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Über dieses Buch

This book reviews a blend of artificial intelligence (AI) approaches that can take e-learning to the next level by adding value through customization. It investigates three methods: crowdsourcing via social networks; user profiling through machine learning techniques, and personal learning portfolios using learning analytics.

Technology and education have drawn closer together over the years as they complement each other within the domain of e-learning, and different generations of online education reflect the evolution of new technologies as researcher and developers continuously seek to optimize the electronic medium to enhance the effectiveness of e-learning. Artificial intelligence (AI) for e-learning promises personalized online education through a combination of different intelligent techniques that are grounded in established learning theories while at the same time addressing a number of common e-learning issues.

This book is intended for education technologists and e-learning researchers as well as for a general readership interested in the evolution of online education based on techniques like machine learning, crowdsourcing, and learner profiling that can be merged to characterize the future of personalized e-learning.



Chapter 1. Introduction

The areas of Artificial Intelligence (AI) Artificial intelligence (AI) and e-Learning from the Computer Science and EducationEducation domains respectively are not usually associated together, not because they are not compatible or complementary but due to a number of otherNon-technical non-technical reasons.

Matthew Montebello

Chapter 2. e-Learning so Far

e-learning has come a long way and it is only thanks to previous versions and numerous evolutions of e-learning that we can propose new routes and design intelligentIntelligentsystemsSystem for futureFuture generations. This also enables us to appreciate and value the meaning of moving forward as we fully understand and acknowledge from where we are coming. A plethora of researchResearch studies have reported conflicting results over the years as some praise and applaud this medium, Medium while others disapprove and critise e-learning in every possible way. The fact that e-learning itself is not regulated by a specific academic body and that best practices are subjective, divergent and too genericGeneric, renders the whole playing gameGame fuzzy, confusing and incredibly frustrating to the learnersLearner. However, pedagogicalPedagogical trends and technologicalTechnological forces have shaped the historyHistory of e-learning and will continue to do so. How have these rubbed off onto each other? And how have they influenced the following generationGeneration of e-learning? What are the factors that will impinge on the future of online education? Online In this chapter a deeper examination and appreciation of these changes and developments over the years is presented in an effort to understand the inevitable evolutionEvolution that occurred and how this affected and influenced the whole environmentEnvironment surrounding e-learning. These include the social implications, the pedagogicalPedagogical repercussions and the technologicalTechnological impacts that gave rise to different e-learning generations.

Matthew Montebello

Chapter 3. MOOCs, Crowdsourcing and Social Networks

Social media took the world by storm and transformed the societySociety and its multiple dimensions in more than one way. The extent of the shock waves that this phenomenon inevitably influenced the way people interacted with the webWeb and with each other, as well as with all web applications and services provided online. Online E-learningE-learning evolved as it embraced the new Web 2.0TechnologiestechnologiesLearning technologies in an attempt to enhance the delivery but at the same time to take full advantage as in the past of the latest cutting-edgeCutting-edge technologies that were available. It has been argued in the previous chapter that this technological shift was no standard evolution but a major unconventional and progressive e-learningE-learning revolution that literally turned the tables around. In this chapter the full impact of this considerable technological contribution to the pedagogicalPedagogical and functional dynamicsDynamics of e-learning will be brought into perspective as innovative techniquesTechnique transpired from the evolution of Web 2.0Web 2.0technologiesTechnologies that slowly but surely got integrated within onlineOnlinelearningLearning systems. These include MOOCsMOOC or Massive OnlineOnline Open Courses, CrowdsourcingCrowdsourcingtechniquesTechnique, and Social NetworksSocial network. The beauty about these technologiesTechnologies that resulted out of the latest technologicalTechnologicalevolutionEvolution addressed particular e-learningE-learning concerns as e-learning had been emanating from the integration of a variety of incongruous emerging technologiesTechnologies that at the time assisted in improving the services provided by such systemsSystem. In the following sections the main e-learningE-learning issues will be discussed together with how emerging technologiesLearning technologies can start addressing them.

Matthew Montebello

Chapter 4. User Profiling and Personalisation

Personalisation, user profiling andPostma, O. the use of machineMachine learning techniques from theComputer computer science arena fall under the umbrella of Artificial IntelligenceArtificial intelligence (AI) or AI. Rather then going through all the technicalTechnical details of machine learning and AI we will be looking into the conceptual application of such techniquesTechnique, as well as the educationalEducational undertones of doing so. PersonalisationPersonalisation features as a main componentComponent in this chapter due to its exceptional and remarkable propertyProperty of improving a service or a product. We shall be looking into how such a widely employed techniqueTechnique in industryIndustry can be similarly applied to educationEducation that promises to alleviate and add-value to e-learningE-learning as we know them. The main concept behind such a techniqueTechnique is the capturing and representationRepresentation of the specific user modelModel or profile. Profile This user representationRepresentation is a living modelModel that evolves over time and requires constant updating to ensure the profileProfile realistically embodies the user or the learnerLearner in our case. As we shall investigate in the next sections the user profileProfile is generally generated and trained using the user patterns and trends but also the interests, needs and choices that all indicate something specific about the user in isolation as well as in combination together. In another section we will also take an in-depth analysis of how user profilingProfiling can be optimised in the case of education in a similar attempt to encapsulate the specific and characteristic learnerProfile profile. Learner We close this chapter with a look at recommenderRecommender systems and how all the different parts mentioned above come together to the cause of enhancing educationEducation and the e-learning medium.

Matthew Montebello

Chapter 5. Personal Learning Networks, Portfolios and Environments

The initial steps towards the model to personalising e-learningE-learning through the injection of AI starts to take shape in this chapter as several of the factoring elements have been covered in the previous two chapters. These will form part of a personal learning environmentPersonal Learning Environment (PLE) that each individual learnerLearner or life-longLife-longlearnerLearner would establish and assemble around oneself in an effort to create andMorrison, D. avail of a sustainable educational systemSystem that has the learner at its centre. Personal learning environmentsPersonal Learning Environment (PLE) or PLEs are ideal vessels to encapsulate all a learner requires due to their personalisationPersonalisation capabilities that truly empower the same learner. Learner Morrison [1] identifies two essential components within a PLEPersonal Learning Environment (PLE) as he depicts its anatomy as shown in Fig. 5.1 overleaf. Each of these componentsComponent play an important role and need to be investigated individually to ensure that they are optimally setup and compatibly designed to generate the expected outcome, an intelligentIntelligent personal learning environmentEnvironment. The Personal Learning Network (PLN)Personal Learning Network (PLN) and the Personal Learning Portfolio (PLP)Personal Learning Portfolio (PLP) form part of the PLEPersonal Learning Environment (PLE) and will be initially presented in the following sections as they bring together essential componentsComponent from the previous chapters. These underlying technologiesTechnologies that source both of them will be justified in terms of their academic relevance, Relevance pedagogicaleffectivenessPedagogical, and theoretical suitability. How will both these componentsComponent take advantage of the latest technologicalTechnological developments and boost well established technologiesTechnologies in an attempt to enrich the learning experience? How will the different technologiesTechnologies compatibly come together to ensure the learnerLearner is not just at the centre of the PLEPersonal Learning Environment (PLE) but also in full control of the medium? Medium The personal learning networkPersonal Learning Network (PLN) and the personal learning portfolio are intended to complement each other as they form part of the proposed modelModel in the next chapter.

Matthew Montebello

Chapter 6. Customised e-Learning – A Proposed Model

This chapter brings all the previous chapters together as they collectively and incrementally built up a crescendo to reach the highlight, namely injecting e-learningE-learning with AI to customiseCustomise the educationEducation process. The proposed modelModel makes us of all the techniquesTechnique discussed in the previous chapters and endeavours to compatibly bring them together to create an intelligentIntelligent personal learning environmentEnvironment. The evolution of e-learning was led imposed by the technology but this modelModel proposes to conveniently employ numerous technologies and techniquesTechnique to directly address specific e-learningE-learning issues. The next generation ofOnline online education is not dictated by technology but by the academic need to personalise learning together with the efficient automationAutomation offered by AI. The first e-learning issue addressed is that of isolation and Chap. 3 undertook this task with the ingenuity of crowdsourcingCrowdsourcing and the popularity of social networks. The connectivism learning theoryConnectivism learning theory has been associated with this phenomenon and this modelModel makes good use of this first factor. MotivationMotivation is another e-learning issue that is addressed through the contributions from Chap. 4 as learnerLearnerprofilingProfiling and learning portfolios support studentStudent to be much more self-determined in their academic endeavour. The third and final issue tackles the issue of impersonalityImpersonality that e-learning is notoriously criticised, and Chap. 5 offers adaptive environments through the combination of a learning portfolio and supportive learning network. A truly intelligent personal learning environmentEnvironment backed and injected by AI techniquesTechnique is being proposed as a compatible combination of all these technologies to enhance e-learningE-learning effectiveness as it leads onlineOnline education to its future and the next e-learning generationGeneration. The rest of this chapter is organised as follows. The section that follows expands further the underlying rationale that led to the proposed modelModel by analysing the contributions from the previous chapters. This is followed by the architectural setup of how these technologies come together within an onlineOnlinesystemSystem to deliver a functional and intelligent PLE. The next section tackles all the implementation details that take place to accomplish and complete the architectural design presented before. Finally operational and pragmatic details of how theOnline online PLE functions are covered in an effort to show how theAI-injected AI-injected e-learning system will operate in reality.

Matthew Montebello

Chapter 7. Looking Ahead

The book draws to an end by looking ahead at potential futureFuture avenues in light of the proposed intelligentIntelligent personal learning environment. Personal Learning Environment (PLE) WebtechnologiesLearning technologies and AI techniques continue to evolve as e-learningE-learning systems continue to take full advantage of both to improve the delivery and the overall holisticHolistic experience. The employment of AI techniquesTechnique in combination with other technologies moved away from the conventional trend of adopting the latest webWebtechnologiesLearning technologies to embellish the e-learning environmentEnvironment and move to the next generationGeneration. The proposed intelligent learning environment had set objectives with specific issues to resolve and employed the different methodologiesMethodologies and practices within an original architectural setup that fulfils the pre-set e-learning needs. Will it be possible to pursue this trend whereby the e-learning needs dictate and prescribe what the technology should be like and impose what it should provide? On the other hand the same architectural setup introduced a novel concept of bringing together numerous technologies to achieve a common goal, personalised e-learning. Will future e-learning generations persist on this line of thought and take full advantage of multiple developments in numerous and diverse domains to collectively achieve a superior added-value outcome that could potentially shape the future of e-learning? This final chapter looks ahead at these possibilities and the potential of influencing futureFuture e-learningE-learning generations by reversing the way e-learning advocates reason and devise such futuristicFuturistic environments. Which technologicalTechnological novelties will leave their impact on future e-learning setups? What exactly is the ideal e-learningE-learningscenarioScenario and which technologies or combination of technologies can pave the way forward?

Matthew Montebello


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