different regulation strategy profiles among medical students (N = 162) across time and to examine to what extent these profiles predict study success in preclinical studies. Four regulation strategy profiles were identified, and connections with study success were found. Students with the lowest self-regulation and with an increasing lack of regulation performed worse than the other groups. As the person-centred approach enables us to individualise students with diverse regulation patterns, it could be used in supporting student learning and in facilitating the early diagnosis of learning difficulties. In Study IV, 91 student teachers participated in a pre-test/post-test design where they answered open-ended questions about a complex science concept both before and after reading either a traditional, expository science text or a refutational text that prompted the reader to change his/her beliefs according to scientific beliefs about the phenomenon. The student teachers completed a questionnaire concerning their regulation and processing strategies. The results showed that the students’ understanding improved after text reading intervention and that refutational text promoted understanding better than the traditional text. Additionally, regulation and processing strategies were found to be connected with understanding the science phenomenon. A weak trend showed that weaker learners would benefit more from the refutational text. It seems that learners with effective learning strategies are able to pick out the relevant content regardless of the text type, whereas weaker learners might benefit from refutational parts that contrast the most typical misconceptions with scientific views. The purpose of Study V was to use eye tracking to determine how third-year medical studets (n = 39) and internal medicine residents (n = 13) read and solve patient case texts. The results revealed differences between medical students and residents in processing patient case texts; compared to the students, the residents were more accurate in their diagnoses and processed the texts significantly faster and with a lower number of fixations. Different reading patterns were also found. The observed differences between medical students and residents in processing patient case texts could be used in medical education to model expert reasoning and to teach how a good medical text should be constructed. The main findings of the thesis indicate that even among very selected student populations, such as high-achieving medical students or student teachers, there seems to be a lot of variation in regulation strategies of learning and text processing. As these learning strategies are related to successful studying, students enter educational programmes with rather different chances of managing and achieving success. Further, the ways of engaging in learning seldom centre on a single strategy or approach; rather, students seem to combine several strategies to a certain degree. Sometimes, it can be a matter of perspective of which way of learning can be considered best; therefore, the reality of studying in higher education is often more complicated than the simplistic view of self-regulation as a good quality and external regulation as a harmful quality. The beginning of university studies may be stressful for many, as the gap between high school and university studies is huge and those strategies that were adequate during high school might not work as well in higher education. Therefore, it is important to map students’ learning strategies and to encourage them to engage in using high-quality learning strategies from the beginning. Instead of separate courses on learning skills, the integration of these skills into course contents should be considered. Furthermore, learning complex scientific phenomena could be facilitated by paying attention to high-quality learning materials and texts and other support from the learning environment also in the university. Eye tracking seems to have great potential in evaluating performance and growing diagnostic expertise in text processing, although more research using texts as stimulus is needed. Both medical and teacher education programmes and the professions themselves are challenging in terms of their multidisciplinary nature and increasing amounts of information and therefore require good lifelong learning skills during the study period and later in work life.