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AI-Based-Personalized Learning in Higher Education: From Tracing Learning Processes to Providing Tailored Educational Support
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The educational community has been interested in personalized learning systems that can adapt itself while providing learning support to different learners to overcome the weakness of ‘one size fits all’ approaches in technology-enabled learning systems. In this paper, one known problem in adaptive learning systems called curriculum sequencing is addressed. A learning path recommendation (LPR) system is designed and implemented that clusters the learners into groups and selects an appropriate learning path for learners based on their prior knowledge. The clustering component uses Fuzzy C-Mean (FCM) algorithm that can recommend more than one learning path to learners located on the cluster boundaries. Using bioinspired ant colony optimization (ACO) algorithm and meaningful learning theory, the ACO path finder component searches for a suitable learning path for the learners while incorporating their continuous improvements. The effectiveness of the LPR system is evaluated by developing and offering a database course to actual learners. The results of the experiment showed that the group using the LPR system had a significantly higher performance and knowledge improvement in the course than the control group. This indicated that the LPR system has a moderate to large impact on the learners’ performance and knowledge improvement.
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Artificial intelligence (AI) is arguably the driving technological force of
the first half of this century, and will transform virtually every industry, if
not human endeavors at large. Businesses and governments worldwide
are pouring enormous sums of money into a very wide array of
implementations, and dozens of start-ups are being funded to the tune of
billions of dollars. It would be naive to think that AI will not have an impact on education—au contraire, the possibilities there are profound yet, for the time being, overhyped as well. This book attempts to provide the right balance between reality and hype, between true potential and wild extrapolations.
In order to develop 21st-century skills, especially with respect to digital technologies, students are supposed to know how to use digital technologies for learning and problem solving. In recent years, approaches to personalized learning have been considered to be particularly promising in this regard. A clearly defined concept of personalized learning is still lacking, however. Rather, it serves an umbrella term for educational approaches that try to do justice to the individual abilities, knowledge and learning needs of each student. Our study deals with theoretical dimensions of this multilayered concept and investigates the in-school use of digital technologies for personalized learning as reported by students in connection with their self-assessed digital skills and beliefs about the usefulness of ICT in learning. For this purpose, we have analyzed data from a survey of N = 860 students (8th grade) from 31 Swiss schools that have introduced personalized learning concepts. A structural equation model shows that increased reported use of digital technologies in learning environments that are characterized by open teaching methods is an important aspect of personalized learning because it has a positive effect on both self-reported digital skills and self-perceived ICT-related beliefs in learning. At the same time, the experienced freedom of choice in learning activities that include digital technologies, which is often considered a crucial dimension of personalized learning, has no significant effect.
Personalisation of learning is a recurring trend in our society, referred to in government speeches, popular media, conference and research papers and technological innovations. This latter aspect—of using personalisation in technology‐enhanced learning (TEL)—has promised much but has not always lived up to the claims made. Personalisation is often perceived to be a positive phenomenon, but it is often difficult to know how to implement it effectively within educational technology.
In order to address this problem, we propose a framework for the analysis and creation of personalised TEL. This article outlines and explains this framework with examples from a series of case studies. The framework serves as a valuable resource in order to change or consolidate existing practice and suggests design guidelines for effective implementations of future personalised TEL.
Over the last decade, several research initiatives have investigated the potentials of the educational paradigm shift from the traditional one-size-fits-all teaching approaches to adaptive and personalized learning. On the other hand, mobile devices are recognized as an emerging technology to facilitate teaching and learning strategies that exploit individual learners’ context. This has led to an increased interest on context-aware adaptive and personalized mobile learning systems aiming to provide learning experiences delivered via mobile devices and tailored to learner’s personal characteristics and situation. To this end, in this paper we present a context-aware adaptive and personalized mobile learning system, namely the Units of Learning mobile Player (UoLmP), which aims to support semi-automatic adaptation of learning activities, that is: (a) adaptations to the interconnection of the learning activities (namely, the learning flow) and (b) adaptations to the educational resources, tools and services that support the learning activities. Initial evaluation results from the use of UoLmP provide evidence that UoLmP can successfully adapt the learning flow of an educational scenario and the delivery of educational resources, tools and services that support the learning activities. Finally, these adaptations can facilitate students to complete successfully the learning activities of an educational scenario.
Looking for ways to handle the transition to a digital economy Robots, artificial intelligence, and driverless cars are no longer things of the distant future. They are with us today and will become increasingly common in coming years, along with virtual reality and digital personal assistants. As these tools advance deeper into everyday use, they raise the question-how will they transform society, the economy, and politics? If companies need fewer workers due to automation and robotics, what happens to those who once held those jobs and don't have the skills for new jobs? And since many social benefits are delivered through jobs, how are people outside the workforce for a lengthy period of time going to earn a living and get health care and social benefits? Looking past today's headlines, political scientist and cultural observer Darrell M. West argues that society needs to rethink the concept of jobs, reconfigure the social contract, move toward a system of lifetime learning, and develop a new kind of politics that can deal with economic dislocations. With the U.S. governance system in shambles because of political polarization and hyper-partisanship, dealing creatively with the transition to a fully digital economy will vex political leaders and complicate the adoption of remedies that could ease the transition pain. It is imperative that we make major adjustments in how we think about work and the social contract in order to prevent society from spiraling out of control. This book presents a number of proposals to help people deal with the transition from an industrial to a digital economy. We must broaden the concept of employment to include volunteering and parenting and pay greater attention to the opportunities for leisure time. New forms of identity will be possible when the "job" no longer defines people's sense of personal meaning, and they engage in a broader range of activities. Workers will need help throughout their lifetimes to acquire new skills and develop new job capabilities. Political reforms will be necessary to reduce polarization and restore civility so there can be open and healthy debate about where responsibility lies for economic well-being. This book is an important contribution to a discussion about tomorrow-one that needs to take place today.
Engagement is a fundamental condition for learning, which the outdated educational system is failing to sustain for the current generation of students, born in a world permeated with digital technologies. This article presents an analysis of high school students’ engagement while playing the roles of programmers and designers of educational digital games in the Community of Practice of the DEMULTS project, which aims to provide an alternative within the traditional educational system. Data collection was performed within an ethnographic approach with participant observation, questionnaires and social network interaction, and analysis was based on the constructs of Activity Theory. Four groups of students were identified with similar needs and motivations, each engaged at different levels according to the nature of the tasks, interaction with peers and educators, and personal expectations. Results reveal that, even in a supposedly fun and innovative context, the relationship between the object of the activity and the students' needs is crucial to promote engagement and learning. Identifying and taking into account students' needs and expectations is reinforced as an indispensable step in educational interventions.
Two recent publications report the emerging technologies that are likely to have a significant impact on learning and instruction: (a) New Media Consortium's 2011 Horizon Report (Johnson, Smith, Willis, Levine & Haywood, 2011), and (b) A Roadmap for Education Technology funded by the National Science Foundation in the USA (to download the report see http://www.cra.org/ccc/edtech.php). Some of the common technologies mentioned in both reports include personalized learning, mobile technologies, data mining, and learning analytics. This paper analyzes and synthesizes these two reports. Two additional sources are considered in the discussion: (a) the IEEE Technical Committee on Learning Technology's report on curricula for advanced learning technology, and, (b) the European STELLAR project that is building the foundation for a network of excellence for technology enhanced learning. The analysis focuses on enablers of (e.g., dynamic online formative assessment for complex learning activities) and barriers to (e.g., accessibility and personalizability) to sustained and systemic success in improving learning and instruction with new technologies. In addition, two critical issues cutting across emerging educational technologies are identified and examined as limiting factors - namely, political and policy issues. Promising efforts by several groups (e.g., the National Technology Leadership Coalition, the IEEE Technical Committee on Learning Technology, Networks of Excellence, etc.) will be introduced as alternative ways forward. Implications for research and particular for assessment and evaluation are included in the discussion as means to establish credible criteria for improvement. © International Forum of Educational Technology & Society (IFETS).
Learning styles which refer to students’ preferred ways to learn can play an important role in adaptive e-learning systems. With the knowledge of different styles, the system can offer valuable advice and instructions to students and teachers to optimise students’ learning process. Moreover, e-leaning system which allows computerised and statistical algorithms opens the opportunity to overcome drawbacks of the traditional detection method that uses mainly questionnaire. These appealing reasons have led to a growing number of researches looking into the integration of learning styles and adaptive learning system. This paper, by reviewing 51 studies, delves deeply into different parts of the integration process. It captures a variety of aspects from learning styles theories selection in e-learning environment, online learning styles predictors, automatic learning styles classification to numerous learning styles applications. The results offer insights into different developments, achievements and open problems in the field. Based on these findings, the paper also provides discussion, recommendations and guidelines for future researches.
Recently, learning analytics (LA) has drawn the attention of academics, researchers, and administrators. This interest is motivated by the need to better understand teaching, learning, “intelligent content,” and personalization and adaptation. While still in the early stages of research and implementation, several organizations (Society for Learning Analytics Research and the International Educational Data Mining Society) have formed to foster a research community around the role of data analytics in education. This article considers the research fields that have contributed technologies and methodologies to the development of learning analytics, analytics models, the importance of increasing analytics capabilities in organizations, and models for deploying analytics in educational settings. The challenges facing LA as a field are also reviewed, particularly regarding the need to increase the scope of data capture so that the complexity of the learning process can be more accurately reflected in analysis. Privacy and data ownership will become increasingly important for all participants in analytics projects. The current legal system is immature in relation to privacy and ethics concerns in analytics. The article concludes by arguing that LA has sufficiently developed, through conferences, journals, summer institutes, and research labs, to be considered an emerging research field.
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