Stratumin ja A+:n toiminnallisuuksien integrointi Moodleen: arkkitehtuuri ja evaluointi

Automated assessment within electronic learning (e-learning) uses computers for the grading of students’ solutions to course assignments, which releases teachers from the burden of manually assessing the submissions and leaves them time for developing other course and teaching activities. The goal of this thesis was to modernize Stratum, an old platform for deploying arbitrary assignments that are automatically assessed and may also be randomly generated in order to provide personalized assignments for each student. The purpose of the modernization was to improve the maintainability and usability of the platform, while retaining its existing core functionality and in particular, its seamless integration in Moodle, a widely used e-learning platform that is also used as the official, university-wide platform at Aalto University, where it is known as MyCourses. This thesis presents alternative approaches for the modernization and identifies the most suitable one for implementation. The selected approach involves another platform, A+, that outsources the implementation of assignments to external exercise services. In this thesis project, a new Moodle plugin was implemented that replicates A+ functionality so that both A+ and Moodle may utilize the same exercise services and function as a front end to courses with automatically assessed assignments. Furthermore, the exercise service framework used with A+, known as the MOOC grader, was extended to support personalized assignments. The new Moodle plugin was named Astra. The new platform implemented in this thesis project realizes the requirements set for the modernization of Stratum. Assignments implemented for Stratum may be ported with feasible effort to the new platform as exercise services. In addition, the new platform replicates enough of the A+ functionality so that a typical course may freely select either Moodle or A+ as its front end without further modifications.Automaattinen harjoitustehtävien arviointi on sähköisen oppimisen (e-oppiminen) osa, jossa tietokoneohjelma arvostelee opiskelijan tekemän tehtävän ratkaisun. Tällöin opettajan ei tarvitse käyttää aikaa tehtävien arvosteluun käsin ja aikaa voi käyttää enemmän muun opetuksen kehittämiseen. Tämän diplomityön tavoitteena oli uudistaa vanha Stratum-järjestelmä, joka on alusta automaattisesti arvioitavien tehtävien toteuttamiseen. Lisäksi Stratumilla voi toteuttaa satunnaisesti luotavia tehtäviä, jolloin jokaiselle opiskelijalle tarjotaan henkilökohtainen tehtävä. Uudistustyön tarkoituksena oli parantaa Stratumin ylläpidettävyyttä ja käytettävyyttä kuitenkaan menettämättä aiempaa ydintoiminnallisuutta. Erityisesti Stratumin saumaton integraatio Moodlessa haluttiin säilyttää. Moodle on laajasti käytetty sähköisen oppimisen alusta, jota käytetään myös Aalto-yliopistossa: kyseinen alusta tunnetaan Aallossa nimellä MyCourses. Tämä diplomityö esittelee vaihtoehtoja uudistustyön toteuttamiseksi ja valitsee niistä parhaan vaihtoehdon. Valittu vaihtoehto viittaa myös erääseen toiseen alustaan, A+:aan, joka ulkoistaa automaattisesti arvioitavien tehtävien toteutuksen ulkopuolisiksi palveluiksi. Tässä diplomityössä toteutettiin uusi Moodle-liitännäinen, joka toistaa A+:n toiminnallisuutta siten, että Moodle voi käyttää samoja tehtäväpalveluja kuin A+ automaattista arviointia varten, jolloin opiskelijat näkevät ja palauttavat tehtävät Moodlessa. Lisäksi tässä työssä laajennettiin A+:n käyttämää tehtäväpalvelujen ohjelmistokehystä, “MOOC graderiä”, jotta se voi tukea henkilökohtaisia tehtäviä kuten Stratum. Uusi Moodle-liitännäinen nimettiin Astraksi. Tässä työssä toteutettu uusi alusta saavuttaa uudistustyölle asetetut tavoitteet. Stratumiin toteutetut vanhat tehtävät on mahdollista siirtää uudelle alustalle kohtuullisella vaivalla. Lisäksi uusi alusta muistuttaa riittävästi A+:aa, jotta tyypilliset kurssit voivat valita vapaasti, kumpaa alustaa ne käyttävät kurssialustana ja käyttöliittymänä, Moodlea vai A+:aa

Mazetec: A Scenario-Based Learning Platform

This work presents Mazetec, a scenario-based learning platform for delivering non-linear scenarios format asynchronously. It enables subject matter experts to create interactive, state-dependent case studies or courses with branching logic for online learning and knowledge testing. Mazetec is a complex web application designed to deliver decision-based or case-based educational scenarios and simulations in a time-limited, non-linear format. There are many e-learning systems in the open source and commercial markets, but while these systems may have similar functions, we have found none that are both domain independent and able to deliver state-dependent content asynchronous and non-linearly. Mazetec can serve as a standalone training system or serve as a supplementary activity provider to an existing course in an organization\u27s existing learning management system (LMS)

Earth Observation Open Science and Innovation

geospatial analytics; social observatory; big earth data; open data; citizen science; open innovation; earth system science; crowdsourced geospatial data; citizen science; science in society; data scienc

From Seminar to Lecture to MOOC: Scripting and Orchestration at Scale

This dissertation investigates the design of large online courses from the pedagogical perspective of knowledge communities. Much of the learning sciences literature has concerned itself with groups of up to 20-30 students, but in universities, courses of several hundred to more than a thousand students are common. At the same time, new models for life-long and informal learning, such as Massive Open Online Courses, are emerging. Amidst this growing enthusiasm for innovation around technology and design in teaching, there is a need for theoretically grounded innovations and rigorous research around practical models that support new approaches to learning. One recent model, known as Knowledge Community and Inquiry (KCI), engages students in the co-construction of a community knowledge base, with a commonly held understanding of the collective nature of their learning, and then provides a sequence of scaffolded inquiry activities where students make use of the knowledge base as a resource. Inspired by this approach to designing courses, the research began with a redesign of an in-service teacher education course, which increased in size from 25 to 75 students. This redesign was carefully analyzed, and design principles extracted. The second step was the design of a Massive Open Online Course for several thousand in-service teachers on technology and inquiry, in collaboration with an affiliated secondary school. A number of innovative design ideas were necessary to accommodate the large number of users, the much larger diversity in terms of background, interest, and engagement among MOOC learners, and the opportunities provided by the platform. The resulting design encompasses a 6- week long curriculum script, and a number of overlapping micro-scripts supported by a custom- written platform that integrated with the EdX platform in a seamless manner. This thesis presents the course structure, including connection to disciplinary principles, its affordances for community and collaboration and its support of individual differentiated learning and collective epistemology. It offers design principles for scripting and orchestrating collective inquiry designs for MOOCS and higher education courses

Bench-Ranking: ettekirjutav analüüsimeetod suurte teadmiste graafide päringutele

Relatsiooniliste suurandmete (BD) töötlemisraamistike kasutamine suurte teadmiste graafide töötlemiseks kätkeb endas võimalust päringu jõudlust optimeerimida. Kaasaegsed BD-süsteemid on samas keerulised andmesüsteemid, mille konfiguratsioonid omavad olulist mõju jõudlusele. Erinevate raamistike ja konfiguratsioonide võrdlusuuringud pakuvad kogukonnale parimaid tavasid parema jõudluse saavutamiseks. Enamik neist võrdlusuuringutest saab liigitada siiski vaid kirjeldavaks ja diagnostiliseks analüütikaks. Lisaks puudub ühtne standard nende uuringute võrdlemiseks kvantitatiivselt järjestatud kujul. Veelgi enam, suurte graafide töötlemiseks vajalike konveierite kavandamine eeldab täiendavaid disainiotsuseid mis tulenevad mitteloomulikust (relatsioonilisest) graafi töötlemise paradigmast. Taolisi disainiotsuseid ei saa automaatselt langetada, nt relatsiooniskeemi, partitsioonitehnika ja salvestusvormingute valikut. Käesolevas töös käsitleme kuidas me antud uurimuslünga täidame. Esmalt näitame disainiotsuste kompromisside mõju BD-süsteemide jõudluse korratavusele suurte teadmiste graafide päringute tegemisel. Lisaks näitame BD-raamistike jõudluse kirjeldavate ja diagnostiliste analüüside piiranguid suurte graafide päringute tegemisel. Seejärel uurime, kuidas lubada ettekirjutavat analüütikat järjestamisfunktsioonide ja mitmemõõtmeliste optimeerimistehnikate (nn "Bench-Ranking") kaudu. See lähenemine peidab kirjeldava tulemusanalüüsi keerukuse, suunates praktiku otse teostatavate teadlike otsusteni.Leveraging relational Big Data (BD) processing frameworks to process large knowledge graphs yields a great interest in optimizing query performance. Modern BD systems are yet complicated data systems, where the configurations notably affect the performance. Benchmarking different frameworks and configurations provides the community with best practices for better performance. However, most of these benchmarking efforts are classified as descriptive and diagnostic analytics. Moreover, there is no standard for comparing these benchmarks based on quantitative ranking techniques. Moreover, designing mature pipelines for processing big graphs entails considering additional design decisions that emerge with the non-native (relational) graph processing paradigm. Those design decisions cannot be decided automatically, e.g., the choice of the relational schema, partitioning technique, and storage formats. Thus, in this thesis, we discuss how our work fills this timely research gap. Particularly, we first show the impact of those design decisions’ trade-offs on the BD systems’ performance replicability when querying large knowledge graphs. Moreover, we showed the limitations of the descriptive and diagnostic analyses of BD frameworks’ performance for querying large graphs. Thus, we investigate how to enable prescriptive analytics via ranking functions and Multi-Dimensional optimization techniques (called ”Bench-Ranking”). This approach abstracts out from the complexity of descriptive performance analysis, guiding the practitioner directly to actionable informed decisions.https://www.ester.ee/record=b553332

Building the Future Internet through FIRE

The Internet as we know it today is the result of a continuous activity for improving network communications, end user services, computational processes and also information technology infrastructures. The Internet has become a critical infrastructure for the human-being by offering complex networking services and end-user applications that all together have transformed all aspects, mainly economical, of our lives. Recently, with the advent of new paradigms and the progress in wireless technology, sensor networks and information systems and also the inexorable shift towards everything connected paradigm, first as known as the Internet of Things and lately envisioning into the Internet of Everything, a data-driven society has been created. In a data-driven society, productivity, knowledge, and experience are dependent on increasingly open, dynamic, interdependent and complex Internet services. The challenge for the Internet of the Future design is to build robust enabling technologies, implement and deploy adaptive systems, to create business opportunities considering increasing uncertainties and emergent systemic behaviors where humans and machines seamlessly cooperate

Analysis of free-cooling system for telecom data centers (Base Transceiver Stations) ¿ big data analytics and pattern detection model.

1.Introductory and Organizational Activities 2.State of Art - Available tools review 3.Programming Skills Development 4.Model creation - statistical approach 5.Model creation with ML techniques 6.Comparison of two approaches/ combined approach 7. Conclusions - energy and monetary savings Appendix Literature revie

Influence of employer support for professional development on MOOCs enrolment and completion: Results from a cross-course survey

Although the potential of open education and MOOCs for professional development is usually recognized, it has not yet been explored extensively. How far employers support non-formal learning is still an open question. This paper presents the findings of a survey-based study which focuses on the influence of employer support for (general) professional development on employees’ use of MOOCs. Findings show that employers are usually unaware that their employees are participating in MOOCs. In addition, employer support for general professional development is positively associated with employees completing MOOCs and obtaining certificates for them. However, the relationship between employer support and MOOC enrollment is less clear: workers who have more support from their employers tend to enroll in either a low or a high number of MOOCs. Finally, the promotion of a minimum of ICT skills by employers is shown to be an effective way of encouraging employee participation in the open education ecosystem.JRC.J.3-Information Societ

Gamification Analytics: Support for Monitoring and Adapting Gamification Designs

Inspired by the engaging effects in video games, gamification aims at motivating people to show desired behaviors in a variety of contexts. During the last years, gamification influenced the design of many software applications in the consumer as well as enterprise domain. In some cases, even whole businesses, such as Foursquare, owe their success to well-designed gamification mechanisms in their product. Gamification also attracted the interest of academics from fields, such as human-computer interaction, marketing, psychology, and software engineering. Scientific contributions comprise psychological theories and models to better understand the mechanisms behind successful gamification, case studies that measure the psychological and behavioral outcomes of gamification, methodologies for gamification projects, and technical concepts for platforms that support implementing gamification in an efficient manner. Given a new project, gamification experts can leverage the existing body of knowledge to reuse previous, or derive new gamification ideas. However, there is no one size fits all approach for creating engaging gamification designs. Gamification success always depends on a wide variety of factors defined by the characteristics of the audience, the gamified application, and the chosen gamification design. In contrast to researchers, gamification experts in the industry rarely have the necessary skills and resources to assess the success of their gamification design systematically. Therefore, it is essential to provide them with suitable support mechanisms, which help to assess and improve gamification designs continuously. Providing suitable and efficient gamification analytics support is the ultimate goal of this thesis. This work presents a study with gamification experts that identifies relevant requirements in the context of gamification analytics. Given the identified requirements and earlier work in the analytics domain, this thesis then derives a set of gamification analytics-related activities and uses them to extend an existing process model for gamification projects. The resulting model can be used by experts to plan and execute their gamification projects with analytics in mind. Next, this work identifies existing tools and assesses them with regards to their applicability in gamification projects. The results can help experts to make objective technology decisions. However, they also show that most tools have significant gaps towards the identified user requirements. Consequently, a technical concept for a suitable realization of gamification analytics is derived. It describes a loosely coupled analytics service that helps gamification experts to seamlessly collect and analyze gamification-related data while minimizing dependencies to IT experts. The concept is evaluated successfully via the implementation of a prototype and application in two real-world gamification projects. The results show that the presented gamification analytics concept is technically feasible, applicable to actual projects, and also valuable for the systematic monitoring of gamification success

Building the Future Internet through FIRE

The Internet as we know it today is the result of a continuous activity for improving network communications, end user services, computational processes and also information technology infrastructures. The Internet has become a critical infrastructure for the human-being by offering complex networking services and end-user applications that all together have transformed all aspects, mainly economical, of our lives. Recently, with the advent of new paradigms and the progress in wireless technology, sensor networks and information systems and also the inexorable shift towards everything connected paradigm, first as known as the Internet of Things and lately envisioning into the Internet of Everything, a data-driven society has been created. In a data-driven society, productivity, knowledge, and experience are dependent on increasingly open, dynamic, interdependent and complex Internet services. The challenge for the Internet of the Future design is to build robust enabling technologies, implement and deploy adaptive systems, to create business opportunities considering increasing uncertainties and emergent systemic behaviors where humans and machines seamlessly cooperate