A literature synthesis of personalised technology-enhanced learning: what works and why

Personalised learning, having seen both surges and declines in popularity over the past few decades, is once again enjoying a resurgence. Examples include digital resources tailored to a particular learner’s needs, or individual feedback on a student’s assessed work. In addition, personalised technology-enhanced learning (TEL) now seems to be attracting interest from philanthropists and venture capitalists indicating a new level of enthusiasm for the area and a potential growth industry. However, these industries may be driven by profit rather than pedagogy, and hence it is vital these new developments are informed by relevant, evidence-based research. For many people, personalised learning is an ambiguous and even loaded term that promises much but does not always deliver. This paper provides an in-depth and critical review and synthesis of how personalisation has been represented in the literature since 2000, with a particular focus on TEL. We examine the reasons why personalised learning can be beneficial and examine how TEL can contribute to this. We also unpack how personalisation can contribute to more effective learning. Lastly, we examine the limitations of personalised learning and discuss the potential impacts on wider stakeholders

Beyond A/B Testing: Sequential Randomization for Developing Interventions in Scaled Digital Learning Environments

Randomized experiments ensure robust causal inference that are critical to effective learning analytics research and practice. However, traditional randomized experiments, like A/B tests, are limiting in large scale digital learning environments. While traditional experiments can accurately compare two treatment options, they are less able to inform how to adapt interventions to continually meet learners' diverse needs. In this work, we introduce a trial design for developing adaptive interventions in scaled digital learning environments -- the sequential randomized trial (SRT). With the goal of improving learner experience and developing interventions that benefit all learners at all times, SRTs inform how to sequence, time, and personalize interventions. In this paper, we provide an overview of SRTs, and we illustrate the advantages they hold compared to traditional experiments. We describe a novel SRT run in a large scale data science MOOC. The trial results contextualize how learner engagement can be addressed through inclusive culturally targeted reminder emails. We also provide practical advice for researchers who aim to run their own SRTs to develop adaptive interventions in scaled digital learning environments

Blending Learning: The Evolution of Online and Face-to-Face Education from 20082015

In 2008, iNACOL produced a series of papers documenting promising practices identified throughout the field of K–12 online learning. Since then, we have witnessed a tremendous acceleration of transformative policy and practice driving personalized learning in the K–12 education space. State, district, school, and classroom leaders recognize that the ultimate potential for blended and online learning lies in the opportunity to transform the education system and enable higher levels of learning through competency-based approaches.iNACOL's core work adds significant value to the field by providing a powerful practitioner voice in policy advocacy, communications, and in the creation of resources and best practices to enable transformational change in K–12 education.We worked with leaders throughout the field to update these resources for a new generation of pioneers working towards the creation of student-centered learning environments.This refreshed series, Promising Practices in Blended and Online Learning, explores some of the approaches developed by practitioners and policymakers in response to key issues in K–12 education, including:Blended Learning: The Evolution of Online and Face-to-Face Education from 2008-2015;Using Blended and Online Learning for Credit Recovery and At-Risk Students;Oversight and Management of Blended and Online Programs: Ensuring Quality and Accountability; andFunding and Legislation for Blended and Online Education.Personalized learning environments provide the very best educational opportunities and personalized pathways for all students, with highly qualified teachers delivering world-class instruction using innovative digital resources and content. Through this series of white papers, we are pleased to share the promising practices in K–12 blended, online, and competency education transforming teaching and learning today

Maximizing Competency Education and Blended Learning: Insights from Experts

In May 2014, CompetencyWorks brought together twenty-three technical assistance providers to examine their catalytic role in implementing next generation learning models, share each other's knowledge and expertise about blended learning and competency education, and discuss next steps to move the field forward with a focus on equity and quality. Our strategy maintains that by building the knowledge and networks of technical assistance providers, these groups can play an even more catalytic role in advancing the field. The objective of the convening was to help educate and level set the understanding of competency education and its design elements, as well as to build knowledge about using blended learning modalities within competency-based environments. This paper attempts to draw together the wide-ranging conversations from the convening to provide background knowledge for educators to understand what it will take to transform from traditional to personalized, competency-based systems that take full advantage of blended learning

A Personalized Travel Recommendation System Using Social Media Analysis

Personalization of recommender systems enables customized services to users. Social media is one resource that aids personalization. This study explores the use of twitter data to personalize travel recommendations. A machine learning classification model is used to identify travel related tweets. The travel tweets are then used to personalize recommendations regarding places of interest for the user. Places of interest are categorized as: historical buildings, museums, parks, and restaurants. To better personalize the model, travel tweets of the user\u27s friends and followers are also mined. Volunteer twitter users were asked to provide their twitter handle as well as rank their travel category preferences in a survey. We evaluated our model by comparing the predictions made by our model with the users choices in the survey. The evaluations show 68% prediction accuracy. The accuracy can be improved with a better travel-tweet training dataset as well as a better travel category identification technique using machine learning. The travel categories can be increased to include items like sports venues, musical events, entertainment, etc. and thereby fine-tune the recommendations. The proposed model lists \u27n\u27 places of interest from each category in proportion to the travel category score generated by the model

Learning in MOOCs: The [Un]democratisation of Learning

Massive open online courses have been signaled as a disruptive and democratizing force in online, distance education. This position paper critiques these claims, examining the tensions between viewing MOOCs as products and students as customers, and the perspective of students as learners who may, or might not, be able to determine their own learning pathway. The capacity, or non-ability, to self-regulate learning leads to inequalities in the ways learners experience MOOCs. While some MOOCs have contributed to change, many replicate and reinforce education that privilege the elite. This paper argues a need to support the development of digital skills and core competencies, including the ability to self-regulate learning, to ensure learners can participate in a new democracy of open, online learning

Challenges in personalisation: supporting mobile science inquiry learning across contexts

The Personal Inquiry project (PI) aimed to develop and implement personal inquiries in secondary schools in order to motivate engagement in scientific inquiry through its focus on inquiries of personal interest to young learners. This paper describes the authors’ experiences working with teachers in one school over three years, iteratively developing the nQuire toolkit* and pedagogical support across different inquiries which can be used in and across different contexts, ranging from the classroom to field trips and at home. As nQuire is web based, and can be accessed in different locations and on a range of networked devices it supports mobile inquiry learning and is the main resource for bridging between contexts. This paper discusses issues related to developing personal inquiries in schools, working across different contexts and focusing on three aspects of personalisation: choice, personal relevance and learner responsibility. It discusses the challenges faced when developing personalised inquiries in science, both in more traditional classroom contexts and in the less formal environment of an after school club. Drawing on technology supported inquiries from both these contexts it reflects on some of the constraints and tensions in providing learners with choice in their inquiries, identifying both the constraints and successes

It's Not a Matter of Time: Highlights From the 2011 Competency-Based Learning Summit

Outlines discussions about the potential and challenges of competency-based learning in transforming the current time-based system, including issues of accountability, equity, personalization, and aligning policy and practice. Includes case summaries

A New Competence-based Approach for Personalizing MOOCs in a Mobile Collaborative and Networked Environment

Massive Open Online Courses (MOOCs) are a new disruptive development in higher education that combines openness and scalability in a most powerful way. They have the potential to widen participation in higher education. Thus, they contribute to social inclusion, the dissemination of knowledge and pedagogical innovation and also the internationalization of higher education institutions. However, one of the critical elements for a massive open language learning experience to be successful is to empower learners and to facilitate networked learning experiences. In fact, MOOCs are designed for an undefined number of participants, thus serving a high heterogeneity of profiles, with diverse learning styles and prior knowledge, and also contexts of participation and diversity of online platforms. Personalization can play a key role in this process. The iMOOC pedagogical model introduced the notion of diversity to MOOC design, allowing for a clear differentiation of learning paths and also virtual environments. In this article, the authors present a proposal based on the iMOOC approach for a new framework for personalizing and adapting MOOCs designed in a collaborative, networked pedagogical approach by identifying each participant's competence profile and prior knowledge, as well as the respective mobile communication device used to generate matching personalized learning. This article also shows the results obtained in a laboratory environment after an experiment has been performed with a prototype of the framework. It can be observed that creating personalized learning paths is possible and the next step is to test this framework with real experimental groups.Los cursos en línea masivos y abiertos (MOOC) son una nueva tendencia rompedora en la educación superior. Estos cursos combinan la propiedad de ser abiertos con la posibilidad de ser escalables de una forma muy potente. Tienen el potencial de permitir la participación en la educación superior para todas las personas, a todos los niveles. Por lo tanto, contribuyen a la inclusión social, la difusión del conocimiento y la innovación pedagógica, así como la internalización de las instituciones de educación superior. Sin embargo, uno de los elementos críticos para que tenga éxito una experiencia de aprendizaje de forma abierta y masiva es potenciar y facilitar una red de aprendizaje. De hecho, los MOOC no están diseñados para un número predefinido de participantes por lo que sirven para un alto número de perfiles heterogéneos, con diversidad de estilos de aprendizaje y conocimientos previos, pero también contextos de participación y diversidad de plataformas online. La personalización puede desempeñar un papel clave en este proceso. El modelo pedagógico iMOOC introdujo el principio de diversidad en el diseño de MOOC, permitiendo una clara diferenciación de caminos de aprendizaje y también entornos virtuales. En este artículo los autores presentan una propuesta basada en el enfoque de iMOOC, sobre un nuevo sistema para la personalización y adaptación de MOOC diseñados en un enfoque colaborativo y en una red pedagógica. El mecanismo es identificar cada competencia del perfil de los participantes, el conocimiento previo que estos tienen así como detectar sus respectivos dispositivos móviles, y se genera un camino de aprendizaje personalizado en base a estos parámetros. Este artículo también muestra los resultados obtenidos en un entorno de laboratorio después de un experimento llevado a cabo con un prototipo del sistema. Se puede observar que es posible crear caminos de aprendizaje personalizados y que el siguiente paso es probar este sistema con grupos experimentales reales