865 research outputs found

    Oppimisanalytiikan käynnistäminen, Tapaus: Aalto Online Learning

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    The digital transformation of learning brings forth data having unprecedented granularity and coverage of learning activity. The research area of Learning Analytics (LA) uses this data to understand and improve learning. The practice of LA is a cyclic process where learning data is collected from different sources and analytics is developed according to stakeholder objectives. Finally, current results are delivered that lead into action which improves learning and produces new data. The goal of this thesis is to bootstrap LA in multiple courses that implement different weekly online learning activities. The term bootstrap underlines the aim to support continuity, further development, and expansion of LA. The research questions were: what learning data the courses currently instrument, and what LA objectives the course staff find most important. This thesis conducts software engineering to construct an LA solution for the research case. Requirements are defined via examination of the case and interviews of the course staff. The developed solution enables real time access to learning data and possibility to integrate data from both Moodle and A-plus learning environments for joined analysis. Novel interactive visualizations are developed according to the user requirements. The work in bootstrapping LA at course level lead to two general findings. First, the integration of learning data from multitude of sources is a common challenge that requires design. Second, teachers' initial LA objectives include aims to monitor expected progress, improve allocation of learning material, identify problematic areas in learning material, and improve interaction with learners.Opetuksen digitaalinen murros synnyttaää ennennäkemättömän tarkkaa ja kattavaa tietoa oppimisaktiviteeteista. Oppimisanalytiikan (OA) tutkimusalue käyttää tätä aineistoa oppimisen ymmärtämiseen ja parantamiseen. OA:n soveltaminen käytäntöön on toistuva prosessi, jossa oppimisaineistoa kerätään erilaisista lähteistä ja analytiikkaa kehitetään omistajiensa tavoitteiden mukaisesti. Lopuksi tuotetaan ajantasaisia tuloksia, jotka johtavat toimintaan, joka parantaa oppimista ja tuottaa uutta aineistoa. Tämän diplomityön tavoitteena on käynnistää OA usealla kurssilla, jotka toteuttavat erilaisia viikoittaisia verkko-oppimisen ratkaisuja. Käynnistäminen pyrkii elinvoimaiseen, kehittyvään ja laajenevaan analytiikkaan. Tutkimuskysymykset olivat, mitä dataa kurssit tällä hetkellä keräävät ja mitkä OA–tavoitteet ovat kurssihenkilökunnalle tärkeimpiä. Työssä rakennetaan ohjelmistotuotannon keinoin OA–ratkaisu tutkittavalle tapaukselle. Ratkaisun vaatimukset määritellään tarkastelemalla tapausta ja haastattelemalla kurssien henkilökuntaa. Kehitetyn ratkaisun avulla aineisto on saatavilla reaaliaikaisesti. Lisäksi ratkaisu mahdollistaa aineiston yhdistämisen Moodle ja A-plus oppimisympäristöistä yhteistä analyysiä varten. Työssä suunnitellaan uusia interaktiivisia tiedon visualisointeja käyttäjävaatimusten mukaisesti. Tutkimus OA:n käynnistämiseksi kurssitasolla tuotti kaksi yleistä tulosta. Ensiksi aineiston yhdistäminen eri lähteistä on tyypillinen haaste, joka vaatii suunnittelua. Toiseksi opettajien tavoitteita OA:ta aloittaessa ovat valvoa odotettua edistymistä, parantaa oppimateriaalin mitoitusta, tunnistaa ongelmakohtia oppimateriaalissa ja parantaa vuorovaikutusta opiskelijoiden kanssa

    Semantic model for mining e-learning usage with ontology and meaningful learning characteristics

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    The use of e-learning in higher education institutions is a necessity in the learning process. E-learning accumulates vast amount of usage data which could produce a new knowledge and useful for educators. The demand to gain knowledge from e-learning usage data requires a correct mechanism to extract exact information. Current models for mining e-learning usage have focused on the activities usage but ignored the actions usage. In addition, the models lack the ability to incorporate learning pedagogy, leading to a semantic gap to annotate mining data towards education domain. The other issue raised is the absence of usage recommendation that refers to result of data mining task. This research proposes a semantic model for mining e-learning usage with ontology and meaningful learning characteristics. The model starts by preparing data including activity and action hits. The next step is to calculate meaningful hits which categorized into five namely active, cooperative, constructive, authentic, and intentional. The process continues to apply K-means clustering analysis to group usage data into three clusters. Lastly, the usage data is mapped into ontology and the ontology manager generates the meaningful usage cluster and usage recommendation. The model was experimented with three datasets of distinct courses and evaluated by mapping against the student learning outcomes of the courses. The results showed that there is a positive relationship between meaningful hits and learning outcomes, and there is a positive relationship between meaningful usage cluster and learning outcomes. It can be concluded that the proposed semantic model is valid with 95% of confidence level. This model is capable to mine and gain insight into e-learning usage data and to provide usage recommendation

    Imssap: after-school interactive mobile learning student support application

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    06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Bu araştırmada, dünya çapında m-öğrenme geliştirme sürecine giriyoruz. M-öğrenme aracımızı tasarlarken sosyal ve pedagojik faktörler kullanılmaktadır. Popülerlik odaklı bir kültürde, kullanıcılar arasındaki etkileşim tartışmaları zenginleştirir ve bilgi paylaşımını teşvik eder. Uygulama android yazılım geliştirme seti ve java programlama dili kullanılarak tasarlandı ve oluşturuldu. Google bulut depolama, verileri kaydetmek için gerçek zamanlı bir veritabanı altyapısı kullanıldı. Test ve geribildirim için Sakarya Üniversitesi, Türkiye. Anket tanıtılmadan önce, öğrenme sürecinin kavramı tartışılmıştır. Uygulamayı kullandıktan sonra, öğrenciler anketi cevapladılar. Anket üç ana noktayı yansıtıyordu: performans, kazanç ve mobil öğrenmenin kabulünü etkileyen sosyal faktörler. Popülerlik, öğrencileri mobil öğrenmeyi benimsemeye teşvik eden en önemli faktörlerden biridir. Anahtar Kelimeler: mühendislik, eğitim, etkileşimli yazılım, sınıf gösterimi, lisans eğitimi.In this research, we are engaging in the process of developing m-learning around the world. Social and pedagogical factors are being used while designing our m- learning tool. In a popularity-driven culture, interaction between users enriches discussions and promotes knowledge share. The application was designed and created using android software development kit and java programming language. Google cloud storage a real-time database infrastructure was used to save data. The application was offered to students in different majors in engineering departments in Sakarya University, Turkey, for testing and feedback. Before conducting the questionnaire we introduced mobile learning concept to students and explained its importance in their own learning process. After using the application, students answered the questionnaire. The questionnaire reflected three major points: performance, gain and the social factors effecting mobile learning acceptance. Popularity is one of the top factors that motivated learners to adopt mobile learning. Keywords: Engineering, Education, Interactive Software, classroom Demonstration, Undergraduate Education

    Technology-Enhanced Learning Environments and Adaptive Learning Systems – Development of Functionality Taxonomies

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    Especially against the background of the current coronavirus crisis, technology-enhanced learning environments (TELEs) increasingly characterize teaching at universities. For the successful use and integration of TELEs, it is important to understand the functionalities of the technologies used. Based on the state of the art and following [1], we develop two taxonomies. The first taxonomy depicts eleven functionalities with different dimensions relevant for successfully designing TELEs. Sound knowledge of the functionalities supports research on adaptive learning within TELEs and the implementation of student-centered learning opportunities, which is structured in a second functionality taxonomy for adaptive learning systems (ALSs). We contribute to current research on TELEs and ALSs by providing a structured overview of functionalities and suggestions for further research with our research opportunities

    The Utilization of Data Analysis Techniques in Predicting Student Performance in Massive Open Online Courses (MOOCs)

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    The growth of the Internet has enabled the popularity of open online learning platforms to increase over the years. This has led to the inception of Massive Open Online Courses (MOOCs) that enrol, millions of people, from all over the world. Such courses operate under the concept of open learning, where content does not have to be delivered via standard mechanisms that institutions employ, such as physically attending lectures. Instead learning occurs online via recorded lecture material and online tasks. This shift has allowed more people to gain access to education, regardless of their learning background. However, despite these advancements in delivering education, completion rates for MOOCs are low. In order to investigate this issue, the paper explores the impact that technology has on open learning and identifies how data about student performance can be captured to predict trend so that at risk students can be identified before they drop-out. In achieving this, subjects surrounding student engagement and performance in MOOCs and data analysis techniques are explored to investigate how technology can be used to address this issue. The paper is then concluded with our approach of predicting behaviour and a case study of the eRegister system, which has been developed to capture and analyse data. Keywords: Open Learning; Prediction; Data Mining; Educational Systems; Massive Open Online Course; Data Analysi

    Adaptive intelligent personalised learning (AIPL) environment

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    As individuals the ideal learning scenario would be a learning environment tailored just for how we like to learn, personalised to our requirements. This has previously been almost inconceivable given the complexities of learning, the constraints within the environments in which we teach, and the need for global repositories of knowledge to facilitate this process. Whilst it is still not necessarily achievable in its full sense this research project represents a path towards this ideal.In this thesis, findings from research into the development of a model (the Adaptive Intelligent Personalised Learning (AIPL)), the creation of a prototype implementation of a system designed around this model (the AIPL environment) and the construction of a suite of intelligent algorithms (Personalised Adaptive Filtering System (PAFS)) for personalised learning are presented and evaluated. A mixed methods approach is used in the evaluation of the AIPL environment. The AIPL model is built on the premise of an ideal system being one which does not just consider the individual but also considers groupings of likeminded individuals and their power to influence learner choice. The results show that: (1) There is a positive correlation for using group-learning-paradigms. (2) Using personalisation as a learning aid can help to facilitate individual learning and encourage learning on-line. (3) Using learning styles as a way of identifying and categorising the individuals can improve their on-line learning experience. (4) Using Adaptive Information Retrieval techniques linked to group-learning-paradigms can reduce and improve the problem of mis-matching. A number of approaches for further work to extend and expand upon the work presented are highlighted at the end of the Thesis

    On data-driven systems analyzing, supporting and enhancing users’ interaction and experience

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    [EN]The research areas of Human-Computer Interaction and Software Architectures have been traditionally treated separately, but in the literature, many authors made efforts to merge them to build better software systems. One of the common gaps between software engineering and usability is the lack of strategies to apply usability principles in the initial design of software architectures. Including these principles since the early phases of software design would help to avoid later architectural changes to include user experience requirements. The combination of both fields (software architectures and Human-Computer Interaction) would contribute to building better interactive software that should include the best from both the systems and user-centered designs. In that combination, the software architectures should enclose the fundamental structure and ideas of the system to offer the desired quality based on sound design decisions. Moreover, the information kept within a system is an opportunity to extract knowledge about the system itself, its components, the software included, the users or the interaction occurring inside. The knowledge gained from the information generated in a software environment can be used to improve the system itself, its software, the users’ experience, and the results. So, the combination of the areas of Knowledge Discovery and Human-Computer Interaction offers ideal conditions to address Human-Computer-Interaction-related challenges. The Human-Computer Interaction focuses on human intelligence, the Knowledge Discovery in computational intelligence, and the combination of both can raise the support of human intelligence with machine intelligence to discover new insights in a world crowded of data. This Ph.D. Thesis deals with these kinds of challenges: how approaches like data-driven software architectures (using Knowledge Discovery techniques) can help to improve the users' interaction and experience within an interactive system. Specifically, it deals with how to improve the human-computer interaction processes of different kind of stakeholders to improve different aspects such as the user experience or the easiness to accomplish a specific task. Several research actions and experiments support this investigation. These research actions included performing a systematic literature review and mapping of the literature that was aimed at finding how the software architectures in the literature have been used to support, analyze or enhance the human-computer interaction. Also, the actions included work on four different research scenarios that presented common challenges in the Human- Computer Interaction knowledge area. The case studies that fit into the scenarios selected were chosen based on the Human-Computer Interaction challenges they present, and on the authors’ accessibility to them. The four case studies were: an educational laboratory virtual world, a Massive Open Online Course and the social networks where the students discuss and learn, a system that includes very large web forms, and an environment where programmers develop code in the context of quantum computing. The development of the experiences involved the review of more than 2700 papers (only in the literature review phase), the analysis of the interaction of 6000 users in four different contexts or the analysis of 500,000 quantum computing programs. As outcomes from the experiences, some solutions are presented regarding the minimal software artifacts to include in software architectures, the behavior they should exhibit, the features desired in the extended software architecture, some analytic workflows and approaches to use, or the different kinds of feedback needed to reinforce the users’ interaction and experience. The results achieved led to the conclusion that, despite this is not a standard practice in the literature, the software environments should embrace Knowledge Discovery and datadriven principles to analyze and respond appropriately to the users’ needs and improve or support the interaction. To adopt Knowledge Discovery and data-driven principles, the software environments need to extend their software architectures to cover also the challenges related to Human-Computer Interaction. Finally, to tackle the current challenges related to the users’ interaction and experience and aiming to automate the software response to users’ actions, desires, and behaviors, the interactive systems should also include intelligent behaviors through embracing the Artificial Intelligence procedures and techniques

    On Data-driven systems analyzing, supporting and enhancing users’ interaction and experience

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    Tesis doctoral en inglés y resumen extendido en español[EN] The research areas of Human-Computer Interaction and Software Architectures have been traditionally treated separately, but in the literature, many authors made efforts to merge them to build better software systems. One of the common gaps between software engineering and usability is the lack of strategies to apply usability principles in the initial design of software architectures. Including these principles since the early phases of software design would help to avoid later architectural changes to include user experience requirements. The combination of both fields (software architectures and Human-Computer Interaction) would contribute to building better interactive software that should include the best from both the systems and user-centered designs. In that combination, the software architectures should enclose the fundamental structure and ideas of the system to offer the desired quality based on sound design decisions. Moreover, the information kept within a system is an opportunity to extract knowledge about the system itself, its components, the software included, the users or the interaction occurring inside. The knowledge gained from the information generated in a software environment can be used to improve the system itself, its software, the users’ experience, and the results. So, the combination of the areas of Knowledge Discovery and Human-Computer Interaction offers ideal conditions to address Human-Computer-Interaction-related challenges. The Human-Computer Interaction focuses on human intelligence, the Knowledge Discovery in computational intelligence, and the combination of both can raise the support of human intelligence with machine intelligence to discover new insights in a world crowded of data. This Ph.D. Thesis deals with these kinds of challenges: how approaches like data-driven software architectures (using Knowledge Discovery techniques) can help to improve the users' interaction and experience within an interactive system. Specifically, it deals with how to improve the human-computer interaction processes of different kind of stakeholders to improve different aspects such as the user experience or the easiness to accomplish a specific task. Several research actions and experiments support this investigation. These research actions included performing a systematic literature review and mapping of the literature that was aimed at finding how the software architectures in the literature have been used to support, analyze or enhance the human-computer interaction. Also, the actions included work on four different research scenarios that presented common challenges in the Human-Computer Interaction knowledge area. The case studies that fit into the scenarios selected were chosen based on the Human-Computer Interaction challenges they present, and on the authors’ accessibility to them. The four case studies were: an educational laboratory virtual world, a Massive Open Online Course and the social networks where the students discuss and learn, a system that includes very large web forms, and an environment where programmers develop code in the context of quantum computing. The development of the experiences involved the review of more than 2700 papers (only in the literature review phase), the analysis of the interaction of 6000 users in four different contexts or the analysis of 500,000 quantum computing programs. As outcomes from the experiences, some solutions are presented regarding the minimal software artifacts to include in software architectures, the behavior they should exhibit, the features desired in the extended software architecture, some analytic workflows and approaches to use, or the different kinds of feedback needed to reinforce the users’ interaction and experience. The results achieved led to the conclusion that, despite this is not a standard practice in the literature, the software environments should embrace Knowledge Discovery and data-driven principles to analyze and respond appropriately to the users’ needs and improve or support the interaction. To adopt Knowledge Discovery and data-driven principles, the software environments need to extend their software architectures to cover also the challenges related to Human-Computer Interaction. Finally, to tackle the current challenges related to the users’ interaction and experience and aiming to automate the software response to users’ actions, desires, and behaviors, the interactive systems should also include intelligent behaviors through embracing the Artificial Intelligence procedures and techniques
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