Dataperusteinen palaute eTerveyspalveluiden sisällöntuotantoon

Web analytics has proven significant potential for constantly improving the provided web-based services and applications. By analyzing interaction data collected from web applications, it is possible to study how the applications are used in detail. The focus of this study is to analyze if interaction data collected with Piwik PRO web analytics platform using JavaScript tagging can provide sufficient detail about user behaviour and interaction in a modern single-page web application. Furthermore, the analysis seeks to answer if the collected data can be refined in a way that will help the content managers of the web application to continuously improve the content and to spot dysfunctional content. The research is based on Omapolku, a Finnish public e-health service providing digital services for personalized healthcare. In this study, the analysis focuses on evaluating digital treatment pathways in Omapolku, which provides various types of information and utilities designed for the needs of specific patient groups. The evaluation is based on the graphical user interface of a treatment pathway view by analyzing a sample dataset consisting of actions performed by the users. The data is analyzed with general web analytics metrics and by applying statistical analyses of web usage mining. The results show that the interaction data can provide necessary detail for evaluating general usage metrics and basic usage patterns. However, the results show that the data does not provide necessary information for identifying most actions performed by the users, which makes it practically impossible to link the data to the front-end components of the user interface. As an outcome of this study, it is recommended that additional identifiers are added to the front-end components of the treatment path interface and that the JavaScript tagging script is modified to record the corresponding identifiers and the action context. In addition, a novel prototype was designed as a solution to the identified challenges and to support the work of the content managers.Web-analytiikka on osoittanut nykypäivänä potentiaalinsa osana web-pohjaisten sovellusten jatkuvaa kehitystä. Web-sovelluksista kerätyn interaktiodatan analysointi mahdollistaa sen, että sovellusten käyttöä voidaan tutkia yksityiskohtaisesti. Tämä työ keskittyy analysoimaan, mikäli Piwik PRO analytiikkapalvelun JavaScript seurantakoodilla kerätty interaktiodata tarjoaa riittäviä yksityiskohtia käyttäjien käyttäytymisestä ja interaktiosta yksisivuisessa web-sovelluksessa. Tämän lisäksi työ keskittyy tutkimaan, mikäli kerättyä dataa voidaan jalostaa siten, että sitä voi hyödyntää toimintahäiriöisten sisältöjen paikantamiseen sekä sisällön jatkuvaan kehittämiseen. Tutkimus perustuu Omapolku-sovellukseen, joka on julkinen suomalainen eTerveyspalvelu. Omapolku tarjoaa digitaalisia palveluita henkilökohtaiseen terveydenhuoltoon. Tässä työssä analyysi perustuu Omapolun digitaalisien hoitopolkujen toimivuuden arvioimiseen. Digitaaliset hoitopolut tarjoavat monipuolista tietoa sekä työkaluja, jotka on suunniteltu potilasryhmäkohtaisesti tietyn hoitotarpeen mukaisesti. Hoitopolkujen toimivuuden arvointi toteutetaan tutkimalla digihoitopolkujen graafisesta käyttöliittymästä kerättyä interaktiodataa. Kerättyä dataa analysoidaan yleisillä web-analytiikan mittareilla sekä tilastollisilla web-tiedonlouhinnan menetelmillä. Työn tulokset osoittavat, että interaktiodata voi tarjota tarpeellista tietoa yleisten mittareiden laskemiseksi sekä yksinkertaisten käyttäytymismallien selvittämiseksi. Tulokset myös osoittavat, että data ei tarjoa tietoa yksityiskohtaisten tapahtumien alkuperän selvittämiseksi käyttöliittymässä. Työn tuloksena suositellaan, että digihoitopolkujen käyttöliittymän komponentteihin lisätään lisätunnisteita ja että JavaScript seurantakoodia muokataan siten, että tapahtuman konteksti ja siihen liittyvä komponenttitunniste tallennetaan tapahtumaan. Tämän lisäksi työssä esitetään prototyyppi ratkaisuna havaittuihin haasteisiin sekä tukemaan sisällöntuottajien työtä

An Enhanced Web Data Learning Method for Integrating Item, Tag and Value for Mining Web Contents

The Proposed System Analyses the scopes introduced by Web 2.0 and collaborative tagging systems, several challenges have to be addressed too, notably, the problem of information overload. Recommender systems are among the most successful approaches for increasing the level of relevant content over the 201C;noise.201D; Traditional recommender systems fail to address the requirements presented in collaborative tagging systems. This paper considers the problem of item recommendation in collaborative tagging systems. It is proposed to model data from collaborative tagging systems with three-mode tensors, in order to capture the three-way correlations between users, tags, and items. By applying multiway analysis, latent correlations are revealed, which help to improve the quality of recommendations. Moreover, a hybrid scheme is proposed that additionally considers content-based information that is extracted from items. We propose an advanced data mining method using SVD that combines both tag and value similarity, item and user preference. SVD automatically extracts data from query result pages by first identifying and segmenting the query result records in the query result pages and then aligning the segmented query result records into a table, in which the data values from the same attribute are put into the same column. Specifically, we propose new techniques to handle the case when the query result records based on user preferences, which may be due to the presence of auxiliary information, such as a comment, recommendation or advertisement, and for handling any nested-structure that may exist in the query result records

Personalised trails and learner profiling within e-learning environments

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

Trace Clustering for User Behavior Mining

Business information systems support a large variety of business processes and tasks, yet organizations rarely understand how users interact with these systems. User Behavior Mining aims to address this by applying process mining techniques to UI logs, i.e., detailed records of interactions with a system\u27s user interface. Insights gained from this type of data hold great potential for usability engineering and task automation, but the complexity of UI logs can make them challenging to analyze. In this paper, we explore trace clustering as a means to structure UI logs and reduce this complexity. In particular, we apply different trace clustering approaches to a real-life UI log and show that the cluster-level process models reveal useful information about user behavior. At the same time, we find configurations in which trace clustering fails to generate satisfactory partitions. Our results also demonstrate that recently proposed representation learning techniques for process traces can be effectively employed in a realistic setting

Wayfinding and Navigation for People with Disabilities Using Social Navigation Networks

To achieve safe and independent mobility, people usually depend on published information, prior experience, the knowledge of others, and/or technology to navigate unfamiliar outdoor and indoor environments. Today, due to advances in various technologies, wayfinding and navigation systems and services are commonplace and are accessible on desktop, laptop, and mobile devices. However, despite their popularity and widespread use, current wayfinding and navigation solutions often fail to address the needs of people with disabilities (PWDs). We argue that these shortcomings are primarily due to the ubiquity of the compute-centric approach adopted in these systems and services, where they do not benefit from the experience-centric approach. We propose that following a hybrid approach of combining experience-centric and compute-centric methods will overcome the shortcomings of current wayfinding and navigation solutions for PWDs

A Location Aware Service to Minimize Travel Costs Using Dynamic Information

Automotive navigation systems have become common accessories in vehicles manufactured today. However, the information provided by these systems is quite limited in that many systems only provide static information. As a result, manufacturers of such systems have not been able to fully capitalize from the potential applications for mobile commerce (mcommerce) which is critically dependent on providing consumers with dynamic information. The objective of this paper is to discuss a novel method, known as Dynamic Location Cost Minimization (DLCM), which can be used with a vehicle’s navigation system to determine the optimum location to purchase gas. With the increasing cost of gas and the possibility of higher prices due to proposed gas price taxes, providing a means for consumers to minimize their costs to travel could prove to be very beneficial, and potentially help drive down prices due to increased competition. In addition, the proposed method could also be used in conjunction with mobile phones to facilitate real-time decisions for other services or purchases. Anecdotal evidence presented in this paper merits further investigation into the usability and acceptance of this technology

noteEd - A web-based lecture capture system

Electronic capture and playback of lectures has long been the aim of many academic projects. Synote is an application developed under MACFoB (Multimedia Annotation and Community Folksonomy Building) project to synchronise the playback of lecture materials. However, Synote provides no functionality to capture such multimedia. This project involves the creation of a system called noteEd, which will capture a range of multimedia from lectures and make them available to Synote. This report describes the evolution of the noteEd project throughout the design and implementation of the proposed system. The performance of the system was checked in a user acceptance test with the customer, which is discussed after screenshots of our solution. Finally, the project management is presented containing a final project evaluation

Uzilla: A new tool for Web usability testing

Web usability testing and research presents challenges for accurate data collection. An instrumented browser solution, Uzilla, is compared with existing solutions, and its contributions to usability testing practice are noted. Uzilla implements a client-server architecture based on the open source Mozilla browser. Instrumentation of the browser facilitates the evaluation of Web sites and applications inside and outside of the laboratory. An integrated data collection and analysis server application decreases the effort required to understand test results and facilitates iterative testing